One year from the target date of the achievement of the Millennium Development Goals (MDGs), the result in Africa is only a work in progress. Africa has made progress towards some of the MDGs but needs to draw lessons and improve performance post 2015. This study investigates the development effectiveness of the African Development Bank Group-financed projects using 229 concluded projects between 2004 and 2012 to assess the major determinants of project performance. The study finds that the Bank Group has on average a high level of development effectiveness. Using econometric analysis the study found that country-level variables’ interactions with project-level variables explain a substantial share of the variations in project performance. In particular, it affirmed that country policies and institutions and country capacity in general is positively correlated with project performance whilst parallel project implementation units were not correlated. At the micro level, the age of a project, the quality of project design and the choice of programing instrument were also important determinants of project success. The findings of this study therefore will inform the policy formulation processes in the post 2015 agenda.
Un an avant la date ciblée de réalisation des Objectifs du Millénaire pour le développement (OMD), le résultat en Afrique n'est encore qu'un travail en cours. L'Afrique a fait des progrès en faveur de certains des OMD, mais doit tirer des leçons et améliorer les résultats post-2015. Cette étude examine l'efficacité du développement des projets financés par le Groupe de la Banque africaine de développement en utilisant 229 projets achevés entre 2004 et 2012 pour évaluer les principaux déterminants des résultats de projet. L’étude constate que le Groupe de la Banque a, en moyenne, un niveau élevé d'efficacité de développement. En utilisant une analyse économétrique, l’étude a révélé que les interactions des variables au niveau national avec des variables au niveau du projet expliquent une part importante des variations des résultats du projet. En particulier, il a affirmé que les politiques nationales, les institutions et la capacité des pays en général sont positivement corrélées avec les résultats du projet, alors que des unités de mise en æuvre de projets parallèles n’étaient pas corrélées. Au niveau micro, l’âge d'un projet, la qualité de la conception du projet et le choix de l'instrument de programmation étaient également d'importants facteurs de réussite du projet. Les résultats de cette étude informeront donc les processus de formulation de l'ordre du jour post-2015.
Africa continues to register unprecedented economic growth. Africa grew by about 4% on average in 2013, compared to 3% for the global economy.
Official development assistance (ODA) has been a source of development finance in many African countries. The international community has made efforts to improve the delivery and quality of ODA to countries through galvanising support using frameworks such as the Paris Declaration of Aid Effectiveness and the Accra Agenda for Action. The 2011 survey on monitoring the implementation of the Paris Declaration showed slow progress in development cooperation, in particular in increasing assistance to African countries, the predictability of aid, the alignment of aid with country priorities and the achievement of results. This prompted several changes including a paradigm shift in development cooperation from ‘aid effectiveness’ to ‘development effectiveness’, as documented in the Bussan partnership for effective development cooperation in 2011. The Bussan partnership has not only reiterated the principles and established commitments by the Paris Declaration and the Accra Agenda for Action, but it has also specifically outlined concrete actions for implementation. The agreed principles at Bussan to advance common goals by 150 countries and over 50 development organisations are (1) ownership of development priorities by developing counties, (2) focus on results, (3) partnerships for development and (4) transparency and shared responsibility.
Following the Bussan agreement, aid flows to countries showed an increase in 2013. Donors provided a total of $134.8 billion in net ODA in 2013. This is a 6.1% increase in real terms in 2013 from $125.9 billion in 2012, amidst the global economic crisis. In spite of this progress, ODA levels continued to be below the internationally agreed target of 0.7% ODA to gross national income (GNI) ratio. For instance, Development Assistance Committee countries provision of net ODA represents 0.29% of their combined GNI in 2012. Consistent with the global trend observed, the net ODA received by Africa as a share of per capita declined from $49.1 in 2011 to $47.5 in 2012.
It is with this spirit that the African Development Bank Group (AfDB) undertook this study to identify key lessons from its projects, which were the main vehicles used to deliver development aid. The study focused on 229 AfDB-financed projects concluded between 2004 and 2012 and their contribution to development effectiveness as measured by the achievement of development outcomes.
This article is organised into five sections. Following the introduction, section two provides a review of the key literature on development effectiveness with a particular focus on the impact of development assistance at the micro level. Section three describes the Bank Group's result measurement. Section four provides insights into the data, methodology and the results of the empirical analysis. Section five provides the conclusion and the key recommendations of the research undertaken.
Studies in the past attempted to measure the impact of external assistance on economic growth and development using time series and cross-sectional data. Such studies influenced the decision-making and economic performance of recipient countries (see Burnside & Dollar
To respond to the above highlighted limitations of the approaches, some authors have tried to establish the impact of development assistance at the micro level, by way of documenting the performance of projects. Most of these studies seemed to establish positive relationships between aid and project success without trying to aggregate the impact of aid at country-level. However, several of these studies also demonstrated difficulty in reaching consensus on the meaning and measure of project success. Kilby (
Deininger, Squire and Basu (
Mubila, Lufumpa and Kayizzi-Mugerwa (
Similarly,
A recently completed study by Denizer, Kaufmann and Kraay (
In sum, the review of the literature seems to concur that analysing the performance of projects and their critical factors for success provided important insights with regard to increasing the effectiveness of development aid. Existing literature further affirms the importance of project-level characteristics (e.g. design, size, age and sector), together with other critical success factors such as country policy and institutional capacities, monitoring efforts and other exogenous factors such as peace. It is also important to note that many of the studies concentrated only on World Bank-financed projects, which have their own characteristics. Thus, this article brings a new perspective by focusing on projects financed by the AfDB and focused only in Africa.
The AfDB Group is made up of three institutions, namely the AfDB (ADB), the African Development Fund (ADF) and the Nigeria Trust Fund (NTF). Currently, it is composed of 54 African countries (regional member countries) and 25 non-African countries (non-regional member countries). The AfDB has expanded its support to 54 regional member countries (RMCs) with the aim to promote the reduction of poverty and contribute to sustainable economic growth.
The AfDB expanded its support in volume on average by more than 20% from an average of $2.6 billion a year in 2003 to $6.5 billion a year in 2013 (
The AfDB developed several strategies to guide its support over time. The current support to RMCs is guided by the ten-year strategy (TYS,
The AfDB provided much of its support through the financing of projects
The AfDB introduced three formats for PCRs to measure results at project level. The methodology to measure results has continued to evolve over time as reflected in the various formats.
The rating on development outcome (project outcome) is scored on a scale from 1 to 4 (four-point scale). 1 signifies ‘Poor’, meaning very limited achievement of objectives with extensive shortcomings; whilst 2 is ‘Fair’, which means partial achievement of result signifying shortcomings. On the other hand, 3 represents ‘Good’ (when most of the objectives are achieved despite a few shortcomings) and 4 is given to mean ‘Very good’ if a project has fully achieved the objectives with no shortcomings.
The unit of analysis for this study is a project financed by the AfDB. As indicated above, the number of projects financed by the AfDB and fully completed was over 2000 as at the time of this study. From these projects there were PCRs for about 496 projects in all the abovementioned three formats. It was decided to take data from PCRs using Format two because (1) the largest number of PCRs followed Format two and (2) their quality of data was considered to be of a higher standards, whilst very few PCRs are presented using Format three. This study selected a sample size of 80% of projects that followed Format two. The projects covered in this study were implemented and completed between 2004 and 2012.
This section presents the descriptive analysis of the data. These projects were implemented by the RMCs whilst the ADB provided financing with some supervisory assistance: project implementation support in the area of monitoring. The sample was well distributed amongst the RMCs as 42 African countries are covered in the study.
Summary of performance of project outcomes.
Project classification | Number of PCRs | Percentage | Average historical rating | Average cost (millions of UA†) | Time for first disbursement (days) | Effective project age (years) |
---|---|---|---|---|---|---|
1. Poor | 7 | 3 | 2.0 | 45.9 | 463 | 8.6 |
2. Fair | 68 | 30 | 2.0 | 24.5 | 514 | 8.8 |
3. Good | 135 | 59 | 2.3 | 52.1 | 357 | 6.7 |
4. Very good | 19 | 8 | 2.4 | 131.0 | 233 | 4.0 |
|
|
|
– | – | – | – |
Effective project age is calculated by taking the time it takes for projects to come to closure from approval. This is arrived at by counting the number of days from board approval to final actual date of closure in the system.
†, The currency used in the AfDB is unit of account (UA) is $1.55. Its rate is determined based on the weighted average of a basket of currencies.
From the 229 projects included in the sample, 8% had ‘Very good’ rating in terms of achieving their development objectives, whilst 59% were rated as ‘Good’. Close to 30% of the projects in the sample were rated ‘Fair’ by task managers of the ADB and the proportion of projects with a rating of ‘Poor’ was only 3%.
It is important to note that projects rated ‘Very good’ were on average large projects with relatively fast first disbursements. In addition, they were implemented in a relatively shorter period of time than the other projects. In contrast, projects rated ‘Poor’ or ‘Fair’ had taken more time (392 days) to receive their first disbursement and their project implementation period exceeded eight years. Furthermore, these projects also received lower ratings during supervision missions undertaken during the project life cycle. In addition, the data showed that 86% of the 229 projects experienced a complete change of task teams during the implementation, which may have affected the quality of the supervision support. This study will further look at these issues in the econometric analysis.
Summary of project performance by sector.
Sector | Projects projects by sector (%) | Average cost (millions of UA) | Delay for first disbursement | Project age (years) | Project classification - Rating (%) | |||
---|---|---|---|---|---|---|---|---|
Poor | Fair | Good | Very good | |||||
Agriculture | 35 | 29.8 | 452 | 8.7 | 6 | 25 | 63 | 5 |
Power | 3.5 | 78.7 | 305 | 6.7 | 0 | 13 | 88 | 0 |
Transport | 7 | 62.9 | 370 | 7.4 | 0 | 19 | 69 | 13 |
Water and sanitation | 6.6 | 65.3 | 565 | 7.1 | 0 | 13 | 67 | 20 |
Social | 22 | 25.2 | 550 | 8.6 | 0 | 48 | 50 | 2 |
Environment | 0.4 | 23.3 | N/A | 7.2 | 0 | 0 | 100 | 0 |
Finance | 1.3 | 112.6 | 195 | 5.7 | 0 | 0 | 67 | 33 |
Industry and minerals | 0.4 | 26.7 | 188 | 7.2 | 0 | 0 | 100 | 0 |
Multi-sector | 24 | 87.3 | 166 | 3.9 | 4 | 32 | 50 | 14 |
In the sample, a large number of projects financed by the ADB supported the agriculture sector (35%) followed by multi-sector interventions (24%) and the social sector (22%). The ADB's support in other sectors such as industry, finance and environment was relatively small. Almost all sectors, with the exception of agriculture, social and multi-sector, in 80% of cases received a ‘Fair’ rating for the development effectiveness of their projects. Both the agriculture and social sector projects seemed to have a longer implementation period in comparison to other sectors. Projects in the agriculture and social sector did not disburse the loan quickly and took respectively 452 days and 550 days on average.
The above analysis provides important insights into the state of project performance and factors that seem to contribute positively or negatively to the performance of the projects. The next section of the article will take the analysis further using econometric analysis based on the same sample.
This section presents the data, estimation method, and the main findings of the econometric analysis.
In order to maximise the use of the data points a set of independent variables were introduced progressively into the analysis. The dependent variable is the overall project outcome rating that will be referred to in this study as the performance of a project. The overall project outcome rating is not a continuous variable. It takes a value ranging from one to four as indicated in Section 3. In this study a binary form of this variable was constructed: all projects with outcome ratings of 3 or 4 are coded as ‘satisfactory’ with a value of 1, and those with a rating of 1 or 2 are coded as ‘not satisfactory’ with a value of 0.
With regard to the independent variables, a set of critical success factors both at country and project levels were introduced progressively. Two models were specified. The first model included basic country and project level variables. The country variables were mainly macroeconomic indicators including: inflation rate (INF), growth rate of real gross domestic product (real GDP), population (POP) and CPIA ratings. For all the macro variables the average during the lifetime of a project was computed. Inflation was entered as a proxy for macroeconomic stability whilst real growth of GDP was inserted in the model to measure the size of an economy. Population size and CPIA were included in the model to measure the complexity of a country
The models specified above were estimated using three methods, namely linear probability ordinary least square (OLS), logit and probit. The OLS estimation method assumes that the dependent variable is continuous and the error term is normally distributed. Whilst logit and probit regressions assume that the error terms follow logistic and normal distribution, respectively.
The goal of the specified models is to assess the major determinant of project performance. Firstly, the study attempted to test a broad hypothesis as to whether country (macro) level variables matter most compared to project-specific factors. The results of the estimations are given at the end the article. The results implied that the model constituted of only country-level variables was insignificant in explaining the change in project performance, although CPIA had a positive and significant coefficient, whilst the model with only project characteristics variables significantly explained the changes in project performance, with age and size of a project being the significant variables. The combined effect of country and project level variables, however, explained more the variation in project performance, which was also closer to the reality on the ground. This finding is consistent with the findings of
Five variables were significant determinants of project performance regardless of the method of estimation. These are CPIA, inflation, the age of the project, the dummy for programing instrument and the size of a project. The result in relation to CPIA is consistent with the findings of Dollar and Levin (
In relation to project characteristics variables, the study discovered a very strong negative relationship between the age of a project and project performance. The age of a project was a significant factor in determining project performance for all estimation. However, the age of a project carries the net effect of complex implementation-related factors. Thus, the underlying causes of extended project implementation should be studied. The coefficient for project size was positive and significant. The project facilitation unit that was introduced to measure the effectiveness of the AfDB effort to augment governments’ capacity had negative but insignificant coefficient.
The dummy to measure the impact of the programming instrument had become significant with a negative coefficient for PBOs. In the case of PBOs, which were commonly known as budget support, success rate was lower than that of investment operations. Thus, it shows that project design affected project performance both in terms of the choice of a programming instrument as well as the size of a project. Surprisingly, all variables to measure the quality of the project managing team, its number and composition, have a positive but insignificant coefficient.
Building on the first model, all variables related to the project management team were dropped and new variables to measure AfDB support were introduced. In order to measure the effectiveness of the AfDB's support, two variables were introduced. The first one was the rating of supervision missions during the project life cycle to see if projects at completion achieved higher or lower rates on average. Secondly, internally generated portfolio management flags were introduced to see if a project was problematic or potentially problematic based on certain indicators and whether that triggered any action to improve performance. A dummy was introduced with a value of one for projects that had not been flagged as problematic or potentially problematic and a value of zero if the project had been flagged in one of the two portfolio indicators. The result indicated that there was positive and significant coefficient for the above two variables. The finding of this study was that lower average supervision rating during the lifetime of a project was related to low performance at the end of a project, indicating the inability of supervision efforts to turn less successful projects around. Similarly, a positive relationship emerged between a project that was not flagged as problematic or potentially problematic and strong project performance. Lastly, regardless of the supervision efforts, projects with implementation problems tend to perform badly.
The dummies to capture the specific nature of fragile countries were not significant.
Most of the African countries, in particular in sub-Saharan Africa, are at a crossroads. They achieved a great deal of social and economic development results over the past decade. The lessons from the past could have a huge impact in informing future policies and programmes. Projects seem to be the major vehicle to deliver development; hence, it is critical to understand their success factors. The findings of this study contribute to an improved understanding of success factors for project performance. The key finding of the study is that project characteristics are the major determinants of project performance, whilst macro level variables also contribute to project performance although to a lesser degree. However, some macroeconomic policy variables were consistently related to project performance, such as CPIA. Hence, countries could improve on development effectiveness by reforming their economies in ways aimed at improving their CPIA ratings and maintaining macroeconomic stability whilst improving project performance through enhancing their project implementation capacities. The AfDB could make efforts to further improve on project design, the selection of programming instruments and pay attention to the complexity of countries.
Finally, although the econometric analysis provided important insights about the determinants of project performance, there remains a significant level of variation in project performance that is not explained with the above models. The models specified thus far only explain at most around 30% of the differences in project performance as shown in the respective adjusted
The following are the main recommendations: Improving a country's CPIA ratings through policy dialogue with RMCs is critical for the success of a project. The policy dialogue should be informed by high-quality empirical studies that can ultimately improve the country's policies and institutional capacities. Strengthening project implementation capacity at the country-level could contribute substantially to improved project performance through enhancing smooth implementation of projects as per the project plan. Improving project design through not only focusing on technical feasibility but also considering the choice of programming instrument and the scale of the intervention. The AfDB should strengthen its supervision and portfolio management tools to achieve greater impact in project performance.
The views expressed in this paper are only the views of the authors and do not represent the view of the African Development Bank Group. We would like to acknowledge the contributions from Houssem Eddine during the econometric analysis and in supervising the data entry. We would like to thank Cherifa Dimassi and Jihen Dridi for their dedicated effort in collecting data. We also like to thank Rodrigo Salvado Cesar for his support at the initial stages of the study.
The authors declare that they have no financial or personal relationship(s) that may have inappropriately influenced them in writing this article.
Y.A. (African Development Bank), O.P.R. (African Development Bank) and D.V. (African Development Bank) equally contributed to the research and writing of this article.
African Economic Outlook 2013.
Goals 2, 3 and 7 are on track to be achieved by the end of 2015 according to the UN report (
Organisation for Economic Co-operations and Development 2013
Development effectiveness refers to achievements at the country level in respect of less attributable, longer-term outcomes and impacts at which the agency efforts are ultimately aimed, and to which it contributes, but which are beyond the manageable, controllable interest of that agency alone (
A project is a set of activities designed to produce a unique product, service or result over a limited time period. The ADB in some instances provided technical support in addition to financial support to countries.
PCRs are prepared for all public sector operations, except in a few cases such as studies and project preparation facilities. Therefore, private sector operations are not subject to PCR submission but subject to an Extended Supervision Report (ESR) in the private sector department of the AfDB.
In the evaluation department database of the ADB, 210 PCRs are available in Format one, 283 PCRs in Format two and three PCRs in Format three as at January 2013.
These countries include: Benin, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Democratic Republic of the Congo, Comoros, Republic of the Congo, Côte d'Ivoire, Djibouti, Egypt, Arab Republic of Eritrea, Ethiopia, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, Mozambique, Niger, Nigeria, Rwanda, Senegal, Seychelles, Sierra Leone, Swaziland, Tanzania, Togo, Tunisia, Uganda and Zambia.
The CPIA index covers a wider range of policy and institutional issues, namely: (1) economic management, (2) structural policies, (3) policies for social inclusion and equity and (4) public sector management and institutions.
Result not reported.
Result not reported in this study.
5464654
Result of the good country or good project hypothesis.
Variable type | Linear probability model | Logit model | Probit model | ||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient | Coefficient | Coefficient | |||||||
|
|||||||||
LINF | 0.0169 | −0.5 | 0.619 | −0.0729 | −0.5 | 0.616 | −0.044 | −0.49 | 0.909 |
LRGDP | 0.0104 | 0.12 | 0.905 | −0.038 | 0.3717 | 0.918 | 0.026 | 0.11 | 0.623 |
LPOP | 0.011 | 0.23 | 0.818 | 0.051 | 0.23 | 0.816 | 0.031 | 0.23 | 0.817 |
CPIA | 0.175 | 1.71 | 0.09 | 0.7499 | 1.71 | 0.089 | 0.465 | 1.72 | 0.086 |
Constant | 0.3174 | 0.5 | 0.619 | −0.753 | −0.26 | 0.791 | −0.491 | −0.28 | 0.782 |
Number of observation | 200 | – | – | 200 | – | – | 200 | – | – |
Prob > |
0.4196 | – | – | 0.4143 | – | – | 0.4095 | – | – |
Adjusted |
0.0004 | – | – | 0.0148 | – | – | 0.015 | – | – |
Result of the good country or good project hypothesis.
Variables | Linear probability model | Logit model | Probit model | ||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient | Coefficient | Coefficient | |||||||
|
|||||||||
Project size | 0.050 | 2.02 | 0.044 | 0.299 | 2.09 | 0.036 | 0.173 | 2.14 | 0.033 |
Number of co-financiers | 0.002 | 0.14 | 0.88 | 0.017 | 0.18 | 0.854 | 0.0153 | 0.28 | 0.781 |
Age of a project | −0.001 | −5.20 | 0.000 | −0.0009 | −4.56 | 0.000 | −0.0005 | −4.74 | 0.000 |
Dummy for PBO | −0.179 | −1.50 | 0.135 | −1.001 | −1.33 | 0.185 | 1.32 | 3.59 | 0.000 |
Constant | 0.917 | 8.29 | 0.0000 | 2.184 | 3.48 | 0.001 | – | – | – |
Number of observation | 200 | – | – | 2000 | – | – | 200 | – | – |
Prob > |
0.000 | – | – | 0.0000 | – | – | 0.0000 | – | – |
Adjusted |
0.134 | – | – | 0.1298 | – | – | 0.129 | – | – |
Binary estimation result for Model 1.
Variables | Linear probability model | Logit model | Probitmodel | ||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient |
|
Coefficient |
|
|
Coefficient |
|
|
||
|
|||||||||
LINF | −0.059 | −1.74 | 0.083 | −0.281 | −1.58 | 0.114 | −0.179 | −1.67 | 0.096 |
LRGDP | 0.031 | 0.39 | 0.698 | 0.216 | 0.49 | 0.626 | 0.170 | 0.65 | 0.518 |
LPOP | 0.030 | 0.77 | 0.440 | 0.230 | 0.90 | 0.368 | 0.133 | 0.87 | 0.382 |
CPIA | 0.160 | 1.72 | 0.086 | 0.906 | 1.80 | 0.070 | 0.533 | 1.79 | 0.074 |
|
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Project size | 0.067 | 2.22 | 0.028 | 0.356 | 2.06 | 0.039 | 0.207 | 2.08 | 0.037 |
Age of a project | −0.001 | −4.78 | 0.000 | −0.009 | −4.30 | 0.000 | −0.0006 | −4.45 | 0.000 |
Number of co-financiers | 0.009 | 0.018 | 0.612 | 0.092 | 0.77 | 0.443 | 0.062 | 0.87 | 0.385 |
Dummy for PBO | −0.228 | −1.80 | 0.073 | −1.28 | −1.55 | 0.122 | −0.772 | −1.64 | 0.101 |
Constant | 1.448 | 2.15 | 0.033 | 4.11 | 1.16 | 0.246 | 1.345 | 1.72 | 0.08 |
Number of observations | 199 | – | – | 200 | – | – | 200 | – |
|
Prob > |
0.000 | – | – | 0.000 | – | – | 0.0000 | – |
|
Adjusted |
0.15 | – | – | 0.161 | – | – | 0.163 | – | – |
Binary estimation result for Model 2A.
Variable type | Linear probability model | Logit model | Probit model | ||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient | Coefficient | Coefficient | |||||||
|
|||||||||
LINF | −0.612 | −1.62 | 0.107 | 0.2378 | 0.04 | 0.966 | −0.238 | −1.64 | 0.101 |
LRGDP | 0.051 | 0.56 | 0.579 | −9.795 | −0.58 | 0.559 | 0.194 | 0.57 | 0.570 |
LPOP | 0.030 | 0.56 | 0.573 | −0.537 | −1.56 | 0.118 | 0.062 | 0.31 | 0.075 |
CPIA | 0.142 | 1.28 | 0.201 | 1.7051 | 1.58 | 0.114 | 0.435 | 1.06 | 0.290 |
Dummy for war | 0.044 | 0.48 | 0.635 | 0.3491 | 0.44 | 0.662 | 0.194 | 0.56 | 0.574 |
|
|||||||||
Project size | 0.041 | 0.58 | 0.562 | 0.187 | 1.4 | 0.161 | 0.187 | 1.4 | 0.610 |
Project age | −0.0007 | −3.14 | 0.002 | −0.002 | −1.72 | 0.080 | −0.002 | −1.72 | −0.085 |
Number of co-financiers | 0.0094 | 0.49 | 0.625 | −0.002 | 0.67 | 0.504 | 0.0600 | 0.67 | 0.504 |
Dummy for PBO | −1.57 | −1.47 | 0.14 | −0.923 | −1.55 | 0.121 | −0.923 | −1.55 | 0.121 |
|
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Supervision rating | 0.271 | 2.38 | 0.018 | 1.864 | 2.06 | 0.039 | 0.918 | 2.16 | 0.031 |
Dummy for problematic/potentially problematic | 0.362 | 4.68 | 0.001 | 2.337 | 4.16 | 0.001 | 1.08 | 4.17 | 0.001 |
Constant | 0.59 | 0.78 | 0.439 | 16.1622 | 1.35 | 0.178 | 1.175 | 0.41 | 0.684 |
Number of observations | 154 | – | – | 154 | – | – | 154 | – | – |
Prob > |
0.000 | – | – | 0.000 | – | – | 0.000 | – | – |
Adjusted |
0.336 | – | – | 0.4 | – | – | 0.288 | – | – |
Binary estimation result for Model 2 B.
Variable type | Linear probability model | Logit model | Probit model | ||||||
---|---|---|---|---|---|---|---|---|---|
Coefficient | Coefficient | Coefficient | |||||||
|
|||||||||
LINF | −0.099 | −1.87 | 0.065 | −0.472 | −1.61 | 0.107 | −0.23 | −1.64 | 0.101 |
LRGDP | −0.0032 | −0.02 | 0.981 | −0.233 | −0.32 | 0.752 | 0.194 | 0.57 | 0.57 |
LPOP | 0.015 | 0.19 | 0.85 | 0.1458 | 0.33 | 0.738 | 0.065 | 0.31 | 0.754 |
CPIA | 0.3094 | 2.18 | 0.032 | 1.895 | 2.36 | 0.018 | 0.435 | 1.06 | 0.290 |
Dummy for war | 0.076 | 0.57 | 0.567 | 0.391 | 0.57 | 0.569 | 0.194 | 0.56 | 0.547 |
|
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Project size | 0.0886 | 1.82 | 0.072 | 0.549 | 182 | 0.069 | 0.3309 | 1.93 | 0.054 |
Age of project | −0.001 | −3.23 | 0.002 | −0.0021 | −3.03 | 0.002 | −0.0007 | −3.14 | 0.002 |
Number of co-financiers | −0.013 | −0.05 | 0.964 | −0.0039 | −0.02 | 0.980 | 0.0026 | 0.03 | 0.978 |
Dummy PBO | −0.344 | −1.75 | 0.084 | −2.147 | −1.88 | 0.060 | −1.451 | −1.90 | 0.057 |
|
|||||||||
Dummy for project facilitation unit | −0.027 | −0.23 | 0.817 | −0.1552 | −0.23 | 0.815 | −0.028 | −0.07 | 0.942 |
Number of task team members | −0.009 | −0.11 | 0.914 | 0.026 | 0.05 | 0.957 | −0.0045 | −0.02 | 0.987 |
Number of task team members with other country experience | 0.472 | 0.95 | 0.345 | 0.010 | 0.01 | 0.917 | 0.1118 | 0.19 | 0.846 |
Number of task team members with other region experience | −0.075 | −0.55 | 0.581 | −0.325 | −0.43 | 0.68 | −0.217 | −0.48 | 0.634 |
Dummy for task team change | 0.017 | 0.17 | 0.867 | 0.273 | 0.46 | 0.646 | 0.158 | 1.45 | 0.651 |
Constant | 1.49 | 1.29 | 0.202 | 6.108 | 1.08 | 0.282 | 3.366 | 1.09 | 0.277 |
Number of observations | 198 | – | – | 197 | – | – | 197 | – | – |
Prob > |
0.03 | – | – | 0.0076 | – | 0.0073 | – | – | |
Adjusted |
0.12 | – | – | 0.229 | – | – | 0.2305 | – | – |