An Analysis of the Relationship between Higher Education Expenditures and Youth Unemployment in TurkeyMuammer Maral, Furkan Yıldız, Yusuf Alpaydın
The problem of unemployment—especially among youth—has been the subject of many scientific studies. Scientists have worked on the main causes of youth unemployment and possible solutions for a long time and are still working on it. However, youth unemployment is a complex, global problem that is affected by many factors. Various scientific disciplines have dealt with different perspectives on the main causes, consequences and possible solutions of youth unemployment. The problem is increasing worldwide, and various opinions and suggestions have been put forward. The main objective of this study is to analyze the determinants of youth unemployment in Turkey for the period 1988– 2019. The dependent variable of the study is youth unemployment, and the main variable that affects the dependent variable is higher education expenditures. Other control variables used in the study are real gross domestic product per capita, gross capital formation and labor productivity, respectively. The Autoregressive Distributed Lag Bounds Testing (ARDL Test) approach, which tests whether there is a short and long-run relationship between the series, was used in the study. The findings obtained from the analysis results indicate that youth unemployment is negatively associated with higher education expenditures and gross capital formation. It is positively associated with real gross domestic product per capita and labor productivity. When the long-term coefficients are compared, the independent variable that has the highest impact on youth unemployment is gross domestic product per capita, followed by gross capital formation, higher education expenditures and labor productivity, respectively.
Türkiye’de Yüksek Öğretim Harcamaları ve Genç İşsizliği İlişkisi Üzerine Bir AnalizMuammer Maral, Furkan Yıldız, Yusuf Alpaydın
İşsizlik ve özellikle genç işsizlik problemi birçok bilimsel araştırmaya konu olmuş bir alandır. Genç işsizliğinin temel nedenleri ve olası çözüm yolları üzerinde bilim insanları uzun süre çalışmış ve halen çalışmaya devam etmektedir. Ancak genç işsizlik, birçok faktörden etkilenen oldukça karmaşık ve küresel bir sorundur. Dünyada giderek artan genç işsizliğin temel nedenleri, sonuçları, olası çözüm yolları üzerine farklı bilim disiplinleri farklı bakış açıları ile eğilmiş, çeşitli görüş ve öneriler ortaya atılmıştır. Bu çalışmanın temel amacı 1988-2019 dönemi için Türkiye’de var olan genç işsizliğini belirleyen faktörleri analiz etmektir. Çalışmanın bağımlı değişkeni genç işsizliği olup bağımlı değişken üzerinde etkisinin test edildiği temel değişken yüksek öğretim harcamalarıdır. Çalışmada kullanılan diğer kontrol değişkenler ise sırasıyla kişi başına düşen reel gayri safi yurtiçi hasıla, brüt sermaye oluşumu ve emek verimliliğidir. Çalışmada seriler arasında kısa ve uzun dönemli bir ilişki olup olmadığını test eden ARDL eş bütünleşme yaklaşımı kullanılmıştır. Analiz sonuçlarından elde edilen bulgular genç işsizliğinin; yükseköğretim harcamaları ve brüt sermaye oluşumu ile negatif, kişi başına düşen reel gayri safi yurtiçi hasıla ve emek verimliliği ile pozitif ilişkili olduğunu göstermektedir. Uzun dönem katsayıları karşılaştırıldığında genç işsizlik üzerinde en yüksek etkiye sahip olan bağımsız değişken kişi başına düşen GSYH olup bu değişkeni sırasıyla brüt sermaye oluşumu, yüksek öğretim harcamaları ve emek verimliliği takip etmektedir.
Unemployment is a serious global problem. The problem of youth unemployment in particular has been the subject of many scientific studies, and the main causes and possible solutions to this problem have been evaluated and various solutions have been put forward. However, youth unemployment is a complex problem with no single solution. It is affected by many different factors. Moreover, the youth unemployment problem is getting bigger day by day. According to the statistics of the International Labor Organization (2020), the young population (15–24 years of age) increased from 1 billion to 1.3 billion between 1999–2019, but the number of young people participating in employment decreased from 568 million to 497 million. Many studies in different disciplines have been conducted on the main causes, results and possible solutions of the increasing youth unemployment problem. All these studies approach youth unemployment from different angles and offer different solutions to the problem. A complex problem such as youth unemployment is affected by many different factors.
This study examines the effects of the variables of real gross domestic product per capita, labor productivity, gross capital formation, and higher education expenditures on youth unemployment—all of which are thought to affect youth unemployment. The study aims to make suggestions about youth unemployment by revealing the effects of these variables on youth unemployment. The study is based on quantitative data analysis and, in particular, time series analysis has been applied. The dataset used in the study was created based on the relevant literature and used the longest possible time interval. In addition, this study differs from the limited number of previous studies in terms of the countries, datasets, and econometric methods used.
The data set used in the study was composed of annual observations that covered the period 1988–2019. The dependent variable of the study is youth unemployment. Independent variables tested for their long-term relationships with youth unemployment are higher education expenditures, real gross domestic product per capita at constant prices in 2010 ($), gross capital formation at constant prices ($) in 2010, and labor productivity.
An autoregressive distributed lag bound test (ARDL test) was used to analyze the data. Unlike Engle and Granger (1987) and Johansen (1988) cointegration tests, this test analyzes whether there is a long-term relationship between the series, regardless of whether the series is stationary at the level or at the first difference. However, this method obtains consistent results even in data with small number of observations (Baek & Kim, 2013; Panopoulou & Pittis, 2004) and a simultaneous estimation of short- and long-term results. First of all, the stationarities of the variables of the study were tested by Augmented Dickey-Fuller and Phillips-Perron unit root tests. According to this, the young unemployment dependent variable is stationary at the 1% significance level in the first difference under both unit root tests, and the gross domestic product and gross capital formation independent variables are stationary at the first difference and 1% significance according to the Augmented DickeyFuller and Phillips-Perron tests. Higher education expenditures and labor productivity independent variables are stationary at the 1% significance level. Then, using the Akaike Information Criterion (AIC), the lag lengths of the variables of the model were determined and, with the F test, the existence of a long-term relationship between youth unemployment, higher education expenditures, real gross domestic product per capita, gross capital formation, and labor productivity. The ARDL test was applied in the next step.
The results of the study are as follows: (1) There is a long-term relationship at a 1% significance level between youth unemployment, higher education expenditures, real gross domestic product per capita, gross capital formation and labor productivity. (2) When the long-term coefficients are compared, the independent variable that has the highest impact on youth unemployment is real gross domestic product per capita, followed by gross capital formation, higher education expenditures and labor productivity, respectively. (3) It has been observed that a 1% increase in higher education expenditures decreases youth unemployment by 0.058%. (4) A 1% increase in real gross domestic product per capita increases youth unemployment by 2.654%. (5) Gross capital formation has a negative and statistically significant effect at a 5% level on youth unemployment, and a 1% increase in gross capital formation reduces youth unemployment by 1.183%. (6) It has been determined that there is a statistically positive relationship between labor productivity and youth unemployment at a 1% significance level. It has been calculated that a 1% increase in labor productivity in the long run increases youth unemployment by 0.01%.