Spatial Regression Models for Demographic Analysis

Chi, Guangqing; Zhu, Jun

doi:10.1007/s11113-007-9051-8

Cited by 212 publications

(161 citation statements)

References 70 publications

(67 reference statements)

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“…Spatial correlation refers to any systematic pattern in the spatial distribution of a variable. With positive spatial autocorrelation, high or low values of a variable tend to cluster in space; with negative spatial autocorrelation, locations tend to be surrounded by neighbors with very different values (Chi and Zhu, 2008). The standard linear regression model depicted in equation (1) assumes that the error term is independently, identically, and normally distributed.…”

Section: Accounting For Spatial Autocorrelationmentioning

confidence: 99%

The effect of investment in children’s education on fertility in 1816 Prussia

2011

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The interaction between investment in children's education and parental fertility is crucial in recent theories of the transition from Malthusian stagnation to modern economic growth. This paper contributes to the literature on the child quantity-quality trade-off with new county-level evidence for Prussia in 1816, several decades before the demographic transition. We find a significant negative causal effect of education on fertility, which is robust to accounting for spatial autocorrelation. The causal effect of education is identified through exogenous variation in enrollment rates due to differences in landownership inequality. A comparison with estimates for 1849 suggests that the preference for quality relative to quantity might have increased during the first half of the nineteenth century.JEL Classification: N33, I20, J13

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Section: Accounting For Spatial Autocorrelationmentioning

confidence: 99%

The effect of investment in children’s education on fertility in 1816 Prussia

2011

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“…The construction and selection of spatial weights is the next important part of a Moran's I test. Currently, there is little theoretical guidance on the selection of neighborhoods, or the spatially weighted matrix used in the test (Chi and Zhu 2007). As a result, a comparison of several spatial weight matrices is often performed to examine the way changing the definition of neighborhoods affects the data (Anselin and Getis 2010;Anselin 2002).…”

Section: Neighborhood Effects Variablesmentioning

confidence: 99%

Do Factory Audits Improve International Labor Standards? An Examination of Voluntary Corporate Labor Regulations in Global Production Networks

2018

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This research examined the effects of voluntary factory audits on labor conditions. Sometimes referred to as corporate social responsibility (CSR) codes of conduct, corporations impose voluntary labor standards coupled with regular auditing to help ensure the protection of workers throughout their global production networks (GPNs). While some believe that auditing factories ensures that CSR codes of conduct are followed and helps private corporations promote higher labor standards, others argue that factory audits have little effect on labor standards at the factory level. Using unique panel data of internal factory audit reports of factories in four Southeast Asian countries between 2003 and 2010, this paper sought to understand whether voluntary GPN audits improve labor standards at the factory level. The results showed that a factory's number of audits between 2003 and 2010 did not improve factory working conditions significantly and that the local neighborhood in which a factory is located has a greater effect on changes in factory audit scores. These findings suggest that CSR codes of conduct and auditing alone are not sufficient to improve labor standards in GPNs. Rather, joint private-public collaboration is needed to improve labor conditions for workers in the global south.

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“…5km kernel distance was arrived at after iteration with increasing bandwidth distance from 1km to 10km, and checking the regression diagnostics to ensure that the model passes and performs best. Three assumptions suggested by Chi et al (2016), Pirdavani et al (2014) and Sinaga et al (2016) The variance of error terms should be constant across observations (also known as homoskedasticity), and [3] The error terms are not auto-correlated and any one residual is not correlated with any other residual in the study area. The main check was the R-squared (ranging from 0.0 (0%) to 1.0(100%)) with the higher value indicating the better model in terms of performance.…”

Section: Spatial Regression: Geographically Weighted Regressionmentioning

confidence: 99%