Employment and Re-Industrialisation in Post Soeharto Indonesia by Mohammad Zulfan Tadjoeddin & Anis Chowdhury

Author:Mohammad Zulfan Tadjoeddin & Anis Chowdhury
Language: eng
Format: epub
Publisher: Palgrave Macmillan UK, London

Fig. 4.2Wage-productivity ratio (%), manufacturing ISIC 2, 2001–2015. (Source: Calculated from BPS data)

4.3.2 Wage-Productivity Gaps in Large-Medium and Micro-Small Establishments

Following the Statistics Indonesia definitions, large firms have 100 workers or more, medium firms have 20–99 workers, small firms have 5–19 workers and micro firms have less than 5 workers (including firms with unpaid workers). Several steps are needed to construct the required data from the existing sources, such as the National Income Account and the National Labour Force Survey (Sakernas), which contain data on sectoral GDP or value added, employment and wages for the overall manufacturing sector disaggregated into nine manufacturing sub-sectors (ISIC 2). Similar data are also available for LM firms from the Large and Medium Manufacturing Statistics. Combining the two datasets (overall manufacturing and LM manufacturing), data for MS manufacturing can be calculated. The data construction steps are explained briefly as follows.

First, wages and employment data for total manufacturing and nine manufacturing sub-sectors from the national statistics are used to generate labour cost, as the product of wages and employment, at the national level. Second, by multiplying labour cost per worker data from the large and medium manufacturing statistics with the number of workers, we get the total labour cost for medium and large manufacturing firms. Third, after deducting the total labour cost of large and medium firms from the labour cost of the overall manufacturing sector, we obtain the labour cost for micro and small firms. Finally, after dividing the labour cost by the number of workers in micro and small manufacturing, we can get the wages for micro and small firms. The number of workers in the MS firms is obtained by deducting LM manufacturing employment from national manufacturing employment.

Thus, we generate employment, wages, total value added and productivity data for the manufacturing sector disaggregated into LM and MS firms. With regard to the nine manufacturing sub-sectors, we focus only on the three with the highest employment shares, namely sub-sector 31 (food), 32 (textile) and 38 (fabricated metal). As mentioned earlier, these sub-sectors represent resource-based and labour-intensive industry (food), footloose labour-intensive industry (textile) and capital-intensive industry (fabricated metal).

As expected, the difference between wages-productivity gaps in LM and MS firms is quite striking. During 2001–2014, the trend of diverging productivity in LM and MS firms was much clearer than that of real wages (Fig. 4.3). Both LM and MS firms showed declining trends of wage-productivity ratio, indicating the delinking of wages and productivity. Although productivity of LM firms increased at a much faster rate than productivity of MS firms, in the past decade, the value-added share of LM firms in overall manufacturing industry stagnated at 54 per cent and their employment share, in fact, declined from 36 per cent to 32 per cent (Fig. 4.4). This observation is consistent with the overall finding on the process of de-industrialisation in the Indonesian economy.

Fig. 4.3Manufacturing: ALL, large-medium (LM) and micro-small (MS), 2001–2014. (Source: Calculated from BPS data)

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.