2 results
13 - Developments in Data for Economic Research
-
- By Roberto Barcellan, Eurostat, Luxembourg, Peter Bøegh Nielsen, Statistics Denmark, Copenhagen, Denmark, Caterina Calsamiglia, CEMFI, Madrid, Spain, Colin Camerer, California Institute of Technology, Pasadena, CA, USA, Estelle Cantillon, Université Libre de Bruxelles, Brussels, Belgium, Bruno Crépon, CREST and JPAL, Paris, France, Bram De Rock, Université Libre de Bruxelles, ECARES, Brussels, Belgium, László Halpern, Hungarian Academy of Science, Budapest Hungary, Arie Kapteyn, University of Southern California, Los Angeles, CA, USA, Asim I. Khwaja, Harvard Kennedy School of Government, Cambridge, MA, Georg Kirchsteiger, Université Libre de Bruxelles, Vigdis Kvalheim, Norway Social Science Data Service, Bergen, Norway, Julia Lane, New York University, New York, USA, Markus Mobius, Microsoft Research, Cambridge, MA, USA, Luke Sibieta, Institute for Fiscal Studies, London, UK, Joseph Tracy, Federal Reserve Bank of New York, New York, USA, Frederic Udina, Idescat, Barcelona, Spain, Gugliemo Weber, University of Padua, Padua, Italy, Lisa Wright, Bureau Van Dijk, Manchester, UK
- Edited by Laszlo Matyas, Central European University, Budapest, Richard Blundell, University College London, Estelle Cantillon, Université Libre de Bruxelles, Barbara Chizzolini, Università Commerciale Luigi Bocconi, Milan, Marc Ivaldi, Wolfgang Leininger, Universität Dortmund, Ramon Marimon, European University Institute, Florence, Frode Steen
-
- Book:
- Economics without Borders
- Published online:
- 24 March 2017
- Print publication:
- 27 April 2017, pp 568-611
-
- Chapter
-
- You have access Access
- Open access
- Export citation
-
Summary
Abstract
There has been a steep increase in empirical research in economics in the past 20–30 years. This chapter brings together several actors and stakeholders in these developments to discuss their drivers and implications. All types of data are considered: official data, data collected by researchers, lab experiments, randomized control trials, and proprietary data from private and public sources. When relevant, emphasis is placed on developments specific to Europe. The basic message of the chapter is that there is no single type of data that is superior to all others. We need to promote diversity of data sources for economic research and ensure that researchers are equipped to take advantage of them. All stakeholders – researchers, research institutions, funders, statistical agencies, central banks, journals, data firms, and policy-makers – have a role to play in this.
Introduction
The past 20–30 years have witnessed a steady rise in empirical research in economics. In fact, a majority of articles published by leading journals these days are empirical, in stark contrast with the situation 40 or 50 years ago (Hamermesh, 2013). This change in the distribution of methodologies used in economic research was made possible by improved computing power but, more importantly, thanks to an increase in the quantity, quality and variety of data used in economics.
This chapter brings together several actors and stakeholders in these changes to discuss their drivers and implications. All types of data are considered. When relevant, emphasis is placed on developments specific to Europe. Sections 13.2 and 13.3 deal with official microdata. Section 13.2 focuses on the level of access to microdata in Europe and its determinants. Section 13.3 focuses on cross-country data harmonization. Section 13.4 then switches gears entirely and discusses the benefits and costs of large-scale data collection efforts led by researchers, instead of statistical offices. Section 13.5 discusses data produced by researchers, either in the context of lab experiments or in the context of randomized control trials. Both types of data have led to major advances; for the first one in our understanding of human behaviour and the robustness of economic institutions; for the second in our understanding of the impact of policies and themechanisms underlying them.
ten - Ethnic inequalities in child outcomes
- Kirstine Hansen, University College London Institute of Education, Heather Joshi, University College London, Shirley Dex, University College London
-
- Book:
- Children of the 21st century (Volume 2)
- Published by:
- Bristol University Press
- Published online:
- 01 September 2022
- Print publication:
- 17 February 2010, pp 169-184
-
- Chapter
- Export citation
-
Summary
Introduction
This chapter reports some findings from the first three surveys of the Millennium Cohort Study (MCS) on the nature and extent of ethnic differences in early childhood environment and outcomes up to age 5. Due to the lack of suitable data, it has not been possible to consider these issues in the UK before – the MCS is one of the first longitudinal surveys in the UK that has the ability to look at this important issue, particularly as the MCS design involved over-sampling individuals from minority ethnic groups and individuals living in disadvantaged areas of the country.
In this chapter we examine ethnic differences in child outcomes, together with background and mediating factors that are likely to have impacted on these outcomes. This draws on earlier work using the MCS looking at ethnic differences in birth outcomes (see Dearden et al, 2006). Our results suggest that in explaining ethnic differences in child outcomes, it is important not only to consider differences in socioeconomic and family characteristics, such as parental education and socioeconomic status (SES), but also family background, family structure and child demographics. It is also important to consider other mediating factors such as family interactions, family health and well-being, the early home learning environment (HLE) and parenting styles and rules. It turns out that these other mediating factors point to possible policy responses to reducing ethnic differences in child outcomes.
The rest of the chapter is organised as follows. First, we outline how we define our ethnic groups used in the rest of the chapter. We then show how our outcomes of interest vary by ethnic groups. This shows that ethnic gaps in childhood development start early in life. Following that, we examine how some possible explanatory factors of this gap vary by ethnic group, namely selected family background characteristics and selected measures of the early childhood environment. We present the multivariate analysis we carried out to see the extent to which family background characteristics and the early childhood environment can act as mediating factors – in some sense ‘explaining’ the ethnic divide in early child development. This analysis is purely descriptive, but provides some clues to policy makers where interventions may be fruitful to lessen the ethnic differences.