Average Hourly Earnings Estimation on District Level Using Small Area Estimation

n social life, wage value is often used as a measure of work quality and living standards. Although not the only indicator, most economists still closely associate the quality of work with economic compensation in the form of wages received every hour (Dahl, Nesheim, Olsen, 2009). Cvrlje Ćorić (2010) also states that the standard of living in a macro perspective can be assessed through the real income that everyone receives. So, it is very natural when high wages are still an attribute to determine the type of decent work by the community.The United Nations (UN) has adopted the average hourly wages received by workers as one indicator to create productive and decent employment for everyone by 2030 (IAEG-SDGs, 2017). This target falls on the Sustainable Development Goal (SDG) 8 by considering gender equality. In addition, the UN also encourages the provision of the above information based on geographical conditions and social background to determine pay gaps that occur (United Nations Economic Commission for Europe, 2015).Indonesia has stated its commitment to implement SDGs up to the Regency / City level. The coordination, budgeting, evaluation and reporting mechanisms are clearly regulated in Presidential Regulation No.59 of 2017 concerning Sustainable Development Goals. The main task of BPS to fulfill this commitment is to provide statistical data for the calculation of SDGs indicators. Associated with the importance of an indicator of average hourly income in the success of SDG 8, BPS felt the need to provide these indicators at the Regency / City level. The availability of this indicator will greatly facilitate the regional government in developing a welfare program for workers / employees in the local environment.As a preliminary study, BPS will estimate the average hourly earnings indicator for 2017. The Small Area Estimation (SAE) technique is applied based on the Fay-Herriot model. The main data sources used were the National Labor Force Survey (SAKERNAS) in August 2017. In addition, the results of the 2010 Population Census (SP2010) and Village Potential Data Collection (PODES) 2014 were also used as sources of auxiliary variables. The types of accompanying variables will be explained in detail in the methodology discussion.