The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

Partial effects in non-linear panel data models with correlated random effects

Summary Nonlinearity and heterogeneity are known to cause difficulties in estimating and interpreting partial effects. This paper provides a systematic characterization of the various partial effects in nonlinear panel data models that might be of interest to empirical researchers. The interpretation of the partial effects depends upon (i) whether the distribution of unobserved heterogeneity is treated as fixed or allowed to vary with covariates, and (ii) whether one is interested in particular covariate values or an average over such values. The characterization covers partial-effects concepts already in the literature but also includes new concepts for partial effects. A simple panel probit design highlights that the different partial effects can be quantitatively very different.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

Small and negative correlations among clustered observations: limitations of the linear mixed effects model

The linear mixed effects model is an often used tool for the analysis of multilevel data. However, this model has an ill-understood shortcoming: it assumes that observations within clusters are always positively correlated. This assumption is not always true: individuals competing in a cluster for scarce resources are negatively correlated. Random effects in a mixed effects model can model a positive correlation among clustered observations but not a negative correlation. As negative clustering effects are largely unknown to the sheer majority of the research community, we conducted a simulation study to detail the bias that occurs when analysing negative clustering effects with the linear mixed effects model. We also demonstrate that ignoring a small negative correlation leads to deflated Type-I errors, invalid standard errors and confidence intervals in regression analysis. When negative clustering effects are ignored, mixed effects models incorrectly assume that observations are independently distributed. We highlight the importance of understanding these phenomena through analysis of the data from Lamers, Bohlmeijer, Korte, and Westerhof (2015). We conclude with a reflection on well-known multilevel modelling rules when dealing with negative dependencies in a cluster: negative clustering effects can, do and will occur and these effects cannot be ignored.

Perceived Pollution and Residential Sorting in Germany: Income May Not Sort, But it Helps to Escape

The disproportionate exposure of minorities and socio-economically disadvantaged households to environmental pollution is often explained by selective migration or sorting mechanisms. Yet, previous empirical findings remain inconclusive. In this study, we offer an explanation for mixed findings by focusing on the selective out-migration process triggered by environmental pollution. We use household-level panel data of the German SOEP from 1986 to 2016 and within-household estimates of correlated random effects probit models. More precisely, we test if the subjective impairment through air pollution selectively affects the probability of out-migration according to income and minority status. We find that perceived air pollution has a stronger effect on the likelihood of moving for households experiencing an income increase. Surprisingly, we find only small and imprecise differences between native German and first generation immigrant households, and a relatively large proportion of this difference can be explained by income. This indicates that selective out-migration processes substantially differ from selective in-migration processes, and environmental inequality research should more carefully distinguish the single steps of neighbourhood sorting.

Financialization and income generation in the 21st century: rise of the petit rentier class?

This article considers the consequences of asset-based accumulation for household income factors and social class structure in 29 countries from 1998 to 2016. Are financialization, asset-based welfare institutions, and rising real estate returns fueling a growing class of petit rentiers in capitalist economies? That is, households who accrue more than a trivial share of income from capital rather than labor or government transfers. The analysis draws on the Luxembourg Income Study data. Contrary to expectations, most countries saw declines in the share of households who accrue more than 10%, or 20% of income from assets. Estimates from correlated random effects models indicate that financialization is associated with between-country differences in the size of the petit rentier, but not within-country change over time. The decline of the petit rentier can be partly explained by declining interest rates, which reduces income from bank savings.

The role of national culture in the relationship between microfinance outreach and sustainability: a correlated random effects approach

PurposeThis study investigates the role of national culture between outreach and sustainability of microfinance institutions (MFIs). Despite microfinance's deep embeddedness in cultural contexts, research on the influence of national culture on MFI performance is rather sparse. This paper seeks to fill this gap and, based on cross-country microfinance data, attempts to explain the outreach-sustainability relationship in reference to cultural factors.Design/methodology/approachAn unbalanced panel, consisting of 5,741 MFI-year observations of 1,232 MFIs from 43 countries in six regions, is drawn from the Microfinance Information Exchange (MIX) Market database. Two different econometric models are tested. Model 1 estimates the direct effect of outreach on sustainability, using a fixed-effects estimator. Model 2 examines the moderation effect of national culture on outreach-sustainability relationship, employing correlated random effects approach.FindingsThe results show that depth of outreach and financial sustainability of MFIs are negatively related, and the relationship is moderated by national culture. Power distance and uncertainty avoidance positively moderate the outreach-sustainability relationship, whereas individualism and masculinity negatively moderate the relationship.Originality/valueThe findings suggest that the national culture where MFIs are located plays an important contingent role in their performance and that the magnitude of the trade-off effect varies from culture to culture. The research thus provides further insight in the trade-off debate and contributes to literatures of both microfinance and cross-cultural management.