Technologies de l’information, productivité et croissance des entreprises : résultats basés sur de nouvelles microdonnées internationales

La relation entre les technologies de l’information (TI), la productivité et la croissance économique a été établie au niveau agrégé. Cependant, les mécanismes par lesquels l’effet se manifeste au niveau de l’entreprise restent à préciser. Les organismes statistiques ont élaboré des indicateurs de l’aptitude des entreprises à utiliser les technologies de l’information (p. ex. l’infrastructure des technologies de l’information, la diffusion de technologies particulières) et certains indicateurs de l’utilisation réelle (p. ex. buts et fréquence d’utilisation). L’étape suivante consiste à produire des estimations de l’impact de l’utilisation des technologies de l’information. Une étude menée récemment par l’OCDE visait à résoudre cette question en utilisant des données agrégées pour les pays membres de l’OCDE, ainsi que des microdonnées pour l’Allemagne et les États‑Unis. Une deuxième phase de l’étude de l’OCDE consistera en une série de projets, regroupant deux ou trois pays, réalisée au moyen de nouvelles microdonnées obtenues récemment pour une douzaine de pays environ. Le présent article décrit l’un de ces projets, destiné à évaluer l’effet des technologies de l’information au Danemark, au Japon et aux États‑Unis. Chacun de ces pays a recueilli récemment de nouvelles données sur l’utilisation des technologies de l’information au niveau de l’entreprise et procédé à l’analyse préliminaire de celles-ci. En outre, chaque pays se distingue des autres par sa structure de marché et sa structure institutionnelle. La prochaine phase du projet consistera à élaborer des estimations de l’effet de l’utilisation des technologies de l’information fondées sur ces nouvelles microdonnées, ainsi qu’à émettre et à tester des hypothèses qui tiennent compte des différences entre les structures de marché et les structures institutionnelles de ces pays.A positive relationship between information technology (IT), productivity, and growth has been established at the aggregate level. What remain unclear are the mechanisms through which the effect operates at the level of specific businesses. Statistical agencies have developed indicators of businesses’ readiness to use IT (e.g. the IT infrastructure, diffusion of specific technologies), and some indicators on actual usage (e.g., purposes, frequency of use). The next phase is using those data to develop estimates of the impact of IT use. A recent study addressed this question using aggregate data for Organization for Economic Cooperation and Development (OECD) countries, and micro data (data for specific businesses) for Germany and the U.S. A second phase of that study envisions a series of two- and three-country studies making use of newly available micro data for roughly a dozen countries. This paper outlines one such study, a three-country project addressing the impact of IT use in Denmark, Japan, and the U.S. Each country recently collected new data at the level of specific businesses on the use of IT by businesses, and has conducted preliminary analyses of its own data. Each country also has different underlying market and institutional structures. The findings presented here are preliminary. They show that network information technology has a significant impact on labour productivity growth in United States. The next phase of this project will develop estimates of the impact of IT use based on these new micro data, developing and testing hypotheses that acknowledge differences among the countries in market and institutional structures

A new stopping rule for surveys

Non-response is a problem for most surveys. In the sample design, non-response is often dealt with by setting a target response rate and inflating the sample size so that the desired number of interviews is reached. The decision to stop data collection is based largely on meeting the target response rate. A recent article by Rao, Glickman, and Glynn (RGG) suggests rules for stopping that are based on the survey data collected for the current set of respondents. Two of their rules compare estimates from fully imputed data where the imputations are based on a subset of early responders to fully imputed data where the imputations are based on the combined set of early and late responders. If these two estimates are different, then late responders are changing the estimate of interest. The present article develops a new rule for when to stop collecting data in a sample survey. The rule attempts to use complete interview data as well as covariates available on non-responders to determine when the probability that collecting additional data will change the survey estimate is sufficiently low to justify stopping data collection. The rule is compared with that of RGG using simulations and then is implemented using data from a real survey. Copyright © 2010 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75787/1/3834_ftp.pd

Using proxy measures and other correlates of survey outcomes to adjust for non-response: examples from multiple surveys

Non-response weighting is a commonly used method to adjust for bias due to unit non-response in surveys. Theory and simulations show that, to reduce bias effectively without increasing variance, a covariate that is used for non-response weighting adjustment needs to be highly associated with both the response indicator and the survey outcome variable. In practice, these requirements pose a challenge that is often overlooked, because those covariates are often not observed or may not exist. Surveys have recently begun to collect supplementary data, such as interviewer observations and other proxy measures of key survey outcome variables. To the extent that these auxiliary variables are highly correlated with the actual outcomes, these variables are promising candidates for non-response adjustment. In the present study, we examine traditional covariates and new auxiliary variables for the National Survey of Family Growth, the Medical Expenditure Panel Survey, the American National Election Survey, the European Social Surveys and the University of Michigan Transportation Research Institute survey. We provide empirical estimates of the association between proxy measures and response to the survey request as well as the actual survey outcome variables. We also compare unweighted and weighted estimates under various non-response models. Our results from multiple surveys with multiple recruitment protocols from multiple organizations on multiple topics show the difficulty of finding suitable covariates for non-response adjustment and the need to improve the quality of auxiliary data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79323/1/j.1467-985X.2009.00621.x.pd

Development of an international survey attitude scale: measurement equivalence, reliability, and predictive validity

Declining response rates worldwide have stimulated interest in understanding what may be influencing this decline and how it varies across countries and survey populations. In this paper, we describe the development and validation of a short 9-item survey attitude scale that measures three important constructs, thought by many scholars to be related to decisions to participate in surveys, that is, survey enjoyment, survey value, and survey burden. The survey attitude scale is based on a literature review of earlier work by multiple authors. Our overarching goal with this study is to develop and validate a concise and effective measure of how individuals feel about responding to surveys that can be implemented in surveys and panels to understand the willingness to participate in surveys and improve survey effectiveness. The research questions relate to factor structure, measurement equivalence, reliability, and predictive validity of the survey attitude scale. The data came from three probability-based panels: the German GESIS and PPSM panels and the Dutch LISS panel. The survey attitude scale proved to have a replicable three-dimensional factor structure (survey enjoyment, survey value, and survey burden). Partial scalar measurement equivalence was established across three panels that employed two languages (German and Dutch) and three measurement modes (web, telephone, and paper mail). For all three dimensions of the survey attitude scale, the reliability of the corresponding subscales (enjoyment, value, and burden) was satisfactory. Furthermore, the scales correlated with survey response in the expected directions, indicating predictive validity

Information systems for collaborating versus transacting: Impact on manufacturing plant performance in the presence of demand volatility⋆

Research at the nexus of operations management and information systems suggests that manufacturing plants may benefit from the utilization of information systems for collaborating and transacting with suppliers and customers. The objective of this study is to examine the extent to which value generated by information systems for collaborating versus transacting is contingent upon demand volatility. We analyze a unique dataset assembled from non‐public U.S. Census Bureau data of manufacturing plants. Our findings suggest that when faced with volatile demand, plants employing information systems for collaborating with suppliers and customers experience positive and significant benefits to performance, in terms of both labor productivity and inventory turnover. In contrast, results suggest that plants employing information systems for transacting in volatile environments do not experience such benefits. Further exploratory analysis suggests that in the context of demand volatility, these two distinct dimensions of IT‐based integration have differing performance implications at different stages of the production process in terms of raw‐materials inventory and finished‐goods inventory, but not in terms of work‐in‐process inventory. Taken together, our study contributes to theoretical and managerial understanding of the contingent value of information systems in volatile demand conditions in the supply chain context.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147128/1/joom313.pd

The development and implementation of the National Comorbidity Survey Replication, the National Survey of American Life, and the National Latino and Asian American Survey

This paper provides an overview of the development and implementation of the Collaborative Psychiatric Epidemiology Surveys (CPES): the National Comorbidity Survey Replication (NCS-R), the National Survey of American Life (NSAL), and the National Latino and Asian American Study (NLAAS). It describes the instrument development and testing phases, the development of training and other project materials, interviewer recruitment and training activities, and data collection procedures and outcomes. The last section offers recommendations for other researchers who undertake similar studies and who might benefit from the experiences learned in the development and operation of NCS-R, NSAL and NLAAS. Copyright © 2004 Whurr Publishers Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34226/1/180_ftp.pd