Exploring the Influence of Government Data Performance on Knowledge Capabilities: Towards a Data-Oriented Political Economy
Department of Political Science, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia
Soc. Sci. 2022, 11(9), 384; https://doi.org/10.3390/socsci11090384
Submission received: 18 July 2022
/
Revised: 22 August 2022
/
Accepted: 23 August 2022
/
Published: 26 August 2022
Abstract
:This article is devoted to the study of the influence of government data performance on knowledge capabilities. Knowledge capabilities play a key role in open innovation and creation of citizen-oriented products and services. However, it is necessary to assess the role of the information environment in the development of knowledge capabilities, including government data as a product and a component of the information environment. Government data performance is expressed through the statistical capacity score and its three dimensions: periodicity assessment of statistical capacity, methodology assessment of statistical capacity, and source data assessment of statistical capacity. Knowledge capabilities are expressed through economic complexity, which reflects the diversity and uniqueness of the production capabilities inherent in each country’s exports. Econometric analysis is based on dynamic panel data models that quantify the effect of government data performance on economic complexity. The final dataset includes 94 countries and their indicators for the selected variables for 2004–2019. The models show that government data performance and its various dimensions influence economic complexity because government data provide a detailed and publicly available description of the economic space, including available resources and potential tasks. Based on these data, agents can produce dissimilar and unique products. This logic may be true in general for the influence of government data performance on knowledge capabilities: structured and complete government data reduces the cost of information analysis and provides information support for decisions. The results of the study contribute to the ideas of a data-oriented political economy. Government participation in value creation includes various forms of indirect influence. The provision of government data is one of these forms. The development of collective data governance and collaborative data projects makes it possible to create more complete datasets and stimulates citizen involvement and deliberation.
1. Introduction
Socio-economic development is associated with improving the quality of life and the development of innovative creativity. At the same time, quality of life, innovative creativity, and sustainable development are highly dependent on knowledge capabilities. Knowledge capabilities include a wide range of abilities to quickly process information from the external and internal environment and generate new knowledge for the decision-making process. The use of knowledge capabilities for the formation of comparative advantages is separately noted (Dawson 2000; Nieves and Haller 2014). Knowledge capabilities allow the creation of innovations that are focused on residents and support economic diversity. “Diversity” in this case indicates the richness of available products/services for consumers, as well as the interaction of different pieces of knowledge to produce these products/services. Consequently, to develop innovation-oriented policy, it is necessary to focus on indicators that evaluate the diversity of products/services as a result of using knowledge capabilities.
Knowledge capabilities are considered as a more general category reflecting the processing and analysis of information for decision making and the production of new knowledge. For the applied analysis of problems related to knowledge capabilities, concrete empirical variables are needed, which may differ depending on the research objectives. Knowledge capabilities can be defined through economic complexity. Economic complexity reflects the diversity and uniqueness of the production capabilities inherent to each country’s exports. The assessment of economic complexity includes several dimensions: (1) the absolute quantity of products that a country produces; (2) the ubiquity of these products (the number of countries exporting the product); and (3) the variety of products produced by other countries (Hidalgo and Hausmann 2009; Hidalgo and Hausmann 2011; Hidalgo et al. 2007). The source of economic complexity is the use of productive knowledge and its dissemination among the population. Productive knowledge is accumulated information about how to produce different products, i.e., know-how. With productive knowledge, it is possible to integrate industries and sub-industries through the search for products that unite organizations of different specializations and profiles. Since the comparative advantages of different industries differ from country to country, creating innovations based on productive knowledge can guarantee that similar products are not ubiquitous in the market. Economic complexity as the expression of knowledge capabilities is particularly important in the context of innovation and production of advanced technology, since it requires the integration of knowledge from different scientific, research, and industrial organizations (Moreno-Hurtado et al. 2020). Next, we will focus on economic complexity as the empirical expression of knowledge capabilities.
The key issue is the role of the government in stimulating economic complexity. As noted above, economic complexity requires the exchange of local knowledge to produce products/services at the intersection of different industries and organizations. To achieve these goals, the government has various information resources, primarily statistics and open data. The government produces data-as-a-product, on the basis of which citizens and organizations assess the situation, coordinate actions, form expectations from their own investments, and make forecasts. It is important to note that government statistics and data comprise information about the state of different industries, so the final official data represent an accumulation of different pieces of knowledge. The data-as-a-product format allows us to share this knowledge and use it to generate innovation, therefore increasing economic diversity.
Current research in this area is mainly related to the impact of open government data on economic development and quality of governance. Citizens and businesses need open government data to ensure transparency, deliberation, and citizen participation in governance (Khayyat and Bannister 2017). The publication and use of open data helps citizens to make more informed decisions, as well as to assess and evenly distribute the risks from their own investments. In this regard, various aspects of the effectiveness of open government and open data have been studied (Ruijer et al. 2020; Milić et al. 2022). However, it is important to further determine how government data performance affects knowledge creativity and economic diversity in the broad context of political economy.
This study contributes to the discussion of the role of the government in the production of innovation and knowledge capabilities. Herein, we focus on the influence of government data performance on economic complexity. We must first distinguish the significance of this research for policy development and the significance for the theory and political economy of the government. These two areas will be discussed based on the results of the study.
The significance for policy making is that the results will help determine which dimensions of public policy data are relevant for economic complexity and innovation. Early studies recognize the importance of organizations’ access to economic information for market analysis and market strategy development (Altayar 2018; Kitsios and Kamariotou 2019; Hanna 2018): government data and statistics are one of the main sources of economic information (and are available for free). However, information support for decision making requires a more detailed view on government data, taking into account different dimensions of data: coverage of areas of economic activity, a variety of data sources, and timely publication of data. This article will explore different dimensions of government data performance, which is important for developing a multi-level and integrated policy in the field of knowledge capabilities and innovation.
Significance for the theory and political economy of the government is based on a critical assessment of the interaction of state categorization and knowledge capabilities. Existing studies indicate that the government seeks to present society in the form of a system of stable categories through which the social space is ordered and fixed (Scott 1998, 2012; Graeber 2015; Dean 2010). This categorization is implemented through standards, government statistics, information systems, data registers, and so on. Criticism of state categorization is related to the fact that it does not take into account local implicit knowledge and experience: state standards ignore established ways of producing innovations, while government data may be fragmented and do not fully reflect economic reality. This paper shows that digitalization and data policy allow the state to identify the diversity of the environment and develop information policies that encourage innovation, knowledge capabilities, and new ways to create value. Thus, it is possible to enrich current theories in the field of political economy of the state.
The remainder of this paper is structured as follows. The methodology, models, and datasets used are described in Section 2. Statistical modeling of the impact of government data performance on economic complexity is presented in Section 3. Section 4 discusses the application of modeling results to (1) innovative policy making and (2) the development of a data-oriented political economy. Finally, the main conclusions and directions of future research are presented in Section 5.
2. Materials and Methods
Modeling the influence of government data performance on economic complexity is based on several assumptions. Government data are a variety of data products that allow economic agents to access information, share knowledge, and coordinate actions to create more diverse and complex products. Government data are also a tool for state categorization, which makes it possible to describe the economic space that is accessible to all. This is the research question: Does government data performance affect economic complexity and allow the creation of a variety of products/services?
One of the most difficult issues is choosing an indicator that corresponds to government data performance. The key aspect is the distinction between government statistics and open data. Open data are government and local government data, published on the internet in the form of datasets. Features of open data include their reuse and free distribution by anyone, so they are considered as an effective tool for stimulating businesses and creating citizen-oriented services (Inkinen et al. 2019). Moreover, by providing their datasets, the government becomes more transparent and accountable to citizens. Open data includes such categories as demographics, quality of life, public procurement, and services.
However, there are several barriers to using indices of open data quality as indicators of government data performance. First, countries differ greatly in the development of open data policies, which is determined by institutional factors. On the one hand, there are centralized open data initiatives for OECD (Organisation for Economic Cooperation and Development) countries that are regularly audited and evaluated; on the other hand, many developing countries are characterized by a lack of open data and a lack of centralized policies for their development. Second, open data are part of a broader concept of e-government, with the bulk of open data initiatives being implemented in the 2010s (Open Data Barometer n.d.; Open Government Data n.d.). However, a comprehensive analysis of the impact of government data on economic complexity requires broader time lapses. To sum up, it is difficult to get a representative sample of countries with a sufficient level of open data indicators.
A more productive option is to use indicators in the field of government statistics. Government statistics are a set of statistical data that the government uses to quantify results in demographics, economic activity, industries, infrastructure, trade, and social policy. In other words, government statistics capture the institutional, technological, and social resources available to agents within the national economy. In the present article, government data will be understood as government statistics.
Government data performance indicates the completeness of coverage of economic indicators and the quality of their evaluation. Given these characteristics, the present study uses statistical capacity scores to assess government data performance. Statistical capacity score is a composite indicator that assesses the potential of a country’s statistical system in such dimensions as (1) methodology, (2) data source, and (3) timeliness of data publication. Countries are evaluated with respect to these dimensions, and then the overall statistical capacity score is calculated as a simple average of the scores for all three dimensions on a scale from 0 to 100. The study will analyze (1) the impact of the overall statistical capacity score on economic complexity, and (2) the impact of each statistical capacity score dimension on economic complexity.
To assess economic complexity, the economic complexity index is used, which evaluates the diversity of a country’s exports. The study also uses several economic indicators as control variables. One of the control variables is the quality of institutions, estimated as a simple average of scores on such dimensions as Voice and Accountability, Political Stability and Lack of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.
Table 1 provides complete information about the variables used. The dependent variable is highlighted in orange, the independent variables in green, and the control variables in yellow.
To build the model, a panel sample of data was formed, which covers 94 countries and their indicators for the selected variables for 2004–2019. The choice of chronological framework is determined by the availability of data on statistical capacity score and economic complexity index. The final sample is characterized by a prevalence of developing countries, for which auditing and institutional recommendations for improving government data were most in demand. This circumstance opens new opportunities for studying the influence of government data performance on economic complexity and knowledge capabilities. Developing countries are characterized by economies in transition and the need to provide information resources and incentives to businesses. In particular, the transition from centralized systems to the market requires disclosure of information about demographics, industries, institutions, macroeconomics, infrastructure, and technologies for economic agents. Based on the generated dataset, it is possible to identify whether government data are actually used by economic agents to create various products/services. For this reason, the prevalence of developing countries in the dataset is an advantage for recommendations in innovation development and complexity policies.
The possibility of using statistical models requires a preliminary analysis of descriptive statistics and correlations between variables based on the number of observations for selected geographical and chronological parameters (Table 2 and Table 3). Indicators of descriptive statistics and correlations will allow us to assess whether the formed sample is optimal for modeling.
As can be seen from Table 2, the spread of values across dependent variables is not critical, which indicates that the average value for the sample is informative.
Based on the data shown in Table 3, there is no high correlation between the main explanatory variables, which avoids multicollinearity. Thus, the coefficient estimates obtained during statistical analysis can be considered as effective and unbiased.
Dynamic panel data models are used to model the influence of government data performance on economic complexity. The approach proposed by R. Blundell and S. Bond (Blundell and Bond 1998) (system GMM, GMM-BB, system generalized method of moments) was used for analysis. This approach is most common for analyzing data that is available for a large sample of countries, but for a limited period of time (T < N, “small T, large N” problem). The chosen approach involves evaluating equations in both levels (Equation (1)) and differences between levels (Equation (2)), which allows us to increase the number of instrumental variables and achieve consistent estimates of the coefficients of equations:
where i and t denote the country and year, respectively; ECI is the economic complexity index; X is a vector of explanatory variables (listed in Table 1); α is a constant; β is a coefficient of the lagged dependent variable; δ is a vector of coefficients for the corresponding explanatory variables; μi is fixed spatial (cross-country) effects; and ε is an error term. The second equation considers differences between levels, so the dependent variable, the vector of explanatory variables, and the error term are denoted by Δ. β is a vector of coefficients for the corresponding explanatory variables, μi is fixed spatial (cross-country) effects, and εit is a random error. The second equation considers differences between levels, so the dependent variable, the vector of explanatory variables, and the random error are denoted by Δ.
ECIit = α + βECIit−1 + δXit + μi + εit,
ΔECIit = α + β(ΔECIit−1) + δ(ΔXit) + (Δεit),
The study examines several dynamic panel models that differ in the explanatory variable reflecting government data performance. First, the influence of statistical capacity score is modeled. Second, the effect of dimensions of this index is estimated (periodicity, methodology, and source). This approach allows us to assess both the overall influence of government data performance on economic complexity and its individual components for better informed decision making.
This section presents data and models that will help assess the influence of government data performance on economic complexity. In the next section, the results of dynamic models will be presented, which will serve as a basis for discussions in the field of policy development and development of political economy in the context of data policy.
3. Results
The current task is to apply the presented dynamic models and assess the influence of government data performance on economic complexity. Each variable that reflects government data performance—statistical capacity score, periodicity, methodology, source—is evaluated in three models due to the sequential addition of control variables.
Initially, the impact of the overall statistical capacity score on economic complexity is estimated. The results are presented in Table 4.
One of the indicators of model quality is a statistically significant positive autoregressive coefficient. In addition, the Hansen and Sargan tests of over-identified restrictions were conducted, which indicate the adequacy of the selected instrumental variables. Therefore, the results of the models can be considered reliable.
Models show that the statistical capacity score has an impact on the economic complexity index. To sum up, this indicates a significant role of government data performance in economic complexity. As noted above, government data performance is a set of characteristics that correspond to the quality, completeness, and diversity of data. Organizations can use government data and statistics to analyze the state of industries and forecast macroeconomic dynamics, thus creating opportunities to find new market niches and create new products. If government data describe economic results, you can explain the impact of statistical capacity on economic complexity by allowing businesses to enter/create new markets or cross-industry products.
The modeling of the impact of statistical capacity score on economic complexity is presented above in Table 4. However, the statistical capacity score includes different dimensions (methodology, data source, and timeliness of data publication). It is useful to assess the influence of each of these dimensions on economic complexity: the results will show which dimensions of government data performance are relevant for policy development in the areas of economic complexity, knowledge, and innovation.
Next, we assess the influence of data periodicity on economic complexity. Periodicity refers to the extent to which government data are made available to users by converting source data into statistical results. In other words, the periodicity indicates that current socio-economic indicators are available to users. Periodicity is calculated as a weighted average of ten basic indicators: periodicity of income poverty, periodicity of child malnutrition, periodicity of child mortality, periodicity of immunization, HIV/AIDS, periodicity of maternal health, periodicity of gender equality in education, primary completion, access to water, and periodicity of GDP growth. The final periodicity estimate is one-third of the total estimate of the statistical potential indicator. The results of the models are presented in Table 5.
A statistically significant autoregressive coefficient indicates the quality of the models, while the Hansen and Sargan tests of over-identified restrictions indicates the adequacy of the selected instrumental variables.
The results of the models show that periodicity has an impact on economic complexity. This can be explained by the fact that timeliness of government data allows us to have an up-to-date assessment of markets and macroeconomic dynamics. Hence, periodicity is an important factor for describing the economic environment and stimulating investments from economic agents. In the context of developing economic complexity, periodicity may be required for evaluating resources and consumers, and creating citizen-oriented products and services.
The next step is to assess the influence of the methodology on economic complexity. Methodology is defined as the use of internationally recommended standards and methods by official statistical offices. Following these standards and methods ensures that statistical results are consistent, which helps to evaluate regional markets based on common indicators. The methodology score is calculated as a weighted average of 10 points for the following basic indicators: national accounts base year, balance of payments manual in use, external debt reporting status, Consumer Price Index base year, industrial production index, import/export prices, government finance accounting concept, enrolment reporting to UNESCO, vaccine reporting to WHO, and IMF’s Special Data Dissemination Standard. The final assessment of the methodology is also one-third of the overall assessment of the statistical capacity score. The results of the modelling are shown in Table 6.
The statistically significant autoregressive coefficient and the results of the Hansen and Sargan tests of over-identified restrictions allow adequate evaluation of the models.
According to the results of the models, the methodology has an impact on economic complexity. First of all, this is explained by synchronization of the results: the methodology covers common databases and evaluation methods, so the final government data make it possible to evaluate the economies of different countries and regions. In turn, this provides economic organizations an opportunity to assess the prospects for creating new markets/products and prospects for inclusion in new value chains—all factors that shape economic complexity.
The last specification evaluates the impact of data source on economic complexity. In the context of the statistical capacity score, the source indicates whether the country provides data from administrative systems in accordance with the internationally recommended frequency. The source score is calculated as a weighted average of five estimates of the base indicator: population census conducted within last ten years, agriculture census conducted within last ten years, number of poverty surveys conducted within last ten years, number of health surveys conducted within last ten years, and completeness of vital registration system. The final estimate of the source data is one-third of the total estimate of the statistical capacity score. The results of the models are shown in Table 7.
One of the indicators of model quality is a statistically significant positive autoregressive coefficient. In addition, the Hansen and Sargan tests of over-identified restrictions was conducted, which indicates the adequacy of the selected instrumental variables. Therefore, the results of the models can be considered reliable.
Based on the results of the models, we can conclude that the data source has an impact on economic complexity. This can be explained by the fact that source standards ensure the synchronicity and unity of data, on the basis of which official services prepare statistics. Thus, economic agents gain access to regular, generally accepted historical and current data, and as a result, they can create new products that are different and not ubiquitous in the market.
Thus, dynamic panel data models show that economic complexity is affected by both the general statistical capacity score and its various dimensions—periodicity, methodology, and source. Together, this indicates the significant role of government data for economic complexity and innovation production. Creating new products or entering new markets requires information support for decision-making and a comprehensive assessment of the environment (consumer demand, price dynamics, quality of life, social policy); that is, government data help in this assessment, providing access to adequate and timely data. This makes it easier to explain the results of the models. More detailed explanations with the prospect of entering the general knowledge capabilities and data-oriented political economy areas are presented in the Discussion section.
4. Discussion
Taking into account the results of this study, their discussion is possible in several stages: (1) the influence of the government data performance on economic complexity, (2) the influence of the government data performance on knowledge capabilities, (3) prospects for supplementing/revising current provisions in the theory of political economy of the government. Thus, it is proposed to scale the results from the level of the selected research object to the level of general theory.
4.1. Government Data Performance and Economic Complexity
In this study, economic complexity was used as a reflection of knowledge capabilities. In other words, economic complexity is a particular object of research, but other variables can be used to express knowledge capabilities. The main task of discussion at this stage is to formulate directions in which the government data performance affects economic complexity. Due to the chosen methods, the proposed directions are probabilistic and require further discussions with other proxy variables.
Let us recall that economic complexity reflects knowledge capabilities in the sense that the variety of innovations produced is a consequence of the use of know-how knowledge and the adaptation of implicit knowledge to new tasks. Knowledge of this type makes it possible to create unique products that are not ubiquitous and differ from alternatives on the market (Hausmann Ricardo et al. 2011). Models show that the government data performance affects economic complexity. However, it cannot be argued that the government data performance directly determines the accumulation of productive knowledge. Therefore, it is important to assume what lies in the gap between economic complexity (productive knowledge) and effective government data.
Effective government data are those that best describe a country’s economic space. Statistical capacity score shows how complete the government statistics data are, and other proxy variables can be used. It can be assumed that effective government data are an important component of the environment that provides information about two parameters. The first parameter is the resources available in the economic space. The second is an approximate list and nature of tasks/problems that products and services can address. For example, information on the resources of the economic space provides data on GDP per capita, demographic data and regional data, while information on potential task/problems can be obtained from data on access to drinking water, vaccination data, poverty data, etc. Thus, the frequency and methodological and source relevance of government data allow us to obtain sufficient information to understand what product can be produced, taking into account the available resources and existing tasks/problems.
The influence of government data performance on economic complexity can be analyzed in the following way. If government data sufficiently describe the economic space (available resources and problems), then organizations have more opportunities to search for information, analyze the market and niches, and apply their knowledge to create value. It is opportunities that determine the influence of government data performance on economic complexity: information about the economic space is shared, but organizations’ differences in knowledge, specialization, and skills allow them to use this information to create different products. It is important to emphasize that government data are publicly available and free, so all agents potentially have access to the description of the economic space.
In summary, government data performance affects economic complexity, since government data shows what opportunities lie in the economic space for agents. Agents use this data to decide how their productive knowledge can be applied to a given economic space. The more complete (and efficient) government data are, the more opportunities agents have to adapt their ideas, products, and innovations to the market, increasing overall complexity and diversity.
4.2. Government Data Performance and Knowledge Capabilities
The influence of government data performance on economic complexity was analyzed above. However, economic complexity is only one of the variables that reflect knowledge capabilities—other variables are also possible. In this section, we propose to discuss the potential impact of data performance on knowledge capabilities in general. This discussion is theoretical and can be used for hypotheses in other papers with other variables reflecting knowledge capabilities.
Government data performance is primarily structured, timely, and detailed information about the resources and problems of the economic space. In this capacity, it is logical to assume that they affect knowledge capabilities and economic complexity, as shown earlier. Knowledge capabilities are necessary to process information flows in detail and to extract and assimilate knowledge and make decisions (Khor and Tan 2012; Balle et al. 2020; Khaksar et al. 2020; Martinez et al. 2019). The advantage of government data performance for knowledge capabilities is that these data provide a wide and rich layer of information, which is already quite detailed and requires lower cognitive and transaction costs to extract knowledge and implement in the decision-making process. This statement is general and logically derived from the coupling of the theory of knowledge capabilities and the potential scaling of the results of this study to knowledge capabilities in general. As a result, it needs to be checked using the example of other proxy variables that reflect knowledge capabilities.
4.3. Government Data Performance, Knowledge Capabilities, and Data-Oriented Political Economy
It can be assumed that the results of the study can also be used to review/supplement current provisions in the political economy of the government. In modern political economy, the government is represented as a consolidated agent of management, whose tasks include (1) creating a system of representation of the management space, expertise and knowledge, decision making, and management of social conflicts, and (2) forming institutions/structures for legitimizing and protecting property rights, creating and extracting value, and distributing risks and rewards from investments in the public sector.
The government is often directly involved in the value creation process. This is implemented through investment programs, infrastructure projects, technology provision, and institutional reforms. However, there are widespread views that it is difficult for governments to interact with implicit knowledge. While local communities rely on implicit knowledge, the government aims to unify knowledge and norms. Unification of knowledge aims to simplify the process of extracting value and is expressed in the introduction of standards, protocols, etc. (Graeber 2004; Scott 2009; Sneddon 2007). The desire to unify the knowledge and economic fields leads to the fact that formal schemes parasitize local informal norms that differ in geographical, professional, and other communities; thus, there is a conflict that hinders innovation and the process of creating and extracting value.
The solution to this conflict is possible if we consider the multidimensional role of the government in knowledge management and value creation. Unification of knowledge involves the direct involvement of the government in the management of knowledge capabilities and the creation/extraction of value. At the same time, indirect government involvement is possible through the provision of government data, statistics, and access to other information resources, all of which are described in the data products category. In this case, the government saturates the information environment to increase knowledge capabilities, but does not form techniques/standards that prescribe ways to manage knowledge. Studies in the field of political economy emphasize that the government’s participation in value creation is often mediated: the development of physical infrastructure, introduction of institutions and incentives, and development of progressive programs and policies (Mazzucato 2013, 2021). It seems fair to include in this list the publication of official data, which is a publicly available resource for organizations to create value through products and services.
In a broader perspective, additional solutions can be identified that involve government participation, government data, and knowledge capabilities. Organizations have different types of data, and combining them represents important information support for decisions and projects. Combining data is possible in collective data management—an organizational policy that includes various forms of joint data collection/analysis/dissemination, such as data trusts, data commons, data marketplaces, and data collaboratives (Mukhametov 2021; Paskaleva et al. 2017). Moreover, other forms of joint information/data collection are relevant, including crowd sensing, crowd casting, crowd sourcing, and collaborative monitoring (Temiz 2021; Karachiwalla and Pinkow 2021; Alvear et al. 2018; Mukhametov 2020). Together, they allow the collection of datasets that help to create a complete description of situations and processes. They also support the participatory nature of information policies and encourage organizations to invest in open and publicly available data. In this context, the government can develop an institutional regulation of collective data management, as well as improve official data, complementing them with data from publicly available repositories and directories. Moreover, with legal guarantees, it is possible to attract citizens and businesses to contribute their own data to such projects. We can expect the growth of different integrated data platforms in the short and the long term.
Thus, government data performance has theoretical and empirical value for both economic complexity and knowledge capabilities, as well as for data-oriented political economy. Government data performance allows for timely and detailed descriptions of the economic space, including descriptions of available resources and potential challenges that require innovation. In this sense, public data are crucial as a support of the decision-making process, as well as a source of structured information for extracting and assimilating knowledge. This shows the influence of government data performance on knowledge capabilities: rich, detailed, and timely data reduce agents’ costs for finding and evaluating information, allowing them to redirect resources to other stages of decision-making. In addition, current trends in the creation of data commons and public data repositories open up new opportunities to improve organizational and data-based decision-making towards deliberation and citizen engagement.
Regarding data-oriented political economy, it is important to note the integration of government data into the theory of value creation/extraction. Current research in the field of data-oriented political economy mainly focuses on surveillance capitalism and unfair value extraction by technology platforms (Zuboff 2019; Sadowski 2020). It is necessary to complement data-oriented political economy with research on public data with respect to creating and extracting value, policy incentives for data discovery and dissemination, and the impact of data on knowledge accumulation and stimulating innovation. This study shows the potential contribution of government data to knowledge accumulation and value creation, but the range of research needed in the future is much more diverse.
5. Conclusions
In conclusion, government data performance affects knowledge capabilities. In this study, we used dynamic panel data models, the results of which demonstrated this effect on the example of statistical capacity score and economic complexity: the timeliness and consistency of the methodology and source base of state statistics has an impact on economic complexity. This is because government data provide a detailed description of the economic space, including available resources and potential challenges. Based on these data, agents can produce innovations, contributing to their diversity and dissimilarity. This logic may extend to other examples of how government data performance affects knowledge capabilities.
From a broader perspective, this study contributes to understanding the relationship between government, government data, technology, knowledge, and innovation. Firstly, state and local knowledge can interact if the government indirectly forms a description of the economic space, but without government intervention in the standardization of local knowledge and experience. Secondly, the accumulation of knowledge and the production of innovations can be stimulated by the government through information support and data. Thirdly, technological innovations allow for the development of collective data governance and collaborative approaches to publishing and using data; these opportunities can significantly improve the contribution of different types of data to knowledge capabilities and open innovation in the future. At the moment, various public data catalogs are being created in the field of urban development, climate, scientific cooperation, etc., the information of which can be integrated into government data. As a result, there are opportunities to use the results of this study in applied and theoretical programs.
Future research may address a large range of themes, including (but not exclusively):
- principles and technologies of building effective government data in the context of a participatory and inclusive approach;
- comparison of different indices and approaches to assessing government data performance;
- assessing the role of government data in policy development and decision-making;
- the impact of government data performance on knowledge capabilities (based on other variables, specific cases, and projects);
- cooperation of government, commercial organizations, civic organizations, and communities in creating publicly available and updated data sources;
- collective data management to create open innovations;
- evolution of open data and its impact on knowledge capabilities and open innovation;
- participatory approaches and incentives to create open and updated data sources.
A collection of related studies will be crucial to complement or revise current scientific positions in line with the complex transformation of the environment.
Funding
The article is prepared according to the results of studies carried out at the expense of budgetary funds on the state task of the Financial University.
Data Availability Statement
Publicly available datasets were analyzed in this study. These data can be found here: https://atlas.cid.harvard.edu/ and https://data.worldbank.org/indicator?tab=all (accessed on 1 May 2022).
Acknowledgments
The author thanks O.R. Mukhametov from the Financial Research Institute of the Ministry of Finance of the Russian Federation (FRI) for recommendations and assistance in conducting sta-tistical modeling.
Conflicts of Interest
The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
References
- Altayar, Mohammed Saleh. 2018. Motivations for open data adoption: An institutional theory perspective. Government Information Quarterly 35: 633–43. [Google Scholar] [CrossRef]
- Alvear, Oscar, Carlos T. Calafate, Juan-Carlos Cano, and Pietro Manzoni. 2018. Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues. Sensors 18: 460. [Google Scholar] [CrossRef] [PubMed]
- Balle, Andrea Raymundo, Mírian Oliveira, and Carla Maria Marques Curado. 2020. Knowledge sharing and absorptive capacity: Interdependency and complementarity. Journal of Knowledge Management 24: 1943–64. [Google Scholar] [CrossRef]
- Blundell, Richard, and Stephen Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115–43. [Google Scholar] [CrossRef]
- Dawson, Ross. 2000. Knowledge capabilities as the focus of organisational development and strategy. Journal of Knowledge Management 4: 320–27. [Google Scholar] [CrossRef]
- Dean, Mitchell. 2010. Governmentality: Power and Rule in Modern Society. London: Sage. [Google Scholar]
- Graeber, David. 2004. Fragments of an Anarchist Anthropology. Chicago: Prickly Paradigm Press. [Google Scholar]
- Graeber, David. 2015. The Utopia of Rules: On Technology, Stupidity, and the Secret Joys of Bureaucracy. Brooklyn: Melville House. [Google Scholar]
- Hanna, Nagy K. 2018. A role for the state in the digital age. Journal of Innovation and Entrepreneurship 7: 5. [Google Scholar] [CrossRef]
- Hausmann Ricardo, César A. Hidalgo, Sebastián Bustos, Michele Coscia, Sarah Chung, Juan Jimenez, Alexander Simoes, and Muhammed A. Yıldırım. 2011. The Atlas of Economic Complexity. New Hampshire: Puritan Press. [Google Scholar]
- Hidalgo, César A., and Ricardo Hausmann. 2009. The building blocks of economic complexity. Proceedings of the National Academy of Sciences of the United States of America 106: 10570–75. [Google Scholar] [CrossRef]
- Hidalgo, César A., and Ricardo Hausmann. 2011. The network structure of economic output. Journal of Economic Growth 16: 309–42. [Google Scholar]
- Hidalgo, César A., Bailey Klinger, Albert-Laszlo Barabasi, and Ricardo Hausmann. 2007. The product space conditions the development of nations. Science 317: 482–87. [Google Scholar] [CrossRef]
- Inkinen, Tommi, Reima Helminen, and Janne Saarikoski. 2019. Port Digitalization with Open Data: Challenges, Opportunities, and Integrations. J. Open Innov. Technol. Mark. Complex 5: 30. [Google Scholar] [CrossRef]
- Karachiwalla, Rea, and Felix Pinkow. 2021. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and innovation management 30: 563–84. [Google Scholar] [CrossRef]
- Khaksar, Seyed Mohammad Sadegh, Mei-Tai Chu, Sophia Rozario, and Bret Slade. 2020. Knowledge-based dynamic capabilities and knowledge worker productivity in professional service firms. The moderating role of organisational culture. Knowledge Management Research & Practice, 1–18. [Google Scholar] [CrossRef]
- Khayyat, Mashael, and Frank Bannister. 2017. Towards a Model for Facilitating and Enabling Co-Creation using Open Government Data. Information Polity 22: 211–31. [Google Scholar] [CrossRef]
- Khor, Lay Kheng, and Cheng Ling Tan. 2012. Weaving the Knowledge Capabilities and Supply Chain Management Skills into the Fabric of Supply Chain Decision Makers: Coexist of Coping Capacity. International Journal of Industrial Management 12: 368–78. [Google Scholar] [CrossRef]
- Kitsios, Fotis, and Maria Kamariotou. 2019. Open data hackathons: An innovative strategy to enhance entrepreneurial intention. International Journal of Innovation Science 10: 519–38. [Google Scholar] [CrossRef]
- Martinez, Marian Garcia, Ferdaous Zouaghi, Teresa Garcia Marco, and Catherine Robinson. 2019. What drives business failure? Exploring the role of internal and external knowledge capabilities during the global financial crisis. Journal of Business Research 98: 441–49. [Google Scholar] [CrossRef]
- Mazzucato, Mariana. 2013. The Entrepreneurial State: Debunking Public vs. Private Myths in Risk and Innovation. London: Anthem Press. [Google Scholar]
- Mazzucato, Mariana. 2021. Mission Economy: A Moonshot Guide to Changing Capitalism. London: Allen Lane-Penguin. [Google Scholar]
- Milić, Petar, Nataša Veljković, and Leonid Stoimenov. 2022. Using OpenGovB Transparency Indicator to Evaluate National Open Government Data. Sustainability 14: 1407. [Google Scholar] [CrossRef]
- Moreno-Hurtado, Carlos, Alexander Plascencia, Ariel Lozano, and Jhonny Cano. 2020. ICT exports: The role of human capital and economic complexity. Paper presented at the 15th Iberian Conference on Information Systems and Technologies (CISTI), Seville, Spain, June 24–27. [Google Scholar]
- Mukhametov, D. R. 2020. Self-organization of Network Communities via Blockchain Technology: Reputation Systems and Limits of Digital Democracy. Paper presented at the 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications, Svetlogorsk, Russia, July 1–3. [Google Scholar]
- Mukhametov, D. R. 2021. Collective Data Governance for Development of Digital Government. Paper presented at the 2021 International Conference on Engineering Management of Communication and Technology, Vienna, Austria, October 20–22. [Google Scholar]
- Nieves, Julia, and Sabine Haller. 2014. Building dynamic capabilities through knowledge resources. Tourism Management 40: 224–32. [Google Scholar] [CrossRef]
- Open Data Barometer. n.d. Available online: https://opendatabarometer.org/3rdedition/report/ (accessed on 20 August 2022).
- Open Government Data—OECD. n.d. Available online: https://www.oecd.org/gov/digital-government/open-government-data.htm (accessed on 20 August 2022).
- Paskaleva, Krassimira, James Evans, Christopher Martin, Trond Linjordet, Dujuan Yang, and Andrew Karvonen. 2017. Data Governance in the Sustainable Smart City. Informatics 4: 41. [Google Scholar] [CrossRef]
- Ruijer, Erna, Francoise Détienne, Michael Bake, Jonathan Groff, and Albert J. Meijer. 2020. The Politics of Open Government Data: Understanding Organizational Responses to Pressure for More Transparency. American Review of Public Administration 50: 260–74. [Google Scholar] [CrossRef]
- Sadowski, Jathan. 2020. The Internet of Landlords: Digital Platforms and New Mechanisms of Rentier Capitalism. Antipode 52: 562–80. [Google Scholar] [CrossRef]
- Scott, James C. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. New Haven: Yale University Press. [Google Scholar]
- Scott, James C. 2009. The Art of Not Being Governed: An Anarchist History of Upland Southeast Asia. New Haven: Yale University Press. [Google Scholar]
- Scott, James C. 2012. Two Cheers for Anarchism: Six Easy Pieces on Autonomy, Dignity, and Meaningful Work and Play. Princeton: Princeton University Press. [Google Scholar]
- Sneddon, Christopher. 2007. Locating Southeast Asia: Geographies of Knowledge and Politics of Space by Paul H. Kratoska, Remco Raben, and Henk Schulte Nordholt. Annals of the American Association of Geographers 97: 462–64. [Google Scholar] [CrossRef]
- Temiz, Serdar. 2021. Open Innovation via Crowdsourcing: A Digital Only Hackathon Case Study from Sweden. Journal of Open Innovation: Technology, Market, and Complexity 7: 39. [Google Scholar] [CrossRef]
- Zuboff, Shoshana. 2019. Surveillance Capitalism and the Challenge of Collective Action. New Labor Forum 28: 10–29. [Google Scholar] [CrossRef]
Table 1.
List of variables.
| Variable | Indicator | Source |
|---|---|---|
| ECI | Economic Complexity Index | The Observatory of Economic Complexity |
| STATCAPACITY | Statistical Capacity Score (Overall Average) (scale: 0–100) | World Development Indicators |
| PERIODICITY | Periodicity and timeliness assessment of statistical capacity (scale: 0–100) | World Development Indicators |
| METHODOLOGY | Methodology assessment of statistical capacity (scale: 0–100) | World Development Indicators |
| SOURCE | Source data assessment of statistical capacity (scale: 0–100) | World Development Indicators |
| POPGROWTH | Population growth (annual %) | World Development Indicators |
| GDPC | GDP per capita (current US$) | World Development Indicators |
| TAXREVENUE | Tax revenue (% of GDP) | World Development Indicators |
| TRADE | Trade (% of GDP) | World Development Indicators |
| QI | Quality of institutions | World Development Indicators |
Table 2.
Descriptive statistics.
| Variable | Observations | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|---|
| ECI | 1503 | −0.312983 | 0.780413 | −2.7989 | 1.7105 |
| STATCAPACITY | 1498 | 72.12253 | 14.28845 | 16.66667 | 98.8889 |
| PERIODICITY | 1498 | 83.86425 | 11.00735 | 33.33333 | 100 |
| METHODOLOGY | 1498 | 62.69693 | 21.79801 | 0 | 100 |
| SOURCE | 1498 | 69.80641 | 20.00073 | 0 | 100 |
| POPGROWTH | 1504 | 1.401657 | 1.614312 | −4.533415 | 17.51221 |
| GDPC | 1491 | 13921.92 | 21056.35 | 284.4877 | 123514.2 |
| TAXREVENUE | 915 | 17.47228 | 6.748043 | 2.331967 | 62.5028 |
| TRADE | 1338 | 97.45959 | 70.92604 | 22.10598 | 863.1951 |
| QI | 1504 | −0.4130177 | 0.5737857 | −2.000013 | 1.251038 |
Source: Compiled by the author.
Table 3.
Correlation between variables.
| ECI | STATCAPACITY | PERIODICITY | METHODOLOGY | SOURCE | POPGROWTH | GDPC | TAXREVENUE | TRADE | QI | |
|---|---|---|---|---|---|---|---|---|---|---|
| ECI | 1.0000 | |||||||||
| STATCAPACITY | 0.5458 | 1.0000 | ||||||||
| PERIODICITY | 0.1830 | 0.6912 | 1.0000 | |||||||
| METHODOLOGY | 0.6042 | 0.8793 | 0.4425 | 1.0000 | ||||||
| SOURCE | 0.4486 | 0.8740 | 0.4944 | 0.6104 | 1.0000 | |||||
| POPGROWTH | −0.1584 | −0.1465 | 0.0834 | −0.2048 | −0.1474 | 1.0000 | ||||
| GDPC | −0.0702 | 0.0280 | 0.1812 | −0.0263 | −0.0099 | −0.0007 | 1.0000 | |||
| TAXREVENUE | −0.1374 | −0.1787 | −0.1816 | −0.1717 | −0.1076 | −0.2063 | 0.2590 | 1.0000 | ||
| TRADE | 0.0705 | 0.1286 | −0.0001 | 0.1287 | 0.1450 | −0.1323 | 0.2528 | 0.3187 | 1.0000 | |
| QI | 0.5831 | 0.5097 | 0.2030 | 0.5085 | 0.4625 | −0.0447 | −0.1189 | 0.0482 | 0.1221 | 1.0000 |
Source: Compiled by the author.
Table 4.
Results of regression models (based on statistical capacity score).
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | ECI | ECI | ECI |
| L.ECI | 0.471 *** | 0.493 *** | 0.467 *** |
| (0.0502) | (0.0676) | (0.0671) | |
| STATCAPACITY | 0.0150 *** | 0.0134 *** | 0.00821 *** |
| (0.00198) | (0.00229) | (0.00203) | |
| POPGROWTH | −0.00511 | −0.0381 * | −0.0603 *** |
| (0.0125) | (0.0223) | (0.0216) | |
| GDPC | −155 × 10−6 * | −8.04 × 10−7 | 5.41 × 10−7 |
| (8.87 × 10−7) | (9.35 × 10−7) | (8.30 × 10−7) | |
| TAXREVENUE | −0.00191 | −0.00898 *** | |
| (0.00375) | (0.00344) | ||
| TRADE | 0.000271 | 0.000132 | |
| (0.000367) | (0.000450) | ||
| QI | 0.280 *** | ||
| (0.0686) | |||
| Constant | −1.196 *** | −1.018 *** | −0.425 ** |
| (0.164) | (0.211) | (0.184) | |
| AB AR(2) | −0.47 | −1.33 | −1.19 |
| Sargan | 14.79 | 16.37 | 17.79 |
| Hansen | 15.00 | 11.20 | 12.82 |
| Observations | 1392 | 784 | 784 |
Notes: 1. Standard errors in parentheses. 2. *** p < 0.01, ** p < 0.05, * p < 0.1. 3. AB AR(2) denotes Arellano–Bond test for AR(2) in first differences. Source: Author’s calculations.
Table 5.
Results of regression models (based on periodicity and timeliness assessment of statistical capacity).
Table 5.
Results of regression models (based on periodicity and timeliness assessment of statistical capacity).
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | ECI | ECI | ECI |
| L.ECI | 0.466 *** | 0.525 *** | 0.482 *** |
| (0.0473) | (0.0560) | (0.0673) | |
| PERIODICITY | 0.00648 *** | 0.00540 *** | 0.00299 |
| (0.00186) | (0.00197) | (0.00184) | |
| POPGROWTH | −0.0173 | −0.0559 *** | −0.0734 *** |
| (0.0173) | (0.0216) | (0.0217) | |
| GDPC | −1.18 × 10−6 | −1.40 × 10−6 | 5.97 × 10−7 |
| (1.06 × 10−6) | (9.48 × 10−7) | (9.09 × 10−7) | |
| TAXREVENUE | −0.00566 | −0.0131 *** | |
| (0.00399) | (0.00372) | ||
| TRADE | 0.000626 | 0.000338 | |
| (0.000405) | (0.000480) | ||
| QI | 0.346 *** | ||
| (0.0743) | |||
| Constant | −0.646 *** | −0.414 ** | 0.0107 |
| (0.175) | (0.199) | (0.183) | |
| AB AR(2) | −0.55 | −1.14 | −1.04 |
| Sargan | 19.58 | 20.58 | 20.10 |
| Hansen | 19.31 | 15.28 | 16.66 |
| Observations | 1392 | 784 | 784 |
Notes: 1. Standard errors in parentheses. 2. *** p < 0.01, ** p < 0.05. 3. AB AR(2) denotes Arellano–Bond test for AR(2) in first differences. Source: Author’s calculations.
Table 6.
Results of regression models (based on methodology assessment of statistical capacity).
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | ECI | ECI | ECI |
| L.ECI | 0.539 *** | 0.582 *** | 0.521 *** |
| (0.0396) | (0.0492) | (0.0553) | |
| METHODOLOGY | 0.00881 *** | 0.00786 *** | 0.00569 *** |
| (0.00109) | (0.00125) | (0.00117) | |
| POPGROWTH | −0.00319 | −0.0199 | −0.0466 ** |
| (0.0109) | (0.0184) | (0.0193) | |
| GDPC | −1.16 × 10−6 | −5.58 × 10−7 | 6.24 × 10−7 |
| (7.80 × 10−7) | (8.11 × 10−7) | (7.40 × 10−7) | |
| TAXREVENUE | −0.000508 | −0.00710 ** | |
| (0.00334) | (0.00322) | ||
| TRADE | 8.08 × 10−5 | −2.00 × 10−5 | |
| (0.000314) | (0.000413) | ||
| QI | 0.251 *** | ||
| (0.0625) | |||
| Constant | −0.669 *** | −0.574 *** | −0.233 * |
| (0.0887) | (0.129) | (0.129) | |
| AB AR(2) | 0.17 | −1.15 | −1.06 |
| Sargan | 11.94 | 12.65 | 13.89 |
| Hansen | 10.18 | 9.32 | 10.03 |
| Observations | 1392 | 784 | 784 |
Notes: 1. Standard errors in parentheses. 2. *** p < 0.01, ** p < 0.05, * p < 0.1. 3. AB AR(2) denotes Arellano–Bond test for AR(2) in first differences. Source: Author’s calculations.
Table 7.
Results of regression models (based on source data assessment of statistical capacity).
| (1) | (2) | (3) | |
|---|---|---|---|
| VARIABLES | ECI | ECI | ECI |
| L.ECI | 0.442 *** | 0.469 *** | 0.470 *** |
| (0.0517) | (0.0745) | (0.0699) | |
| SOURCE | 0.00796 *** | 0.00759 *** | 0.00307 ** |
| (0.00115) | (0.00148) | (0.00138) | |
| POPGROWTH | −0.00508 | −0.0394 | −0.0652 *** |
| (0.0128) | (0.0244) | (0.0227) | |
| GDPC | −1.68 × 10−6 * | −5.54 × 10−7 | 9.99 × 10−7 |
| (9.31 × 10−7) | (9.77 × 10−7) | (8.72 × 10−7) | |
| TAXREVENUE | −0.00438 | −0.0121 *** | |
| (0.00378) | (0.00356) | ||
| TRADE | 0.000440 | 0.000197 | |
| (0.000385) | (0.000476) | ||
| QI | 0.323 *** | ||
| (0.0696) | |||
| Constant | −0.671 *** | −0.556 *** | 0.0110 |
| (0.102) | (0.149) | (0.134) | |
| AB AR(2) | −0.94 | −1.38 | −1.12 |
| Sargan | 19.59 | 22.82 * | 21.27 * |
| Hansen | 20.78 | 13.80 | 15.41 |
| Observations | 1392 | 784 | 784 |
Notes: 1. Standard errors in parentheses. 2. *** p < 0.01, ** p < 0.05, * p < 0.1. 3. AB AR(2) denotes Arellano–Bond test for AR(2) in first differences. Source: Author’s calculations.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Mukhametov, D. Exploring the Influence of Government Data Performance on Knowledge Capabilities: Towards a Data-Oriented Political Economy. Soc. Sci. 2022, 11, 384. https://doi.org/10.3390/socsci11090384
AMA Style
Mukhametov D. Exploring the Influence of Government Data Performance on Knowledge Capabilities: Towards a Data-Oriented Political Economy. Social Sciences. 2022; 11(9):384. https://doi.org/10.3390/socsci11090384
Chicago/Turabian StyleMukhametov, Daniyar. 2022. "Exploring the Influence of Government Data Performance on Knowledge Capabilities: Towards a Data-Oriented Political Economy" Social Sciences 11, no. 9: 384. https://doi.org/10.3390/socsci11090384
APA StyleMukhametov, D. (2022). Exploring the Influence of Government Data Performance on Knowledge Capabilities: Towards a Data-Oriented Political Economy. Social Sciences, 11(9), 384. https://doi.org/10.3390/socsci11090384
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
