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Intro -- CONTENTS -- LIST OF FIGURES -- LIST OF TABLES -- ABBREVIATION -- 1. INTRODUCTION -- 1.1 INSTITUTIONS IN A REGIONAL ECONOMIC GROWTH MODEL -- 1.2 A CHINESE SUCCESS STORY: THE GREATER PEARL RIVER DELTA -- 1.3 TOWARD A NEW ECONOMIC GROWTH MODEL? -- 1.4 AIM AND SCOPE -- 1.5 ACKNOWLEDGEMENTS -- 2. THEORETICAL AND CONCEPTUAL BACKGROUND -- 2.1 INSTITUTIONAL CHANGE AND THE REGIONAL ECONOMY -- 2.2 INNOVATION AND INDUSTRIAL UPGRADING IN CHINA -- 2.3 DEVELOPING AN ANALYTICAL FRAMEWORK -- 3. METHODOLOGY -- 3.1 EMPIRICAL RESEARCH DATA -- 3.2 MEASUREMENT CONCEPTS -- 3.3 CRITICAL METHOD EVALUATION AND LIMITATIONS -- 4. INSTITUTIONAL CHANGE AND STRATEGIC ADAPTATION. A FIRM-LEVEL PERSPECTIVE ON THE POST-CRISIS ECONOMY OF THE CHINESE PRD -- 4.1 INTRODUCTION -- 4.2 THEORETICAL AND CONCEPTUAL BACKGROUND -- 4.3 DATA AND METHODS -- 4.4 EMPIRICAL ANALYSIS OF FIRM ADAPTATION PROCESSES -- 4.5 CONCLUSION AND DISCUSSION -- 5. THE IMPACT OF FINANCING CHANNELS ON FIRM INNOVATION - IS THERE AN 'INFORMALITY TRAP?' -- 5.1 INTRODUCTION -- 5.2 THEORETICAL AND CONCEPTUAL BACKGROUND -- 5.3 DATA AND METHODS -- 5.4 FINANCE AND INNOVATION IN THE ELECTRONICS INDUSTRY OF THE PRD -- 5.5 CONCLUSION -- 6. INTER-FIRM LEARNING IN THE ELECTRONICS SUPPLY CHAIN - PROSPECT FOR ENHANCING LOCAL FIRMS' INNOVATION PERFORMANCE? -- 6.1 INTRODUCTION -- 6.2 THE PROCESS OF INNOVATION FROM A SUPPLY-CHAIN PERSPECTIVE -- 6.3 DATA AND METHODS -- 6.4 ECONOMETRIC ANALYSIS -- 6.5 DISCUSSION -- 6.6 CONCLUSION -- 7. POLICIES OF RESTRUCTURING ECONOMIC GROWTH - TOWARDS A NEW GROWTH MODEL IN CHINA AND ITS PROVINCES? -- 7.1 INTRODUCTION -- 7.2 AN INSTITUTIONAL PERSPECTIVE ON THE CHINESE POLITICAL SYSTEM -- 7.3 DATA AND METHODS -- 7.4 THE CHINESE NATIONAL GROWTH MODEL -- 7.5 POLITICAL LEADERSHIP AND INDUSTRIAL DEVELOPMENT IN GUANGDONG PROVINCE -- 7.6 INTERIM CONCLUSION.

This evaluation report investigates the impact of ten years of comprehensive
land-use planning in the Philippines. Characterized by fundamental
developmental challenges associated with scarce land resources,
environmental degradation, natural hazards and persistent poverty,
land-use planning plays a crucial role in finding answers to these pressing
challenges.
The impact evaluation assesses a technical approach to enhanced land-use
planning and capacity development from community to national level,
supporting decentralized planning, natural resource governance, and
resilience to natural hazards and climate change. The so-called SIMPLE
(Sustainable Integrated Management and Planning for Local Government
Ecosystems) approach by the Philippine-German cooperation, managed
by the Deutsche Gesellschaft für internationale Zusammenarbeit (GIZ),
was implemented in two regions of the Visayas. The ambitious intervention
operated in a challenging environment with multiple stakeholders,
overlapping mandates, and imprecise legal frameworks. In cooperation
with GIZ, the Housing and Land Use Regulatory Board (HLURB) rolled
out the related enhanced Comprehensive Land Use Planning (eCLUP)
guidelines nationwide.
Based on a mixed-methods and quasi-experimental design, the evaluation
generates relevant findings for the improvement of land-use planning and
local governance interventions, for sustainable natural resource management,
disaster risk management, and for welfare improvements of communities
and beneficiaries. It shows relevant factors for the successful implementation.
The report draws important lessons for local planning and the national
framework, and suggests solutions to the fundamental gap between
planning and plan implementation, improved innovation diffusion and
efficient processes, effective community participation, and public
accountability.

Eine systematische Nutzung von RS-Daten ermöglicht durch Hinzunahme einer räumlichen Dimension eine bessere Beantwortung der Evaluierungsfragen. In diesem Policy Brief wird der methodische Ansatz von DEval zur Analyse von hochauflösenden RS-Daten unter Anwendung von Bildklassifizierungstechniken und Techniken des maschinellen Lernens (ML) vorgestellt. DEval hat den Ansatz in enger Zusammenarbeit mit RS-Fachleuten der Fakultät für Geoinformationswissenschaften und Erdbeobachtung (ITC) an der Universität Twente in den Niederlanden entwickelt.

A systematic use of Remote Sensing (RS) data opens a door for evaluators to better address evaluation questions by adding a spatial dimension. This policy brief highlights DEval's methodological approach to the analysis of high-resolution RS data through the application of image classification and machinelearning (ML) techniques. DEval has been developing this approach in close cooperation with RS experts from the Faculty of Geo-information Science and Earth Observation (ITC) at the University of Twente in the Netherlands.

Land requires fair and transparent management to allow for equal participation and for its sustainable use among rivaling stakeholders. Land use planning is the mechanism to allow for this kind of resource management and the reconciliation of diverging interests. It is thus not surprising that the governance of land resources has become a prominent topic among donors and development practitioners in the last decade. It is theorized that good administration and management of land is crucial to poverty reduction, conflict transformation, disaster risk management, improvement in the quality of local governance and ultimately sustainable economic growth. The report at hand presents first results derived from a quantitative impact evaluation of an intervention for enhanced land use planning in the Philippines. The SIMPLE (Sustainable Integrated Management and Planning for Local Government Ecosystems) approach embedded in the Philippine-German cooperation's "Environment and Rural Development (EnRD)" program was implemented between 2006 and 2015, managed by the Gesellschaft für internationale Zusammenarbeit (GIZ). The report draws upon quantitative cross-sectional data collected in 2012 on household, village and municipal level. It provides first insights into program outcomes and impacts. A follow-up impact evaluation of the intervention, based on a rigorous before-after design, will be published in 2017.

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.