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Can business play a role in building resilience among vulnerable communities? Can (marketing) employment contribute to resilience – and if so, how? These are increasingly important and relevant questions in our current world as the recent COVID-19 pandemic showed many households to be just one shock away from falling into poverty. Consequently, understanding how resilience, the ability to cope both psychologically and economically with adversity, can be built has become a top priority for many policymakers and practitioners. This paper explores a marketing solution to building resilience. In this paper, we seek to answer the following question: “Can sales employment help people build resilience and enable them to cope with adversity?” We offer a mechanism through which sales improves resilience: by facilitating opportunities to connect. We show that by enabling opportunities to connect with a wide set of individuals, sales employment can build resilience. We differentiate between types of connections and show that different connections can lead to different forms of resilience. We present experimental evidence from a randomized controlled trial involving saleswomen in rural India. With our partner organization, we recruited, trained, and randomly assigned 1,048 women to either treatment, placebo, or control condition. We exogenously assign individuals to an employment position that facilitates opportunities to connect and measure the impact on resilience. In doing so, not only do we vary opportunities to connections, but we also provide a plausible mechanism of why they have more connections than others: certain jobs – in our case sales employment. To the best of our knowledge, there is no empirical evidence showing the impact of (changing) connections on resilience when facing adversity. In doing so, we are the first to randomly assign participants to different employment forms and causally estimate the impact of employment on personal outcomes, such as resilience. Our results show that those engaged in sales employment are more resilient than those in placebo employment or no-employment conditions. In fact, we find that sales employment significantly improves one’s ability to be economically resilient when facing adversity by increasing: i) the likelihood of being aware of external sources of help and ii) the ability to take-up external sources of help. Second, our results show that engagement in sales is associated with having a wider set of connections. This effect is largely driven by increased connections with local government representatives and local community leaders. We find a strong association between the width of connections and economic and psychological resilience. Last, we examine heterogeneous effects and find that those from lower caste benefit most from engaging in sales employment in terms of building resilience compared. To the best of our knowledge, no one has looked at the impact of employment on resilience – we show that marketing employment holds the power to improve resilience when facing adversity.

This thesis is a contribution to macroeconomics and macro-finance research. It focuses on two areas, the interactions between debt markets and the macroeconomy, in the first two chapters, and consumption heterogeneity’s role in the transmission of shocks, in the third. The first chapter is my job market paper, “Monetary Policy and the Maturity Structure of Public Debt”. It studies the mediating impact of the maturity structure of public debt in the transmission of monetary policy shocks to economic activity. A longer debt maturity attenuates greatly the effect of monetary policy: going from the average historical duration of US debt to very short term debt doubles the impact of a rise of the policy rate on output. A similar result holds in UK data. There is no differential effect on inflation. I show that these effects can be traced back to a quantitatively important financing channel. A model featuring an interaction between an empirically estimated primary market friction and a standard financial accelerator is able to account for these facts. The second chapter of the thesis is “The Fiscal Consequences of Missing an Inflation Target”. This is a joint work with Helene Rey. We show, in Euro Area countries and in the US, the effect of the reliability of central banks’ monetary policy on public finances. When a credible central bank misses its inflation target, this has large fiscal consequences for the fiscal authority. These misses are quantitative sizable in the past low inflation decade, especially for countries with a high level and a high maturity of public debt. We link our results to the optimum currency area literature as the monetary-fiscal nexus is key to the functioning of the Euro Area: the ECB sets monetary policy for sovereigns with heterogeneous debt profiles. The third chapter of my thesis, “LESS is MORE: Consumer Spending and the Size of Economic Stimulus Payments”, is a joint work with Paolo Surico. We study the consumption response to unexpected transitory income gains of different size, using hypothetical survey questions. Affluent households exhibit a higher Marginal Propensity to Consume (MPC) out of large gains while families with low cash-on-hand display a higher MPC out of small gains. The spending of higher earners is consistent with the predictions of a model with non-homothetic preferences on consumption, while spending of low-income families can be accounted for by borrowing constraints. Our results suggest that, for a given level of public spending, a fiscal transfer of smaller size paid to a larger group of low-income households stimulates aggregate consumption more than a larger transfer paid to a smaller group.

Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at the subpixel level and estimate their corresponding proportions (fractional abundances). Recently, the rapid development of deep learning networks has provided us with a new method to solve the problem of spectral unmixing. In this paper, we propose a spatial-information-assisted spectral information learning unmixing network (SISLU-Net) for hyperspectral images. The SISLU-Net consists of two branches. The upper branch focuses on the extraction of spectral information. The input of the upper branch is a number of pixels randomly extracted from the hyperspectral image. The data are fed into the network as a random combination of different pixel blocks each time. The random combination of batches can boost the network to learn global spectral information. Another branch focuses on learning spatial information from the entire hyperspectral image and transmitting it to the upper branch through the shared weight strategy. This allows the network to take into account the spectral information and spatial information of HSI at the same time. In addition, according to the distribution characteristics of endmembers, we employ Wing loss to solve the problem of uneven distributions of endmembers. Experimental results on one synthetic and three real hyperspectral data sets show that SISLU-Net is effective and competitive compared with several state-of-the-art unmixing algorithms in terms of the spectral angle distance (SAD) of the endmembers and the root mean square error (RMSE) of the abundances.

This paper shows that endogenous adjustments in the composition of labour supplies magnify the effects of changes in commodity prices on the measured skill premium under quite plausible conditions. These composition effects arise from decisions of individuals with heterogeneous inherent abilities about acquiring human capital. They reinforce the well-known Stolper – Samuelson effect on the measured skill premium in countries with a sufficiently high relative supply of skilled labour, but compensate them otherwise. As a result, the model can account for the observation of a worldwide increase in the skill premium during the last two decades.

In this paper, we present a method for segmenting lung lobe regions and segmental lobe regions from 3D CT datasets. In a CAD system for the chest, it is very important to understand structures of the chest by a computer. It is required to develop algorithms that automatically segment organ regions of the chest area from input 3-D chest CT datasets. This paper tries to divide lung regions into lung lobe regions and segmental lobe regions by the figure decomposition process and the Voronoi division process. In this method, we enhance sheet structures on CT images by using eigenvalues of a Hessian matrix. Also, the lung regions are segmented by simple thresholding and morphological filtering. Then, we subtract sheet structures from the lung regions. By applying the figure decomposition process, we obtain each lung lobe region. Segmental lobe regions are obtained by the Voronoi division process using bronchial branch information The proposed method was applied to thirteen cases of 3-D chest CT images. Experimental results showed that the proposed method can extract lung lobe regions and segmental lobe regions even for cases of incomplete fissure or over-extraction of interlobar pleura.

We know the Nordic countries are leaders in sustainable developments in social and eco-friendly practices, and measurements of multiple bottom lines are becoming common for many organizations. Also, the European Union has enacted laws to practice sustainability and submit reports on it. However, these reports are not compulsory for all the organizations in the EU. If the organizations have more than 500 workers, they are obliged to report on it. Yet multiple bottom-line measurements for sustainability practices are becoming a trend these days, so many organizations are measuring multiple bottom lines for sustainability practices. The interesting fact is that many organizations are voluntarily measuring multiple bottom lines for sustainability practices. For private companies, these phenomena are not uncommon. But if an organization is owned by a local municipality, this is not so common. One such company is Roskilde Kongres- og Idrætscenter (an event management organization) which is owned by Roskilde municipality. Event management companies organize events, congregations, or social occasions. It is obvious that these functions will create waste, and a huge amount of energy and natural resources are connected to them. These activities have a non-eco-friendly impact on society and the environment. Although this organization is not required by law to measure multiple bottom lines for sustainability practices, it is measuring four bottom lines for the sustainability agenda. In the beginning, we did not know the exact reasons but assumed that stakeholders in this organization might demand multiple bottom-line measurements for sustainability practices. The purpose of this thesis paper was to learn about the influence of stakeholders on measuring multiple bottom lines for sustainability practices. The journey began with the theoretical framework used to study the situation. The theory of stakeholders guided which stakeholders particularly had to be considered. By exploring the different kinds of stakeholders, it was easier to know whether they influenced measuring multiple bottom lines for the sustainability agenda or not, and even if they did, which stakeholder was the most influential. The primary data was collected through semi-structured interviews, and the design of a case study was used. The analysis was done through narrative analysis due to the fact that qualitative data was collected from personal interviews. The findings of this research paper reveal that a few stakeholder groups influence measuring multiple bottom lines for sustainability practices. Moreover, influence from stakeholders can force an organization to make choices it is not required to make.