Measuring producer welfare under output price uncertainty and risk non-neutrality

Procedures to measure the producer welfare effects of changes in an output price distribution under uncertainty are reviewed. Theory and numerical integration methods are combined to show how for any form of Marshallian risk-responsive supply, compensating variation of a change in higher moments of an output price distribution can be derived numerically. The numerical procedure enables measurement of producer welfare effects in the many circumstances in which risk and uncertainty are important elements. The practical ease and potential usefulness of the procedure is illustrated by measuring the producer welfare effects of USA rice policy.price uncertainty, risk non-neutrality, welfare economics, Demand and Price Analysis, Risk and Uncertainty,

TOWARDS MEASURING PRODUCER WELFARE UNDER OUTPUT PRICE UNCERTAINTY AND RISK NON-NEUTRALITY

We combine theory with numerical integration methods to show that for any form of uncompensated supply, compensating variation of a change in higher moments of an output price distribution can be numerically derived.Producer welfare, price uncertainty, risk, Demand and Price Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty,

Relationships Between Soft TQM, Hard TQM, and Organisational Performance

Many empirical studies have demonstrated that only a handful of soft total quality management (TQM) elements contribute to organisational performance while elements of hard TQM have no relationship with performance. Despite these findings, a review of literature suggests that the elements of hard TQM in fact have a profound impact on organisational performance. The empirical studies which have investigated the relationship between hard TQM and performance have investigated the impact of each dimension of TQM on performance separately. We argue that it is more appropriate to investigate the direct impact of soft TQM on the diffusion of hard TQM in organisations and then assess the direct impact of hard TQM on performance. Besides direct effects, it is also important to investigate the indirect effect of soft TQM on performance through its effect on hard TQM elements. Analysis of 260 Australian manufacturing companies revealed that both soft TQM and hard TQM contribute directly to organisational performance. The results also indicate that there are significant positive relationships between the elements of soft TQM and those of hard TQM. Moreover, in addition to its direct affect, the elements of soft TQM also indirectly affects an organisation’s performance through its effect on hard TQM elements

TravelSmart: A Critical Appraisal

Travel behaviour modification, also called TravelSmart®, Indimark® and Travel Blending®, has been offered as a solution to the dependence of urban populations on the car. Travel behaviour modification is a voluntary programme aimed at changing travel behaviour through providing better information about transport options, rather than through investments in public transport, or through disincentive programmes for the car. The policy has been implemented in Australia in Perth, Adelaide, and Brisbane, and is under active consideration at least in Melbourne and Sydney. The basis of this increasingly widespread potential application of travel behaviour modification is the claim that the program can deliver a shift of travel mode choices through the provision of better information about travel behaviour and travel choices. The claims that are made for this programme are that it can lead to reductions in car use of the order of 10 to 14 percent. If these claims are real, then travel behaviour modification is an enormously valuable programme, with the potential to achieve what has never been done before, i.e. provide a doubling or more of public transport ridership and a significant drop in car use. Such a program would be the answer to the dilemma of how to reduce car use significantly and consequently reduce congestion and vehicular emissions. It is, therefore, appropriate to undertake a critical appraisal to determine if travel behaviour modification is able to deliver these major mode shifts, as its proponents claim. In this paper, we review a number of published articles, primarily based on the Australian experience with travel behaviour modification, and also review several reports, and materials from the application areas. From these reviews, analyses are performed to see what the actual expected shift is in mode use for the whole population. It is found that there appears to be evidence that the claims of 10 or more percent shift out of car driver are over-stated, and that real shifts may be of the order of six to seven percent. Second, some sampling issues are discussed that indicate that the numbers reported to date may not be as reliable as one would like. Third, the locations of the test applications are examined and discussed, and it is suggested that there may be some significant bias in these locations towards a larger uptake of the shifts into environmentally-friendly modes of travel. In sum, the paper concludes that travel behaviour modification is capable of making changes in the use of environmentally-friendly modes, but not at the rates that have often been claimed. It is suggested that the target populations may need to be limited and that expectations of the size of the shifts in mode use need to be tempered

Measuring Bus Performance using GPS Technology

Assessing the running times of bus services has traditionally been a difficult and expensive task for the majority of bus operators in Australia, and in other parts of the world. Up until recently, travel times have been collected by time keepers positioned at key points along a given route or service corridor who record bus arrival and departure times. These data then need to be manually collated before any kind of analysis can be undertaken. The time consuming nature of this process restricts the ability of operators to collect large and meaningful samples of data. Furthermore, it is difficult, if not impossible, to identify congestion points from such data, and to evaluate the impact that they might have on overall service levels. Passive Global Positioning System (GPS) technology offers a low-cost means of collecting large amounts of highly accurate data, which can be used in an on-going performance assessment program. Although raw GPS track points can be viewed on most standard GIS packages, on-screen visual analysis is extremely time consuming for even small amounts of data. Programming skills are therefore required to break continuous GPS data into records that are more meaningful to an operator. A number of important tasks need to be undertaken before analysis can take place. Firstly, periods of in-service or out-of-service running need to be defined, and routes need to be identified. This can be a complicated task because operators often design shifts so that buses may switch between different areas and routes, from run to run, to maximise vehicle utilisation. Once routes are identified, records must then be separated into individual runs and matched with a timetable to compare scheduled and actual running times. This paper provides an overview of a number of software applications developed for processing and analysing large GPS data records collected by a bus operator in Sydney in late 2002/early 2003. The data collection process is described, and some examples are presented of output produced by the main trip processing and timetable query program. It is concluded that passive GPS is a highly attractive method of collecting data on performance, even for very small operators

Monte Carlo Simulation of Sydney Household Travel Survey Data with Bayesian Updating using Different Local Sample Sizes

There is increasing interest in the potential to simulate household travel survey data as an alternative to collecting large sample household travel surveys, or as a means to augment sample sizes, well beyond what can usually be considered. In prior research on simulating such data, it has been shown that it is possible to reproduce, within reasonable bounds of accuracy, an actual household travel survey, It has also been found that the procedure of updating the distributions of the simulated variables, using Bayesian updating with subjective priors, can provide significant improvement in the accuracy with which an actual household travel survey can be simulated. In work performed to date, it has not been determined what the optimal size would be for the update sample to be used in the Bayesian updating. Rather, prior work has used a sample of approximately 500 households, largely as a matter of convenience and cost. In this paper, we report on further research that compares different sample sizes for the local update data. It was found that a reasonable updating could be obtained from a sample as small as 300 households, chosen through a stratified sampling procedure, and that results improved substantially when the update sample was increased to 500. However, an increase in the sample to 750 did not produce very much additional improvement, suggesting that sample sizes of this size and larger may not be economically justified. At the same time, the research suggests that there may be room for a more targeted sampling procedure which could allow smaller samples to be more cost-effective

GPS Surveys and the Internet

ITS has been pioneering the use of GPS to provide more accurate data on where and when people travel, their routes, travel distance, and travel time. GPS provides no information on the number of people travelling together, trip purposes, and travel costs. ITS has pioneered the development of a method of collecting this additional information called the prompted recall survey, designed to be conducted some days after the GPS data are collected, using maps and tabular presentations from the GPS records to prompt the respondent’s memory. We describe these surveys and document some of the results. As an improvement on the paper and pencil version, we developed an internet-based survey. This provides animation of each GPS trip, and gives respondents the ability to stop the trip part way through to indicate a trip end that the analysis of the GPS data had not detected, to restart the trip, and to indicate that a stop was only a traffic stop, not a destination. The paper describes the animation, shows the types of data that can be collected, and describes the advantages offered. Some examples are provided of the results of people using the prompted recall survey version

Exploring the Use of Passive GPS Devices to Measure Travel

Global Positioning System devices are emerging as a potential means to collect improved data on the spatial aspects of personal travel. In many applications, the GPS device is coupled with a Personal Data Assistant of some type and the respondent who is using the GPS device is required to enter various data items at the start of each trip made with the device. This procedure has the disadvantage that it relies on the memory of the respondent to use the PDA, and also is subject to being missed if the respondent is in a hurry. This paper builds on earlier work by Stopher and others to develop a passive GPS device, for which additional non- GPS data may be added either through inference or through a subsequent prompted recall survey. The paper describes the use of both in-vehicle and personal versions of a GPS device that logs position in one- or five-second intervals and has a number of other capabilities, such as turning off automatically when speed drops below 1 knot. Experiments have been performed in which the devices are tested for a range of different situations, including collecting data for one month, collecting data on trains, buses, and ferries, and experimenting with automatic on/off procedures. The paper reports on a number of experiments, describes the procedures undertaken to download and analyse the data, and processing of the data for the prompted recall surveys. Initial results are included on experiments with the prompted recall, and options to develop this as an internet survey are explored. In addition, analysis of the data is conducted to investigate congestion and the amount of time spent under congested travel conditions. Potential applications of this analysis to a variety of purposes is described in the paper

Simulated Household Travel Survey Data: Synthetic Data in Australia

method has been developed to synthesize household travel survey data from a combination of Census and national transport survey data sources. The procedure, described in other papers, involves creating distributions of pertinent variables (numbers of trips by purpose, mode of travel, time of day of travel, and trip length) that can be used to estimate travel-demand models. A sample of local residents is then drawn from disaggregate census data, providing detailed information on the socioeconomic characteristics of the sample. Using these socioeconomic characteristics, travel data are simulated from the transport data distributions using Monte Carlo simulation. This procedure was developed in the United States in the past four years. The paper describes the application of this procedure to Adelaide, South Australia, for which an actual household travel survey exists from 1999. The paper describes results obtained from applying the generic data as the basis of the simulation. Results are compared between the synthetic and real data to determine the closeness of the match between the data sets. The procedure uses data derived from a nationwide travel survey in the U.S., but uses census data for Adelaide from the 1996 ABS Census, using the one percent sample. The purpose of this research was to determine the extent to which the trip characteristics distributions from the U.S. could be used in Australia. It is concluded that the procedure performs about as well as the process was shown to perform in Dallas, Salt Lake City, and Baton Rouge in the U.S. This process holds out considerable promise as a means to increase available samples for local and corridor planning, as well as to provide data for regions that have typically not been able to undertake household travel surveys on the scale of those being conducted in the Melbourne and Sydney regions