Do remittances cause Dutch Disease? A meta-analytic review

Anwar, Amar; Mang, Colin F.

doi:10.1080/00036846.2021.2022091

Cited by 10 publications

(11 citation statements)

References 71 publications

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“…Meta-analysts often want to synthesize the results of a linear regression model where the effect of interest is the relation between an independent variable and a dependent variable when controlling for other variables in the model. Meta-analyses on these so-called partial effects are becoming more common 1 and are regularly conducted in, for example, psychology 2,3,4,5,6 and economics 7,8,9,10,11 . The partial correlation coefficient (PCC) can be used as effect size measure in these meta-analyses.…”

Section: Introductionmentioning

confidence: 99%

“…Meta-analyses on these so-called partial effects are becoming more common 1 and are regularly conducted in, for example, psychology [2][3][4][5][6] and economics. [7][8][9][10][11] The partial correlation coefficient (PCC) can be used as effect size measure in these meta-analyses. The PCC quantifies the relationship between the independent and dependent variables where there is controlled for the effect of the other variables in both the independent and dependent variable.…”

Section: Introductionmentioning

confidence: 99%

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A critical reflection on computing the sampling variance of the partial correlation coefficient

Aert

Goos

2023

Research Synthesis Methods

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The partial correlation coefficient quantifies the relationship between two variables while taking into account the effect of one or multiple control variables. Researchers often want to synthesize partial correlation coefficients in a meta-analysis since these can be readily computed based on the reported results of a linear regression analysis. The default inverse variance weights in standard meta-analysis models require researchers to compute not only the partial correlation coefficients of each study but also its corresponding sampling variance. The existing literature is diffuse on how to estimate this sampling variance, because two estimators exist that are both widely used. We critically reflect on both estimators, study their statistical properties, and provide recommendations for applied researchers. We also compute the sampling variances of studies using both estimators in a meta-analysis on the partial correlation between self-confidence and sports performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A critical reflection on computing the sampling variance of the partial correlation coefficient

Aert

Goos

2023

Research Synthesis Methods

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“…Although these two studies provide important insights, they fail to account for publication bias. Publication bias occurs when researchers and journals have preferences for estimates that are statistically significant or have a certain sign (Anwar & Mang 2022;Xue et al 2021). These preferences work to filter out less preferred estimates so that the literature presents a distorted picture of reality.…”

Section: Introductionmentioning

confidence: 99%

“…Meta-analysis is a statistical tool that allows researchers to appropriately weight contrasting estimates while accounting for the influence of publication bias (Anwar & Mang 2022;Ogundari 2022;Ogundari & Bolarinwa 2018;Ridhwan et al 2022). The goal is to produce an estimate of the overall, average effect of a given treatment or intervention.…”

Section: Introductionmentioning

confidence: 99%

“…previous studies(Anwar & Mang 2022;Duan et al 2020;Ogundari 2022;Ridhwan et al 2022;Xue et al 2021), we employ two weighted least squares (WLS) estimators to address this problem: WLS-Fixed Effects (FE) and WLS-Random Effects (RE).2 The WLS-FE model assumes that there is a single underlying true effect and the reason for different estimates across studies is solely due to sampling error. The corresponding weight is given by the inverse of the variance of the estimated effect, that is, 1∕[𝑆𝐸(𝑃𝐶𝐶 𝑖 ) 2 ] (Xue et al 2021).…”

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confidence: 99%

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Yield effects of agricultural cooperative membership in developing countries: A meta‐analysis

Hong

Reed

et al. 2023

Ann Public Coop Econ

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This study uses a meta-analysis to synthesize the effects of agricultural cooperative membership on the yield of crops and livestock. It collects 158 estimated yield effects from 42 studies, covering 19 developing countries.Our analysis finds evidence that there exists positive publication bias in the empirical literature, confirming that researchers and journals have a preference to publish articles that report positive and significant results. After correcting for publication bias, we find that cooperative membership has a small-sized and insignificant effect on the yield. The meta-regression analysis reveals that variation in the reported yield effects can be largely explained by the study attributes such as the sample type (full sample vs. subsample), membership ratio, econometric approaches (instrumental-variable based parametric approach, non-parametric approach or ordinary least square regression), effect size types (average treatment effects on the treated, average treatment effects, or coefficient), agro-product type (grain or others), and climate zones (tropical or non-tropical).

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Meta‐analyzing partial correlation coefficients using Fisher's z transformation

Aert

2023

Research Synthesis Methods

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The partial correlation coefficient (PCC) is used to quantify the linear relationship between two variables while taking into account/controlling for other variables. Researchers frequently synthesize PCCs in a meta‐analysis, but two of the assumptions of the common equal‐effect and random‐effects meta‐analysis model are by definition violated. First, the sampling variance of the PCC cannot assumed to be known, because the sampling variance is a function of the PCC. Second, the sampling distribution of each primary study's PCC is not normal since PCCs are bounded between −1 and 1. I advocate applying the Fisher's z transformation analogous to applying Fisher's z transformation for Pearson correlation coefficients, because the Fisher's z transformed PCC is independent of the sampling variance and its sampling distribution more closely follows a normal distribution. Reproducing a simulation study by Stanley and Doucouliagos and adding meta‐analyses based on Fisher's z transformed PCCs shows that the meta‐analysis based on Fisher's z transformed PCCs had lower bias and root mean square error than meta‐analyzing PCCs. Hence, meta‐analyzing Fisher's z transformed PCCs is a viable alternative to meta‐analyzing PCCs, and I recommend to accompany any meta‐analysis based on PCCs with one using Fisher's z transformed PCCs to assess the robustness of the results.

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Do remittances cause Dutch Disease? A meta-analytic review

Cited by 10 publications

References 71 publications

A critical reflection on computing the sampling variance of the partial correlation coefficient

A critical reflection on computing the sampling variance of the partial correlation coefficient

Yield effects of agricultural cooperative membership in developing countries: A meta‐analysis

Meta‐analyzing partial correlation coefficients using Fisher's z transformation

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