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Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry

Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry Article Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry Maman Setiawan *, Nury Effendi, Rina Indiastuti, Mohamad Fahmi and Budiono Faculty of Economics and Business, Universitas Padjadjaran, Jl. Dipati Ukur No. 35, Bandung 40132, Indonesia * Correspondence: maman.setiawan@unpad.ac.id Abstract: This paper examines the relationship between innovation and dynamic productivity growth in the Indonesian food and beverage industry. Dynamic productivity growth is calculated using a Luenberger indicator, and innovation is represented by a process innovation. This research uses firm-level data for the period 1980–2015 sourced from the Indonesian Central Bureau of Statistics. This research uses a panel data regression model to estimate the relationship between innovation and dynamic productivity growth. This research finds that innovation is relatively low in the Indo- nesian food and beverage industry. Dynamic productivity growth declines steadily during the pe- riod of estimation. This research also found that innovation positively affected dynamic productiv- ity growth only after the introduction of the competition law in Indonesia. Keywords: dynamic productivity growth; innovation; competition law; food and beverage industry JEL Classification: L11; L44; L51; M21; O31 Citation: Setiawan, M.; Effendi, N.; Indiastuti, R.; Fahmi, M.; Budiono. 1. Introduction Innovation and The Indonesian food and beverage industry is a manufacturing sector that proceeds Dynamic Productivity Growth in the raw materials from agriculture, fisheries, and plantations into value-added products. Since Indonesian Food and Beverage 2010, the Indonesian food and beverage industry has contributed almost 20% of the GDP Industry. Resources 2022, 11, 98. annually, making it a significant contributor to the Indonesian economy. In addition, the https://doi.org/10.3390/ Indonesian Central Bureau of Statistics [1] reported that the food and beverage industry resources11110098 accounts for over half of all household spending. Given the importance of the industry, pro- Academic Editor: Eva Pongrácz duction security should be guaranteed. To secure production performance in the industry, firms should continually innovate in their operations (see [2]). For example, innovation in Received: 21 August 2022 food production using robots or new improved machines may double production. Regard- Accepted: 19 October 2022 ing innovation activities in the Indonesian manufacturing industry, Setiawan et al. [3] re- Published: 25 October 2022 ported that only nine subsectors of the food and beverage industry were included in the Publisher’s Note: MDPI stays neu- twenty subsectors of the Indonesian manufacturing industry with the highest R&D expend- tral with regard to jurisdictional itures during the periods 1994–1995 and 2017. Nevertheless, the percentage of R&D expend- claims in published maps and institu- itures for those subsectors was still low, at less than 1% of their output. This indicates that tional affiliations. innovation in the Indonesian food and beverage industry may still be low. Regarding the impact of innovation on production performance, previous research has investigated the relationship between innovation and productivity growth. Geroski [4], Vivero [5], Huergo and Jaumandreu [6], and Mañez et al. [7] investigated the effects Copyright: © 2022 by the authors. Li- of innovation on productivity growth in the European manufacturing industry. Their re- censee MDPI, Basel, Switzerland. This article is an open access article search concluded that innovation positively affected productivity growth. On the con- distributed under the terms and con- trary, Mansury and Love [8] found that innovation did not affect productivity growth in ditions of the Creative Commons At- US business service firms. Previous research has suggested that the effect of innovation tribution (CC BY) license (https://cre- on productivity growth could be different between regions or sectors. A factor that may ativecommons.org/licenses/by/4.0/). cause the different effects of innovation on productivity between regions or sectors can be Resources 2022, 11, 98. https://doi.org/10.3390/resources11110098 www.mdpi.com/journal/resources Resources 2022, 11, 98 2 of 13 economic institutional infrastructure (see [9]). Economic infrastructure institutions can be regulated, such as the competition law suggested by Setiawan et al.[10]. Setiawan et al. [10] found that the introduction of competition law in Indonesia since 1999 has decreased inefficiency allocative. The latter may suggest that the introduction of competition law, as an economic institution infrastructure can affect productivity growth. Thus, research in- vestigating the effect of innovation on productivity growth in the Indonesian food and beverage industry is still relevant, especially including the effect of the introduction of Indonesian competition law. Moreover, research investigating the effect of innovation on productivity growth is rarely found in the Indonesian food and beverage industry. Additionally, the effect of the introduction of competition law on the way innovation affects dynamic productivity growth, as well as the effect of competition law implementation on dynamic productivity growth, are rarely investigated in Indonesia. Previous research has only investigated the impact of industrial concentration on R&D in the industry (see [3]). In addition, Setiawan [11] only investigated productivity growth and its determinants without including the impact of innovation on productivity growth. Setiawan et al. [10] also investigated only the effect of competition law’s introduction on the price–cost margin. Thus, research in- vestigating the impact of innovation on dynamic productivity growth, including the in- fluence of the implementation of competition law, is important. Previous research investigating the relationship between innovation and productiv- ity growth also applies to static productivity growth. The adjustment costs of investments in quasi-fixed factors of production were not taken into account by static productivity growth. Failure to account for adjustment costs in productivity growth assessment, ac- cording to Kapelko et al. [12–14], Setiawan and Lansink [15], and Setiawan [11], may in- correctly ascribe adjustment costs to productivity growth. A cost that is either internally created, such as learning expenses, or externally generated, such as expansion planning fees, is referred to as a transaction or rearrangement cost [12,16,17]. Although adjust- ment costs are not visible, their impacts are expressed as increased input costs and/or re- duced output levels. As a result, a study on the relationship between innovation and productivity growth using dynamic productivity growth is important. Research on the relationship between innovation and dynamic productivity growth with the influence of competition law can generate important policy implications. Policy- makers such as the Ministry of Economics, the Ministry of Industry, and the Ministry of Trade can facilitate firms’ innovation if the innovation can secure the productivity growth of the industry. With this information, policymakers can design regulations and incen- tives to support firms’ innovation in the industry and to improve their productivity growth. Additionally, the positive effect of competition law on dynamic productivity growth as well as on the way innovation affects dynamic productivity growth may sug- gest that policymakers, such as the Indonesian Competition Commission, strengthen the effectiveness of competition law in Indonesia. Based on the previous background, this research freshly investigates the relationship between innovation and dynamic productivity growth in the Indonesian food and bever- age industry. This research also has novelty with respect to the application of dynamic productivity growth in relating innovation to productivity growth. Moreover, this re- search also includes the influence of competition law on the effect of innovation on productivity growth. Both novelties can be useful for firms and policymakers. The following is a breakdown of the paper’s structure. The second section is devoted to a review of the literature. The modeling approach is described in Section 3. Section 4 presents the data description, and Section 5 contains the presentation of the empirical model and outcomes. The final section summarizes and draws conclusions from the find- ings. Resources 2022, 11, 98 3 of 13 2. Literature Review Research investigating the relationship between innovation and productivity growth has been conducted previously among countries and sectors. The innovation measures are mostly sourced from the survey. According to OECD-EUROSTAT [18], a firm is said to implement product innovation if a new and improved product has been introduced in the market. A firm is said to implement process innovation if a new and improved man- ufacturing process is used within the production process. Due to data unavailability, most of the previous research defined innovation as the process of innovation. For example, Geroski [4] investigated the relationship between firm entry, innovation, and productivity growth in 79 industries in the UK during the period 1976–1979. Innovation was measured by the annual count of major innovations constructed by SPRU at Sussex. The research found that innovation activity increased productivity growth. Vivero [5] also investigated the relationship between innovation and productivity growth of firms in Spain. The re- search used two measures of innovation, i.e., R&D intensity and the number of process innovations that a firm obtained in a year. The research found that innovation positively affected static productivity growth. Mañez et al. [7] investigated the effect of process in- novation on the total factor productivity growth of small and medium enterprises in Span- ish manufacturing during the period 1991–2002. Process innovation was defined as a modification of the productive process using a question in the survey. The research con- cluded that the introduction of process innovation increased productivity growth. Huergo and Jaumandreu [6] investigated the impact of (process) innovations on productivity growth. The research used 2300 Spanish firms surveyed during the period 1990–1998. They defined process innovation as activities related to the modification of the productive process (affecting machines, organization, or both). The research concluded that process innovation affected productivity growth. Rochina-Barrachina et al. [2] investigated the ef- fect of process innovation using a sample of Spanish manufacturing firms during the pe- riod 1991–1998. The data on the process of innovation was sourced from the survey, where the process of innovation was assumed to occur if the firms answered positively to the question on whether the firms introduced some important modifications to the productive process. The research concluded that process innovation increased the total factor produc- tivity growth. In contrast to other previous research, Mansury and Love [8] concluded that innovation did not affect productivity growth. They investigated the impact of inno- vation on the productivity and growth of US business service firms. They used a ques- tionnaire to collect data on innovative firm activities. Later research may suggest that an investigation of the relationship between innovation and productivity growth may still be relevant. Regarding the ambiguous effect of innovation activity on productivity growth, pre- vious research suggested that the ambiguous effect could be caused by a poor economic institution in the country that might affect the effectiveness of innovation in improving productivity growth (see [9]). Poor economic institutions, i.e., monopolization and cartel- ization, may significantly create higher uncertainty about the benefits of having more in- novation since innovation activity may increase the costs of developing new products and services. Thus, innovation may inversely affect productivity growth in countries with poor economic institutions. For example, the monopolization or cartelization of a sector by a few companies may negatively affect the productivity growth of other companies with more innovation in the same sector since market power is still owned by the monop- olists. Thus, the implementation of competition law in Indonesia in 1999 is hypothesized to turn the effect of innovation into a positive effect on productivity growth. Regarding the effect of the competition law on productivity growth, Setiawan et al. [10] found that the introduction of competition might lower the inefficiency allocative, i.e., lower the price–cost margin. The lower inefficiency allocative may increase productivity growth since firms will increase capacity utilization to get higher returns. Dynamic productivity growth can also be affected by other variables, such as foreign ownership and export activity. For example, Setiawan [11] suggested that foreign Resources 2022, 11, 98 4 of 13 ownership had a positive effect on dynamic productivity growth. Additionally, Kimura and Kiyota [19] also found that exports could increase the productivity growth of firms. This research still applies the measure of innovation as a modification of the produc- tive process because of data unavailability of product innovation. This research does not use R&D to measure innovation since the R&D data were only available for a few years (less than 5 years with no consecutive years). In addition, the adjustment cost from the investment in quasi-fixed input, which is attributed to the productivity growth measure, is taken into account in this study, which was not taken into account in earlier similar research. Regarding previous research, this research hypothesizes that the effect of innovation and other variables on dynamic productivity growth can be written in the equation (1). The trend variable is included in the equation (1) following the research of Setiawan [11] to reconfirm the trend of dynamic productivity growth. DTFPG = f(Innov, Foreign, Export, Law, InnovLaw, Trend) (1) where > 0 or < 0 , > 0 , > 0 , > 0 , > ¶ ¶ ¶ ¶ ¶ ¶ 0, and < 0 or > 0. DTFPG is the dynamic productivity growth, Innov is the ¶ ¶ process innovation, Export is the export activity of the firm, Foreign is the foreign owner- ship, Law is the dummy to reflect the period of competition law implementation, Inno- vLaw is the interaction variables between dummy of competition law and innovation, and Trend is the trend variable. 3. Modelling Approach This research defines process innovation as the expenditures for purchasing and re- pairing machines and equipment to significantly improve the process of production (see [18]). The use of expenditures to measure process innovation can be better than the R&D measure since the expenditures can reflect the actual use of the new improved process of production (see also [5]). The expenditure on R&D may not directly be implemented in the process of production. This research applies the ratio of innovation to the output of firms as the final measure of innovation. The shift in firm productivity growth over time is represented by dynamic produc- tivity growth. Current decisions have an impact on future productivity, according to this dynamic productivity concept. This dynamic measure takes into account investment-re- lated adjustment costs, which, in static models, could be wrongly attributed to improve- ments in technological efficiency and production. The intertemporal connection of pro- duction choice in this dynamic framework is provided by the adjustment costs related to changes in the level of quasi-fixed elements [13,14]. A Luenberger indicator of dynamic productivity gain in practice can be used to calculate it. The Luenberger indicator was created using the idea of a dynamic directional distance function. The function is based on production technology at time t, and it can be written as Vt(yt:kt) = {(xt, It) can produce yt, given kt}. The vector of outputs (yt) is formed using the vector of inputs (xt) and quasi- fixed input (kt), with the gross investment in kt (It). Silva and Stefanou [20] and Silva et al. [21] both cited the following qualities as being included in the production input require- ment list. The intertemporal connection of production choice in this dynamic framework is derived from the adjustment costs associated with changes in the level of quasi-fixed components [13,14]. Using a Luenberger indicator of dynamic productivity development, it can be practically estimated. The production input requirement set is considered to have the following characteristics in accordance with Silva and Stefanou [20] and Silva et al. [21]: The closed, nonempty set Vt(yt:kt) has a lower bound, is positive monotonic in vari- able inputs xt, and is negative monotonic in gross investments It. Its output levels rise with the quasi-fixed inputs kt and are freely dispensable. It also has a strictly convex set. The feature connected to the gross investment, which suggests that there is a positive cost Resources 2022, 11, 98 5 of 13 when there is an investment in quasi-fixed inputs, plainly demonstrates the incorporation of the adjustment costs. The input-oriented dynamic directional distance function is first applied to estimate the dynamic technical inefficiency using directional vectors for inputs to estimate dy- ( ) namic productivity growth (gx) and investment (gI) or , , , ; , : ( ) , , , ; , = max{ ∈ ℜ: ( − , + ) ∈ ( : )}, (2) ∈ ℜ , ∈ ℜ , ( , ) ≠ (0 , 0 ) If (x g ,I g )V (y :k ) for some β, ( , , , ; , ) = −∞ , other- t x I t t t wise. The directional distance function (xt, It) provides the maximum translation in the direction defined by the vector ( , ), maintaining the translated input combination in- side the set Vt(yt:kt). Firm i’s dynamic technical inefficiency is represented by the coefficient of β. By incorporating a dynamic directional distance function, the static Luenberger indi- cator of productivity growth from Chambers et al. [22] is transformed into dynamic productivity growth. Using the constant return-to-scale assumption, the dynamic Luen- berger productivity growth indicator (DTFPG) can be expressed as follows: ̇ ̇ ⃗ ⃗ = [ ( , , , ; , ) − ( , , , ; , )] (3) ̇ ̇ ⃗ ⃗ +[ ( , , , ; , ) − ( , , , ; , )] The DTFPG indicator provides the arithmetic average of the productivity change measured by technology at time t+1 (the first two terms in (3)) and the productivity change measured by technology at time t (the last two terms in (3)). The positive (negative) value of DFPG indicates whether productivity increased (decreased) between time t and time t+1. Using the dynamic directional distance function, Lansink et al. [23] split the dynamic productivity growth from the Luenberger indicator into components of dynamic technical change (TCH) and dynamic technical inefficiency change (TEI) under CRS: (4) = + Dynamic technical change (TCH), which occurs between time t and time t+1, denotes a change in the technology of dynamic production brought on by the reduction of variable inputs and an increase in investments. It is calculated using the following formula: ⃗ ⃗ = [ ( , , , ; , ) − ( , , , ; , )] (5) ⃗ ⃗ ( ) ( ) + , , , ; , − , , , ; , The difference in technology (the frontier) between time t and time t+1, as assessed at time t and time t+1’s input and output, is referred to as dynamic technical change. Furthermore, the difference between dynamic technical inefficiency at time t and time t+1 is used to calculate the dynamic technical inefficiency change under CRS: ⃗ ⃗ ( ) ( ) (6) , , ; , − , , ; , Equation (6), unlike the last two terms in (3), calculates the changes in dynamic tech- nical inefficiency at periods t and t+1. To assess dynamic scale inefficiencies, both CRS and VRS are used to estimate dynamic technical inefficiency. Kapelko et al. [13,14] used a pri- mal perspective to divide dynamic technical and scale inefficiency change into: ⃗ ⃗ = ( , , ; , | ) − ( , , ; , | ) (7) ⃗ ⃗ = [ ( , , ; , | ) − ( , , ; , | )] Resources 2022, 11, 98 6 of 13 ⃗ ⃗ −[ ( , , ; , | ) − ( , , ; , | )] The dynamic technical inefficiency changes under VRS and the dynamic scale ineffi- ciency changes are represented by ΔVTEI and ΔSE, respectively. The difference in the firm’s position in terms of CRS and VRS dynamic technologies over the two time periods was measured by dynamic scale inefficiencies. Additionally, using the dynamic directional distance function, data envelopment analysis is used to assess dynamic technical inefficiency: ( | , , , ; , ) = max s.t ≤ ∑ , = 1, … , ; (8) ≤ − , = 1, … , ; ∑ ∑ + − ≤ − K , = 1 , … , ; = 1; ≥ 0, = 1, … , . where a vector of variable weights is indexed by γ, the depreciation rate is indexed by δ, the outputs are indexed by m, the inputs are indexed by n, the firms are indexed by j, and the quasi-fixed inputs are indexed by f. According to Kapelko et al. [13,14], the value of the directional vector of investments (gI) is determined by the depreciation rate (0.2) mul- tiplied by the value of the fixed assets, and the value of the directional vector of inputs (gx) is determined by the actual value of the inputs. Dynamic productivity growth can be characterized as follows in terms of the break- down of the dynamic Luenberger indicator of productivity growth: (9) DTFPG = ∆TCH + ∆VTEI + ∆SE The DTFPG’s positive (negative) value implies an increase in production (decrease). Additionally, the positive (negative) DTFG components denote positive (negative) dy- namic productivity development. The relationship between innovation and dynamic productivity growth is derived from the mathematical equation as written in equation (1) and estimated using Equation (10) as follows: DTFPGit = βi + α1Innovit + α2Foreignit + α3Exportit + α4Lawit + α5InnovLawit (10) + α6Trendit + eit where i and t index firm and year, respectively. Equation (10) is estimated using a panel data regression model, either applying fixed-effect or random effect models, based on the Hausman [24] test. A multicollinearity test was applied to the model using the variance inflation factor (VIF). The model suffers from a multicollinearity problem if the VIF for each variable exceeds 10. Moreover, the Levin et al. [25] test was applied to test whether all variables were stationary at the level form. The Breusch–Godfrey test is also applied for the autocorrelation problem. 4. Data The data for this study comes from an Indonesian manufacturing survey conducted by the Indonesian Central Bureau of Statistics. The data relates to the five-digit level of the 2009 Klasifikasi Baku Lapangan Usaha Indonesia (KBLI), which is analogous to the Inter- national Standard Industrial Classification (ISIC) system. Moreover, dynamic Resources 2022, 11, 98 7 of 13 productivity growth can only be provided until 2015, when this research was conducted. The Indonesian Central Bureau of Statistics published a different format of manufacturing survey data after 2015, which made it difficult to estimate the dynamic productivity growth at the firm and ISIC levels. Because subsectors with fewer than 30 observations were combined into groups of comparable products or groupings at the four-digit ISIC level, this study employed 44 subsectors from the original data set, which originally included around 96 subsectors. For example, the subsector of 10390 is a combination of the subsectors of 10391, 10392 and 10399. This research used subsectors as the basis for calculating the dynamic productivity of firms. Firm-level data was applied for the final estimation of the relationship between innovation and dynamic productivity growth. Panel-data regression estimation was also based on the combination of firm and year data. Using two variable inputs-raw materials and labor-as well as one quasi-fixed element or input-capital in machinery and equipment, where associated investment was distin- guished-this study calculates dynamic productivity growth. Output was defined as the value of the gross output produced by a firm following Setiawan et al. [26,27] and Se- tiawan and Lansink [15], deflated by the wholesale price index (WPI). The WPI of ma- chinery (excluding electrical products), transport equipment, and residential and non-res- idential buildings deflated capital in machinery and equipment. Additionally, this re- search used the labor efficiency unit to measure labor, as also applied by Setiawan et al. [26]. The raw materials included the entire cost of materials, including energy, which was deflated by the WPI of raw materials reported by the Indonesian Bureau of Central Statis- tics. Furthermore, the investment variable was formulated as new fixed asset acquisitions minus fixed asset sales. The variables used to estimate dynamic productivity growth are described statisti- cally in Table 1, along with the factors that influence dynamic productivity growth, such as innovation. The coefficient of variation for each variable was more than 1. This indi- cated that the variables varied significantly across years and firms. The variables of capital and investment were the variables with the highest coefficient of variation. A few outliers in each variable were removed for each subsector and year, but this would still not remove the variation of the variables in the panel data. Table 1. Descriptive statistics of the variables applied in the analysis of the Indonesian food and beverage industry (firm-level of 44 subsectors), 1980–2015. Standard Coefficient of Variable Mean Deviation Variation Material (million Rupiah) 160.480 1555 9.690 Labor efficiency units 163.006 633.001 3.883 Capital (million Rupiah) 371.554 2.50×10 67.285 Output (million Rupiah) 208.244 1708.324 8.203 Investment (million Rupiah) 81.965 9911 120.917 Innovation (Innov) 0.022 0.109 6.056 Foreign (%) 3.128 15.973 5.106 Export 0.225 0.418 1.858 Source: Indonesian Bureau of Central Statistics and authors’ calculation. Unbalanced panel data with n = 95,177. From Table 1, it can be seen that the average innovation was 0.022 during the period 1980–2015. This indicates that the average modification expenditure for machines and equipment was 2.2% relative to the output of a firm. Furthermore, the firms had about 3.128% foreign ownership, on average, during the same period. Moreover, the average dummy variable for export activity was 0.225, close to 0. This indicated that most of the Resources 2022, 11, 98 8 of 13 firms in the Indonesian food and beverage industry were not involved in export activities and had small foreign ownership. 5. Results and Discussion Table 2 shows that the dynamic productivity growth of the firms in the industry ex- perienced a declining trend. The average dynamic productivity growth was 0.260% dur- ing the period 1981/1980–2015/2014. The dynamic productivity growth was 1.75% in the interval period of 1981/1980–1985/1984 and it reached to −6.190% in the interval period of 2011/2010–2015/2014. The dynamic productivity growth declined continually after the in- terval period of 1996/1995–2000/1999 when the Indonesian crisis happened in 1997–1998. The dynamic productivity growth was the highest during the interval period of 1991/1990–1995/1994 reaching to 5.560%. The latter period was the era of an overheating economy in Indonesia. During the period from 1981/1980 to 2015/2014, the averages of technical inefficiency change, technical change, and scale efficiency change were 0.027, −0.028, and 0.005, respectively. The technical change was the component that made the highest contribution to the negative dynamic productivity growth compared to the tech- nical inefficiency change and scale inefficiency change. This may indicate that technolog- ical progress in the industry slowed during the period of estimation. In addition, the de- clining trends of technical inefficiency change and scale efficiency change may be a sign that the industry may experience lower competitiveness in the long run. Table 2. Trend of average dynamic productivity growth (TFPG) and innovation ratio for 44 subsec- tors, 1980–2015. Interval Period TFPG (%) TIC (%) TC (%) SEC (%) Innovation Ratio 1981/1980–1985/1984 1.750 0.021 −0.014 0.011 0.0246 1986/1985–1990/1989 1.100 0.043 −0.046 0.014 0.0211 1991/1990–1995/1994 4.560 0.042 −0.013 0.016 0.0144 1996/1995–2000/1999 2.780 0.066 −0.058 0.020 0.0508 2001/2000–2005/2004 2.250 0.024 −0.004 0.002 0.0150 2006/2005–2010/2009 −4.410 0.034 −0.069 −0.009 0.0141 2011/2010–2015/2014 −6.190 −0.043 0.004 −0.021 0.0149 1981/1980–2015/2014 0.260 0.027 −0.028 0.005 0.0221 Source: authors’ calculation. From Table 2, it is also seen that higher or lower innovation was not always positively related to higher or lower dynamic productivity growth. For example, there was higher average dynamic productivity growth in the interval period of 1991/1990–1995/1994, but innovation was lower in that interval period. Additionally, the highest average dynamic productivity growth was in the interval period of 1996/1995–2000/1999, but the highest dynamic productivity growth was in the interval period of 1991/1990–1995/1994. Moreo- ver, the trend of the two variables mostly moved in the opposite direction during consec- utive interval periods. Regarding the average dynamic productivity growth of the firms in Table 3, there were 10 subsectors with the highest average dynamic productivity growth of the firms. The subsector with the highest average dynamic productivity growth of the firms was subsectors of 10802 (animal feed concentrate), followed by subsectors of 10635 (corn mill- ing and cleaning; rice and corn flour; and rice and corn starch) and 10296 (salted, dried, smoked, frozen, fermented, extracted, iced, and pulverized other aquatic biotas). From Table 3, it is also seen that only 4 of the 10 subsectors with the highest average innovation ratio were included in the 10 subsectors with the highest average dynamic productivity growth. The four subsectors included 10625 (other palm starch, glucose, and other starch processes), 10722 (brown sugar), 10425 (flour and other coconut processes), and 10223 (canned fish, water biota, and shrimp). Resources 2022, 11, 98 9 of 13 Table 3. 10 (ten) subsectors with the highest average dynamic productivity growth and innovation for 44 subsectors, 1980–2015. 10 (Ten) Subsectors with Highest Average Dynamic 10 (Ten) Subsectors with Highest Average Productivity Growth Innovation ISIC Dynamic productivity growth ISIC Innovation 10802 0.072 10721 0.055 10635 0.067 10425 0.046 10296 0.056 10722 0.044 10625 0.052 10431 0.042 10722 0.047 10760 0.039 10792 0.041 10625 0.036 11011 0.038 11050 0.035 10425 0.034 10223 0.035 10214 0.031 10550 0.034 10223 0.027 11070 0.032 Source: authors’ calculation. Table 4 shows the subsectors with the lowest average innovation and dynamic productivity growth of firms. Table 4 shows that ISIC 11050 (mineral water) was the sub- sector with the lowest average dynamic productivity growth, followed by 10710 (bakery product), and 10210 (meat processing). Moreover, none of the 10 subsectors with the low- est average innovation were included in the 10 subsectors with the lowest average dy- namic productivity growth. This may indicate that innovation does not always boost dy- namic productivity growth. Poor economic institutions in Indonesia may turn innovation into lower dynamic productivity growth. Table 4. 10 (ten) subsectors with lowest average dynamic productivity growth and innovation for 44 subsectors, 1980–2015. 10 (Ten) Subsectors with Lowest Average Dynamic 10 (Ten) Subsectors with Lowest Average Productivity Growth Innovation ISIC Dynamic productivity growth ISIC Innovation ratio 11050 −0.045 10214 0.006 10710 −0.035 10390 0.008 10210 −0.034 10771 0.009 11070 −0.033 10790 0.009 10760 −0.031 10794 0.009 10431 −0.030 10631 0.009 10140 −0.027 10620 0.010 10315 −0.023 10792 0.010 10220 −0.019 10793 0.011 10312 −0.019 10740 0.012 Source: authors’ calculation. Table 5 provides an estimation of the effect of innovation on dynamic productivity growth using the innovation measure of the new machine and equipment expenditure ratio (innovation ratio). The model was estimated using a random effect model, since the Hausman test rejected the fixed effect model. Based on the VIF, there was no multicollin- earity problem in the model because the VIF for each variable was less than 10. For exam- ple, the VIF for the Law variable was 4.01, which was the highest. The estimations also applied White-heteroscedasticy consistent covariance, since the model suffered from the heteroscedasticity problem. All variables were stationary at the 5% critical level using the Resources 2022, 11, 98 10 of 13 test of Levin et al. (2002). The Breusch–Godprey test suggested that there was no autocor- relation problem in the model at the 5% critical level. Table 5. Results of the regression of innovation on dynamic productivity growth. Dependent Variable: Dynamic Productivity Growth Independent Variable (DTFPG) Coefficients 0.025 *** Intercept (0.002) −0.030 *** Innov (0.007) 0.005 *** Export (0.002) −4 1.577×10 *** Foreign −5 (3.91×10 ) Law 0.017 *** (0.002) 0.031 *** InnovLaw (0.011) −0.002 *** Trend −4 (1.285×10 ) p-value of Wald-statistics 0.000 Notes: *** denotes the significance of the test statistic at the 1% level. Unbalanced panel data with n = 95,177. Standard errors are in parentheses. Innov = Innovation ratio with a measure of the new ma- chine and equipment expenditure ratio. HHI = Herfindahl–Hirschman Index. Foreign = foreign ownership. Export = dummy variable to reflect the export activity of a firm. Law = Dummy variable to reflect the period after the introduction of the competition law in 1999. Innov x Law = interaction between Innov and Law variables. Source: Authors’ calculation. Table 5 shows that before the introduction of the competition law, innovation af- fected dynamic productivity growth significantly at 1%, with a coefficient of innovation ratio of −0.030. This indicates that an increase in the innovation ratio by 0.1 units decreased dynamic productivity growth by 0.003, ceteris paribus. After the introduction of the com- petition law, innovation affected dynamic productivity growth positively and signifi- cantly at the 1% critical level. The coefficient of innovation ratio after the introduction of competition law was 0.001 (= 0.031 − 0.030). This indicates that an increase in the innova- tion ratio by 0.1 units increased dynamic productivity growth by 0.0001 units, ceteris pari- bus. This may be in line with the finding of Silve and Plekhanov [9], which suggested that innovation could boost firm growth only in good-quality economic institutions. Process innovation that increases productivity growth, as suggested by Geroski [4], Huergo and Jaumandreu [6], Mañez et al. [7], and Rochina-Barrachina et al. [2], may occur in the Indo- nesian industry only if the business environment is competitive. The export activity of the firm had a positive effect on dynamic productivity growth with a coefficient of 0.005. The coefficient was significant at the 1% critical level. This in- dicates that the firm with export activity had a higher dynamic productivity growth of 0.005 compared to the firm with no export activity, ceteris paribus. The result supports the findings of Kimura and Kiyota [19], who also found that exports positively affected productivity growth. −4 Foreign ownership had a coefficient of 1.577*10 and it had a significant effect on the dynamic productivity growth at the 1% critical level. This indicates that an increase in −4 foreign ownership by 1% increased dynamic productivity growth by 1.577*10 , ceteris Resources 2022, 11, 98 11 of 13 paribus. This may support the findings of Xu, Liu, and Abdoh [28], which suggested that firms with foreign ownership were positively related to firm productivity. Competition law implementation had a positive effect on dynamic productivity growth with a coefficient of 0.017. The coefficient was significant at the 1% critical level. This supports the finding of Buccirossi et al. [29], which concluded that competition policy had a positive impact on total factor productivity growth. Moreover, the coefficient of the trend was −0.002, indicating that dynamic productiv- ity growth declined by 0.2% every year during the period of estimation, on average. This was in line with Table 2, which shows how dynamic productivity growth declined con- tinuously during the interval periods. This also supports the finding of Setiawan [11], who reported a declining trend of dynamic productivity growth, although the research had a different period of estimation. This research implies that Indonesian policymakers should strengthen economic in- stitutions to ensure that the business environment in Indonesia is conducive to competi- tion and innovation (see also [30]). The economic institution covers not only competition law but also other economic institutions, such as property rights or patent law and the effective rule of law. Moreover, the government and the House of Representatives may also amend any regulations that restrict investment and innovation in Indonesia. With this higher quality of institutions, investment, and innovation will increase with a greater effect on the rise of productivity. Regarding the positive effect of export activity, the manager of a firm should choose an export-orientation strategy to increase the productivity of the firm. The Indonesian government should also facilitate firms in exporting their products to the global market, such as by providing the ease of having export licensing, technological assistance, process, and product innovation training, and credit assistance with low interest. The government should also facilitate the spillover effect of foreign investment, in addition to opening In- donesia’s market for foreign investment. Thus, local firms can learn from the best practices of foreign firms. 6. Conclusions This research investigated the effect of innovation on dynamic productivity growth, including the influence of competition laws on the way innovation affects dynamic productivity growth. This research found that average innovation expenditure was rela- tively small relative to the output of the firm. Innovation negatively affected dynamic productivity before the introduction of the competition law in 1999. Following the imple- mentation of the competition law, the effect of innovation on dynamic productivity growth became positive. Additionally, dynamic productivity growth was higher after the implementation of the competition law. Regarding the effect of other variables, export activity and foreign ownership posi- tively affected dynamic productivity. Exposure to the world market and more foreign control may induce firms to be more productive. The trend variable also indicated that dynamic productivity was declining continuously, which could serve as a warning to In- donesian policymakers. Because this study discovered that innovation had a positive impact on dynamic productivity growth only after the implementation of competition law, future research or theoretical foundations should not view innovation as a stand-alone variable impacting industrial performance. Further research may investigate other variables, such as regula- tions and other economic variables, that may moderate the effect of innovation on indus- trial performance. Additionally, innovation should be taken into account by both busi- nesses and policymakers in terms of both costs and benefits. To reduce the potential neg- ative impact of innovation on economic performance, a cost–benefit analysis should be carried out prior to the implementation of the innovation strategy and policy. This research also recommends investigating the effect of product innovation on productivity growth in future research. This can be relevant since product innovation is Resources 2022, 11, 98 12 of 13 delivered directly to the consumer, which may have a different impact on productivity growth. Furthermore, this research may also suggest considering the endogeneity prob- lem in the variable of innovation in future research, which may change the estimation strategy. Author Contributions: Writing the draft of paper, M.S.; literature review and conceptual frame- work, M.S. and B.; data cleaning and analysis, M.S. and M.F.; resources and reading, R.I. dan B.; supervision and editing, N.E.; project administration, M.S. and R.I. All authors have read and agreed to the published version of the manuscript. Funding: This research received funding from PDUPT-Dikti 2022 and supported from ALG facili- ties. The APC is also funded by Universitas Padjadjaran. Institutional Review Board Statement: Ethical review and approval were waived for this study, due to “Not applicable” for studies not involving humans or animals Informed Consent Statement: Not applicable for studies not involving humans Data Availability Statement: Not Applicable Acknowledgments: The authors thank all the reviewers for the thoughtful comments. Conflicts of Interest: The authors declare no conflict of interest References 1. Indonesian Central Bureau of Statistics. Gross Domestic Product. 2020. Available online: https://www.bps.go.id/subject/169/produk-domestik-bruto--pengeluaran-.html#subjekViewTab3 (accessed on April 2022) 2. Rochina-Barrachina, M.E.; Mañez, J.A.; Sanchis-Llopis, J.A. Process innovations and firm productivity growth.Small Business Economics. 2010, 34, 147–166. https://doi.org/10.1007/s11187-008-9110-5. 3. Setiawan, M.; Indiastuti, R.; Hidayat, A.K.; Rostiana, E. R&D and Industrial Concentration in the Indonesian Manufacturing Industry. J. Open Innov. Technol. Mark. Complex. 2021, 7, 112. https://doi.org/10.3390/joitmc7020112 4. Geroski, P.A. Entry, innovation and productivity growth. The Review of Economics and Statistics. 1989, 572-578. 5. Vivero, R.L. The impact of process innovations on firm’s productivity growth: The case of Spain.Applied Economics. 2002, 34, 1007–1016. https://doi.org/10.1080/00036840010019684. 6. Huergo, E.; Jaumandreu, J. Firms’ age, process innovation and productivity growth. International Journal of Industrial Organiza- tion. 2004, 22, 541–559. https://doi.org/10.1016/j.ijindorg.2003.12.002 7. Mañez, J.A.; Barrachina, M.E.R.; Sanchis, A.; Sanchis, J.A. Do process innovations boost SMEs productivity growth?. Empirical economics. 2013, 44, 1373–1405. https://doi.org/10.1007/s00181-012-0571-7. 8. Mansury, M.A.; Love, J.H. Innovation, productivity and growth in US business services: A firm-level analysis. Technovation 2008, 28, 52–62. https://doi.org/10.1016/j.technovation.2007.06.002. 9. Silve, F.; Plekhanov, A. Institutions, Innovation and Growth: Cross-Country Evidence; European Bank for Reconstruction and De- velopment: London, UK, 2015; Working Paper No. 177. http://dx.doi.org/10.2139/ssrn.3119688 10. Setiawan, M.; Emvalomatis, G.; Lansink, A.O. Industrial Concentration and Price Cost Margin in Indonesian Food and Beverage Industry. Applied Economics. 2012, 44, 3805–3814. https://doi.org/10.1080/00036846.2011.581220 11. Setiawan, M. Dynamic Productivity Growth and Its Determinants in the Indonesia Food and Beverage Industry. Int. Rev. Appl. Econ. 2019, 33, 774–788. https://doi.org/10.1080/02692171.2019.1606900 12. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Assessing dynamic inefficiency of the Spanish construction sector pre- and post- financial crisis. European journal of operational research. 2014, 237, 349–357. https://doi.org/10.1016/j.ejor.2014.01.047. 13. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis. PLoS ONE. 2015, 10, e0128217. https://doi.org/10.1371/journal.pone.0128217. 14. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Analyzing the impact of investment spikes on dynamic productivity growth. Omega 2015, 54, 116–124. https://doi.org/10.1016/j.omega.2015.01.010. 15. Setiawan, M.; Lansink, A.G.J.M.O. Dynamic technical inefficiency and industrial concentration in the Indonesian food and bev- erages industry. Br. Food J. 2018, 120, 108–119. https://doi.org/10.1108/bfj-04-2017-0226. 16. Brechling, F. Investment and employment decisions; Manchester University Press, Manchester, UK, 1975 17. Nickel, S. J. The investment decisions of firms; Cambridge University Press, Cambridge, UK, 1978. 18. OECD-EUROSTAT. Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. In Oslo Manual; OECD: France, Paris, 1997. 19. Kimura, F.;Kiyota, K. Exports, FDI, and Productivity: Dynamic Evidence from Japanese Firms, Review of World Economics. 2006, 142, 695-719. https://doi.org/10.1007/s10290-006-0089-1 20. Silva, E.; Stefanou, S.E. Nonparametric dynamic production analysis and the theory of cost. Journal of Productivity Analysis. 2003, 19, 5–32. https://doi.org/10.1023/A:1021865018717 Resources 2022, 11, 98 13 of 13 21. Silva, E.; Lansink, A.O; Stefanou, S.E. The adjustment-cost model of the firm: Duality and productive efficiency. International Journal of Production Economics. 2005, 168, 245-256. https://doi.org/10.1016/j.ijpe.2015.06.027 22. Chambers, R.G.; Chung, Y.; Färe, R. Benefit and distance functions. Journal of Economic Theory. 1996, 70, 407–419. https://doi.org/10.1006/jeth.1996.0096 23. Lansink, A.O.; Stefanou, S.; Serra, T. Primal and dual dynamic Luenberger productivity indicators. European Journal of Opera- tional Research. 2015, 241, 555–563. https://doi.org/10.1016/j.ejor.2014.09.027. 24. Hausman, J.A. Specification Tests in Econometrics. Econometrica. 1978, 46, 1251–1271. https://doi.org/10.2307/1913827. 25. Levin, A.; Lin, C.-F.; Chu, C.-S.J. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of econometrics. 2002, 108, 1–24. https://doi.org/10.1016/s0304-4076(01)00098-7. 26. Setiawan, M.; Emvalomatis, G.; Lansink, A.O. The relationship between technical efficiency and industrial concentration: Evi- dence from the Indonesian food and beverages industry. Journal of Asian Economics. 2012, 23, 466–475. https://doi.org/10.1016/j.asieco.2012.01.002. 27. Setiawan, M. Persistence of Price–Cost Margin and Technical Efficiency in the Indonesian Food and Beverage Industry. Int. J. Econ. Bus. 2019, 26, 315–326. https://doi.org/10.1080/13571516.2019.1592996. 28. Xu, J.; Liu, Y.; Abdoh, H. Foreign ownership and productivity. International Review of Economics & Finance. 2022, 80, 624-642. https://doi.org/10.1016/j.iref.2022.02.079 29. Buccirossi, P.; Ciari, L.; Duso, T.; Spagnolo, G.; Vitale, C. Competition Policy and Productivity Growth: An Empirical Assess- ment. Review of Economics and Statistics. 2013, 95, 1324–1336. https://doi.org/10.1162/rest_a_00304. 30. Setiawan, M.; Effendi, N. Survey of the Industrial Concentration and Price-cost Margin of the Indonesian Manufacturing In- dustry. 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Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry

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Article Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry Maman Setiawan *, Nury Effendi, Rina Indiastuti, Mohamad Fahmi and Budiono Faculty of Economics and Business, Universitas Padjadjaran, Jl. Dipati Ukur No. 35, Bandung 40132, Indonesia * Correspondence: maman.setiawan@unpad.ac.id Abstract: This paper examines the relationship between innovation and dynamic productivity growth in the Indonesian food and beverage industry. Dynamic productivity growth is calculated using a Luenberger indicator, and innovation is represented by a process innovation. This research uses firm-level data for the period 1980–2015 sourced from the Indonesian Central Bureau of Statistics. This research uses a panel data regression model to estimate the relationship between innovation and dynamic productivity growth. This research finds that innovation is relatively low in the Indo- nesian food and beverage industry. Dynamic productivity growth declines steadily during the pe- riod of estimation. This research also found that innovation positively affected dynamic productiv- ity growth only after the introduction of the competition law in Indonesia. Keywords: dynamic productivity growth; innovation; competition law; food and beverage industry JEL Classification: L11; L44; L51; M21; O31 Citation: Setiawan, M.; Effendi, N.; Indiastuti, R.; Fahmi, M.; Budiono. 1. Introduction Innovation and The Indonesian food and beverage industry is a manufacturing sector that proceeds Dynamic Productivity Growth in the raw materials from agriculture, fisheries, and plantations into value-added products. Since Indonesian Food and Beverage 2010, the Indonesian food and beverage industry has contributed almost 20% of the GDP Industry. Resources 2022, 11, 98. annually, making it a significant contributor to the Indonesian economy. In addition, the https://doi.org/10.3390/ Indonesian Central Bureau of Statistics [1] reported that the food and beverage industry resources11110098 accounts for over half of all household spending. Given the importance of the industry, pro- Academic Editor: Eva Pongrácz duction security should be guaranteed. To secure production performance in the industry, firms should continually innovate in their operations (see [2]). For example, innovation in Received: 21 August 2022 food production using robots or new improved machines may double production. Regard- Accepted: 19 October 2022 ing innovation activities in the Indonesian manufacturing industry, Setiawan et al. [3] re- Published: 25 October 2022 ported that only nine subsectors of the food and beverage industry were included in the Publisher’s Note: MDPI stays neu- twenty subsectors of the Indonesian manufacturing industry with the highest R&D expend- tral with regard to jurisdictional itures during the periods 1994–1995 and 2017. Nevertheless, the percentage of R&D expend- claims in published maps and institu- itures for those subsectors was still low, at less than 1% of their output. This indicates that tional affiliations. innovation in the Indonesian food and beverage industry may still be low. Regarding the impact of innovation on production performance, previous research has investigated the relationship between innovation and productivity growth. Geroski [4], Vivero [5], Huergo and Jaumandreu [6], and Mañez et al. [7] investigated the effects Copyright: © 2022 by the authors. Li- of innovation on productivity growth in the European manufacturing industry. Their re- censee MDPI, Basel, Switzerland. This article is an open access article search concluded that innovation positively affected productivity growth. On the con- distributed under the terms and con- trary, Mansury and Love [8] found that innovation did not affect productivity growth in ditions of the Creative Commons At- US business service firms. Previous research has suggested that the effect of innovation tribution (CC BY) license (https://cre- on productivity growth could be different between regions or sectors. A factor that may ativecommons.org/licenses/by/4.0/). cause the different effects of innovation on productivity between regions or sectors can be Resources 2022, 11, 98. https://doi.org/10.3390/resources11110098 www.mdpi.com/journal/resources Resources 2022, 11, 98 2 of 13 economic institutional infrastructure (see [9]). Economic infrastructure institutions can be regulated, such as the competition law suggested by Setiawan et al.[10]. Setiawan et al. [10] found that the introduction of competition law in Indonesia since 1999 has decreased inefficiency allocative. The latter may suggest that the introduction of competition law, as an economic institution infrastructure can affect productivity growth. Thus, research in- vestigating the effect of innovation on productivity growth in the Indonesian food and beverage industry is still relevant, especially including the effect of the introduction of Indonesian competition law. Moreover, research investigating the effect of innovation on productivity growth is rarely found in the Indonesian food and beverage industry. Additionally, the effect of the introduction of competition law on the way innovation affects dynamic productivity growth, as well as the effect of competition law implementation on dynamic productivity growth, are rarely investigated in Indonesia. Previous research has only investigated the impact of industrial concentration on R&D in the industry (see [3]). In addition, Setiawan [11] only investigated productivity growth and its determinants without including the impact of innovation on productivity growth. Setiawan et al. [10] also investigated only the effect of competition law’s introduction on the price–cost margin. Thus, research in- vestigating the impact of innovation on dynamic productivity growth, including the in- fluence of the implementation of competition law, is important. Previous research investigating the relationship between innovation and productiv- ity growth also applies to static productivity growth. The adjustment costs of investments in quasi-fixed factors of production were not taken into account by static productivity growth. Failure to account for adjustment costs in productivity growth assessment, ac- cording to Kapelko et al. [12–14], Setiawan and Lansink [15], and Setiawan [11], may in- correctly ascribe adjustment costs to productivity growth. A cost that is either internally created, such as learning expenses, or externally generated, such as expansion planning fees, is referred to as a transaction or rearrangement cost [12,16,17]. Although adjust- ment costs are not visible, their impacts are expressed as increased input costs and/or re- duced output levels. As a result, a study on the relationship between innovation and productivity growth using dynamic productivity growth is important. Research on the relationship between innovation and dynamic productivity growth with the influence of competition law can generate important policy implications. Policy- makers such as the Ministry of Economics, the Ministry of Industry, and the Ministry of Trade can facilitate firms’ innovation if the innovation can secure the productivity growth of the industry. With this information, policymakers can design regulations and incen- tives to support firms’ innovation in the industry and to improve their productivity growth. Additionally, the positive effect of competition law on dynamic productivity growth as well as on the way innovation affects dynamic productivity growth may sug- gest that policymakers, such as the Indonesian Competition Commission, strengthen the effectiveness of competition law in Indonesia. Based on the previous background, this research freshly investigates the relationship between innovation and dynamic productivity growth in the Indonesian food and bever- age industry. This research also has novelty with respect to the application of dynamic productivity growth in relating innovation to productivity growth. Moreover, this re- search also includes the influence of competition law on the effect of innovation on productivity growth. Both novelties can be useful for firms and policymakers. The following is a breakdown of the paper’s structure. The second section is devoted to a review of the literature. The modeling approach is described in Section 3. Section 4 presents the data description, and Section 5 contains the presentation of the empirical model and outcomes. The final section summarizes and draws conclusions from the find- ings. Resources 2022, 11, 98 3 of 13 2. Literature Review Research investigating the relationship between innovation and productivity growth has been conducted previously among countries and sectors. The innovation measures are mostly sourced from the survey. According to OECD-EUROSTAT [18], a firm is said to implement product innovation if a new and improved product has been introduced in the market. A firm is said to implement process innovation if a new and improved man- ufacturing process is used within the production process. Due to data unavailability, most of the previous research defined innovation as the process of innovation. For example, Geroski [4] investigated the relationship between firm entry, innovation, and productivity growth in 79 industries in the UK during the period 1976–1979. Innovation was measured by the annual count of major innovations constructed by SPRU at Sussex. The research found that innovation activity increased productivity growth. Vivero [5] also investigated the relationship between innovation and productivity growth of firms in Spain. The re- search used two measures of innovation, i.e., R&D intensity and the number of process innovations that a firm obtained in a year. The research found that innovation positively affected static productivity growth. Mañez et al. [7] investigated the effect of process in- novation on the total factor productivity growth of small and medium enterprises in Span- ish manufacturing during the period 1991–2002. Process innovation was defined as a modification of the productive process using a question in the survey. The research con- cluded that the introduction of process innovation increased productivity growth. Huergo and Jaumandreu [6] investigated the impact of (process) innovations on productivity growth. The research used 2300 Spanish firms surveyed during the period 1990–1998. They defined process innovation as activities related to the modification of the productive process (affecting machines, organization, or both). The research concluded that process innovation affected productivity growth. Rochina-Barrachina et al. [2] investigated the ef- fect of process innovation using a sample of Spanish manufacturing firms during the pe- riod 1991–1998. The data on the process of innovation was sourced from the survey, where the process of innovation was assumed to occur if the firms answered positively to the question on whether the firms introduced some important modifications to the productive process. The research concluded that process innovation increased the total factor produc- tivity growth. In contrast to other previous research, Mansury and Love [8] concluded that innovation did not affect productivity growth. They investigated the impact of inno- vation on the productivity and growth of US business service firms. They used a ques- tionnaire to collect data on innovative firm activities. Later research may suggest that an investigation of the relationship between innovation and productivity growth may still be relevant. Regarding the ambiguous effect of innovation activity on productivity growth, pre- vious research suggested that the ambiguous effect could be caused by a poor economic institution in the country that might affect the effectiveness of innovation in improving productivity growth (see [9]). Poor economic institutions, i.e., monopolization and cartel- ization, may significantly create higher uncertainty about the benefits of having more in- novation since innovation activity may increase the costs of developing new products and services. Thus, innovation may inversely affect productivity growth in countries with poor economic institutions. For example, the monopolization or cartelization of a sector by a few companies may negatively affect the productivity growth of other companies with more innovation in the same sector since market power is still owned by the monop- olists. Thus, the implementation of competition law in Indonesia in 1999 is hypothesized to turn the effect of innovation into a positive effect on productivity growth. Regarding the effect of the competition law on productivity growth, Setiawan et al. [10] found that the introduction of competition might lower the inefficiency allocative, i.e., lower the price–cost margin. The lower inefficiency allocative may increase productivity growth since firms will increase capacity utilization to get higher returns. Dynamic productivity growth can also be affected by other variables, such as foreign ownership and export activity. For example, Setiawan [11] suggested that foreign Resources 2022, 11, 98 4 of 13 ownership had a positive effect on dynamic productivity growth. Additionally, Kimura and Kiyota [19] also found that exports could increase the productivity growth of firms. This research still applies the measure of innovation as a modification of the produc- tive process because of data unavailability of product innovation. This research does not use R&D to measure innovation since the R&D data were only available for a few years (less than 5 years with no consecutive years). In addition, the adjustment cost from the investment in quasi-fixed input, which is attributed to the productivity growth measure, is taken into account in this study, which was not taken into account in earlier similar research. Regarding previous research, this research hypothesizes that the effect of innovation and other variables on dynamic productivity growth can be written in the equation (1). The trend variable is included in the equation (1) following the research of Setiawan [11] to reconfirm the trend of dynamic productivity growth. DTFPG = f(Innov, Foreign, Export, Law, InnovLaw, Trend) (1) where > 0 or < 0 , > 0 , > 0 , > 0 , > ¶ ¶ ¶ ¶ ¶ ¶ 0, and < 0 or > 0. DTFPG is the dynamic productivity growth, Innov is the ¶ ¶ process innovation, Export is the export activity of the firm, Foreign is the foreign owner- ship, Law is the dummy to reflect the period of competition law implementation, Inno- vLaw is the interaction variables between dummy of competition law and innovation, and Trend is the trend variable. 3. Modelling Approach This research defines process innovation as the expenditures for purchasing and re- pairing machines and equipment to significantly improve the process of production (see [18]). The use of expenditures to measure process innovation can be better than the R&D measure since the expenditures can reflect the actual use of the new improved process of production (see also [5]). The expenditure on R&D may not directly be implemented in the process of production. This research applies the ratio of innovation to the output of firms as the final measure of innovation. The shift in firm productivity growth over time is represented by dynamic produc- tivity growth. Current decisions have an impact on future productivity, according to this dynamic productivity concept. This dynamic measure takes into account investment-re- lated adjustment costs, which, in static models, could be wrongly attributed to improve- ments in technological efficiency and production. The intertemporal connection of pro- duction choice in this dynamic framework is provided by the adjustment costs related to changes in the level of quasi-fixed elements [13,14]. A Luenberger indicator of dynamic productivity gain in practice can be used to calculate it. The Luenberger indicator was created using the idea of a dynamic directional distance function. The function is based on production technology at time t, and it can be written as Vt(yt:kt) = {(xt, It) can produce yt, given kt}. The vector of outputs (yt) is formed using the vector of inputs (xt) and quasi- fixed input (kt), with the gross investment in kt (It). Silva and Stefanou [20] and Silva et al. [21] both cited the following qualities as being included in the production input require- ment list. The intertemporal connection of production choice in this dynamic framework is derived from the adjustment costs associated with changes in the level of quasi-fixed components [13,14]. Using a Luenberger indicator of dynamic productivity development, it can be practically estimated. The production input requirement set is considered to have the following characteristics in accordance with Silva and Stefanou [20] and Silva et al. [21]: The closed, nonempty set Vt(yt:kt) has a lower bound, is positive monotonic in vari- able inputs xt, and is negative monotonic in gross investments It. Its output levels rise with the quasi-fixed inputs kt and are freely dispensable. It also has a strictly convex set. The feature connected to the gross investment, which suggests that there is a positive cost Resources 2022, 11, 98 5 of 13 when there is an investment in quasi-fixed inputs, plainly demonstrates the incorporation of the adjustment costs. The input-oriented dynamic directional distance function is first applied to estimate the dynamic technical inefficiency using directional vectors for inputs to estimate dy- ( ) namic productivity growth (gx) and investment (gI) or , , , ; , : ( ) , , , ; , = max{ ∈ ℜ: ( − , + ) ∈ ( : )}, (2) ∈ ℜ , ∈ ℜ , ( , ) ≠ (0 , 0 ) If (x g ,I g )V (y :k ) for some β, ( , , , ; , ) = −∞ , other- t x I t t t wise. The directional distance function (xt, It) provides the maximum translation in the direction defined by the vector ( , ), maintaining the translated input combination in- side the set Vt(yt:kt). Firm i’s dynamic technical inefficiency is represented by the coefficient of β. By incorporating a dynamic directional distance function, the static Luenberger indi- cator of productivity growth from Chambers et al. [22] is transformed into dynamic productivity growth. Using the constant return-to-scale assumption, the dynamic Luen- berger productivity growth indicator (DTFPG) can be expressed as follows: ̇ ̇ ⃗ ⃗ = [ ( , , , ; , ) − ( , , , ; , )] (3) ̇ ̇ ⃗ ⃗ +[ ( , , , ; , ) − ( , , , ; , )] The DTFPG indicator provides the arithmetic average of the productivity change measured by technology at time t+1 (the first two terms in (3)) and the productivity change measured by technology at time t (the last two terms in (3)). The positive (negative) value of DFPG indicates whether productivity increased (decreased) between time t and time t+1. Using the dynamic directional distance function, Lansink et al. [23] split the dynamic productivity growth from the Luenberger indicator into components of dynamic technical change (TCH) and dynamic technical inefficiency change (TEI) under CRS: (4) = + Dynamic technical change (TCH), which occurs between time t and time t+1, denotes a change in the technology of dynamic production brought on by the reduction of variable inputs and an increase in investments. It is calculated using the following formula: ⃗ ⃗ = [ ( , , , ; , ) − ( , , , ; , )] (5) ⃗ ⃗ ( ) ( ) + , , , ; , − , , , ; , The difference in technology (the frontier) between time t and time t+1, as assessed at time t and time t+1’s input and output, is referred to as dynamic technical change. Furthermore, the difference between dynamic technical inefficiency at time t and time t+1 is used to calculate the dynamic technical inefficiency change under CRS: ⃗ ⃗ ( ) ( ) (6) , , ; , − , , ; , Equation (6), unlike the last two terms in (3), calculates the changes in dynamic tech- nical inefficiency at periods t and t+1. To assess dynamic scale inefficiencies, both CRS and VRS are used to estimate dynamic technical inefficiency. Kapelko et al. [13,14] used a pri- mal perspective to divide dynamic technical and scale inefficiency change into: ⃗ ⃗ = ( , , ; , | ) − ( , , ; , | ) (7) ⃗ ⃗ = [ ( , , ; , | ) − ( , , ; , | )] Resources 2022, 11, 98 6 of 13 ⃗ ⃗ −[ ( , , ; , | ) − ( , , ; , | )] The dynamic technical inefficiency changes under VRS and the dynamic scale ineffi- ciency changes are represented by ΔVTEI and ΔSE, respectively. The difference in the firm’s position in terms of CRS and VRS dynamic technologies over the two time periods was measured by dynamic scale inefficiencies. Additionally, using the dynamic directional distance function, data envelopment analysis is used to assess dynamic technical inefficiency: ( | , , , ; , ) = max s.t ≤ ∑ , = 1, … , ; (8) ≤ − , = 1, … , ; ∑ ∑ + − ≤ − K , = 1 , … , ; = 1; ≥ 0, = 1, … , . where a vector of variable weights is indexed by γ, the depreciation rate is indexed by δ, the outputs are indexed by m, the inputs are indexed by n, the firms are indexed by j, and the quasi-fixed inputs are indexed by f. According to Kapelko et al. [13,14], the value of the directional vector of investments (gI) is determined by the depreciation rate (0.2) mul- tiplied by the value of the fixed assets, and the value of the directional vector of inputs (gx) is determined by the actual value of the inputs. Dynamic productivity growth can be characterized as follows in terms of the break- down of the dynamic Luenberger indicator of productivity growth: (9) DTFPG = ∆TCH + ∆VTEI + ∆SE The DTFPG’s positive (negative) value implies an increase in production (decrease). Additionally, the positive (negative) DTFG components denote positive (negative) dy- namic productivity development. The relationship between innovation and dynamic productivity growth is derived from the mathematical equation as written in equation (1) and estimated using Equation (10) as follows: DTFPGit = βi + α1Innovit + α2Foreignit + α3Exportit + α4Lawit + α5InnovLawit (10) + α6Trendit + eit where i and t index firm and year, respectively. Equation (10) is estimated using a panel data regression model, either applying fixed-effect or random effect models, based on the Hausman [24] test. A multicollinearity test was applied to the model using the variance inflation factor (VIF). The model suffers from a multicollinearity problem if the VIF for each variable exceeds 10. Moreover, the Levin et al. [25] test was applied to test whether all variables were stationary at the level form. The Breusch–Godfrey test is also applied for the autocorrelation problem. 4. Data The data for this study comes from an Indonesian manufacturing survey conducted by the Indonesian Central Bureau of Statistics. The data relates to the five-digit level of the 2009 Klasifikasi Baku Lapangan Usaha Indonesia (KBLI), which is analogous to the Inter- national Standard Industrial Classification (ISIC) system. Moreover, dynamic Resources 2022, 11, 98 7 of 13 productivity growth can only be provided until 2015, when this research was conducted. The Indonesian Central Bureau of Statistics published a different format of manufacturing survey data after 2015, which made it difficult to estimate the dynamic productivity growth at the firm and ISIC levels. Because subsectors with fewer than 30 observations were combined into groups of comparable products or groupings at the four-digit ISIC level, this study employed 44 subsectors from the original data set, which originally included around 96 subsectors. For example, the subsector of 10390 is a combination of the subsectors of 10391, 10392 and 10399. This research used subsectors as the basis for calculating the dynamic productivity of firms. Firm-level data was applied for the final estimation of the relationship between innovation and dynamic productivity growth. Panel-data regression estimation was also based on the combination of firm and year data. Using two variable inputs-raw materials and labor-as well as one quasi-fixed element or input-capital in machinery and equipment, where associated investment was distin- guished-this study calculates dynamic productivity growth. Output was defined as the value of the gross output produced by a firm following Setiawan et al. [26,27] and Se- tiawan and Lansink [15], deflated by the wholesale price index (WPI). The WPI of ma- chinery (excluding electrical products), transport equipment, and residential and non-res- idential buildings deflated capital in machinery and equipment. Additionally, this re- search used the labor efficiency unit to measure labor, as also applied by Setiawan et al. [26]. The raw materials included the entire cost of materials, including energy, which was deflated by the WPI of raw materials reported by the Indonesian Bureau of Central Statis- tics. Furthermore, the investment variable was formulated as new fixed asset acquisitions minus fixed asset sales. The variables used to estimate dynamic productivity growth are described statisti- cally in Table 1, along with the factors that influence dynamic productivity growth, such as innovation. The coefficient of variation for each variable was more than 1. This indi- cated that the variables varied significantly across years and firms. The variables of capital and investment were the variables with the highest coefficient of variation. A few outliers in each variable were removed for each subsector and year, but this would still not remove the variation of the variables in the panel data. Table 1. Descriptive statistics of the variables applied in the analysis of the Indonesian food and beverage industry (firm-level of 44 subsectors), 1980–2015. Standard Coefficient of Variable Mean Deviation Variation Material (million Rupiah) 160.480 1555 9.690 Labor efficiency units 163.006 633.001 3.883 Capital (million Rupiah) 371.554 2.50×10 67.285 Output (million Rupiah) 208.244 1708.324 8.203 Investment (million Rupiah) 81.965 9911 120.917 Innovation (Innov) 0.022 0.109 6.056 Foreign (%) 3.128 15.973 5.106 Export 0.225 0.418 1.858 Source: Indonesian Bureau of Central Statistics and authors’ calculation. Unbalanced panel data with n = 95,177. From Table 1, it can be seen that the average innovation was 0.022 during the period 1980–2015. This indicates that the average modification expenditure for machines and equipment was 2.2% relative to the output of a firm. Furthermore, the firms had about 3.128% foreign ownership, on average, during the same period. Moreover, the average dummy variable for export activity was 0.225, close to 0. This indicated that most of the Resources 2022, 11, 98 8 of 13 firms in the Indonesian food and beverage industry were not involved in export activities and had small foreign ownership. 5. Results and Discussion Table 2 shows that the dynamic productivity growth of the firms in the industry ex- perienced a declining trend. The average dynamic productivity growth was 0.260% dur- ing the period 1981/1980–2015/2014. The dynamic productivity growth was 1.75% in the interval period of 1981/1980–1985/1984 and it reached to −6.190% in the interval period of 2011/2010–2015/2014. The dynamic productivity growth declined continually after the in- terval period of 1996/1995–2000/1999 when the Indonesian crisis happened in 1997–1998. The dynamic productivity growth was the highest during the interval period of 1991/1990–1995/1994 reaching to 5.560%. The latter period was the era of an overheating economy in Indonesia. During the period from 1981/1980 to 2015/2014, the averages of technical inefficiency change, technical change, and scale efficiency change were 0.027, −0.028, and 0.005, respectively. The technical change was the component that made the highest contribution to the negative dynamic productivity growth compared to the tech- nical inefficiency change and scale inefficiency change. This may indicate that technolog- ical progress in the industry slowed during the period of estimation. In addition, the de- clining trends of technical inefficiency change and scale efficiency change may be a sign that the industry may experience lower competitiveness in the long run. Table 2. Trend of average dynamic productivity growth (TFPG) and innovation ratio for 44 subsec- tors, 1980–2015. Interval Period TFPG (%) TIC (%) TC (%) SEC (%) Innovation Ratio 1981/1980–1985/1984 1.750 0.021 −0.014 0.011 0.0246 1986/1985–1990/1989 1.100 0.043 −0.046 0.014 0.0211 1991/1990–1995/1994 4.560 0.042 −0.013 0.016 0.0144 1996/1995–2000/1999 2.780 0.066 −0.058 0.020 0.0508 2001/2000–2005/2004 2.250 0.024 −0.004 0.002 0.0150 2006/2005–2010/2009 −4.410 0.034 −0.069 −0.009 0.0141 2011/2010–2015/2014 −6.190 −0.043 0.004 −0.021 0.0149 1981/1980–2015/2014 0.260 0.027 −0.028 0.005 0.0221 Source: authors’ calculation. From Table 2, it is also seen that higher or lower innovation was not always positively related to higher or lower dynamic productivity growth. For example, there was higher average dynamic productivity growth in the interval period of 1991/1990–1995/1994, but innovation was lower in that interval period. Additionally, the highest average dynamic productivity growth was in the interval period of 1996/1995–2000/1999, but the highest dynamic productivity growth was in the interval period of 1991/1990–1995/1994. Moreo- ver, the trend of the two variables mostly moved in the opposite direction during consec- utive interval periods. Regarding the average dynamic productivity growth of the firms in Table 3, there were 10 subsectors with the highest average dynamic productivity growth of the firms. The subsector with the highest average dynamic productivity growth of the firms was subsectors of 10802 (animal feed concentrate), followed by subsectors of 10635 (corn mill- ing and cleaning; rice and corn flour; and rice and corn starch) and 10296 (salted, dried, smoked, frozen, fermented, extracted, iced, and pulverized other aquatic biotas). From Table 3, it is also seen that only 4 of the 10 subsectors with the highest average innovation ratio were included in the 10 subsectors with the highest average dynamic productivity growth. The four subsectors included 10625 (other palm starch, glucose, and other starch processes), 10722 (brown sugar), 10425 (flour and other coconut processes), and 10223 (canned fish, water biota, and shrimp). Resources 2022, 11, 98 9 of 13 Table 3. 10 (ten) subsectors with the highest average dynamic productivity growth and innovation for 44 subsectors, 1980–2015. 10 (Ten) Subsectors with Highest Average Dynamic 10 (Ten) Subsectors with Highest Average Productivity Growth Innovation ISIC Dynamic productivity growth ISIC Innovation 10802 0.072 10721 0.055 10635 0.067 10425 0.046 10296 0.056 10722 0.044 10625 0.052 10431 0.042 10722 0.047 10760 0.039 10792 0.041 10625 0.036 11011 0.038 11050 0.035 10425 0.034 10223 0.035 10214 0.031 10550 0.034 10223 0.027 11070 0.032 Source: authors’ calculation. Table 4 shows the subsectors with the lowest average innovation and dynamic productivity growth of firms. Table 4 shows that ISIC 11050 (mineral water) was the sub- sector with the lowest average dynamic productivity growth, followed by 10710 (bakery product), and 10210 (meat processing). Moreover, none of the 10 subsectors with the low- est average innovation were included in the 10 subsectors with the lowest average dy- namic productivity growth. This may indicate that innovation does not always boost dy- namic productivity growth. Poor economic institutions in Indonesia may turn innovation into lower dynamic productivity growth. Table 4. 10 (ten) subsectors with lowest average dynamic productivity growth and innovation for 44 subsectors, 1980–2015. 10 (Ten) Subsectors with Lowest Average Dynamic 10 (Ten) Subsectors with Lowest Average Productivity Growth Innovation ISIC Dynamic productivity growth ISIC Innovation ratio 11050 −0.045 10214 0.006 10710 −0.035 10390 0.008 10210 −0.034 10771 0.009 11070 −0.033 10790 0.009 10760 −0.031 10794 0.009 10431 −0.030 10631 0.009 10140 −0.027 10620 0.010 10315 −0.023 10792 0.010 10220 −0.019 10793 0.011 10312 −0.019 10740 0.012 Source: authors’ calculation. Table 5 provides an estimation of the effect of innovation on dynamic productivity growth using the innovation measure of the new machine and equipment expenditure ratio (innovation ratio). The model was estimated using a random effect model, since the Hausman test rejected the fixed effect model. Based on the VIF, there was no multicollin- earity problem in the model because the VIF for each variable was less than 10. For exam- ple, the VIF for the Law variable was 4.01, which was the highest. The estimations also applied White-heteroscedasticy consistent covariance, since the model suffered from the heteroscedasticity problem. All variables were stationary at the 5% critical level using the Resources 2022, 11, 98 10 of 13 test of Levin et al. (2002). The Breusch–Godprey test suggested that there was no autocor- relation problem in the model at the 5% critical level. Table 5. Results of the regression of innovation on dynamic productivity growth. Dependent Variable: Dynamic Productivity Growth Independent Variable (DTFPG) Coefficients 0.025 *** Intercept (0.002) −0.030 *** Innov (0.007) 0.005 *** Export (0.002) −4 1.577×10 *** Foreign −5 (3.91×10 ) Law 0.017 *** (0.002) 0.031 *** InnovLaw (0.011) −0.002 *** Trend −4 (1.285×10 ) p-value of Wald-statistics 0.000 Notes: *** denotes the significance of the test statistic at the 1% level. Unbalanced panel data with n = 95,177. Standard errors are in parentheses. Innov = Innovation ratio with a measure of the new ma- chine and equipment expenditure ratio. HHI = Herfindahl–Hirschman Index. Foreign = foreign ownership. Export = dummy variable to reflect the export activity of a firm. Law = Dummy variable to reflect the period after the introduction of the competition law in 1999. Innov x Law = interaction between Innov and Law variables. Source: Authors’ calculation. Table 5 shows that before the introduction of the competition law, innovation af- fected dynamic productivity growth significantly at 1%, with a coefficient of innovation ratio of −0.030. This indicates that an increase in the innovation ratio by 0.1 units decreased dynamic productivity growth by 0.003, ceteris paribus. After the introduction of the com- petition law, innovation affected dynamic productivity growth positively and signifi- cantly at the 1% critical level. The coefficient of innovation ratio after the introduction of competition law was 0.001 (= 0.031 − 0.030). This indicates that an increase in the innova- tion ratio by 0.1 units increased dynamic productivity growth by 0.0001 units, ceteris pari- bus. This may be in line with the finding of Silve and Plekhanov [9], which suggested that innovation could boost firm growth only in good-quality economic institutions. Process innovation that increases productivity growth, as suggested by Geroski [4], Huergo and Jaumandreu [6], Mañez et al. [7], and Rochina-Barrachina et al. [2], may occur in the Indo- nesian industry only if the business environment is competitive. The export activity of the firm had a positive effect on dynamic productivity growth with a coefficient of 0.005. The coefficient was significant at the 1% critical level. This in- dicates that the firm with export activity had a higher dynamic productivity growth of 0.005 compared to the firm with no export activity, ceteris paribus. The result supports the findings of Kimura and Kiyota [19], who also found that exports positively affected productivity growth. −4 Foreign ownership had a coefficient of 1.577*10 and it had a significant effect on the dynamic productivity growth at the 1% critical level. This indicates that an increase in −4 foreign ownership by 1% increased dynamic productivity growth by 1.577*10 , ceteris Resources 2022, 11, 98 11 of 13 paribus. This may support the findings of Xu, Liu, and Abdoh [28], which suggested that firms with foreign ownership were positively related to firm productivity. Competition law implementation had a positive effect on dynamic productivity growth with a coefficient of 0.017. The coefficient was significant at the 1% critical level. This supports the finding of Buccirossi et al. [29], which concluded that competition policy had a positive impact on total factor productivity growth. Moreover, the coefficient of the trend was −0.002, indicating that dynamic productiv- ity growth declined by 0.2% every year during the period of estimation, on average. This was in line with Table 2, which shows how dynamic productivity growth declined con- tinuously during the interval periods. This also supports the finding of Setiawan [11], who reported a declining trend of dynamic productivity growth, although the research had a different period of estimation. This research implies that Indonesian policymakers should strengthen economic in- stitutions to ensure that the business environment in Indonesia is conducive to competi- tion and innovation (see also [30]). The economic institution covers not only competition law but also other economic institutions, such as property rights or patent law and the effective rule of law. Moreover, the government and the House of Representatives may also amend any regulations that restrict investment and innovation in Indonesia. With this higher quality of institutions, investment, and innovation will increase with a greater effect on the rise of productivity. Regarding the positive effect of export activity, the manager of a firm should choose an export-orientation strategy to increase the productivity of the firm. The Indonesian government should also facilitate firms in exporting their products to the global market, such as by providing the ease of having export licensing, technological assistance, process, and product innovation training, and credit assistance with low interest. The government should also facilitate the spillover effect of foreign investment, in addition to opening In- donesia’s market for foreign investment. Thus, local firms can learn from the best practices of foreign firms. 6. Conclusions This research investigated the effect of innovation on dynamic productivity growth, including the influence of competition laws on the way innovation affects dynamic productivity growth. This research found that average innovation expenditure was rela- tively small relative to the output of the firm. Innovation negatively affected dynamic productivity before the introduction of the competition law in 1999. Following the imple- mentation of the competition law, the effect of innovation on dynamic productivity growth became positive. Additionally, dynamic productivity growth was higher after the implementation of the competition law. Regarding the effect of other variables, export activity and foreign ownership posi- tively affected dynamic productivity. Exposure to the world market and more foreign control may induce firms to be more productive. The trend variable also indicated that dynamic productivity was declining continuously, which could serve as a warning to In- donesian policymakers. Because this study discovered that innovation had a positive impact on dynamic productivity growth only after the implementation of competition law, future research or theoretical foundations should not view innovation as a stand-alone variable impacting industrial performance. Further research may investigate other variables, such as regula- tions and other economic variables, that may moderate the effect of innovation on indus- trial performance. Additionally, innovation should be taken into account by both busi- nesses and policymakers in terms of both costs and benefits. To reduce the potential neg- ative impact of innovation on economic performance, a cost–benefit analysis should be carried out prior to the implementation of the innovation strategy and policy. This research also recommends investigating the effect of product innovation on productivity growth in future research. This can be relevant since product innovation is Resources 2022, 11, 98 12 of 13 delivered directly to the consumer, which may have a different impact on productivity growth. Furthermore, this research may also suggest considering the endogeneity prob- lem in the variable of innovation in future research, which may change the estimation strategy. Author Contributions: Writing the draft of paper, M.S.; literature review and conceptual frame- work, M.S. and B.; data cleaning and analysis, M.S. and M.F.; resources and reading, R.I. dan B.; supervision and editing, N.E.; project administration, M.S. and R.I. All authors have read and agreed to the published version of the manuscript. Funding: This research received funding from PDUPT-Dikti 2022 and supported from ALG facili- ties. The APC is also funded by Universitas Padjadjaran. Institutional Review Board Statement: Ethical review and approval were waived for this study, due to “Not applicable” for studies not involving humans or animals Informed Consent Statement: Not applicable for studies not involving humans Data Availability Statement: Not Applicable Acknowledgments: The authors thank all the reviewers for the thoughtful comments. Conflicts of Interest: The authors declare no conflict of interest References 1. Indonesian Central Bureau of Statistics. Gross Domestic Product. 2020. Available online: https://www.bps.go.id/subject/169/produk-domestik-bruto--pengeluaran-.html#subjekViewTab3 (accessed on April 2022) 2. Rochina-Barrachina, M.E.; Mañez, J.A.; Sanchis-Llopis, J.A. Process innovations and firm productivity growth.Small Business Economics. 2010, 34, 147–166. https://doi.org/10.1007/s11187-008-9110-5. 3. Setiawan, M.; Indiastuti, R.; Hidayat, A.K.; Rostiana, E. R&D and Industrial Concentration in the Indonesian Manufacturing Industry. J. Open Innov. Technol. Mark. Complex. 2021, 7, 112. https://doi.org/10.3390/joitmc7020112 4. Geroski, P.A. Entry, innovation and productivity growth. The Review of Economics and Statistics. 1989, 572-578. 5. Vivero, R.L. The impact of process innovations on firm’s productivity growth: The case of Spain.Applied Economics. 2002, 34, 1007–1016. https://doi.org/10.1080/00036840010019684. 6. Huergo, E.; Jaumandreu, J. Firms’ age, process innovation and productivity growth. International Journal of Industrial Organiza- tion. 2004, 22, 541–559. https://doi.org/10.1016/j.ijindorg.2003.12.002 7. Mañez, J.A.; Barrachina, M.E.R.; Sanchis, A.; Sanchis, J.A. Do process innovations boost SMEs productivity growth?. Empirical economics. 2013, 44, 1373–1405. https://doi.org/10.1007/s00181-012-0571-7. 8. Mansury, M.A.; Love, J.H. Innovation, productivity and growth in US business services: A firm-level analysis. Technovation 2008, 28, 52–62. https://doi.org/10.1016/j.technovation.2007.06.002. 9. Silve, F.; Plekhanov, A. Institutions, Innovation and Growth: Cross-Country Evidence; European Bank for Reconstruction and De- velopment: London, UK, 2015; Working Paper No. 177. http://dx.doi.org/10.2139/ssrn.3119688 10. Setiawan, M.; Emvalomatis, G.; Lansink, A.O. Industrial Concentration and Price Cost Margin in Indonesian Food and Beverage Industry. Applied Economics. 2012, 44, 3805–3814. https://doi.org/10.1080/00036846.2011.581220 11. Setiawan, M. Dynamic Productivity Growth and Its Determinants in the Indonesia Food and Beverage Industry. Int. Rev. Appl. Econ. 2019, 33, 774–788. https://doi.org/10.1080/02692171.2019.1606900 12. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Assessing dynamic inefficiency of the Spanish construction sector pre- and post- financial crisis. European journal of operational research. 2014, 237, 349–357. https://doi.org/10.1016/j.ejor.2014.01.047. 13. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis. PLoS ONE. 2015, 10, e0128217. https://doi.org/10.1371/journal.pone.0128217. 14. Kapelko, M.; Lansink, A.O.; Stefanou, S.E. Analyzing the impact of investment spikes on dynamic productivity growth. Omega 2015, 54, 116–124. https://doi.org/10.1016/j.omega.2015.01.010. 15. Setiawan, M.; Lansink, A.G.J.M.O. Dynamic technical inefficiency and industrial concentration in the Indonesian food and bev- erages industry. Br. Food J. 2018, 120, 108–119. https://doi.org/10.1108/bfj-04-2017-0226. 16. Brechling, F. Investment and employment decisions; Manchester University Press, Manchester, UK, 1975 17. Nickel, S. J. The investment decisions of firms; Cambridge University Press, Cambridge, UK, 1978. 18. OECD-EUROSTAT. Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. In Oslo Manual; OECD: France, Paris, 1997. 19. Kimura, F.;Kiyota, K. Exports, FDI, and Productivity: Dynamic Evidence from Japanese Firms, Review of World Economics. 2006, 142, 695-719. https://doi.org/10.1007/s10290-006-0089-1 20. Silva, E.; Stefanou, S.E. Nonparametric dynamic production analysis and the theory of cost. Journal of Productivity Analysis. 2003, 19, 5–32. https://doi.org/10.1023/A:1021865018717 Resources 2022, 11, 98 13 of 13 21. Silva, E.; Lansink, A.O; Stefanou, S.E. The adjustment-cost model of the firm: Duality and productive efficiency. International Journal of Production Economics. 2005, 168, 245-256. https://doi.org/10.1016/j.ijpe.2015.06.027 22. Chambers, R.G.; Chung, Y.; Färe, R. Benefit and distance functions. Journal of Economic Theory. 1996, 70, 407–419. https://doi.org/10.1006/jeth.1996.0096 23. Lansink, A.O.; Stefanou, S.; Serra, T. Primal and dual dynamic Luenberger productivity indicators. European Journal of Opera- tional Research. 2015, 241, 555–563. https://doi.org/10.1016/j.ejor.2014.09.027. 24. Hausman, J.A. Specification Tests in Econometrics. Econometrica. 1978, 46, 1251–1271. https://doi.org/10.2307/1913827. 25. Levin, A.; Lin, C.-F.; Chu, C.-S.J. Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of econometrics. 2002, 108, 1–24. https://doi.org/10.1016/s0304-4076(01)00098-7. 26. Setiawan, M.; Emvalomatis, G.; Lansink, A.O. The relationship between technical efficiency and industrial concentration: Evi- dence from the Indonesian food and beverages industry. Journal of Asian Economics. 2012, 23, 466–475. https://doi.org/10.1016/j.asieco.2012.01.002. 27. Setiawan, M. Persistence of Price–Cost Margin and Technical Efficiency in the Indonesian Food and Beverage Industry. Int. J. Econ. Bus. 2019, 26, 315–326. https://doi.org/10.1080/13571516.2019.1592996. 28. Xu, J.; Liu, Y.; Abdoh, H. Foreign ownership and productivity. International Review of Economics & Finance. 2022, 80, 624-642. https://doi.org/10.1016/j.iref.2022.02.079 29. Buccirossi, P.; Ciari, L.; Duso, T.; Spagnolo, G.; Vitale, C. Competition Policy and Productivity Growth: An Empirical Assess- ment. Review of Economics and Statistics. 2013, 95, 1324–1336. https://doi.org/10.1162/rest_a_00304. 30. Setiawan, M.; Effendi, N. Survey of the Industrial Concentration and Price-cost Margin of the Indonesian Manufacturing In- dustry. International Economic Journal. 2016, 30, 123–146. https://doi.org/10.1080/10168737.2015.1136666.

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Resources – Multidisciplinary Digital Publishing Institute

Published: Oct 25, 2022

Keywords: dynamic productivity growth; innovation; competition law; food and beverage industry

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