Dynastic inequality,mobility and equality of opportunity

One often heard counter to the concern about rising income and wealth inequality is that it is wrong to focus on inequality of outcomes in a “snapshot”. Intergenerational mobility and “equality of opportunity”, so the argument goes, is what matters for normative evaluation. In response to this counter, we ask what pattern of intergenerational mobility leads to lower inequality not between individuals but between the dynasties to which they belong? And how does this pattern in turn relate to commonly held views on what constitutes equality of opportunity? We revive and revisit here our earlier contributions which were in the form of working papers (Kanbur and Stiglitz 1982, 1986) in order to engage with the current debate. Focusing on bistochastic transition matrices in order to hold constant the steady state snapshot income distribution, we develop an explicit partial ordering which ranks matrices on the criterion of inequality between infinitely lived dynasties. A general interpretation of our result is that when comparing two transition matrices, if one matrix is “further away” from the identity matrix then it will lead to lower dynastic inequality. More specifically, the result presents a computational procedure to check if one matrix dominates another on dynastic inequality. We can also assess “equality of opportunity”, defined as identical prospects irrespective of starting position. We find that this is not necessarily the mobility pattern which minimizes dynastic inequality.     

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: Keynote and panel discussion on communicating heterogeneous treatment effects across populations

How do we communicate nuanced regulatory information to different audiences, recognizing that the consumer audience is very different from the physician audience? In particular, how do we communicate the heterogeneity of treatment effects - the potential differences in treatment effects based on sex, race, and age? That is a fundamental question at the heart of this panel discussion. Each panelist addressed a specific “challenge question” during their 5-minute presentation, and the list of questions is provided. The presentations were followed by a question and answer session with members of the audience and the panelists.     

Non‐compliance with India's Factories Act: Magnitude and patterns

While non‐compliance with legislation and regulations is said to be rife in developing countries, there is limited systematic evidence of the magnitude of non‐compliance. The authors quantify non‐compliance with India's Factories Act in 2010 and find that the number of non‐compliant firms is nearly twice that of compliant firms, and much larger than the number of firms “adjusting out” of the legislation. Thus, non‐compliance with the Factories Act is a key feature of India's “missing middle”. The main trends and patterns of non‐compliance are explored, and a number of key issues highlighted for further analytical and policy research.     

Lean Six Sigma implementation: modelling the interaction among the enablers

Abstract

Lean Six Sigma (LSS) methodology has been acquiring a prominent position in organisations. The aim of this study is to demonstrate an approach to LSS implementation in organisations using the development of a hypothetical model based on interpretive structural modelling (ISM) and fuzzy Matriced Impacts Croisés Multiplication Appliquée á un Classement (fuzzy MICMAC) analysis phenomenon. Seventy Lean Six Sigma enablers (LSSEs) have been identified through extensive literature review and out of which 40 most important LSSEs were finalised through opinions of experts both from industry and academia. Furthermore, the valuable expert opinions have been applied to determine contextual relationships between these significant LSSEs and a hierarchical model has been created based on an ISM. The fuzzy MICMAC analysis has also been utilised to classify the enablers based on the dependence and driving power, and validate the created ISM-based model. The developed hierarchical model will assist to understand interrelationships and interdependencies among the identified LSSEs. Having high driving and low dependence power, the LSSEs have strategic significance because of their driving character. On the other hand, having high dependence and low driving power, LSSEs are more performance orientated. The mutual influence, driving and dependence power of LSSEs render valuable information to top management to distinguish between independent and dependent LSSEs. An organisation desiring of adopting LSS may get benefited by the understanding of LSSEs and their interactions.     

Social Capital,Labour Markets,and Job‐Finding in Urban and Rural Regions: comparing paths to employment in prosperous cities and stressed rural communities in Canada1,2

This paper compares paths to employment (job‐finding) in prosperous cities and economically‐stressed rural communities in Canada. Since the pioneering work of Mark Granovetter (1973; 1974 ), sociologists have investigated the role of social capital in job‐finding (specifically, the use of strong and weak social ties to find out about employment opportunities). To date, however, there have been few direct comparisons of job‐finding in urban and rural settings (see Lindsay et al., 2005 ; Wahba and Zenou, 2005 ). Using data from two major surveys and a qualitative interview project, we uncover several important differences in urban and rural paths to employment. First, we find that both strong and weak ties are used more frequently by rural residents to find a job, while city‐dwellers rely more often on formal or impersonal means. Second, we find much stronger evidence of differentiation within rural regions. Long‐time rural residents are much more likely to use strong and weak ties to find employment than are newcomers. However, rural residents who used weak ties as paths to employment have significantly lower incomes. None of these patterns are evident in the cities. Together, these findings lead us to conclude that job‐finding in rural settings is strongly affected by constraints – in the labour market and in social capital resources – that are not present in cities.     

Middlemen,fair traders,and poverty

We propose a spatial model of producer market access where local middlemen reap market power due to match friction, and fair traders enter to present an alternative. The model features location as a key determinant of the impact of fair trader entry on the market share of fair traders, the distribution of consumer willingness to pay between middlemen and producers, and intra- / inter-regional poverty incidence. For governments who wish to minimize the poverty gap, our results support directing resources to subsidize fair trade organizations, and/or to producers with no access to markets, rather than to local middlemen intermediaries.     

An Improved Security Requirement for Data Perturbation with Implications for E‐Commerce

With the rapid increase in the ability to store and analyze large amounts of data, organizations are gathering extensive data regarding their customers, vendors, and other entities. There has been a concurrent increase in the demand for preserving the privacy of confidential data that may be collected. The rapid growth of e‐commerce has also increased calls for maintaining privacy and confidentiality of data. For numerical data, data perturbation methods offer an easy yet effective solution to the dilemma of providing access to legitimate users while protecting the data from snoopers (legitimate users who perform illegitimate analysis). In this study, we define a new security requirement that achieves the objective of providing access to legitimate users without an increase in the ability of a snooper to predict confidential information. We also derive the specifications under which perturbation methods can achieve this objective. Numerical examples are provided to show that the use of the new specification achieves the objective of no additional information to the snooper. Implications of the new specification for e‐commerce are discussed.     

Comparison of cancer slope factors using different statistical approaches.

The U.S. Environmental Protection Agency's cancer guidelines ( USEPA, 2005 ) present the default approach for the cancer slope factor (denoted here as s*) as the slope of the linear extrapolation to the origin, generally drawn from the 95% lower confidence limit on dose at the lowest prescribed risk level supported by the data. In the past, the cancer slope factor has been calculated as the upper 95% confidence limit on the coefficient (q*₁) of the linear term of the multistage model for the extra cancer risk over background. To what extent do the two approaches differ in practice? We addressed this issue by calculating s* and q*₁ for 102 data sets for 60 carcinogens using the constrained multistage model to fit the dose‐response data. We also examined how frequently the fitted dose‐response curves departed appreciably from linearity at low dose by comparing q₁, the coefficient of the linear term in the multistage polynomial, with a slope factor, s_c, derived from a point of departure based on the maximum liklihood estimate of the dose‐response. Another question we addressed is the extent to which s* exceeded s_c for various levels of extra risk. For the vast majority of chemicals, the prescribed default EPA methodology for the cancer slope factor provides values very similar to that obtained with the traditionally estimated q*₁. At 10% extra risk, q*₁/s* is greater than 0.3 for all except one data set; for 82% of the data sets, q*₁ is within 0.9 to 1.1 of s*. At the 10% response level, the interquartile range of the ratio, s*/s_c, is 1.4 to 2.0.