Analisis Pengaruh Pertumbuhan Ekonomi, Upah Minimum dan Indeks Pembangunan Manusia Terhadap Tingkat Penganggruan di Provinsi Sumater Utara

Aisyaturridho;  Ahmad Albar Tanjung;  Weni Hawariyuni

doi:10.33059/jse.v5i2.3945

Analisis Pengaruh Pertumbuhan Ekonomi, Upah Minimum dan Indeks Pembangunan Manusia Terhadap Tingkat Penganggruan di Provinsi Sumater Utara

Jurnal Samudra Ekonomika ◽

10.33059/jse.v5i2.3945 ◽

2021 ◽

Vol 5 (2) ◽

pp. 114-126

Author(s):

Aisyaturridho ◽

Ahmad Albar Tanjung ◽

Weni Hawariyuni

Keyword(s):

Time Series ◽

Cross Section ◽

P Value ◽

Program Software

Aisyaturridho. Analisis Pengaruh Pertumbuhan Ekonomi, Upah Minimum dan Indeks Pembangunan Manusia Terhadap Tingkat Pengangguran di Sumatera Utara. Tesis. Medan: Program Pascasarjana Universitas Sumatera Utara, 2021. Penganguran merupakan masalah yang harus dihadapi oleh Sumatera Utara. Tingkat Pengangguran Terbuka di Provinsi Sumatera Utara pada tahun 2017-2020 masih mengalami fluktuatif. Penelitian ini bertujuan untuk menguji secara empiris pengaruh pertumbuhan ekonomi, upah minimum dan indeks pembangunan manusia terhadap pengangguran di Provinsi Sumatera Utara. Penelitian ini menggunakan data time series dan cross section tahun 2017 sampai 2020 dari BPS Sumatera Utara. Untuk analisis data menggunakan Metode Analisis Data Panel yang didasarkan atas pengolahan data dengan menggunakan program software eviews 10. Hasil penelitian menunjukkan nilai R2 sebesar 0.94. ini berarti bahwa sebesar 94% proporsi variabel-variabel bebas yang digunakan mampu menjelaskan variasi variabel terikat dalam model tersebut, sedangkan sisanya sebesar 6% dijelaskan oleh variable lain yang tidak digunakan dalam penelitian ini. Semua variable bebas memiliki Pvalue lebih kecil dari 0.05. Ini berarti bahwa semua variable bebas yang digunakan dalam estimasi model analisis ini, yaitu pertumbuhan ekonomi, upah minimum dan indeks pembangunan manusia bersama-sama berpengaruh secara signifikan terhadap pengangguran di Provinsi Sumatera Utara. Variabel pertumbuhan ekonomi signifikan berpengaruh negatif terhadap tingkat pengangguran terbuka di Provinsi Sumatera Utara, memiliki p-value sebesar 0.0001 dengan koefisien regresi sebesar -24.14609 yang berarti setiap kenaikan 1 milyar rupiah pertumbuhan ekonomi maka akan menurunkan 24.14609% tingkat pengangguran terbuka. Variabel upah minimum signifikan berpengaruh positif terhadap tingkat pengangguran terbuka di Provinsi Sumatera Utara memiliki p-value sebesar 0.0005 dengan koefisien regresi 8.005012 berarti apabila terjadi kenaikan 1 juta rupiah upah minimum maka akan meningkatkan 8.005012% tingkat pengangguran terbuka. Sementara variabel indeks pembangunan manusia signifikan berpengaruh positif terhadap tingkat pengangguran terbuka di Provinsi Sumatera Utara karena p-value sebesar 0.0454 dengan koefisien regresi sebesar 0.997629 berarti apabila indeks pembangunan manusia mengalami kenaikan sebesar 1 rasio, maka akan menaikkan tingkat pengangguran terbuka sebesar 0.997629%. Untuk mengurangi tingkat pengangguran terbuka maka pemerintah harus meningkatkan pertumbuhan ekonomi dan indeks pembangunan manusia sedangkan uoah minimum harus distabilkan agar terjadinya penurunan tingkat pengangguran terbuka. Kata Kunci: Tingkat Pengangguran Terbuka, Pertumbuhan Ekonomi, Upah Minimum, Indeks Pembangunan Manusia.

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Analisis Pengaruh Rasio Profitabilitas dan Economic Value Added terhadap Harga Saham pada Sub Sektor Industri Semen yang Terdaftar di Bursa Efek Indonesia

Jurnal Manajemen dan Organisasi ◽

10.29244/jmo.5.2.139-153 ◽

2016 ◽

Vol 5 (2) ◽

pp. 139

Author(s):

Anggita Mugi ◽

Abdul Kohar Irwanto ◽

Yusrina Permanasari

Keyword(s):

Time Series ◽

Cross Section ◽

Economic Value ◽

Value Added ◽

Profit Margin ◽

P Value ◽

Economic Value Added ◽

Net Profit

Penelitian ini bertujuan untuk mengetahui pengaruh Return on Asset (ROA), Return on Equity (ROE), Net Profit Margin (NPM), Earning Per Share (EPS) dan Economic Value Added (EVA) terhadap harga saham. Sampel yang digunakan adalah perusahaan sub sektor industri semen yang terdaftar di BEI periode 2009-2011. Variabel independen yang digunakan adalah ROA, ROE, NPM, EPS dan EVA dengan variabel dependennya adalah harga saham. Metode analisis yang digunakan adalah regresi data panel yang meliputi data cross section dan time series. Hasil analisis regresi data panel menunjukkan bahwa secara simultan variabel ROA, ROE, NPM, EPS dan EVA berpengaruh secara signifikan terhadapa harga saham karena nilai p-value (0.000) < alpha (0.05). Sedangkan secara parsial variabel EVA tidak berpengaruh secara signifikan terhadap harga saham karena nilai p-value pada EVA (0.0683) > alpha (0,05) dan variabel ROA, ROE, NPM dan EPS berpengaruh secara parsial terhadap harga saham dengan nilai p-value berturut-turut sebesar (0.0002; 0.0013; 0.0008; 0.000). Hasil uji analisis koefisien determinasi adalah sebesar 95.51% hal ini bahwa variabel independen yang ada pada regresi ini mampu menjelaskan sebesar 95.51% dan sisanya 4.49% kemungkinan dijelaskan oleh faktor lain yang tidak dijelaskan ke dalam model ini.Kata kunci : ROA, ROE, NPM, EPS, EVA, Harga Saham

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Analisis Pengaruh Rasio Profitabilitas dan Economic Value Added terhadap Harga Saham pada Sub Sektor Industri Semen yang Terdaftar di Bursa Efek Indonesia

Jurnal Manajemen dan Organisasi ◽

10.29244/jmo.v5i2.12154 ◽

2016 ◽

Vol 5 (2) ◽

pp. 139

Author(s):

Anggita Mugi ◽

Abdul Kohar Irwanto ◽

Yusrina Permanasari

Keyword(s):

Time Series ◽

Cross Section ◽

Economic Value ◽

Value Added ◽

Profit Margin ◽

P Value ◽

Economic Value Added ◽

Net Profit

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Does the Covid-19 Outbreak Impacts On Economic Growth? An Evidence from Indonesia

International Journal of Global Operations Research ◽

10.47194/ijgor.v2i3.111 ◽

2021 ◽

Vol 2 (3) ◽

pp. 88-95

Author(s):

Hilda Aprina ◽

M. Sabri Abd. Majid ◽

Vivi Silvia

Keyword(s):

Economic Growth ◽

Time Series ◽

Cross Section ◽

Direct Investment ◽

Time Series Data ◽

Regression Coefficient ◽

Negative Impact ◽

Percent Level ◽

Series Data ◽

P Value

This study aims to analyze the effectS of the COVID-19 pandemic, labor, domestic direct investment (DDI), AND foreign direct investment (FDI) on economic growth in Indonesia. The type of data used in this study is panel data, which is a combination of cross-section and THE time series data (Silvia, 2020). The cross-section data involves 34 provinces and time-series data covers the period from the first quarter of 2018 to the second quarter of 2021. The result found out that the regression coefficient of labor has a positive and significant effect at the 5 percent level, which means that if the number of workers increases by 1 percent, economic growth will increase by 0.03 percent. Furthermore, the FDI variable also has a significant and positive effect on economic growth in Indonesia. We can see in table 3.2 that the FDI variable is significant at the 5 percent level with a regression coefficient of 0.012, this means that an increase in FDI by 1 percent will accelerate economic growth by 0.012 percent. From the results of data processing obtained by the author, it can be seen that the DDI variable has a positive but not significant effect on economic growth in Indonesia, this can be seen from the p-value which is greater than 5 percent. The regression coefficient of -0.001 proves that the COVID-19 pandemic has a negative impact on economic growth in Indonesia. When the COVID-19 pandemic reached the territory of Indonesia, economic growth slowed by 0.001 percent.

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Pooling of Time Series and Cross Section Data

Econometrica ◽

10.2307/1912807 ◽

1969 ◽

Vol 37 (3) ◽

pp. 552

Author(s):

V. K. Chetty

Keyword(s):

Time Series ◽

Cross Section ◽

Cross Section Data ◽

Section Data

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The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

Peace Economics Peace Science and Public Policy ◽

10.1515/peps-2020-0053 ◽

2020 ◽

Vol 26 (3) ◽

Author(s):

Rex W. Douglass ◽

Thomas Leo Scherer ◽

Erik Gartzke

Keyword(s):

Time Series ◽

Cross Section ◽

Observational Studies ◽

Data Science ◽

Future Trends ◽

Methodological Issues

AbstractOne of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.

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Predicting US recessions with stock market illiquidity

The B E Journal of Macroeconomics ◽

10.1515/bejm-2015-0009 ◽

2016 ◽

Vol 16 (1) ◽

Cited By ~ 3

Author(s):

Shiu-Sheng Chen ◽

Yu-Hsi Chou ◽

Chia-Yi Yen

Keyword(s):

Time Series ◽

Stock Market ◽

Stock Returns ◽

Cross Section ◽

Market Liquidity ◽

Credit Spreads ◽

Stock Market Liquidity ◽

Predictive Content ◽

Dynamic Link

AbstractIn this paper, we investigate the dynamic link between recessions and stock market liquidity by examining the predictive content of illiquidity for US recessions. After controlling for other commonly featured recession predictors such as term spreads and credit spreads, we find that the illiquidity measure proposed by (Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-Section and Time-Series Effects.”

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Time is Not A Theoretical Variable

Political Analysis ◽

10.1093/pan/mpq012 ◽

2010 ◽

Vol 18 (3) ◽

pp. 293-294 ◽

Cited By ~ 13

Author(s):

Nathaniel Beck

Keyword(s):

Time Series ◽

Cross Section ◽

Duration Dependence ◽

Cross Section Data ◽

Section Data ◽

Data Analyst ◽

Binary Time Series ◽

Simple Cubic ◽

Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

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An External Agribusiness Risk Analysis Using KBDI: A Case of Veldfires in the Northern Territory of Australia

Environmental Sciences Proceedings ◽

10.3390/iecf2020-08065 ◽

2020 ◽

Vol 3 (1) ◽

pp. 37

Author(s):

Toyi Maniki Diphagwe ◽

Bernard Moeketsi Hlalele ◽

Dibuseng Priscilla Mpakathi

Keyword(s):

Time Series ◽

Probability Distribution ◽

Crop Production ◽

Time Series Data ◽

Northern Territory ◽

Return Level ◽

Series Data ◽

Burned Area ◽

Trend Test ◽

P Value

The 2019/20 Australian bushfires burned over 46 million acres of land, killed 34 people and left 3500 individuals homeless. Majority of deaths and buildings destroyed were in New South Wales, while the Northern Territory accounted for approximately 1/3 of the burned area. Many of the buildings that were lost were farm buildings, adding to the challenge of agricultural recovery that is already complex because of ash-covered farmland accompanied by historic levels of drought. The current research therefore aimed at characterising veldfire risk in the study area using Keetch-Byram Drought Index (KBDI). A 39-year-long time series data was obtained from an online NASA database. Both homogeneity and stationarity tests were deployed using a non-parametric Pettitt’s and Dicky-Fuller tests respectively for data quality checks. Major results revealed a non-significant two-tailed Mann Kendall trend test with a p-value = 0.789 > 0.05 significance level. A suitable probability distribution was fitted to the annual KBDI time series where both Kolmogorov-Smirnov and Chi-square tests revealed Gamma (1) as a suitably fitted probability distribution. Return level computation from the Gamma (1) distribution using XLSTAT computer software resulted in a cumulative 40-year return period of moderate to high fire risk potential. With this low probability and 40-year-long return level, the study found the area less prone to fire risks detrimental to animal and crop production. More agribusiness investments can safely be executed in the Northern Territory without high risk aversion.

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