Confirmatory factor analysis in Stata

I will present a set of routines to conduct a one-factor confirmatory factor analysis in Stata. The use of Mata in programming will be highlighted. Corrections for non-normality, as common in the structural equation modeling literature, will be demonstrated. Indications for further development into multifactor models and, eventually, structural equation models, will be given.

Structural equation models with latent variables

This talk will introduce the main ideas of structural equation models (SEMs) with latent variables and Stata tools that can be used for such models. The two approaches most often used in the applied work are numeric integration of the latent variables and covariance structure modeling. The first approach is implemented in Stata via -gllamm- (developed by Sophia Rabe-Hesketh). The second approach is currently implemented in -confa- for confirmatory factor analysis models. Also, introduction of the generalized method of moments (GMM) estimation and testing framework in version 11 of Stata made it possible to estimate SEMs by using moderately complex parameter and matrix manipulations. Working examples will be provided with some popular data sets (Holzinger-Swineford factor analysis model and Bollen's industrialization and political democracy model).

Survey bootstrap and bootstrap weights

In this presentation, I will review the bootstrap for complex surveys with designs featuring stratification, clustering, and unequal probability weights. I will present the Stata module bsweights, which creates the bootstrap weights for designs specified through and supported by svy. I will also provide simple demonstrations highlighting the use of the procedure and its syntax. I will discuss various tuning parameters and their impact on the performance of the procedure, and I will give arguments for the bootstrap by the method of weights in nonsurvey settings.

2004), “Determinants of interregional mobility in Russia: Evidence from panel data

Abstract The paper studies the determinants of internal migration in Russia. Using panel data on gross region-to-region migration flows in 1992-99, we estimate the effect of economic, political and social factors. Although overall migration is rather low, it turns out that its intensity does depend on economic factors even controlling for fixed effects for each origin-destination pair. People move from poorer and job scarce regions with worse public good provision to those which are richer and prospering better both in terms of employment prospects and public goods. Migration is, however, constrained by the lack of liquidity; for the poorest regions, an increase in income raises rather than decreases outmigration. Our estimates imply that up to a third of Russian regions are locked in poverty traps. JEL classifications: P23, J61, P36, R23

Exploring new statistical frontiers at the intersection of survey science and big data: convergence at "BigSurv18"

Held in October 2018, The Big Data Meets Survey Science conference, also known as "BigSurv18," provided a first-of-its-kind opportunity for survey researchers, statisticians, computer scientists, and data scientists to convene under the same roof. At this conference, scientists from multiple disciplines were able to exchange ideas about their work might influence and enhance the work of others. This was a landmark event, especially for survey researchers and statisticians, whose industry has been buffeted of late by falling response rates and rising costs at the same time as a proliferation of new tools and techniques, coupled with increasing availability of data, has resulted in "Big Data" approaches to describing and modelling human behavio

2005), “Attaching workers through in-kind payments: Theory and evidence from

for their comments and support. We are especially grateful to three anonymous referees and Jaime de Melo for their comments and suggestions. We also acknowledge the comments of colleagues at seminars in Caen, Carnegie- Abstract As a result of external shocks, the productivity of fixed capital may sometimes decrease in certain regions of an economy. There are exogenous obstacles to migration that make it hard for workers to reallocate to more profitable regions. We point to an endogenous obstacle that has not been considered before. Firms may devise "attachment" strategies to keep workers from moving out of a local labor market. When workers are compensated in kind, they find it difficult to raise the cash needed for migration. We show, first, that the feasibility of attachment depends on the inherited structure of local labor markets: Attachment can exist in equilibrium only if the labor market is sufficiently concentrated. Second, attachment is beneficial for both employers and employed workers, but it hurts unemployed and self-employed. An analysis of matched household-firm data from Russia corroborates our theory