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The aim of this paper is to learn about some patterns of sectoral and industrial structural change of the Chinese economy over the 1995-2010 period, which also complements a previous paper of the author. The chosen period is about (and conveniently) bounded by two international crises: the Southeast Asian crisis of 1997 and the world crisis that started in 2007/2008.

To such a purpose, this paper set up a quantitative methodology via input-output modelling, which allows us to decompose gross output into some key demand sources or contributions. These are then analyzed over the full period.

It can be shown that the trajectory of the main structural patterns over the period was not smooth and was pretty unbalanced and that they generally responded to both domestic policy and international shocks. Export demand and heavy industry appeared to be the main engines of the economy, which showed massive increases in their share of output, at the expense of domestic demand, services and agriculture. Despite the high growth rates over this period, the Chinese economy seemed to be in need of rebalancing, which seems to have started toward the end of the authors’ period.

The decomposition method has been applied before by the author and others, but the variations in this paper are original, just as original is the application to China (never been done before), which in addition is not confined to two or so snapshots separated by many years, as is the usual use, but to the full year-after-year change of the sectoral and industrial structure over this study’s focus period.

This paper deals with some structural indicators and their evolution, in China and its regions, over the period 1981-2010.

The paper uses a quantitative approach. Linear programming and structural growth decompositions were used. The authors first produce estimates of the optimal productivities of incremental capital and the optimal incremental income elasticity of capital by means of a linear programming exercise. They then produce an accounting growth decomposition to assess the changes in the contribution of capital productivity, capital intensity and labour participation to the growth rate of output per capita. Finally, they combine an accounting growth decomposition with a standard production function, growth accounting, decomposition to assess the contribution of both capital productivity and capital intensity to total factor productivity (TFP). They also show in the Appendix the difference in the TFP growth contribution when marginal elasticities are assumed variable over time and when scale returns are assumed to be increasing rather than constant.

The main conclusion of the paper is that capital intensity, rather than capital productivity or labour participation, has been the main growth contributor. Capital productivity has fallen, while capital intensity has increased significantly, but that does not mean that quantity in itself, rather than quality, is behind such growth, as total factor productivity, which is significantly more than engineering technical change, has been relatively important over the period.

Both the use of linear programming to assess the evolution of incremental capital productivity and the decomposition of TFP.

Surging natural disasters globally has precipitated renewed interests in disaster risk management. Though several global and regional disaster risk management policy frameworks have been put in place, it is necessary to evaluate their successes and capacities to deliver. This chapter reviews key disaster management frameworks, particularly the Yokohama Strategy, the Hyogo Framework for Action and the Sendai Framework for Disaster Risk Reduction. It examines the extent to which these policies shaped Africa’s regional disaster risk management processes, with an emphasis on sub-Saharan Africa (SSA). Through documentary analysis and scientific literature review, this chapter identifies key parameters that shaped SSA’s disaster risk reduction (DRR) processes and their implications for DRR policy instruments and impact studies. The analysis reveals a number of findings. First, the roll-out process of global disaster reduction and management policy processes and instruments is yet to optimally impact SSA, in terms of effective disaster management. Second, a more comprehensive understanding of the magnitude and severity of natural disasters could contribute to stem the damages linked to their occurrence. This is yet to be achieved. Third, paradigm shifts towards fully appreciating underlying disaster risk factors and manifestations could potentially support the practical drift from disaster coping and management towards risk identification, reduction and resilience building in SSA. Finally, instruments that prioritise capacity building (such as extension services training, research and development, information and communication), organisational governance, sustainable financing and technology, still relatively weak in SSA, should be stepped up to promote DRR capacities and strategies.

The purpose of this paper is to investigate humanitarian supply chains in the context of the Ukrainian crisis as example of complex emergency. The paper focuses on a selection of support modes: in-kind donations, cash-based assistance and local procurement.

This paper adopts a case-study approach and interpretive paradigm. Findings are based on the analysis of primary sources including interviews with three Polish humanitarian organizations, internal documents, and secondary sources such as published reports.

Findings indicate that in a middle-income urbanized country such as Ukraine non-standard modes such as cash transfer programs and local procurement can be employed, since the necessary infrastructure and market are operational. However, each mode has limitations, so they should match the local context and the needs of diverse social groups.

The findings and recommendations are specific to the case analyzed, Ukraine, and its socio-economic context. The research contributes to discussions about mode selection, stressing the links between mode, stage of the disaster response and local context.

Applying cash transfers and local procurement can reduce supply chain costs, such as transport and warehousing. Shortened supply chains enable faster responses and increased agility.

Cash transfers and procurement involve the local community and beneficiaries, and can better fulfill needs maintaining people’s dignity. However, for vulnerable groups and those in conflict zones, in-kind goods are a better option.

The author argues that the much-discussed dichotomy of cash or goods does not reflect reality; local and regional procurement should be added as important support modes in middle-income countries in crisis.

The purpose of this paper is to evaluate the effect of floods and the role of financial development on per capita gross state domestic product (GSDP) growth, controlling for growth-enhancing factors across Indian states.

The paper uses the pooled mean group (PMG) method using state-level panel data for 19 Indian states over the period 1981-2011.

The PMG estimate shows that floods negatively affect the per capita GSDP growth in the long run. The results show that the mean of economic losses, the population affected and the area affected by floods increase by 10 per cent, leading to a decline in per capita GSDP growth by 0.0303, 0.0633 and 0.0232 per cent, respectively, in the long run. Furthermore, the population affected by floods exerts a higher adverse impact on the per capita GSDP growth compared to other flood measures. The results further show that states with better financial development experience a higher per capita GSDP growth, supported by additional capital expenditure, enrolment in higher education, better road infrastructure and higher urbanization. The crime rate is negatively correlated with per capita GSDP growth.

The results based on PMG estimates suggest that not only floods but also crime activities adversely affect the per capita GSDP growth across Indian states. Better financial market increases the per capita GSDP growth in the long run. This study not only contributes to empirical growth literature but also provides some useful policy suggestions. Moreover, the results lead to the conclusion that long-term flood management policies are essential to mitigate the adverse impact of floods on per capita GSDP growth across Indian states.

The area of disaster studies is plagued by dubious statistical data and widespread conceptual disagreements. This is the major focus of discussion in the paper. We detail the limitations in much of the numerical data that are both specifically and generally used in discussions of disasters. Factors that are responsible for this, including inadequate conceptualizations about disasters are discussed. We also show that there is not much consensus by researchers and others about many of the most central concepts used such as “disaster”, “hazards,” “risk,” etc. In our call for more reliable statistics and more relevant concepts, we provide examples and suggestions of how this could be done.

Prior studies have paid close attention to the impact of political risk on financial markets. Following this strand of literature, this paper aims to focus on the causality link between political shocks and their impacts on emerging stock markets.

This paper highlights an innovative counterfactual model for political risk assessment. Based on a natural experiment, i.e. the Taiwan Strait Crisis in 1995-1996, this study utilizes one data-driven approach, e.g. the synthetic control methods (SCMs), to estimate causal impact of this political shock on Taiwan’s stock market.

Major findings in this study are consistent with existing literature on the price of political risk, e.g. political uncertainty commands a risk premium. The SCM estimations suggest that Taiwan’s stock prices dramatically underperformed its newly industrialized peers and other developed markets during the crisis. The SCM results are statistically significant and robust to various cross-validation tests.

Findings in this study indicate that political risks could generate enormous impacts on emerging financial markets. In particular, political uncertainty following new geopolitical dynamics requires proper identification and assessment.

To the author’s knowledge, this paper is the first rigorous counterfactual study to the causality relationship between political uncertainty and stock prices in emerging markets. This paper is distinct from previous studies in applying a data-driven approach to combine the features of learning from others (cross-sectional) and learning from the past (time series).