1. Introduction and Context of the Study
As the open government movement has been a major global concern in recent years, the way in which open government influences the sustainable development of countries has become an important issue. Since the Open Government Partnership (OGP), a central international body, was founded in 2011 by eight countries (i.e., Brazil, Indonesia, Mexico, Norway, the Philippines, South Africa, the United Kingdom and the United States), the OGP has currently grown to 78 national members and 56 local jurisdiction members. Member countries have made over 3100 commitments to making their governments more open and accountable [1
]. To move towards improving the degree of government openness, African countries have also made noteworthy efforts and achievements. As of March 2021, fifteen African countries have joined the OGP; as a part of the requirements to join, they have made action plans committing to opening government tenets and reforms, such as bringing about the development of an openness in governance, legislature and laws, and the rights of citizens.
To increase an awareness of open government in African countries, some governments have hosted conferences. In 2013, the Africa Freedom of Information Centre organized a conference for thirty civil society organizations to share knowledge and experience related to open government from the countries that first joined the OGP (e.g., Ghana, Kenya, Liberia, South Africa and Tanzania [2
]). Ranchod [3
] interviewed key players from those five countries and Malawi, which had implemented OGP initiatives. She found that the major developments of these countries were identifying the roles of civil society organizations, creating an awareness of open government and promoting Access to Information acts.
In 2016, Burkina Faso’s National Agency hosted the first Francophone African Conference on Open Data and Open Government and brought representatives from 22 French-speaking countries to share their commitment to open government initiatives and to promote ICT (information and communication technology) [4
]. At this conference, the countries agreed that the principles of open government are key to sustainable social and economic development through the free use of public data. It is beneficial to see that certain African countries have experienced some level of reduction in corruption. Chiviru [5
] reported that Cameroon’s Fako Division of Buea community was able to save about 5 million XAF (USD 50,500) for a school construction project after the Ministry of Public Contracts implemented open contracting. The Nigeria Extractive Industries Transparency Initiative, a partnership between government and civil society, was able to recover USD 2.4 billion from oil corruption [5
]. In 2017, the Africa Open Data Conference was held to host and share various open data groups from African countries [6
]. The Code for Africa initiated the largest public OpenAFRICA repository [7
], allowing citizens to upload and access open datasets across Africa. The government of Kenya launched the Kenya Open Data Initiative (KODI) [8
] portals, which allowed citizens to access government data about the government census, expenditures, and public services.
Regardless of these efforts, the United Nations Economic Commission for Africa (UN ECA) [9
] identified that African countries are faced with several challenges in progressing open government activities. Africans seem to prioritize economic development above information governance issues; therefore, core open government indicators may not matter to average African citizens as much as concerns relating to income generation, employment and personal development. As Ranchod [3
] addressed, most African countries have limited knowledge of open government in both the national levels and organized civil society, since poor information culture is another impediment: information in the public sector is usually inaccessible. Public officials do not understand their obligation to provide services and public goods and information. As such, laws and policies to open up the information space in African countries may not be effective. In addition, because of poor infrastructure, citizens do not have access to government information either, as they are not used to putting demands on revealing information about their governments as a result of their limited capacity to use information technologies [9
Relevant studies on African countries’ government openness largely deal with the following topics: (1) e-government [10
], (2) freedom of information access [12
], and (3) open data initiatives in Africa [16
]. For example, Adu et al. [10
] investigated the implementation progresses of electronic government in Ghana and found that although e-government resources are available and appropriately used to facilitate the activities of government ministries and agencies, public sector organizations are still faced with infrastructure, economic and legal challenges.
The studies of the 2nd group investigated Freedom of Information Act (FOIA) or Access to Information Act in Ghana [12
], Nigeria [13
], South Africa [19
], Sierra Leone [14
] and Liberia [15
] and examined the acts’ impacts on government openness. These studies agreed that the implementation of the acts is unsatisfactory, or, despite implementing FOIA, corrupt practices continue to flourish. As Svärd [15
] also remarked, when the acts are not implemented satisfactorily in African countries, public information or records are instead used to exercise control over citizens by governments and other political institutions; government transparency in Africa is still poor.
The studies of the 3rd group of governments describe open data usage to engage citizens in government practices and innovate in African countries. Afful-Dadzie and Afful-Dadzie’s study [16
] analyzed the infrastructure of seven open government web portals in Africa and compared their data quality through the perspectives of five media practitioners. The study found that data quality features of web portals are not consistent with users’ preferences.
These studies point out that African governments are in a state of lost trust; open government could be one of the major actions needed to restore the trust and dignity of public service in African governments [11
]. Similarly, Chiviru [5
] asserted that the failure to eradicate poverty in Africa is a result of a failure to deal with corruption, inequality, lack of accountability and bad governance. Although the African continent boasts abundant natural resources, the number of people living in extreme poverty is over 100 million. Chiviru [5
] sees that, because open government strengthens the checks and balances within government and enables the identification of corrupt individuals, opening information to citizens enables the building of accountability and makes civil servants responsible for integrity in providing services to citizens. Razzano [19
] conducted a study with OGP member countries and addressed that, from South African interviews, working together with government departments and agencies was difficult because South Africa did not have a strong coordination framework in holding the same objectives and progressing the implementation of OGP commitments. In Africa, where OGP member countries have made more activities and programs than non-OGP countries, they tend to identify more challenges than counterparts. It is also a concern that, while open government-related activities and programs have been centered on OGP member countries in Africa, non-OGP countries may be alienated from main open government activities.
In light of the above situation of African governments in response to the global open government movement, it is meaningful to conduct Africa-focused research to take a closer look at the African regional context. As the open government movement has been driven by developed countries to date, studies on African countries’ open government have been rare. Thus, this study aims to examine how African countries have made effort towards improving their open government levels and to analyze the relationships between the factors of open government. Research questions are formulated as: (1) What levels of open government are African countries positioned in?; (2) What areas need to improve in African countries?; and (3) How do the OGP member countries perform open government in comparison to non-OGP member countries? This assessment of open government in African countries will be useful to analyze the current state of African countries and to examine the areas in which they should improve. The findings of this study will present insights for countries that have similar conditions as African countries to help make policy plans and strategies.
The remainder of this study is organized as follows: Section 2
presents the conceptual framework and objectives of the study. Section 3
explains the data, methods and procedures of conducting the analyses. Section 4
provides the results of the analyses. Section 5
discusses the key points of the results and policy implication. Section 6
concludes with further studies.
2. Conceptual Framework and Objectives
With regard to research on open government, there are several global index studies that provide the world’s progress scores in open government, such as the Global Open Data Index [20
], International Open Data Charter [21
], Open Data Readiness Assessment [22
], Open Government Index [23
], Open Government Standards [24
], and Global Government Openness Index [25
]. These indices provide various aspects of government openness with different factors for the world [26
]. For example, the Open Government Standards [24
], developed by Access Info Europe, assess open government with a focus on the right of access to information and consist of three core areas (e.g., accountability, transparency, and participation). The Global Open Data Index [20
] aims to evaluate the state of government data release regarding 15 categories in the administration of government, such as government budget, national statistics, procurement, national laws, administrative boundaries, etc. The Open Data Readiness Assessment [22
] provides a methodological toolkit for a government agency to use and plan open government programs with eight dimensions (e.g., senior leadership, policy/legal framework, institutional structures, responsibilities and capabilities within government, government data management policies and procedures, demand for open data, civic engagement and capabilities for open data, funding for an open data program, and national technology and skills infrastructure). The Open Government Index [23
] measures open government with legal aspects in four dimensions (e.g., publicized laws and government data, right to information, civic participation, and complaint mechanisms). The Open Government Partnership [27
] assessed the state of member countries’ commitments and action plans when joining the OGP through four dimensions (e.g., accountability, transparency, citizen participation, and technology and innovation).
Accountability indicates the juridical dimensions of governments, governance-related laws or policies in administration. Although some African countries have implemented FOIA or Access to Information Acts, those have been still insufficient; thus, Africa is still confronted with a lack of accountability and bad governance. Transparency indicates transparent government operations and assesses the degree of corruption in governments. Considering that Africa is broadly faced with corruption practices, without the implementation of FOIA—or experiencing unsatisfactory progress even with it—this factor is closely related to the factor of accountability. Citizen participation and freedom indicates the broad engagement and participation of citizens in government operations and social areas. It is based on the perspective that participation by citizens in government should be encouraged in order to build open government. Information and communication technology measures the degree to which ICT facilities and equipment are in place.
Among the existing indices with various viewpoints and focuses, Park and Oh’s study examined the different factors and their relationships used in the existing indices and identified that the four factors of the OGP, including “the common aspects of the existing indices and contain broader coverages of open government related aspects comprehensively and impartially” [26
] (p. 21). The UN ECA [9
] also remarked that open government is based on the philosophy that democracy is a participatory process in which citizens have access to legal rights and play a part in the process, which builds the transparency and accountability of government. Thus, to facilitate the process, the broader and balanced aspects of open government are important. Thus, the Global Government Openness Index adopted the four factors to measure the degree of open government with 134 countries [26
]. This study also adopts the four factors of the Global Government Openness Index as a conceptual framework for estimating African countries’ Government Openness Index (AGOI), including: (1) accountability (ACC hereafter), (2) transparency (TRA hereafter), (3) citizen participation and freedom (CPF hereafter), and (4) information and communication technology (ICT hereafter).
Although the existing index studies include Africa, they do not focus on African countries, which are currently needed to diagnose the situation related to government openness in African countries and help overcome problems. To fill the gap in existing index studies, the objectives of this study are to:
Estimate African countries’ Government Openness Index (AGOI) to see the updated progresses of their open government;
Examine what areas African countries should improve further for sustainable development, and;
Compare the performances of OGP member countries to those of non-OGP countries to see if OGP membership helps improve their progress in open government.
4.1. African Countries’ Government Openness Index
First, this study tests the dimensionality of the four factors by applying a principal component analysis. Table 2
shows the result of principal component analysis for the four factors, which are ACC, TRA, CPF, and ICT.
According to Kaiser’s rule [36
], the number of the principal component is taken if the eigenvalue is larger than 1. The table shows that only the eigenvalue of the first principal component (2.782) is larger than 1. Its proportion (cumulative proportion) is 0.696 (approximately 70%). Thus, one principal component exists, which implies that the four factors are placed in the same dimension (e.g., the assumption is accepted). This test validates that the four factors are closely related and the adoption of the four factors in this test is feasible.
Continuously, Table 3
shows the mean values of African countries by their AGOI scores in 2019 and lists them by the rank of their AGOI scores.
As displayed in Table 3
, Cabo Verde is ranked the highest, with an AGOI of 0.707, followed by Tunisia, Botswana, and Ghana. Looking at ACC values, Botswana is ranked at the top, followed by Cabo Verde, South Africa and Namibia. As for TRA scores, Cabo Verde is placed at the top, followed by Botswana and Ghana. These three countries have higher scores than the rest of the countries. CPF shows South Africa is ranked first with a high score, followed by Tunisia, Ghana, and Cabo Verde. These four countries have very high scores when compared to the rest of the countries. ICT scores show that Tunisia places 1st with a remarkably high score, Morocco in 2nd, and South Africa 3rd. It is worth noting that the top ranked countries tend to score relatively higher in all four factors, while lower countries tend to score lower in all respects. This implies that the scores of the factors in one country in Africa are likely to be connected to each other.
illustrates the descriptive statistics of the factors included in this study.
The descriptive statistics include the overall picture of the data, such as mean, median, minimum, maximum, and standard deviation values for AGOI and four factors. The magnitude of AGOI ranges from 0.131 to 0.711. The mean of AGOI in 32 African countries (0.389) is above the median value (0.367). Among the four factors, ACC has the highest mean value (0.553) as well as the highest standard deviation value (0.179), while ICT has the lowest mean (0.238) and the lowest standard deviation values (0.149). ACC’s high standard deviation score shows that a bigger gap exists between African countries than that of ICT.
To examine the overall trends of AGOI with African countries, Figure 1
displays the trend of AGOI and four factors with their values in 2006 and 2019, differences and CAGR (cumulative annual growth rate) for the period of 2006–2019. The differences in the values are taken by subtracting the value of 2006 from that of 2019.
and Table 5
shows that AGOI has continuously increased 0.328 in 2006 to 0.449 in 2019 with a CAGR of 2.45%, which is a positive sign. Considering the trends of the four factors for the period, ICT has increased the most from 0.097 in 2006 to 0.371 in 2019, resulting in a difference between the two years of 0.274. The CAGR of ICT during the period is 10.89%, which is the biggest amount among the four factors, as well as AGOI. The second highest increase among the four factors is CPF, which has increased from 0.312 in 2006 to 0.492 in 2019, resulting in a difference of 0.180 and a CAGR of 3.56%. TRA has also increased from 0.349 in 2006 to 0.383 in 2019 with a CAGR of 0.74%. ACC, however, has slightly decreased during the period, resulting in a negative difference of −0.005. Even though its decrease is a tiny amount, it is noticeable to see that only ACC has been reduced among the four factors. To keep AGOI constantly increasing, it seems that ICT and CPF have mainly contributed to an increase in AGOI during the period.
Over the time period, the ACC line of African countries remains almost the same or is a slightly lower line, while the TRA line slightly increases from 2006 to 2012 and then lowers. Surprisingly, ICT has rapidly and dramatically increased during the period. CPF also shows constant increases over the period, making a parallel line with ICT. AGOI performs well with a constant increase over the period, looking like a middle line in the four factor lines.
shows correlative relations of AGOI with four factors for the full period by using panel data of all countries.
As for AGOI, the correlations with ACC, TRA and CPF tend to be high with the highest ACC score (0.892). These three factors have a positive impact on the levels of AGOI. In the case of ACC, a correlation with TRA is high, followed by CPF and ICT. In the meantime, the correlations of ICT tend to have lower scores and a correlation of ICT and AGOI is the highest (0.696).
4.3. Comparisons of OGP and NOGP
To examine the differences of OPG and non-OGP countries in building AGOI scores and to see which factor affects their difference, this section makes a comparative analysis of the two groups. Table 3
lists the countries of OGP members (OGP hereafter) and their non-OGP counterparts (NOGP hereafter) as of April 2020 from the OGP site (2021). From the 32 African countries selected for the study, while the number of OGP member countries is 11, that of non-OGP countries is 21.
To see the comparative performances of OGP and non-OGP countries in constructing AGOI, Table 7
displays the mean values of the two country groups by the values of AGOI and four factors at the beginning year of 2006 and in the final year, 2019. The differences in each column are taken by subtracting the value of 2006 from that of 2019.
For the OGP group, AGOI has increased from 0.395 in 2006 to 0.545 in 2019, resulting in a positive change of 0.150. Among the four factors, ICT has performed the most with a difference of 0.328 over the period. CPF places in second with a difference of 0.230 and TRA is third with 0.055. The value of ACC for the OGP group has been reduced from 0.650 in 2006 to 0.636 in 2019, resulting in a difference of −0.014.
For the non-OGP group, AGOI has also increased from 0.292 in 2006 to 0.398 in 2019, resulting in a positive change of 0.106. In terms of the magnitude of change, the order is ICT, CPF, TRA and ACC. Among the four factors, ACC of the OGP group is negative, and that of the non-OGP group also shows a negative change from 2006 to 2019.
Looking at the differences in the OGP group from the non-OGP group by subtracting the value of 2006 from that of 2019, the OGP group’s differences in AGOI, ICT, CPF, and TRA are larger than those in the non-OGP group. The difference of ACC between OGP and non-OGP has decreased from 0.148 in 2006 to 0.135 in 2019. It is conspicuous that both the OGP and non-OGP countries show the same pattern of increases in AGOI, ICT, CPF and TRA by order, as well as a decrease in ACC. Most of all, OGP member countries have performed better than the non-OGP counterparts in terms of AGOI, ICT, CPF and TRA except for ACC. These observations imply that participating in the OGP seems helpful in enhancing their government openness and the differences between the two groups (0.043) is meaningful to OGP countries.
To visually present the means of AGOI and four factors by OGP members and non-OGP countries, Figure 2
demonstrates the clear differences between the two groups in graphs.
In all five graphs, the lines of OGP are placed in higher positions than those of their non-OGP counterparts, which boosts OGP countries. The two lines of AGOI moved upwards in parallel until 2012, when the gap between the lines became larger. Since 2017, they have been going in parallel again. The year was after the OGP began in 2011, and since then, more member countries have joined the OGP. The lines of ACC have been steadily in parallel from 2006 to 2016, and after a small decrease in 2015, they returned into parallel. The lines of TRA show that a difference in the two lines has increased and decreased by 2011. Then, the gap between the two TRA lines increased from 2014 to 2019. Overall, the cases of ACC and TRA are likely to ensure that the gap between the two lines remains relatively steady.
In the case of CPF, with a decrease from 2006 to 2008, the gap in the two CPF lines was significantly high from 2014 to 2018, with a decrease in 2019. This agrees with the gap of AGOI lines, which also became larger in 2014. The case of ICT is prominent in order to see that the gap between the two groups constantly and steadily increased from 2006 to 2019.
Looking at the comparative performances of the two groups in the past year, 2019, as the AGOI scores of OGP are much higher than those of the non-OGP group, the values of non-OGP could only reach approximately 73% in AGOI, 79% in ACC, 73% in TRA, 68% in CPF, and 71% in ICT, respectively. Among the five cases, while the differences of ACC, TRA and ICT are similar amounts, the difference of CPF is noticeably the highest. This implies that the CPF score of the OGP group tends to be much higher than those of the OGP countries, which reflects the fact that OGP countries perform especially well in the CPF score.
4.4. Panel Regressions
For the first regression analysis with AGOI as a dependent variable, three models are used for both the fixed effect model and the random effect model, depending on the independent variables. The first model has a constant term and GDP per capita (PGDP) as independent variables. The second model has a constant term and human capital index (HCI) as independent variables. The third model has three independent variables such as constant term, PGDP, and HCI. In order to select a model between the fixed effect model and random effect model, the Hausman test is performed and applied to Model 1 vs. Model 4, Model 2 vs. Model 5, and Model 3 vs. Model 6.
demonstrates the results of the panel regression and the Hausman test for three groups: ALL (32 African countries), OGP (11 countries) and Non-OGP (21 countries).
The table provides adjusted R2 as overall model fitness. For the ALL group with 32 African countries, based on the Hausman test, Models 2, 3 and 4 are chosen. Both Model 3 and Model 4 show that the income variable has a positive and statistically significant effect on AGOI. Both Model 2 and Model 3 also show that the HCI variable has a positive and statistically significant effect on AGOI. Thus, the two hypotheses for the ALL group are accepted at the 5% significance level. Comparing the magnitude of income and HCI variables in Model 3, the income effect (0.295) dominates the HCI effect (0.089).
For the OGP group in Africa, Models 1, 2, and 3 are selected. The panel regression results show that both income and HCI have positive and statistically significant effects on AGOI, as observed in the ALL case. Again, the relative effect of income is much larger than that of HCI. Thus, the two hypotheses for the OGP group are accepted at the 5% significance level. For non-OGP countries in Africa, Models 2, 4 and 6 are selected, based on the Hausman test. Like ALL and OGP groups, the income variable is positively significant, as shown in the selected models. HCI, however, is either insignificant (Model 2) or has a negatively significant effect (Model 6). Thus, for the non-OGP group, the first hypothesis is accepted and the second hypothesis is rejected at the 5% significance level.
Comparing the three groups together—ALL, OGP, and non-OGP—income has a positively and statistically significant effect at the 1% significance level, which is robust. Income effect in OGP has a more dominant effect than that of non-OGP. In the case of HCI, only ALL and OGP groups have a positively and statistically significant effect, which is robust. That of the non-OGP group is either insignificant or has a negatively significant effect. Considering the HCI effects between ALL and OGP, the HCI effect of OGP dominates that of non-OGP.
As the second regression analysis, this study tests the existence and relative strength of the effect of joining the OGP on AGOI while controlling for the effects of PGDP and HCI. The panel regression results are shown in Table 9
To see the overall results, Figure 3
illustrates the main findings of this study in a visual form.