TY - BOOK AU - Guerrero, Omar A. AU - Castañeda, Gonzalo TI - Complexity economics and sustainable development : computational framework for policy priority inference SN - 9781009016544 U1 - 338.927 PY - 2024/// CY - UK PB - Cambridge University Press KW - Economic Development And Growth/ Sustainable Development N1 - The Sustainable Development Goals are global objectives set by the UN. They cover fundamental issues in development such as poverty, education, economic growth, and climate. Despite growing data across policy dimensions, popular statistical approaches offer limited solutions as these datasets are not big or detailed enough to meet their technical requirements. Complexity Economics and Sustainable Development provides a novel framework to handle these challenging features, suggesting that complexity science, agent-based modelling, and computational social science can overcome these limitations. Building on interdisciplinary socioeconomic theory, it provides a new framework to quantify the link between public expenditure and development while accounting for complex interdependencies and public governance. Accompanied by comprehensive data of worldwide development indicators and open-source code, it provides a detailed construction of the analytic toolkit, familiarising readers with a diverse set of empirical applications and drawing policy implications that are insightful to a diverse readership; Contents List of Figures List of Tables Foreword by Luis F. López Calva and Robert Axtell Acknowledgements List of Abbreviations PART 1 A COMPLEXITY APPROACH TO SUSTAINABLE DEVELOPMENT 1 Introduction 1.1 Motivation for This Book 1.2 Cutting-Edge Methods for Challenging Goals 1.3 The ‘Policy Priority Inference’ Research Programme 1.4 Target Audience 1.5 Structure of the Book 2 Policy Prioritisation, Complexity, and Agent Computing 2.1 Modelling the Expenditure–Development Link 2.2 Generative Causation and Social Mechanisms 2.3 On Causal Inference and Agent Computing 2.3.1 The Identification of Counterfactuals 2.3.2 The Workings of the Dependency and Generative Accounts 2.3.3 The Validity of Agent-Computing Counterfactuals 2.3.4 The Benefits of Using Agent Computing for Policy Evaluations 2.4 Summary and Conclusions 3 Relevant Data and Empirical Challenges 3.1 A Worldwide Look at Sustainable Development through Data 3.1.1 SDGs and Indicators 3.1.2 Pre-processing Indicators and Descriptive Statistics 3.1.3 Countries and Government Spending 3.2 Popular Modelling Frameworks and Their Limitations 3.2.1 Benchmark Analysis 3.2.2 Regression Analysis 3.2.3 General Equilibrium Models 3.2.4 System Dynamics 3.2.5 Network Analysis 3.3 Empirical Challenges 3.3.1 Adapting to Coarse-Grained Indicators 3.3.2 Moving beyond Associations 3.3.3 Handling Complex Expenditure Linkages 3.3.4 Embedding Vertical Mechanisms 3.3.5 Estimating Interdependency Networks 3.4 Summary and Conclusions 4 A Computational Model 4.1 Policy Instruments 4.2 Indicator Dynamics 4.3 Public Servants 4.4 Central Authority 4.5 Development Outcomes 4.6 Summary and Conclusions 5 Calibration and Validation 5.1 Calibration Strategy 5.2 Optimisation Algorithm 5.3 Goodness of Fit 5.4 On Statistical Confidence and Testing 5.5 Validation 5.5.1 Out-of-Sample Inference 5.5.2 Comparison with Alternative Models 5.5.3 Sensitivities and Specification Checks 5.6 Summary and Conclusions PART 2 APPLICATIONS TO POLICY AND DEVELOPMENT 6 The ‘Policy Priority Inference’ Framework 6.1 The Policy Space 6.2 The Space of Mechanisms 6.3 Identification of Social Mechanisms 6.3.1 The Search for Dependencies 6.3.2 The Role of Theory and Generative Mechanisms 6.3.3 Mechanistic Consistency 6.4 Policy Inference and the Estimation of Policy Effects 6.5 Sensitivity Analysis and Robustness Checks 6.6 Summary and Conclusions 7 Case Studies in Development Policy 7.1 Education 7.1.1 The Role of Public Spending CONTENTS xi PART III A FOCALISED VIEW OF SUSTAINABLE DEVELOPMENT 10 Subnational Development and Fiscal Federalism 10.1 On Fiscal Federalism 10.1.1 Fiscal Decentralisation in Mexico 10.2 Data 10.2.1 Development Indicators 10.2.2 Development Clusters 10.2.3 Expenditure Data 10.3 Simulation Strategy 10.4 Results 10.4.1 The Impact of Contributions 10.4.2 Optimising Contributions 10.4.3 Policy Priorities and Contributions 10.5 Summary and Conclusions 11 Accelerators and Systemic Bottlenecks 11.1 Accelerators, Bottlenecks, and Their Empirical Quantification 11.2 Data 11.2.1 Government Expenditure 11.2.2 Development Indicators 11.3 Simulation Strategy 11.3.1 Counterfactual Budgets 11.3.2 Detection of Bottlenecks and Accelerators 11.4 Results 11.4.1 Identification of Systemic Bottlenecks and Accelerators 11.4.2 Comparison against Naïve Approaches 11.4.3 Disaggregation of Systemic Bottlenecks and Accelerators 11.5 Summary and Conclusions xii CONTENTS 12 Deprivation, Income Shocks, and Remittances 12.1 Socioeconomic Deprivation in the Mexican Context 12.1.1 The Importance of Remittances and Research Design 12.2 Data 12.2.1 Indicators 12.2.2 Social Expenditure 12.2.3 Household Spending and Remittances 12.2.4 The Complex Structure of Government Spending and Development 12.3 Simulation Strategy 12.4 Results 12.4.1 Impact Evaluation by Expenditure Source 12.4.2 Shock Mitigation via Government Expenditure 12.5 Summary and Conclusions 13 Lessons and Reflections 13.1 Lessons Learnt 13.2 From Analysis to Policy Guidelines 13.2.1 Workflow for Strategic Planning 13.3 A Call for Computational Social Scientists 13.3.1 Necessary Infrastructure 13.3.2 Upgrading Skills in Technical Teams 13.3.3 Updating Social Science Programmes Bibliography Index ER -