Construction Time Determination Model, Automatically Detected Resource Needs Using Programs

This research is discussing about the construction project scheduling that is integrated between the implementation time with the resources needed periodically and in total. Because, determining the time of implementation of construction is very important as a basic reference, when the commencement of work and when the work must be completed. So far, the project scheduling method that is often used by engineers is the time schedule bar chart method, S curve method, Critical Path Method (CPM) method and other methods. After that, the time schedule is developed into scheduling material procurement and manpower, but the scheduling planning system is often out of sync with the implementation time schedule, material supply schedule and manpower supply schedule, therefore construction project delays often occur due to improper supply of material and manpower. That is part of the cause of the loss, therefore there needs to be an integrated scheduling program. How to produce an integrated program is, conducting research by combining methods namely, time schedule bar chart method, S curve method, CPM method, BOW analysis, SNI analysis, statistics, and supported by 60 random sampling data of project documents, consisting of, construction drawings, BOQ, and cost budget plan. Through the process according to these methods, an integrated program will be produced. Why is it necessary to use a program that results from this research, because this program is easy to use, simply enter the building area and planned implementation time at the input layer, the implementation schedule, and the required resources, will be detected automatically, both periodically and in total. This program uses Microsoft Excel applications and is very simple. This program can only be used for scheduling residential construction projects 1 level to 2 levels, with a building area between 36 m2 to 290 m2 . Cannot be used in other projects because each construction project has different characteristics.

Using Elasticities to Derive Optimal Bankruptcy Exemptions

This article studies the optimal determination of bankruptcy exemptions for risk averse borrowers who use unsecured contracts but have the possibility of defaulting. In a large class of economies, knowledge of four variables is sufficient to determine whether a bankruptcy exemption level is optimal or should be increased or decreased. These variables are 1. the composition of households’ liabilities, 2. the sensitivity of the credit supply schedule to exemption changes, 3. the probability of filing for bankruptcy with non-exempt assets, and 4. the value given by households to a marginal dollar in different states, which can be mapped to changes in households’ consumption. I recover empirical estimates of the sufficient statistics using U.S. data over the period 2008–16 and find that increasing exemption levels improves overall welfare, although there is substantial variation in estimated welfare gains across U.S. states and income quintiles.

Detecting Liquidity Traders

We develop a measure (based on the relative slopes of the demand and supply schedules) quantifying the asymmetric presence of liquidity traders in the market: a steeper slope of the demand (supply) schedule indicates a concentration of liquidity traders on the demand (supply) side. Using the opening session of the Tel Aviv Stock Exchange, we demonstrate the predictive power of our measure. Consistent with theory, we find that the concentration of liquidity traders on the demand (supply) side is negatively (positively) correlated with future returns. We find that liquidity traders are likely to arrive at the market together (commonality).