JF
J. Francisco Conceicao
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The Problem of Uncertain Contextual Characteristic (PUCC)
Does it matter how contextual poverty is measured for the neighbourhood effect estimation?
This paper investigates the sensitivity of neighbourhood effect estimates to the operationalization of contextual poverty. It introduces the Problem of Uncertain Contextual Characteristic (PUCC), which refers to uncertainty surrounding what is measured and represented when constructing contextual variables, potentially resulting in estimation bias. Using longitudinal micro-data from Dutch population registers (2011–2020), we assess four key parameters when operationalizing poverty: poverty dimensions, reference groups, poverty-line thresholds, and aggregation statistics. We undertake a systematic analysis modelling the effect of each poverty indicator while keeping all other factors constant. We also generate models including different residential context scales and geographies to compare the effects of PUCC with other sources of estimation variation. Results show that the operationalization of contextual poverty substantially influences the estimated neighbourhood effects on individual income. In our analyses, the operationalization of contextual poverty introduced greater variation than the residential context’s scale or the geographical extent of the study. Findings further suggest that PUCC and the Modifiable Areal Unit Problem (MAUP) are closely related, as the impact of contextual poverty measures varies significantly across spatial scales.
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This paper investigates the sensitivity of neighbourhood effect estimates to the operationalization of contextual poverty. It introduces the Problem of Uncertain Contextual Characteristic (PUCC), which refers to uncertainty surrounding what is measured and represented when constructing contextual variables, potentially resulting in estimation bias. Using longitudinal micro-data from Dutch population registers (2011–2020), we assess four key parameters when operationalizing poverty: poverty dimensions, reference groups, poverty-line thresholds, and aggregation statistics. We undertake a systematic analysis modelling the effect of each poverty indicator while keeping all other factors constant. We also generate models including different residential context scales and geographies to compare the effects of PUCC with other sources of estimation variation. Results show that the operationalization of contextual poverty substantially influences the estimated neighbourhood effects on individual income. In our analyses, the operationalization of contextual poverty introduced greater variation than the residential context’s scale or the geographical extent of the study. Findings further suggest that PUCC and the Modifiable Areal Unit Problem (MAUP) are closely related, as the impact of contextual poverty measures varies significantly across spatial scales.