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Register a new userBottleneck Congestion And Work Starting Time Distribution Considering Household Travels
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Flextime is one of the efficient approaches in travel demand management to reduce peak hour congestion and encourage social distancing in epidemic prevention. Previous literature has developed bi-level models of the work starting time choice considering both labor output and urban mobility. Yet, most analytical studies assume the single trip purpose in peak hours (to work) only and do not consider the household travels (daycare drop-off/pick-up). In fact, as one of the main reasons to adopt flextime, household travel plays an influential role in travelers decision making on work schedule selection. On this account, we incorporate household travels into the work starting time choice model in this study. Both short-run travel behaviours and long-run work start time selection of heterogenous commuters are examined under agglomeration economies. If flextime is not flexible enough, commuters tend to agglomerate in work schedule choice at long-run equilibrium. Further, we analyze optimal schedule choices with two system performance indicators. For total commuting cost, it is found that the rigid school schedule for households may impede the benefits of flextime in commuting cost saving. In terms of total net benefit, while work schedule agglomeration of all commuters leads to the maximum in some cases, the polarized agglomeration of the two heterogenous groups can never achieve the optimum.
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Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.
The objective of this study is to understand the different behavioral considerations that govern the choice of people to engage in a crowd-shipping market. Using novel data collected by the researchers in the US, we develop discrete-continuous models. A binary logit model has been used to estimate crowd-shippers willingness to work, and an ordinary least-square regression model has been employed to calculate crowd-shippers maximum tolerance for shipping and delivery times. A selectivity-bias term has been included in the model to correct for the conditional relationships of the crowd-shippers willingness to work and their maximum travel time tolerance. The results show socio-demographic characteristics (e.g. age, gender, race, income, and education level), transporting freight experience, and number of social media usages significant influence the decision to participate in the crowd-shipping market. In addition, crowd-shippers pay expectations were found to be reasonable and concurrent with the literature on value-of-time. Findings from this research are helpful for crowd-shipping companies to identify and attract potential shippers. In addition, an understanding of crowd-shippers - their behaviors, perceptions, demographics, pay expectations, and in which contexts they are willing to divert from their route - are valuable to the development of business strategies such as matching criteria and compensation schemes for driver-partners.
Each individual in society experiences an evolution of their income during their lifetime. Macroscopically, this dynamics creates a statistical relationship between age and income for each society. In this study, we investigate income distribution and its relationship with age and identify a stable joint distribution function for age and income within the United Kingdom and the United States. We demonstrate a flexible calibration methodology using panel and population surveys and capture the characteristic differences between the UK and the US populations. The model here presented can be utilised for forecasting income and planning pensions.
We use geospatial data to examine the unprecedented national program currentlyunderway in the United States to distribute and administer vaccines against COVID-19. We quantify the impact of the proposed federal partnership with the companyDollar General to serve as vaccination sites and compare vaccine access with DollarGeneral to the current Federal Retail Pharmacy Partnership Program. Although dollarstores have been viewed with skepticism and controversy in the policy sector, we showthat, relative to the locations of the current federal program, Dollar General stores aredisproportionately likely to be located in Census tracts with high social vulnerability;using these stores as vaccination sites would greatly decrease the distance to vaccinesfor both low-income and minority households. We consider a hypothetical alternativepartnership with Dollar Tree and show that adding these stores to the vaccinationprogram would be similarly valuable, but impact different geographic areas than theDollar General partnership. Adding Dollar General to the current pharmacy partnersgreatly surpasses the goal set by the Biden administration of having 90% of the popu-lation within 5 miles of a vaccine site. We discuss the potential benefits of leveragingthese partnerships for other vaccinations, including against influenza.
Productivity levels and growth are extremely heterogeneous among firms. A vast literature has developed to explain the origins of productivity shocks, their dispersion, evolution and their relationship to the business cycle. We examine in detail the distribution of labor productivity levels and growth, and observe that they exhibit heavy tails. We propose to model these distributions using the four parameter L{e}vy stable distribution, a natural candidate deriving from the generalised Central Limit Theorem. We show that it is a better fit than several standard alternatives, and is remarkably consistent over time, countries and sectors. In all samples considered, the tail parameter is such that the theoretical variance of the distribution is infinite, so that the sample standard deviation increases with sample size. We find a consistent positive skewness, a markedly different behaviour between the left and right tails, and a positive relationship between productivity and size. The distributional approach allows us to test different measures of dispersion and find that productivity dispersion has slightly decreased over the past decade.
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