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In this paper we study how differences in the quality of countries' institutions affect the impact of natural hazards in these countries. To do so, we first build a new data set that allows us to adequately control for countries' development and geological characteristics and, importantly, the physical intensity of the natural hazard. We then analyze our data using an output distance frontier model to assess two important aspects of the relation-ship between institutions and hazard impacts. First, the model allows us to estimate the trade-offs between different types of (negative) outcomes (e.g., deaths, affected, and damages). Second, it enables us to estimate the excess deaths, affected inhabitants and damages that countries, all else equal, suffer relative to the best performing countries. We can refer to this as the countries' (in)efficiency at managing natural hazards. Our results show that countries differ a lot in their disaster management efficiencies, with richer countries performing better than poorer countries. Richer countries also incur higher capital losses in exchange for fewer lives affected, controlling for their overall level of development and population density. For rich and poor countries we show that institutions of higher quality indeed correlate with higher disaster relief efficiencies. Most important are indicators of good governance and government effectiveness, whereas the de jure indicators are not informative. Our estimates suggest that a country with a 10%higher disaster relief efficiency will save one more life and protect four more people at the cost of $8 million in capital losses in an average intensity natural hazard.

We investigate which industries drive the trade-specialization nexus in the european union over the 1997–2006 period. We study the impact of the reallocation of resources within industries. We find that the true drivers of the trade-specialization nexus are productive firms, who benefit from the increase in trade openness by appropriating resources from less productive firms, coinciding with the expansion of the industry in which they operate, at the expense of other industries, in which there is no room to make such moves.keywordstrade barrierslatent class modelgravity model.

After Reading This Chapter, You Will: Have a general knowledge of Institutional Review Board (IRB) procedures Have the capacity to anticipate the basic ethical pitfalls in research designs Know how to counter common ethical objections Be able to design an informed consent form

This paper studies how banks adjust their asset structure in response to changes in loan demand after natural disasters. We show how banks help clients smoothen consumption and support local recovery through their asset diversification strategy. In the empirical part, using a difference-in-difference method, we find that U.S. commercial banks increase real estate lending after disasters and sell government bonds to finance such a disaster-driven demand surge. In the theoretical part of this paper we present a novel multiple-asset dynamic credit rationing model that explains these empirical findings. Using simulations of our model we can then predict and quantify the possible impact of climate change on the asset structure and profitability of banks for different scenarios.

We investigate the extent to which local banking market characteristics can explain where small enterprises (SEs) are located and how they thrive. We start from a simple theory that explains the channels along which local banking market structure can affect the growth of SEs. Subsequently, we make use of a highly detailed data set, exploit unique features of the Italian banking system and find that the structure of the banking system explains the regional distribution of SEs in Italy, in particular through the historical presence of small banks.

This paper examines the impact of natural disaster experiences on banks’ business practices. Using earthquake and banking data for California, we find that banks that have had stronger earthquake experiences change their practices, both as a result of the natural disasters’ effects on local deposit supply and through changes in banks’ risk perceptions. These banks have a smaller exposure to real estate, maintain higher equity levels, and are more likely to lend to high-income borrowers. This paper confirms, therefore, that institutional memory exists in the banking sector and that banks and communities adapt to natural disasters interactively.

Empirical literature and related legal practice using concentration as a proxy for competition measurement are prone to a fallacy of division, as concentration measures are appropriate for perfect competition and perfect collusion but not intermediate levels of competition. Extending the classic Cournot model used to derive the Hirschman–Herfindahl Index (HHI) of market concentration, we propose an adaptation of this model intended to remove the aggregation bias and omitted variable bias that exist in many empirical studies. Application of our model and new critical mass measures to data for US commercial banks in the period 1984–2013 confirms that concentration measures are indeed unreliable competition metrics. Our results lead us to conclude that critical mass builds upon HHI as a market power metric that incorporates competitive information not contained in HHI. Policy and future research implications are briefly discussed.

Firms depend heavily on trade credit. This paper introduces a trade credit network into a structural model of the economy. In an empirical analysis of the model, we find that trade credit is an elusive insurance: as long as a firm is financially unconstrained and times are good, more trade credit enhances sales stability and insures against shocks to the firm’s suppliers. However, if a firm becomes financially constrained or times are bad, trade credit fails to insure against supplier shocks. Moreover, if the firm is low on cash, trade credit propagates shocks from a supplier to its customer.

To what extent has input reallocation contributed to aggregate productivity growth in the banking sectors of Europe and the United States? Interestingly, under-performing banks capture market share, while more productive banks lose market share, in particular in the US. The pattern of reallocation is markedly different between the geographical regions: European productivity has grown by reallocating inputs through the first half of the sample period, at the same time when reallocation diminished growth in the US. The long-run positive effects of creative destruction are especially apparent in the US, where reallocation is an important driver of increases in productivity.

This paper attempts to estimate comparable efficiency scores for European banks operating in the Single Market in the EU. Using a data set of more than 5000 large commercial banks from all major European banking markets over the period 1993-2004, the application of meta-frontiers enables us to assess the existence of a single and integrated European banking market. We find evidence in favor of a single European banking market characterized by cost and profit meta-frontiers. However, compared to the meta-frontier estimations, pooled frontier estimations tend to underestimate efficiency levels and correlate poorly with country-specific frontier efficiency ranks.

In this paper, we show the importance of accounting for heterogeneity among sample firms in stochastic frontier analysis. For a fairly homogenous sample of German savings and cooperative banks, we analyze how alternative theoretical assumptions regarding the nature of heterogeneity can be modeled and the extent to which the respective empirical specifications affect estimated efficiency levels and rankings. We find that the level of efficiency scores is affected in the case of both cost and profitmodels. On the cost side especially, level and rank correlations show that different specifications identify different banks as being best or worst performers. Our main conclusion is that efficiency studies in general and bank efficiency studies in particular should account for heterogeneity across sample firms. Especially when efficiency measures are employed for policy purposes, a careful choice of models and transparency regarding maximization methods are essential to be able to make inferences about managerial behavior.

The inability of most bank merger studies to control for hidden bailouts may lead to biased results. In this study, we employ a unique data set of approximately 1,000 mergers to analyze the determinants of bank mergers. We use data on the regulatory intervention history to distinguish between distressed and non-distressed mergers. We find that, among merging banks, distressed banks had the worst profiles and acquirers perform somewhat better than targets. However, both distressed and non-distressed mergers have worse CAMEL profiles than our control group. In fact, non-distressed mergers may be motivated by the desire to forestall serious future financial distress and prevent regulatory intervention.

This paper brings to the forefront the assumptions that we make when focusing on a particular type of explanation for bank profitability. We evaluate a broad field of research by introducing a general framework for a profit maximizing bank and demonstrate how different types of models can be fitted into this framework. Next, we present an overview of the current major trends in European banking and relate them to each model's assumptions, thereby shedding light on the relevance, timeliness and shelf life of the different models. This way, we arrive at a set of recommendations for a future research agenda. We advocate a more prominent role for output prices, and suggest a modification of the intermediation approach. We also suggest ways to more clearly distinguish between market power and efficiency, and explain why we need time-dependent models. Finally, we propose the application of existing models to different size classes and sub-markets. Throughout we emphasize the benefits from applying several, complementary models to overcome the identification problems that we observe in individual models.

Because of the fast development in technologies, our lives have been constantly changing and the impact of changes on labor markets is essential for economic growth and inequality. This dissertation explores the relationship between economic growth, inequality, and the challenges in adapting to changes in the labor market. The study focuses on understanding the difficulties workers face when their skills need to change or when they need to switch to different industries. It examines how these adjustments affect productivity, wages, and the perception of technological changes that favor certain skills. Additionally, it explores the obstacles workers encounter when trying to move between different industries due to factors like high costs or specific skills needed in particular industries. The research also discusses the role of labor market policies in managing unemployment and inflation. It considers the trade-offs involved and the challenges of implementing effective policies. Overall, the dissertation highlights the importance of understanding and addressing these challenges to achieve both economic growth and a fairer society.

The dissertation focuses on debt pricing. Main findings suggest that banks and non-bank lenders have different pricing behaviour in the presence of informational frictions associated with geographical distance in the commercial mortgage-backed securities (CMBS) market: The distance is priced by banks, but not by non-bank lenders. The findings shed light on loan pricing practices of different types of lenders and provide evidence that risk considerations are not the same across lenders. Moreover, findings of the dissertation show that signalling through disclosure lowers the firms’ cost of debt in the syndicated loan market and in the green bond market.