Price connectedness between green bond and financial markets

We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.     

Credit constraints and productivity of SMEs: Evidence from Canada

To what extent firms are constrained by external credit is usually unobserved in commonly used firm-level data. We use a survey of financing among Canadian small and medium-sized enterprises to measure the likelihood of a firm being constrained by credit. We find that firm size, current debt-to-asset ratio and cash flow are robust indicators of being financially constrained, while long-term debt to asset ratio is not a significant indicator of credit constraints. We then estimate the firm-level total factor productivity, taking into account the measured credit constraints. Omitting credit constraints leads to an upward bias of productivity estimates, by 4 percent. In addition, we find no strong evidence that suggests credit constraints lead to slower productivity growth. Finally, we confirm that both investment and employment growth are negatively affected by the measured credit constraints.     

Financial development,economic growth and convergence clubs

To study the role of financial development in economic growth, we apply an array of convergence tests designed to capture nonlinear transitional dynamics to real outputs per capita. Strong evidence of multiple convergence clubs is observed, implying that the clubs are formed based on the initial level of real output per capita and average growth rate. Our empirical results show that the stage of economic growth of each country plays an important role for the composition of the convergence clubs. Furthermore, financial development emerges to be a significant determinant, albeit plays differently in the economic growth of each convergence club.     

A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region

Bogotá and the 17 neighboring municipalities make up one of the biggest metropolitan areas in Latin America. However, despite strong functional interactions within the area, there is no official government body at this level in charge of coordinating authorities and providing solutions to the wide variety of issues arising in the regional urban land system. Aiming at providing an insight on future land-use developments linked to new transport infrastructures and at offering a tool to support territorial decision-making, this paper presents a cellular automata-based (CA) model based in Metronamica® software, that allows testing different scenarios based on potential land-use policies, environmental suitability and transport alternatives.There has not been, so far, an urban planning tool that accounts for the complexities of this region. CA-based land-use simulations constitute a useful approach to understanding the impacts of urban planning policies and regulations. This tool can help to improve inter-territorial and inter-institutional coordination, which through planning and management policies seek a spatially integrated development, with a long-term perspective.The CA-based model proposed was calibrated to reproduce land-use changes between 2007 and 2016 using different methods and indicators. The model was used to simulate and analyze eight scenarios with different policy directions of transport infrastructure in the future of the region. The results of the simulations reflect the dynamics of territorial occupation. The calibration indices in the experiment indicate a high degree of suitability for the CA Bogotá model, proving its effectiveness and potential as a useful tool for decision-making. The results show that occupation scenarios with restricted developable zones within the city, tend to have the greater dispersion rate in the study area, compared to scenarios where land development plans are promoted in Bogotá, which representing a more compact development.     

The use of landslide hazard maps to determine mitigation priorities in a dam reservoir and its protection area

Despite longstanding research on the landslide hazard assessment and mapping, the use this information in regional planning applications remains largely unclear. There is still a need for novel methods to interpret the results of hazard analyses. In this respect, the main research question of this study is how the landslide hazard maps can be evaluated in planning practice. To evaluate this issue, a comprehensive case study was carried out and a new methodology, defining the order of priority for the locations where mitigation measures are applied was developed. The investigations were carried out in the Melen Dam reservoir, which provides potable water to the city of Istanbul, and the dam protection area (Duzce, Turkey) in four main stages: (i) Evaluation of the general characteristics of the study area, (ii) preparation of landslide inventory, (iii) development of landslide hazard model, and (iv) implementation of the results of hazard analyses in planning. As a consequence, a decision support system that can evaluate analytically complicated outputs “P_Combined”, “P_{AL min.}”, and “L_Total” and provide clear decisions “Urgent”, “Primary”, and “Secondary” was suggested. Considering the limited resources available for the mitigation measures, the determination of mitigation priorities for the micro-catchments constitutes the key land-use policy for the protection of the dam reservoir.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Incorporating textual information in customer churn prediction models based on a convolutional neural network

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates a framework that explains how textual data can be incorporated in a predictive model. First, the results confirm previous research showing that the inclusion of textual data in a CCP model improves its predictive performance. Second, CNNs outperform current best practices for text mining in CCP. Third, textual data are an important source of data for CCP, but unstructured textual data alone cannot create churn prediction models that are competitive with models that use traditional structured data. A calculation of the additional profit obtained from a customer retention campaign through the inclusion of textual information can be used by practitioners directly to help them make more informed decisions on whether to invest in text mining.     

Does more finance mean more inequality in times of crisis?

Many recent empirical studies show that both banking crises and financial development (FD) play an important role in understanding the dynamics of income inequality (IncI) over the last decades. However, so far no study has investigated the role of FD in the amplification of IncI following banking crises. This paper seeks to address this issue based on a sample of 69 banking crises in 54 countries over the 1977–2013 period. Our analysis suggests that FD is associated with a significant increase in IncI in the aftermath of banking crises. This result is robust to a broad range of alternative specifications and is unaffected by various potential sources of endogeneity. We also show that the relationship between FD and the redistributive consequences of banking crises is not subject to a threshold effect and is stronger for developing countries.     

New ventures in Cleantech: Opportunities,capabilities and innovation outcomes

Facing the challenge of climate change, innovations that imply environmental benefits create business opportunities for entrepreneurs. This paper analyzes innovation capabilities of startups in Cleantech and how the innovation outcomes of those startups develop over time. Based on the Mannheim Foundation Panel and applying propensity score matching, a cohort of 567 Cleantech startups is analyzed and compared with a control cohort of non‐Cleantech startups. We find that startups in Cleantech have, on average, higher technological capabilities compared with all other startups. Our econometric evidence shows that Cleantech startups are more likely to combine existing technology in a novel way. Finally, we find that Cleantech startups develop more market novelties in subsequent years when compared with their control group peers.