Alif Noorachmad Muttaqin, Muharman Lubis, Tomi Mulhartono, Arif Ridho Lubis

Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia

Introduction

Quantifying the causal impact of employment trends on academic performance using time-series and public interest data in indonesia. Quantify employment trends' causal impact on academic performance in Indonesia. Uses time-series, Google Trends, and Granger causality to link labor indicators with GPA determinants. Essential for education policy.

Abstract

This study quantifies the causal impact of employment trends on academic performance using a hybrid model of survey data and time-series public interest data from Google Trends in Indonesia. Employing Granger causality and regression analysis, the research investigates eight determinants of GPA and their relationship to labor indicators. A purposive sample of 40 respondents and secondary data from 2011–2019 were analyzed. Granger tests reveal significant one-way causality from employment to GPA indicators, particularly in parental monitoring (F = 7.06; p < 0.05) and learning motivation (F = 9.68; p < 0.05). Regression analysis supports these findings with R² values above 0.50. Results highlight the potential of integrating behavioral data into educational analytics. This research contributes methodological innovation by incorporating public interest data to explain academic outcomes, with implications for predictive modeling in education policy and planning.

Review

The study titled "Quantifying the Causal Impact of Employment Trends on Academic Performance Using Time-Series and Public Interest Data in Indonesia" presents an ambitious and methodologically innovative approach to understanding complex educational dynamics. The integration of traditional survey data with time-series public interest data from Google Trends offers a novel avenue for exploring the interrelationships between socio-economic factors and academic outcomes. The stated objective of quantifying causal impact is highly relevant for educational policy and planning, particularly in a developing economy like Indonesia, where employment trends can significantly influence family and individual circumstances. A key strength of this research lies in its interdisciplinary approach, leveraging Google Trends as a proxy for public interest to potentially capture broader societal shifts relevant to employment. The application of Granger causality tests to identify directional relationships between employment and specific GPA indicators, such as parental monitoring and learning motivation, is particularly interesting, offering concrete insights into potential pathways of influence. The reported statistical significance (p < 0.05) and R² values above 0.50 from regression analysis suggest a degree of explanatory power. The authors' claim of methodological innovation through incorporating behavioral data into educational analytics is well-founded and represents a promising direction for future research. Despite these strengths, several aspects warrant closer scrutiny. The use of a "purposive sample of 40 respondents" for survey data, combined with time-series analysis spanning nearly a decade for a country as vast and diverse as Indonesia, raises significant concerns about the generalizability and robustness of the "causal impact" claims. While Granger causality indicates temporal precedence and correlation, a sample of 40 might not be sufficient to establish a broadly applicable causal relationship, especially when linked to macro-level employment trends and public interest data. Further, the abstract mentions "eight determinants of GPA" and "labor indicators" without elaboration, making it difficult to fully assess the scope and specific mechanisms explored. Future work would benefit greatly from a larger, more representative sample, a clearer articulation of the specific variables employed, and a more nuanced discussion of how the micro-level survey data integrates with the macro-level time-series data to support the strong causal inferences drawn.

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