The first objective of this research is to calculate the total factor productivity (TFP) of large and medium-sized manufacturing firms in Indonesia; second, this paper will identify the determinant of manufacturing productivity; and third, to analyze technical and efficiency changes at the sub-sector level. The higher the output-to-input ratio, the higher the company's performance. In other words, the production curve reflects the company's level of technology use.
It is a comparison of the output of certain companies with the maximum output produced by other companies using the same set of inputs. As explained above, the productivity level is depicted by the slope of the straight line from point O. We can measure the allocative efficiency only if we know the value of those input costs.
After using the price of output and input, we can derive a simplified productivity measure like the equation below. Here we compare the productivity of the two companies using the actual output and the actual input.
Data Envelopment Analysis
However, under the assumption of variable returns to scale, along with these two sources of productivity, there are also operational scale and efficiency scale. The DEA model uses ratio; for each company, we need to calculate the ratio of aggregate output to aggregate input. We can solve the DEA model formulation in LP above, however, the constraint will increase with the number of companies, and so we need to specify the LP form above in Dual Programming (DP).
On the DEA Constant Return to Scale (CRS) it assumes that all the DMU operate at the optimal economic scale. To deal with this, we can use DEA model under assumption of Variable Return to Scale (VRS). The economic scale estimated from the above model does not indicate whether the company's returns to scale increase or decrease.
For this reason, we impose a non-increasing NIRS (Return to Scale) constraint on the DEA model. On the other hand, if the TE NIRS is equal to TE VRS, then there is decreasing Return to Scale (DRS). Simple example, if a certain firm can produce the same output in period t and t+1, but uses only 75% of the input period t in period t+1, the TFP index increases by 1/0.75=1.3 .
Similarly, if the firm uses the same inputs in both periods, but produces 30% higher output in period t+1 compared to period t, then the TFP index is 1.3. In addition to the MTFPI explained in detail above, there are two other TFP indexes; Hicks-Moorsteen TFP (HM TFP) Index and TFP Index by Profitability Ratio. However, the HM TFP index cannot explain the sources of productivity growth (technical change, efficiency change) and does not take into account the price effect.
On the other hand, the second approach (Profitability Ratio) estimates the TFP index based on revenues and costs (price corrected between period s and t).
Research on the TFP of Manufacturing sector in Indonesia
In the SFA group, Ikhsan (2007) investigated the TFP growth and changes in technical efficiency in the Indonesian manufacturing industry during the period 1988–2000. Using the Medium and Large Industry Statistic (SIBS), the study concluded that average TFP growth was 1.55 percent. The contributors to TFP growth came mainly from technical progress at about 1.89 percent, while the contribution from economies of scale and technical efficiency is -0.13%.
In terms of technical efficiency changes, Ikhsan found a visible learning-by-doing process in technology adoption, as the company is not operating at its maximum production capacity. After a slowdown in the period 2000-2004, probably due to post-crisis consolidation, industrial productivity growth started to pick up in 2004-2007. On the other hand, technology growth and economies of scale contributed negatively to TFP, -0.17% and -0.45% respectively.
Prabowo and Cabanda (2011) investigated the productivity of Indonesian manufacturing companies listed in the Indonesia Stock Exchange period 2000-2005. Using stochastic frontier approach (SFA), Prabowo and Cabanda found technical inefficiency in these firms. Using the data at the UNIDO 3-digit ISIC level, they concluded that there are 5 sub-sectors with the highest efficiency, Tobacco; Iron and steel; Transportation equipment; Non-ferrous metal; and chemistry.
In general, basic industry returns were higher than traditional industry in the low- and high-tech industry category. The DEA method was applied to the five categories of industrial manufacturing companies listed on the stock exchange between 2001 and 2007; Food and drink; clothing and textile product; plastic and glassware; Automotive products; and Pharmaceuticals. The TFP for efficient business was positively related to Return on Assets (ROA), reflecting the higher efficiency of marketing productivity, the higher the financial performance will be.
METHODOLOGY 3.1. Methodology
Data, Variable, and Proxy
The main data used in this study is the Survey of Large and Medium (Survei Industri Besar dan Sedang, SIBS) published by the Central Bureau of Statistics (BPS). For each company we use the following set of variables: output, capital, labour, raw materials and energy. This study uses the first proxy, as the firm uses all of its resources (capital, labor, raw materials, and energy) to produce some outputs despite being sold or stored as inventory.
The production value will be deflated using Indonesian Wholesale Price Index accordingly for each sub-sector. The proxy for capital is estimated fixed or durable assets, including land, buildings, machinery, vehicles and other durable assets. We use the capital of year t to estimate the capital of other years, using the fixed investment (purchase or maintenance), the value of the sale and depreciation (assumed equal to 14%) during the missing period.
To obtain real capital data, we use the gross domestic fixed capital formation (GFCF) deflator to deflate nominal capital. For employment data, we consider the use of working hours to be appropriate, since the same amount of labor in a company can generate different output when their working hours change (due to overtime or temporary production stops). However, due to lack of data, this study used the number of workers as a proxy for employment.
The intermediate input included raw materials and support materials originating from both the domestic market and imports. This intermediate input is deflated using the wholesale price index, which is assumed to be the same across firms. We deflate fuel and lubricants using their corresponding wholesale price index (premium, kerosene, diesel oil, diesel oil, fuel oil and lubricants).
RESULT AND ANALYSIS
The Agregate TFP of Manufacture Industry
The company improved the use of intermediate inputs, improved production layout to reduce switching between workstations, aligned workflow across workstations (the concept of pull systems) to reduce the accumulation of semi-finished products between workstations and the application of Lean Manufacturing concepts to reduce idle time between workstations. During the delayed technical change means the decline of the production frontier, due to the decreasing production capacity of the machines. One possible reason is disruption to machine replacement, as evidenced by low investment growth (BVA/PMTB) and low investment realization (both FDI and domestic).
The average growth rate of investments and the realization of domestic and foreign investments were higher compared to the previous period (Figures 18 and 19). The increase in total investments and the realization of domestic and foreign investments generally bring new technologies. Although technical change increased during this period, the effects of efficiency change or catch-up decreased.
Similar productivity studies in other countries explain the reason for the decline in the catch-up effect as technical change increases, the lack of human resource capacity to adapt new technologies. Inability to catch up indicates low job skills, either due to low education or low skills. In the long run, this may result in foreign investment in Indonesia being limited to low-tech ones.
Unlike Indonesia, the increase in technical change in Malaysia was not followed by a decline in efficiency change (Figure 20). The Global Competitiveness Index ranking during 2012-2013 for Indonesia was apparently far behind Malaysia, especially for pillar four (health and basic education) and pillar five (secondary education and training).
TFP and Its Component Across Manufacturing Subsector
Subsector Quadrant of Industry and Its Characteristics
In contrast, the number of subsectors in quadrant II has increased over the two corresponding periods. The decrease in the number of industry subsectors in quadrant I and the increase in the number of subsectors in quadrant II indicates a lack of development and innovation in management (work procedures) throughout the production process. This affects the firm in two aspects, first, the ability of the production sector to operate at its potential level; and second, the ability of workers to adapt to increasing technology.
One way to solve this problem is to develop the skills of the employees to deal with higher technology. Mapping the sub-sector to the four quadrants may depend on the characteristic of the company within the industry sub-sector. Some variables collected from the Survey of Medium and Large Industry (SIBS) include: the intensity of research and development (R&D) activities;.
We recall that in terms of technical change and efficiency change, quadrant I is better than quadrant II and III, and quadrant II or III is better than quadrant IV. We see some interesting ones that emerge from the survey; first, the use of foreign investment facilities is associated with higher technical change and also higher efficiency change, leading them to quadrant I. Third, if the company is located in industrial area, the probability of having better infrastructure support increases.
On the other hand, companies are more motivated to learn from each other. These will help them increase their productivity; therefore more likely to place them in quadrant I, II, and then quadrant III.
CONCLUSION
