Is America Exporting Misguided Telecommunications Policy? The U.S.-Japan Telecom Trade Negotiations and Beyond

Global telecommunications markets have traditionally been closed to foreign trade and investment. Recent World Trade Organization negotiations resulted in a Basic Telecommunications agreement that sought to construct a multilateral framework to reverse that trend and begin opening telecom markets worldwide. Regrettably, this new WTO framework is quite ambiguous and open to pro-regulatory interpretations by member states. In fact, during recent bilateral trade negotiations with Japan, U.S. government officials adopted the position that the new framework allowed them to demand that the Japanese government adopt very specific regulatory provisions regarding telecom network interconnection and pricing policies. The Office of the U.S. Trade Representative argued that Japanese officials should require their domestic telecom providers to share their networks with rivals at a generously discounted price to encourage greater resale competition. Those interconnection and line-sharing rules were borrowed directly from the U.S. Telecommunications Act of 1996, a piece of legislation that remains the subject of intense debate within the United States. Good evidence now exists that those rules generally retard net-work investment and innovation by encouraging infrastructure sharing over facilities-based investment. Consequently, the USTR has generated resentment on the part of Japan and other trading partners as it has attempted to force them to adopt heavy-handed telecommunications mandates that have very little to do with legitimate free-trade policy. The USTR must discontinue efforts to impose American telecommunications regulations on other countries as part of free-trade negotiations and should instead focus on reforming or eliminating the most serious barriers to foreign direct investment both here and abroad

Estimating the critical and sensitive periods of investment in early childhood: A methodological note

This paper provides an overview of different quantitative methods available for the statistical analysis of longitudinal data regarding child development, and in particular the identification of critical and sensitive periods for later abilities. It draws heavily on the work on human skill formation developed by the economist James Heckman, which treats ability as a latent variable and explains its formation through the simultaneous estimation of structural equations of investments and achieved abilities across time. We distinguish between two specifications of the ability formation function. One of them (the ‘recursive’) format explains current ability as a function of the ability and investment at the immediately preceding period. The other (the ‘non-recursive’) format explains current ability as a function of a series of past investments. In order to fully examine critical and sensitive periods of investments, the non-recursive formulation needs to be used. Furthermore, true abilities of an individual cannot be directly observed: what we observe are the test scores, for example, on reading and writing. We outline an approach based on structural models that treats actual test scores as measurements of the latent ability variable, and show how it can be used in the recursive and non-recursive formulation. In order to fully examine critical and sensitive periods of investments, we argue that the non-recursive formulation of this structural model is necessary. However, the non-recursive formulation requires more data than the recursive formulation, and to the best of our knowledge, has never been used in the identification of critical and sensitive periods in early childhood development. (254wds

Common scale valuations across different preference-based measures: estimation using rank data

Background: Different preference-based measures (PBMs) used to estimate Quality Adjusted Life Years (QALYs) provide di¤erent utility values for the same patient. Differences are expected since values have been obtained using different samples, valuation techniques and descriptive systems. Previous studies have estimated the relationship between pairs of PBMs using patient self-reported data. However, there is a need for an approach capable of generating values directly on a common scale for a range of PBMs using the same sample of general population respondents and valuation technique but keeping the advantages of the different descriptive systems. Methods: General public survey data (n=501) where respondents ranked health states described using subsets of six PBMs were analysed. We develop a new model based on the mixed logit to overcome two key limitations of the standard rank ordered logit model, namely, the unrealistic choice pattern (Independence of Irrelevant Alternatives) and the independence of repeated observations. Results: There are substantial differences in the estimated parameters between the two models (mean di¤erence 0.07) leading to di¤erent orderings across the measures. Estimated values for the best states described by di¤erent PBMs are substantially and significantly di¤erent using the standard model, unlike our approach which yields more consistent results. Limitations: Data come from a exploratory study that is relatively small both in sample size and coverage of health states. Conclusions: This study develops a new, �exible econometric model specifically designed to reflect appropriately the features of rank data. Results support the view that the standard model is not appropriate in this setting and will yield very different and apparently inconsistent results. PBMs can be compared using a common scale by implementation of this new approach

It's all in the name, or is it? The impact of labelling on health state values

Many descriptions of health used in vignettes and condition-specific measures refer to the medical condition. This paper assesses the impact of referring to the medical condition in the descriptions of health states valued by members of the general population. A sample of 241 members of the UK general population each valued 8 health states using time trade-off. All respondents valued essentially the same health states, but for each respondent the descriptions featured either an irritable bowel syndrome label, a cancer label or no label. Regression techniques were used to estimate the impact of each label and experience of the condition on health state values. We find that the inclusion of a cancer label in health state descriptions affects health state values and that the impact is dependent upon the severity of the state. A condition label can affect health state values, but this is dependent upon the specific condition and severity. It is recommended to avoid condition labels in health state descriptions (where possible) to ensure that values are not affected by prior knowledge or preconception of the condition that may distort the health state being valued

Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices

Suppose that one has a clinical dataset with only non-preference-based QOL data, and that one nevertheless would like to perform a cost/QALY analysis. This study reports on some efforts to establish a “mapping” relationship between AQLQ (a non-preference-based QOL instrument for asthma) and EQ5D (a preference-based generic instrument). Various methods are described in terms of associated assumptions regarding the measurement properties of the instruments. This is followed by empirical mapping, based on regressing EQ5D on AQLQ. Six main regression models and two supplementary models are identified, and the regressions carried out. Performance of each model is explored in terms of goodness of fit between observed and predicted values, and of robustness of predictions on external data. The results show that it is possible to predict mean EQ5D indices given AQLQ data. The general implications for methods of mapping non-preference-based instruments onto preference-based measures are discussed.EQ5D; AQLQ; mapping

Deriving preference-based single indices from non-preference based condition-specific instruments: Converting AQLQ into EQ5D indices

Suppose that one has a clinical dataset with only non-preference-based QOL data, and that one nevertheless would like to perform a cost/QALY analysis. This study reports on some efforts to establish a "mapping" relationship between AQLQ (a non-preference-based QOL instrument for asthma) and EQ5D (a preference-based generic instrument). Various methods are described in terms of associated assumptions regarding the measurement properties of the instruments. This is followed by empirical mapping, based on regressing EQ5D on AQLQ. Six main regression models and two supplementary models are identified, and the regressions carried out. Performance of each model is explored in terms of goodness of fit between observed and predicted values, and of robustness of predictions on external data. The results show that it is possible to predict mean EQ5D indices given AQLQ data. The general implications for methods of mapping non-preference-based instruments onto preference-based measures are discussed

Using rank and discrete choice data to estimate health state utility values on the QALY scale

Objective: Recent years has seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off. However, in order to use these health state values in cost effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. This study addresses this challenge and examines how rank and discrete choice experiment data can be used to elicit health state utility values anchored on the full health-dead scale and compares the results to time trade-off (TTO) results. Methods: Two valuation studies were conducted using identical methods for two health state classification systems: asthma and overactive bladder. Each valuation study involved interviews of 300 members of the general population using ranking and TTO plus a postal survey using discrete choice experiment sent to all consenting interviewees and a "cold" sample of the general population who were not interviewed. Results: Overall DCE produced different results to ranking and time trade-off, whereas ranking produced similar results to TTO in one study, but not the other. Conclusions: Ordinal methods offer a promising alternative to conventional cardinal methods of standard gamble and TTO. However, the results do not appear to be robust across different health state classification systems and potentially different medical conditions. There remains a large and important research agenda to address

Using rank and discrete choice data to estimate health state utility values on the QALY scale

Objective: Recent years have seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off. However, in order to use these health state values in cost effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. This study addresses this challenge and examines how rank and discrete choice experiment data can be used to elicit health state utility values anchored on the full health-dead scale and compares the results to time trade-off (TTO) results. Methods: Two valuation studies were conducted using identical methods for two health state classification systems: asthma and overactive bladder. Each valuation study involved interviews of 300 members of the general population using ranking and TTO plus a postal survey using discrete choice experiment sent to all consenting interviewees and a "cold" sample of the general population who were not interviewed. Results: Overall DCE produced different results from ranking and time trade-off, whereas ranking produced similar results to TTO in one study, but not the other. Conclusions: Ordinal methods offer a promising alternative to conventional cardinal methods of standard gamble and TTO. However, the results do not appear to be robust across different health state classification systems and potentially different medical conditions. There remains a large and important research agenda to address.ranking; discrete choice experiment; preference-based measures; QALYs