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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.

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.

Reading is a commandment from Allah. commanded through al-Qur'an Surah al-Alaq verse 1 which was revealed through the intermediary angel Gabriel to the Prophet Muhammad. Which will be preached to all the human Ummah specifically to the Ummah who always follows the teachings of Islam. Therefore, we must read the Koran, both reading directly (written) and reading with signs of Allah's power. (implied). Which has a variety of discussions ranging from reading the Koran, mentadabburi, practice and practice so as to create a generation of Qur'ani. In this paper using library research with the method of using library data collection techniques (Library Research) that uses various kinds of existing literature such as books, books of hadith, books of interpretation and the Qur'an that will help in using methods, deductive, inductive and comparative. And it was concluded that the command to read in the Koran so that it can be seen that reading is not just a job, not a mere hobby but as a very important command that will underlie all our activities in this world, let alone a prosecutor of knowledge, reading also is a foundation to go to the path that is blessed by Allah.

E-commerce is anything that involves an online transaction. E-commerce provides multiple benefits to the consumers in form of availability of goods at lower cost, wider choice, and saving time. The general category of e-commerce can be broken down into two parts: E-merchandise and E-Finance. E-commerce involves conducting business using modern communication instruments: telephone, fax, e-payment, money transfer systems, e-data interchange, and the Internet. Online businesses like financial services, travel, entertainment, and groceries are all likely to grow. Forces influencing the distribution of global e-commerce and its forms include economic factors, political factors, cultural factors, and supranational institutions. Goods and Service Tax or GST as it is known is all set to be a game-changer for the Indian economy. Overall it is known to be beneficial to both the consumer, business, and the Government. In India, there are different indirect taxes applied on goods and services by central and state governments. GST is intended to include all these taxes into one tax with seamless ITC and charged on both goods and services. Thus excise duty, special additional duty, service tax, VAT to name a few will get repealed and will be added into GST. For this, GST will have 3 parts CGST, SGST, and IGST. The central taxes like excise duty will be subsumed into CGST and state taxes like VAT into SGST. This going to be forward on all transactions of both goods and services, only one tax will apply which is GST comprising of CGST and SGST. IGST would be applied instead of SGST for interstate transactions. Input credit of all these taxes will be available against all the respective outputs. This paper is the outcome of a review of various research studies carried out on the Impact of GST on E-commerce. This paper examines different aspects like No threshold for GST registration, No Benefit under Composition Scheme, Tax Collection at Source by Marketplace Operator. Finally in the conclusion, one country and one tax will help the Indian economy to grow rapidly.

The quality of asphalt pothole repair in carriageways depends on several important factors, such as the selection of patching material and the choice of repair technique. Conventional hot mix plants operate to support large paving projects, making production more economic with high volume output. When small repairs and maintenance are needed, it can be challenging to maintain small quantities of hot asphalt at a sufficient temperature, especially in the case of winter maintenance. Consequently, the repair materials cannot be compacted to the desired level on some occasions. Thus, producing high quality asphalt materials for low-volume and hand-laid applications including potholes patching and utility reinstatements has lways been a problem. This research aimed to develop a pelletised polymer modified bitumen (PPMB) with reduced temperature sensitivity for low volume applications. The effect of the developed binder PPMB on the asphalt has been evaluated in terms of stiffness, permanent deformation (wheel track test, creep test and indentation test) and fatigue life test, durability (water sensitivity and freeze-thaw cycle) in comparison with the conventional patching materials. The results showed that the highest penetration index (low temperature sensitivity) has been achieved by modifying bitumen with 20% rubber and 2% wax to produce polymer modified bitumen (PMB). Hence, the developed PMB has been incorporated with 5% filler (limestone) and then pelletised and coated with 1% paper sludge ash to produce PPMB. The outcomes showed that PPMB improved the asphalt properties in terms of stiffness, permanent deformation and fatigue life test, water sensitivity and freeze-thaw cycle; this is due to the development of a strong bonds between the PPMB and the aggregate resulting in stronger and more durable asphalt compared to the traditional hot mix asphalt. Furthermore, the self-compacted mastic asphalt has been developed by incorporating PPMB and fine aggregate. The results demonstrated that modifying the mastic asphalt with 2% gilsonite and 3% low density polyethene has improved the sensitivity to permanent deformation (creep test, wheel track test and indentation test), resistance for water sensitivity and long-term ageing compared to the unmodified mastic asphalt. This implies that the modified mastic asphalt could be used in heavy traffic due to its mechanical and durability properties. The overall results indicated that using the PPMB to produce asphalt and modified mastic asphalt is a successful alternative to the traditional patching materials in different applications.

Delays on construction projects are w result in several negative effects like disputes, lawsuits, total dissertation and abandonment. Thus, identifying, ranking and confronting the causes of delay This paper identifies the causes construction projects in Addis Ababa. categorized in 8 major groups. 43 Contractors, 21 Consultants, and 19 Aluminum Fabricators were asked to evaluate frequency and severity of each cause. Data were analyzed with frequency and importance indices. Agreement of the rankings of importance of the causes between groups was tested. Findings of the research revealed that a consultants experienced a 42% and 52% of delay the groups of respondents attest that cause of delay. They also share the view that important. Consultants distinctly perceive whereas Contractors regard Fabricators view that Material Related causes are second in line. Relative agreement of rank of causes between groups showed a high degree of correlation among the groups of respondents. viii worldwide phenomena. Construction projects delays are important. of delay in aluminum fabrication and installation in This study revealed a total of 52 causes . about 90% of the contractors and 85% of the average delay over original contract period. All Owner induced factors are the most frequent and severe Contractor Related causes are third most that Project related causes are second most severe, related causes as runner-ups. Fabricators hold the

Sequencing can provide genomic characterisation of a specific organism, as well as of a whole environmental or clinical sample. High Throughput Sequencing (HTS) makes it possible to generate an enormous amount of genomic data at gradually decreasing costs and almost in real-time. HTS is used, among others, in medicine, veterinary medicine, microbiology, virology and epidemiology. The paper presents practical aspects of the HTS technology. It describes generations of sequencing, which vary in throughput, read length, accuracy and costs ̶ and thus are used for different applications. The stages of HTS, as well as their purposes and pitfalls, are presented: extraction of the genetic material, library preparation, sequencing and data processing. For success of the whole process, all stages need to follow strict quality control measurements. Choosing the right sequencing platform, proper sample and library preparation procedures, as well as adequate bioinformatic tools are crucial for high quality results.

Time series regression models are commonly used in time series analysis. However, in modern real-world applications, serially correlated data with an ultra-high dimension and fat tails are prevalent. This presents a challenge in developing new statistical tools for time series analysis. In this paper, we propose a novel Bernstein-type inequality for high-dimensional linear processes and apply it to investigate two high-dimensional robust estimation problems: (1) time series regression with fat-tailed and correlated covariates and errors, and (2) fat-tailed vector autoregression. Our proposed approach allows for exponential increases in dimension with sample size under mild moment and dependence conditions, while ensuring consistency in the estimation process.

The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devising effective disease-control measures. Qin et al. (2020) designed a cross-sectional and forward follow-up study to collect the duration times between a specific observation time and the onset of COVID-19 symptoms for a number of individuals. They further proposed a mixture forward-incubation-time epidemic model, which is a mixture of an incubation-period distribution and a forward time distribution, to model the collected duration times and to estimate the incubation-period distribution of COVID-19. In this paper, we provide sufficient conditions for the identifiability of the unknown parameters in the mixture forward-incubation-time epidemic model when the incubation period follows a two-parameter distribution. Under the same setup, we propose a likelihood ratio test (LRT) for testing the null hypothesis that the mixture forward-incubation-time epidemic model is a homogeneous exponential distribution. The testing problem is non-regular because a nuisance parameter is present only under the alternative. We establish the limiting distribution of the LRT and identify an explicit representation for it. The limiting distribution of the LRT under a sequence of local alternatives is also obtained. Our simulation results indicate that the LRT has desirable type I errors and powers, and we analyze a COVID-19 outbreak dataset from China to illustrate the usefulness of the LRT., 34 pages, 2 figures, 2 tables

Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information. In this paper, we consider a high-dimensional linear regression model with blockwise missing covariates and a partially observed response variable. Under this framework, we propose a computationally efficient estimator for the regression coefficient vector based on carefully constructed unbiased estimating equations and a blockwise imputation procedure, and obtain its rate of convergence. Furthermore, building upon an innovative projected estimating equation technique that intrinsically achieves bias-correction of the initial estimator, we propose a nearly unbiased estimator for each individual regression coefficient, which is asymptotically normally distributed under mild conditions. Based on these debiased estimators, asymptotically valid confidence intervals and statistical tests about each regression coefficient are constructed. Numerical studies and application analysis of the Alzheimer's Disease Neuroimaging Initiative data show that the proposed method performs better and benefits more from unsupervised samples than existing methods., Comment: V2: 40 pages, 2 figures. Accepted at Statistica Sinica