Zidny Maulida Nafahatir, Dhia Zalfa Nadhifah, Sazkia Jasuma Putri, Anzar Alfat Firdaus

Pengaruh Literasi Digital, IPM, UMP terhadap TPT di 5 Provinsi di Indonesia

Introduction

Pengaruh literasi digital, ipm, ump terhadap tpt di 5 provinsi di indonesia. Pengaruh Literasi Digital, IPM, UMP pada TPT di 5 provinsi Indonesia dikaji. Hasilnya, kebijakan pasar kerja terintegrasi penting untuk mengurangi tingkat pengangguran.

Abstract

Reducing the Open Unemployment Rate (OUR) remains a major challenge in several Indonesian provinces with different development characteristics, including DKI Jakarta, West Java, Banten, the Riau Islands, and Papua. Differences in economic development, labor market structure, and human resource quality contribute to variations in unemployment rates across regions. This study aims to analyze the effect of Digital Literacy, the Human Development Index (HDI), and the Provincial Minimum Wage (PMW) on the Open Unemployment Rate in these five provinces. Digital literacy reflects workforce readiness in responding to technological transformation, HDI represents the quality of education, health, and purchasing power, while PMW indicates wage policy affecting labor costs and employment decisions. This research employs a quantitative approach using panel data from 2015 to 2024 and is analyzed using the Fixed Effect Model (FEM). The results show that individually, digital literacy, HDI, and PMW do not have a significant effect on unemployment. However, simultaneously, these variables significantly influence variations in unemployment across provinces. These findings highlight the importance of integrated labor market policies that consider regional characteristics to achieve sustainable unemployment reduction.

Review

This study investigates a crucial socio-economic issue in Indonesia, analyzing the impact of Digital Literacy, the Human Development Index (HDI), and the Provincial Minimum Wage (PMW) on the Open Unemployment Rate (OUR) across five diverse provinces. The selection of provinces, ranging from highly urbanized DKI Jakarta to Papua, is commendable as it promises to capture a wide spectrum of development characteristics and labor market dynamics. The quantitative approach utilizing panel data from 2015 to 2024 with a Fixed Effect Model (FEM) is an appropriate methodological choice for examining such dynamics over time and across distinct entities. The research question is well-defined, and the chosen variables are relevant indicators for understanding modern labor market challenges and human capital development. However, the findings present an interesting nuance that warrants further elaboration and critical discussion in the full paper. While the abstract states that individually, digital literacy, HDI, and PMW do not have a significant effect on unemployment, it simultaneously concludes that these variables collectively significantly influence variations in unemployment. This juxtaposition suggests potential complexities in the underlying relationships, such as multicollinearity among the independent variables, or that the individual effects are too subtle to be statistically significant on their own but collectively contribute to a broader trend. A detailed discussion on the implications of these individual non-significant findings, perhaps exploring interaction effects or alternative model specifications, would substantially strengthen the paper's analytical depth and interpretation of results. Without this, the reader is left to wonder about the precise mechanisms through which these variables jointly exert influence. Despite this analytical gap, the study's concluding emphasis on the importance of integrated labor market policies that consider regional characteristics is a salient and practical implication. Future research stemming from this study could delve deeper into the specific ways digital literacy and HDI interact with the minimum wage policy within each provincial context to affect employment outcomes. Exploring the magnitude and direction of the simultaneous effect, which is not detailed in the abstract, would also provide valuable insights. Additionally, incorporating a discussion on the operationalization of "digital literacy" for panel data, given its often qualitative nature, would enhance the methodological transparency and robustness of the findings.

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