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Regional Economic Outlook 2019 - East Africa
04/04/2019 18:35
Regional Economic Outlook 2019 - East Africa
East Africa Economic Outlook 2018
12/03/2018 13:36
East Africa Economic Outlook 2018
The <em>East Africa Economic Outlook </em>reviews economic performance in 2017 and forecasts the next two years by highlighting the region’s key drivers of growth, opportunities, and challenges. It covers major macroeconomic developments in the region’s 13 countries and discusses structural issues affecting future growth, poverty, and inequality. It also presents in part II a synopsis of manufacturing activity in the region, drawing on a previous study of seven of the region’s countries. The outlook selects manufacturing as the sector to cover due to its potential to drive future growth and employment in the subregion. Economic growth in East Africa was a robust 5.9 percent in 2017 and is forecast to persist in 2018 and 2019. It would have been even higher, had it not been for political instability in the region’s fragile states. The service sector is generally the main driver of East Africa’s growth as agriculture, which has for a longtime played a leading role, is receding. Services grew 12.4 percent in 2017, compared with 12.0 percent for industry and 7.1 percent for agriculture. The mineral and industrial sectors’ role in driving growth is also increasing. On the demand side, household consumption is the main driver of growth, followed by public investment in infrastructure, mineral exploration, and construction.Read more
Land Tenure Regularization in Rwanda: Good practices in land reform
20/01/2017 11:34
Land Tenure Regularization in Rwanda: Good practices in land reform
East Africa Quarterly Bulletin - Fourth Quarter 2014
24/04/2015 14:21
East Africa Quarterly Bulletin - Fourth Quarter 2014
AfDB Partner of Choice for East Africa - EARC Report 2014
08/10/2014 12:27
AfDB Partner of Choice for East Africa - EARC Report 2014
East Africa Quarterly Bulletin - Second Quarter 2014
02/10/2014 12:01
East Africa Quarterly Bulletin - Second Quarter 2014
East Africa Quarterly Bulletin - First Quarter 2014
09/06/2014 13:38
East Africa Quarterly Bulletin - First Quarter 2014
Rwanda - 2014 - Country Profile - Improving economic competitiveness to bring about shared growth - Full Report
14/05/2014 09:40
Rwanda - 2014 - Country Profile - Improving economic competitiveness to bring about shared growth - Full Report
Rwanda - 2014 - Country Profile - Improving economic competitiveness to bring about shared growth - Summary Report
14/05/2014 09:40
Rwanda - 2014 - Country Profile - Improving economic competitiveness to bring about shared growth - Summary Report
Analysis of Gender and Youth Employment in Rwanda
07/05/2014 09:02
Analysis of Gender and Youth Employment in Rwanda

Categories: Rwanda, Gender, Employment, Youth

East Africa Quarterly Bulletin - Fourth Quarter 2013
02/04/2014 10:09
East Africa Quarterly Bulletin - Fourth Quarter 2013
Working Paper 120 - Community Based Health Insurance Schemes in Africa: The Case of Rwanda
15/01/2014 18:52
Working Paper 120 - Community Based Health Insurance Schemes in Africa: The Case of Rwanda
Every year, around 100 million people are driven into poverty due to burden of health expenditure. Most of these newly poor reside in resource poor countries such as Sub Saharan Africa (SSA) where health care systems significantly lag behind developed country counterparts and are characterized by either dysfunctional or non-existent health insurance schemes. The result has been a high disease burden propagating a sickly, unproductive labor force. In Sub-Saharan Africa, formal and well functioning health insurance schemes generally exist for the very few who are employed in the formal sector. Households in poorer countries generally tend to spend as much as those living in relatively richer countries. This paper evaluates the impact of the Community-based health insurance schemes (Mutuelles) in Rwanda on demand for modern health care, mitigation of out-of-pocket catastrophic health expenditure and social inclusiveness based on a nationally representative household survey using traditional regression approach and matching estimator popular in the evaluation literature. The data used in this study was collected in 2005/06 covering around 6,900 households with about 35,000 individual histories. The data is a typical living standard survey where information on household demographics, educational attainment, health, consumption, income sources, migration, agriculture, labor market condition, household assets, living conditions and other variables were collected. The preferred method of estimation is the matching estimator that uses data organized along the dichotomy: “treated” vs. “control” conditional on observed covariates. Such a dichotomy allows estimation of three statistics relevant for evaluation. The Average Treatment Effect (ATE) compares outcomes between “treated” vs. “control” group by taking randomly selected individuals from both samples so that impact of a program is evaluated directly. The results based on simple probit model suggest that membership into CBHISs had a potential of increasing health care utilization by about 15% following an illness episode. The effect is slightly higher for poor households than the non-poor. With regard to catastrophic expenditure, there is significant effect returned by the probit model where insured households had a much lower probability of experiencing catastrophic expenditure compared to the uninsured and more so among the poor than the non-poor. Similarly, the results from the matching estimator indicate that households that were members of the CBHISs had a 15 percentage point higher utilization of health care facilities than uninsured ones following an illness episode. According to our preferred method, higher utilization of health care services was found among the insured non-poor than insured poor households, with comparable effect in reducing health-related expenditure shocks. Utilization of modern health care services among the insured did not have statistically significant effect on health utilization, particularly among the poor. The non-poor did show 21 percentage point increase in the use of health services. The CBHISs succeeded however in reducing significantly health related consumption shocks in all households, more among the poor than the non-poor. This result is very encouraging since health related shocks have the potential of persisting for a long time in typical poor households. The study also shows that if the insurance scheme was extended to non-members, heath utilization would increase by 18 percentage points. This figure is close to 30 percentage points for non-poor households and about 10 percentage points among poor households. With respect to income protection, the potential of CBHIs is still very high. It could reduce catastrophic expenditure by 17 percentage points and much more significantly among the poor than the non-poor households. Overall the matching estimator indicates stronger evidence of better utilization of health care facilities and income protection due to CBHISs in Rwanda.Read more
East Africa Quarterly Bulletin - Third Quarter 2013
22/11/2013 10:18
East Africa Quarterly Bulletin - Third Quarter 2013
Policy Brief - Perfomance Contracts and Social Service Delivery - Lessons from Rwanda
11/09/2013 14:04
Policy Brief - Perfomance Contracts and Social Service Delivery - Lessons from Rwanda

Categories: Rwanda

East Africa Quarterly Bulletin - Second Quarter 2013
28/08/2013 12:44
East Africa Quarterly Bulletin - Second Quarter 2013
Working Paper 177 - A Macroeconometric Model for Rwanda
09/07/2013 09:46
Working Paper 177 - A Macroeconometric Model for Rwanda
This paper presents a macroeconometric model of Rwanda built to analyze both endogenous and exogenous shocks. The model is developed considering the supply-constrained nature of the economy. On the supply side, total output is disaggregated into agricultural sector and non-agricultural sector. On the demand side, the households' aggregate consumption expenditure and private investment expenditure functions are specified. The government investment and consumption are assumed to be exogenous. In addition to the public and private expenditure components, the domestic demand for imports (disaggregated into consumption goods import and intermediate goods import) and export supply functions are specified on the demand side. The monetary sector contains a behavioral money demand equation and money supply identity. The money supply equation is endogenous to the model to capture the monetization of deficit. The price and the real exchange equations are also specified and hence determined endogenously. The model's operation is consistent with the general equilibrium framework in which price serves as equilibrating variable. The value of export, terms of trade and real exchange rate determine the level of imports, which in turn affect the level of private investment. Imports and exports determine trade balance that may spillover to the monetary sector and affects money supply. The fiscal deficit has a feedback effect on prices through its effect on money supply. The level of output also determined the aggregate demand by affecting consumption and investment. The excess demand over the total output is assumed to be financed by foreign financial flows. However, for a given level of foreign financial flows, the disequilibrium between aggregate demand and aggregate supply is assumed to spillover to the domestic price so as to achieve market clearing through price adjustment. The behavioral equations of the model are estimated individually in a cointegration framework using Pesaran et al (2001) autoregressive distributed lag (ARDL) model. The individual equations fit the data quite well. Given that good individual equation fit does not necessarily imply the overall fit of the model once the interactions among all the model variables are allowed, the Theil's inequality coefficient and its decompositions are used to assess the overall fit of the model. The model exhibits the desirable properties of low bias and variance proportions, implying that there is low systematic error in the model. Two sets of simulations are carried out using the model. The two scenarios experimented are scaling up infrastructural spending by 20 to 75 percent, and aid cut to the tune of 10 to 30 percent under different assumptions. The first set of the scenario, higher infrastructural spending, indicates that further scaling up of infrastructure spending by 20 percent may be accommodated with 2.6 to 3.8 percentage points increase in inflation in the medium term. The inflationary impact of the scaling up could be subdued when the productivity effect of the physical infrastructure kicks in. If the infrastructure spending starts paying off by 2014/15, the inflationary impact of the additional spending will be significantly lower (1.4 percentage points vs. 3.8 percentage points). This underscores the importance of institutional effectiveness in timely implementation of infrastructural projects. Nevertheless, excessive expansion of infrastructure spending may destabilize the macroeconomic environment considerably. Our scenario of scaling up spending by 75% provides an alarming picture. Under this scenario, inflation rate would go easily to the lower twenties territory in the medium term. The inflationary effect would be lower when the productivity effect sets in. However, disinflation would be very costly at such high inflation rate as inflationary expectations may be engrained in the economy. Moreover, the significant appreciation of real exchange rate under this scenario may substantially erode export competitiveness. The second set of simulation considers a decline in aid flows by 10 to 30 percent, with and without partial domestic matching of the decline in aid through domestic borrowing. The results indicate that deeper aid cuts (by 20 to 30 percent) would have considerable effect on the economy and it could reverse the growth momentum that Rwanda has enjoyed over the last decade. The model could be further refined through different channels. First, the production side could be disaggregated by product category, such as exportable and non-exportable, and the production function for each exportable item could be linked with the export supply function. This would accentuate the supply constraint nature of the economy. Second, the labor demand functions could be derived from the production technologies specified and the labor market equilibrium could be derived assuming exogenous labor supply. Third, different tax categories could be specified to look at the effects of government policies on different categories. The refinement of the model is, however, determined by the available data.Read more
East Africa Quarterly Bulletin - First Quarter 2013
06/06/2013 13:17
East Africa Quarterly Bulletin - First Quarter 2013
Economic Brief - State of Infrastructure in East Africa
23/04/2013 14:28
Economic Brief - State of Infrastructure in East Africa
East Africa Quarterly Bulletin - Fourth Quarter 2012
04/04/2013 15:41
East Africa Quarterly Bulletin - Fourth Quarter 2012
East Africa Quarterly Bulletin - Third Quarter 2012
04/04/2013 15:36
East Africa Quarterly Bulletin - Third Quarter 2012
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