Mind the Gap: Analyzing the Impact of Data Gap in Millennium Development Goals’ (MDGs) Indicators on the Progress toward MDGs

•The paper analyzes data gaps in the MDG performance monitoring.•Paper finds that higher data gaps reduced the probability of MDG success.•Countries that measured their performance better were able to perform better.•Points at the need to take the performance measurement of SDGs seriously. This pape...

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Vydáno v:World development Ročník 93; s. 260 - 278
Hlavní autor: Jacob, Arun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.05.2017
Pergamon Press Inc
Témata:
Bias
Data
Deviation
Empirical analysis
Estimation
Evaluation
goal setting
Impact analysis
Indicators
MDG
Millennium
monitoring and evaluation
Objectives
Performance evaluation
Performance indicators
Performance measurement
SDGs
Social factors
Socioeconomic data
Socioeconomic factors
Socioeconomics
Statistical analysis
Success
Sustainability
Sustainable development
value of data
MDG
goal setting
monitoring and evaluation
SDGs
value of data
performance measurement
ISSN:0305-750X, 1873-5991
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Shrnutí:•The paper analyzes data gaps in the MDG performance monitoring.•Paper finds that higher data gaps reduced the probability of MDG success.•Countries that measured their performance better were able to perform better.•Points at the need to take the performance measurement of SDGs seriously. This paper analyzes the impact of data gap in Millennium Development Goals’ (MDGs) performance indicators on actual performance success of MDGs. Performance success, within the MDG framework, is quantified using six different ways proposed in the existing literature, including both absolute and relative performance and deviation from historical transition paths of MDG indicators. The empirical analysis clearly shows that the data gap in performance measurement is a significant predictor of poor MDG performance in terms of any of the six progress measures. Larger the data gap or weaker the performance measurement system, lesser is the probability of MDG performance success. The empirical methodology used in the paper combines a Heckman correction and instrumental variable estimation strategies to simultaneously account for potential endogeneity of the key data gap variable and bias due to sample selection. This result holds true even after controlling for overall national statistical capacity and a variety of socioeconomic factors. The paper underlines the need to strengthen the performance measurement system attached to the 2030 agenda for sustainable development and the associated Sustainable Development Goals (SDGs). This paper is the first attempt at empirically evaluating the value of data in the context of international development goals and gives empirical evidence for the need to harness the “data revolution” for sustainable development.
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ISSN:0305-750X
1873-5991
DOI:10.1016/j.worlddev.2016.12.016