TY - JOUR AU - Nwaubani, Joel Chiadikobi AU - Zelka, Magdalini AU - Tsianta, Artemis PY - 2020/01/14 Y2 - 2024/03/29 TI - A Model for Comparing Government Expenditure on Civil Servants-Gross Wages And Salaries in the EU24 JF - European Journal of Business and Management Research JA - EJBMR VL - 5 IS - 1 SE - Articles DO - 10.24018/ejbmr.2020.5.1.147 UR - https://ejbmr.org/index.php/ejbmr/article/view/147 SP - AB - <p>Following government responses to the economic crisis, aimed at restabilising financial markets, maintaining employment and mitigating the effects of unemployment, public budgets have come increasingly under strain. The debt crisis that struck some EU members and is far from having been surmounted, has prompted governments to embark on a policy of strict budgetary austerity. Recent developments in the European Union reflects the reformation needs of most EU governments, in form of wage and employment moderation and, in some cases, even cuts in public sector wages and employment. All over Europe the public sectors have been the main target of governments’ reformation policies. Public sector employers have bypassed established collective bargaining procedures and wages and jobs have been cut or frozen, most frequently by unilateral state decision. Against the background of governments’ reformation strategies, this study compare and estimate the most accurate association model of the Categorical Data Analysis (CDAS) for government expenditure on civil servants– gross wages and salaries in 24 countries of the European Union from 2002 - 2011. The analysis of association (ANOAS) table is given in order to ascertain the percentage of the data which is covered by each model. We estimate the association model to find the model that has the best fit, but none proved accepted. Consequently, we proceeded to the multivariate model to find the model with the best fit and in conclusion we find out that the multivariate Row-Column Effects Association Model (RC) of the (M = 9) has the best fit because it covers 100% of the data -&nbsp; giving it the best fit among all.</p> ER -