Public Sector Digital Transformation Impact on Rural Multidimensional Poverty Alleviation (Living Standard Dimension)

Description

This is the dataset for my article "The Impact of Public Sector Digital Transformation on Rural Multidimensional Poverty Alleviation (Living Standards Dimension): Evidence from Indonesia". All raw data sourced from Indonesia Village Potential Data (Potensi Desa - PODES) which officially reported by Indonesian Central Bureau of Statistics (Badan Pusat Statistik - BPS) from year 2014, 2018 and 2021.

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Our raw data comes from 71,805 villages per year across three time periods: 2014, 2018, and 2021, based on the official Village Potential Statistics (PODES) data published by BPS. The treatment group is defined using the 2018 cohort, as it was the first year PODES provided comprehensive data on digital transformation and its components. As a result, the 2021 period is used to assess the long-term causal effects. This database includes detailed information for each village in Indonesia including sources of drinking water for most households, the sanitation facilities used by most households, the cooking fuel used by most households, the presence of residents using mobile phones, the avaibility of village’s flagship products and its exports, the condition of internet connectivity, the avaiability of village information system, electricity quality, and road quality. Our study also using Polychoric Principal Component Analysis (PCA) to create living standard index, because this was not incorporated directly in our raw data. Principal Component Analysis is used to reduce the complexity of correlations between variables. Unlike other methods, PCA maximizes the explanation of variance in its first component (Ariawan, 2006). Van Phan & O’Brien (2019) adapted PCA using polychoric correlations to calculate well-being, incorporating dimensions like assets, education, health, and housing. Polychoric PCA is more suitable for ordinal data compared to standard PCA (Ariawan, 2006). This study focuses on living standards as defined by UNDP and OPHI, due to data limitations in PODES database.

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