Firm Performance and the Determinants in the Textile and Textile Product Industry of Indonesia Pre- and Post-COVID-19 Pandemic

Abstract

This research aimed to examine firm performance and its determinants in the textile and textile product (TPT) industry of Indonesia before and after the COVID-19 pandemic. The analysis used data from the manufacturing survey conducted by Indonesia’s Bureau of Central Statistics (BPS) for the period 2018–2021. It further incorporated the fixed-effect model on the subsectors by applying least-square dummy variables. The results show that firm performance declined during the COVID-19 pandemic while the price–cost margin was affected by firm size, export orientation, foreign ownership, and the pandemic. However, the Herfindahl–Hirschman index did not have a significant influence on firm performance. This research addresses the gaps identified in previous publications, which had limitations regarding sample data. It further contributed to the literature by applying price–cost margin (PCM) as a proxy for firm performance and investigating the determining factors in the TPT industry before and after the COVID-19 pandemic, particularly in Indonesia.

1. Introduction

The Indonesian textile and textile product (TPT) industry contributed about 6.056% to the output of the manufacturing sector during the period 2011–2021. Despite the importance of this industry, there has been a declining trend in its growth and contribution to the manufacturing sector. For example, the growth and contribution of the industry to the Indonesian manufacturing sector were 0.305% and 6.380% in 2011, respectively, which further declined to −7.506% and 5.577% in 2021. This shows that there has been a declining performance of the firms in the industry.

Regarding the performance of the firms in the TPT industry, previous research from Amaliyah et al. (2022) found that the performance of the firms, proxied by average efficiency, experienced a decline from 2007 to 2013. This decline in performance could potentially lead to instability in both production and price, causing welfare losses. Therefore, there is an urgency to investigate the performance of the firms in the industry, and also the determinants.

Previous research on firm performance and its determinants in the TPT industry remains limited, particularly for Indonesia. For example, Abbas et al. (2013) investigated firm performance and the determinants in the textile sector in Pakistan. Demertzis (2022) also examined firm performance in the textiles and apparel industry of the United States, while Mahmud et al. (2021) investigated firm performances in the textile industry in Bangladesh. However, these publications are limited in terms of data samples, with Arianto and Kurniasih (2023) examining the textile and garment industry in Indonesia, but the research only investigated the financial distress of the firms listed on the stock exchange. In this context, Amaliyah et al. (2022) investigated the technical efficiency of the Indonesian textile and TPT during the 2007–2013 period. Based on this context, examining the current performance of the TPT industry and the determinants in Indonesia is relevant.

Regarding the determinants of productivity, factors that may affect firm performance in the TPT industry include the firm size (Abbas et al., 2013; Mahmud et al., 2021; Demertzis, 2022)1. In the industry’s context, performance can be generally affected by the market structure as a representative of competition (Setiawan, 2023). Furthermore, foreign ownership can affect firm performance by providing better skills and technology (Webster et al., 2022; Putri & Setiawan, 2023). Export activity can also improve firm performance due to external pressures, as observed in Putri and Setiawan (2023). However, previous research has rarely combined the effect of the market structure and firms’ characteristics as the determinants of productivity. Research combining the effects of the characteristics and the market structure on productivity can contribute valuable insights to the literature.

The COVID-19 pandemic began in 2020, which caused a deeper decline in the performance of the TPT industry. For example, the TPT industry declined by about 6.963%, and the contribution to the manufacturing sector also declined by about 6.12% in the same year. The COVID-19 pandemic further affected Indonesian economic productivity, which influenced firm performance (Syarifuddin & Setiawan, 2022). Consequently, the crisis can also be an important factor affecting the performance of the TPT industry. Regarding previous research focusing on firm performance and the determinants, productivity levels pre- and post-COVID-19 pandemic have not been investigated. Although some research in other countries correlated performance with the impact of the COVID-19 pandemic, the use of a price–cost margin (PCM) as a proxy is rarely found. The use of an average PCM has the benefit of being a measure of the market power of a firm. Therefore, the research aimed to address the gap by investigating performance (using PCM as a proxy) and the determinants in the Indonesian TPT industry pre- and post-COVID-19 pandemic.

Furthermore, this research is different from the previous publications by virtue of investigating current performances in the Indonesian TPT industry using firm-level data2. Having the firm-level data analysis will provide a clear picture of the real business conditions in the industry. Additionally, the analysis of the effect of the COVID-19 pandemic on firm performance in the industry introduces a crucial aspect to the research.

The research investigating the performance of the TPT industry and its determinants pre- and post-COVID-19 pandemic has policy implications. For example, policymakers support organizations in the TPT industry to increase the size, which further affects productivity. Opening the industry to higher foreign investment and export activity may also be suggested when the two variables significantly improve firm performance. Furthermore, policymakers may design appropriate programs to improve the industry’s performance when overall firm performance declines after the COVID-19 pandemic. The declining performance of the TPT industry may cause deindustrialization in the long run without an intervention.

The analysis is further organized into the following sections, where Section 2 provides a literature review explaining the previous research that investigated firm performance and the determinants in the TPT industry. Subsequently, the relationship between firm performance and its determinants is modeled using the econometrics model. Section 3 further provides the empirical model, while Section 4 presents data and variable measures. Additionally, Section 5 provides the results of the research, and Section 6 shows the discussion. Section 7 draws all conclusions from the research and policy implications.

2. Literature Review

Previous research investigated the performance of the textile and TPT, as well as its determinants, in various countries. However, previous research was limited in terms of the data samples and did not use the price–cost margin (PCM) as the performance variable. For example, Abbas et al. (2013) investigated the determinants of firm financial performance in the Pakistan textile sector using Return on Investment (ROI) as a measure of performance. The results show that short-term leverage, firm size, risk, tax, and non-debt tax shields significantly influenced financial performance3. The publication was limited to data from the Karachi Stock Exchange for the period 2005–2010.

Mahmud et al. (2021) further investigated the profitability of the firm in the textile sector of Bangladesh from 2011 to 2019 using Return on Assets (ROA) to measure firm performance. The results show that the growth and age of the firm possessed a negative paradoxical relationship with profitability. This research was also limited, using only 31 firms listed on the Dhaka Stock Exchange.

Demertzis (2022) examined the determinants affecting the financial performance of firms in the textiles and apparel industry of the United States from 2004 to 2021, using ROA as a measure of financial performance. The result shows that the ROA was significantly influenced by both solvency and long-term leverage. However, the research only investigated four NYSE-listed firms using Thomson Reuters.

In addition, Santos and Castanho (2022) investigated the performance of the textile industry in Portugal during the COVID-19 pandemic. The research used Return on Equity (ROE) and Return on Capital Employed (ROCE) for profitability indicators from 2019 to 2020. The results show that smaller firms performed better than larger organizations due to the higher fixed costs of the latter when orders declined worldwide. The analysis had limitations because even though the search returned 29 firms, only five had financial reports for 2020. The year 2020 was important in assessing firm performance during the pandemic.

Related to previous research, investigating firm performance using limited organizations in the TPT industry could not capture the whole business situation in the industry. Therefore, research investigating firm performance using survey data is still relevant. These publications rarely investigate the impacts of the COVID-19 pandemic on firm performance. Ignoring the effect of the pandemic in evaluating firm performance could cause bias. Consequently, including the COVID-19 pandemic as a factor affecting firm performance was important, and would make an academic contribution.

Furthermore, firm performance and its determinants in the Indonesian TPT industry pre- and post-COVID-19 pandemic were hardly addressed in the nationally or internationally published literature. For example, Amaliyah et al. (2022) investigated the technical efficiency and determinants in the textile, as well as textile products, during the period 2007–2013. The results show that size, market concentration, exports, and foreign ownership affected firm performance significantly. This publication still did not cover the conditions during the COVID-19 pandemic. It also used technical efficiency as the measure of firm performance instead of a real financial indicator of performance such as profitability or price–cost margin. Therefore, the research investigating the determinants of firm (financial) performance pre- and post-COVID-19 pandemic still makes a contribution and shows relevance.

Based on the literature review, previous research examining the relationship between firm performance and COVID-19 remained limited, both internationally and in Indonesia. Additionally, these publications also possessed constraints in terms of the data sample. Although certain research attempted to correlate performance with the impacts of the COVID-19 pandemic, none used PCM as a proxy for firm performance in the nationally and internationally published literature. The use of PCM had several advantages over other variables such as ROA and ROE, because it more accurately reflected the efficiency of a firm in generating profits from the sale of the products or services without being influenced by the asset or equity structure. PCM was a more suitable performance proxy for market analysis and industry structure, as it provides direct insights into the firm pricing and market power. Therefore, this research offered a crucial contribution by addressing the gap, specifically by using PCM to evaluate firm performance in the pre- and post-COVID-19 pandemic context. This provides a deeper and more specific perspective on understanding firm performance during the pandemic. The comparative analysis was crucial for policymakers seeking to adapt effectively to the dynamics of COVID-19 and similar crises, which created a significant gap in the existing literature.

3. Modeling Approach

This research used an econometrics approach to investigate the determinants of firm performance in the Indonesian TPT industry. A fixed-effect regression model was also used by introducing the dummy variables to differentiate the firm performance between subsectors of the Indonesian TPT industry4, as follows:

$${Perf}_{it}={\alpha }_i+{\beta }_1{Size}_{it}+{\beta }_2{IC}_{kt}+{\beta }_3{Export}_{it}+{\beta }_4{Foreign}_{it}+{\beta }_5{Cov19}_t+{\displaystyle {\sum }_{j=1}^k}{\delta }_k{DI}_k+e_{it}$$

(1)

where $i$ and $t$ index each firm and time period, $Perf$ denotes the firm performance, $Size$ represents the size of the firms, $IC$ indicates industrial concentration, $Export$ is the dummy variable for export activity, $Foreign$ measures the proportion of foreign ownership in the firm, and $Cov19$ is a dummy variable representing the COVID-19 pandemic. Furthermore, $DI$ refers to the dummy variables for subsectors, with $k$ indexing each subsector.

Equation (1) was estimated using the fixed-effect model with least-square dummy variable (LSDV) estimation. LSDV was applied since the manufacturing survey has not provided balanced panel data since 20175. This approach benefitted by considering some unobserved heterogeneities, such as different characteristics of the subsector and scale effect. Equation (1) was further corrected using the Newey–West estimator when there was a problem of heteroscedasticity and autocorrelation or using only White-robust standard error during the problem of heteroscedasticity. The method possessed an advantage, since it only corrected the standard error of the parameters when the parameters suffered from the heteroscedasticity and autocorrelation problems without performing a transformation on the variables. The heteroscedasticity and autocorrelation assumptions were further tested using the simple and robust White and Durbin–Watson tests, respectively (Setiawan, 2019).

4. Data

This research used the data from the Indonesian Bureau of Central Statistics (BPS) manufacturing survey, covering the period from 2018 to 2021. The data included the periods before COVID-19 (2018–2019) and during and after the pandemic (2020–2021). Organizations were classified using the five-digit Klasifikasi Baku Lapangan Usaha Indonesia (KBLI) standard, similar to the International Standard Industrial Classification (ISIC) level. During 2018–2021, there were about 2241 firms surveyed by BPS in the TPT industry on average. This research used all the firms as the sample to represent the industry.

As previously discussed, the TPT industry was an important sector for Indonesia, especially regarding the contribution to the output of the manufacturing industry. However, there were indications of a decline in the performance of the industry due to the COVID-19 pandemic. This decline in performance could impact the industry’s contribution to the output of the manufacturing sector, production, and prices. Therefore, investigating the performances of firms in the industry and the determinants before and after the COVID-19 pandemic became essential.

This research investigated the textile and textile products (TPT) industry, including 30 subsectors divided into two main categories, namely, 20 subsectors from the textile industry and 10 subsectors of products derived from the textile industry. Examples of subsectors from the textile industry include textile fiber preparation (13111), yarn spinning (13112), sewing thread spinning (13113), weaving excluding jute and other sacks (13121), as well as ikat weaving fabric (13122) industries. Furthermore, examples of subsectors based on textile products included household (13921), embroidered (13922), pillows and similar (13923), knitted and embroidered (13924), as well as jute sack (13925) textile products industries.

Regarding the variables, this research measured the performance using PCM, which represented the profitability or markup (Abraham et al., 2024). The firm size was further measured by the natural logarithm of fixed assets (Mahmud et al., 2021). This research used the Herfindahl–Hirschman index (HHI) to measure industrial concentration (Amaliyah et al., 2022). The dummy for COVID-19 pandemic was represented by 1 for the 2020–2021 period, otherwise it was 0. Export was also a dummy variable that assumed the value 1 when a firm possessed an export activity, otherwise it was 0. Foreign ownership was also a dummy variable that assumed the value 1 when a firm possessed a proportion of foreign ownership, and otherwise it was 0. The fixed asset and output variables were deflated using the wholesale price index, using the constant price of the year 2010.

Table 1. Descriptive statistics of the variables in the TPT industry, 2018–2021.

Variable	Mean	Standard Deviation	Coefficient of Variation	Min	Max
PCM	0.276	0.204	0.738	−1.986	0.998
Size	9.955	2.467	0.248	−1.216	20.160
HHI	0.122	0.138	1.132	0.017	1
Export	0.064	0.245	3.825	0	1
Foreign (%)	14.666	34.693	2.365	0	100
Dcov	0.456	0.498	1.093	0	1

Source(s): Indonesian Bureau of Central Statistics and authors’ calculation. Note(s): Unbalanced panel data with n = 8926.

shows the descriptive statistics of the variables used to estimate firm performance, and the determinants. The statistics show that most variables were relatively heterogeneous, with relatively high standard deviations and coefficients of variation for all variables, which were more than 1 for almost all variables in all groups across subsectors and periods6.

During the period covered by the data, the average PCM of 0.276 shows that the firms in the sector had a positive price markup. The average PCM value of 0.276 suggests that organizations in the TPT industry were on average selling products at a markup of 27.6%. This value suggests the presence of market power, where organizations could set prices above marginal costs, in contrast to perfect competition, which typically led to PCM values close to zero. However, the standard deviation of 0.204 and a coefficient of variation of 0.738 show that not all organizations have the same level of profitability, possibly due to differences in market position. This phenomenon is related to the intense competition in the TPT industry, where organizations must continuously innovate and improve product quality to maintain or enhance profit margins.

The TPT industry in Indonesia further showed many organizations operating on a relatively large scale with an average size of 9.955. The relatively low variation in firm size (standard deviation of 2.467 and a coefficient of variation of 0.248) shows consistency in operational scale. This large firm size allowed organizations to access greater resources and leverage economies of scale, contributing to improved profitability. On the other hand, smaller organizations struggled to compete, particularly in terms of market access and technology.

The research observed that the average HHI was 0.122, which characterizes the Indonesian TPT industry as a moderately concentrated market based on the criteria of Shepherd (1999). The average HHI showed a significant standard deviation of 0.138 and a coefficient of variation of 1.132, suggesting that some organizations dominated the market more significantly than others. This reflected a scenario where several large organizations captured a greater market share. In this context, smaller organizations struggled to survive, which led to a reduction in the diversity of products available in the market.

From the export variables, few firms engaged in the export activity where the average of the dummy variable was close to zero. A very high variation, including standard deviation of 0.245 and a coefficient of variation of 3.825, showed that some firms were very successful in penetrating international markets, while others were more focused on the domestic market.

Furthermore, the average foreign ownership of firms in the Indonesian TPT industry only reached 14.666%, suggesting that most firms operating in the TPT industry were predominantly domestic. The very high standard deviation (34.693) and coefficient of variation (2.365) further showed considerable variation in foreign ownership levels among firms. This affected firm performance, where those with higher foreign ownership gained better access to technology and international markets. However, this increased the risk of dependence on foreign investment, which impacted the independence of the domestic TPT industry.

5. Results

Table 2. Trend of the variables.

Year	PCM	Size	HHI
2018	0.275	9.858	0.089
2019	0.289	9.874	0.188
2020	0.243	10.083	0.116
2021	0.296	10.049	0.091

Source(s): authors’ calculation.

provides a summary of the determinants of the price–cost margin during 2018–2021. The averages of PCM, Size, and HHI were 0.276, 9.966, and 0.121, respectively. The positive average of PCM suggests that firms could manage to make a profit in the industry over time, where the PCM increased from 0.275 in 2018 to 0.296 in 2021. This was also related to the increase in the size, since average firm size could successfully grow over the period, as it increased from 9.858 in 2018 to 10.049 in 2021. Furthermore, the positive average of HHI suggests an increase in industrial concentration in Indonesian TPT industry from 0.089 in 2018 to 0.091 in 2021. The higher HHI is an indication that there was lower competition in the TPT industry.

Regarding the trend of PCM in the subsectors,

Table 3. Average variables for five highest and lowest average PCMs, 2018–2021.

Period	ISIC	Names of the Subsectors	Average PCM	Average Size	Average HHI
2018–2019	13942	Products made from rope industry	0.452	9.839	0.151
	13921	Household textile products industry	0.395	9.597	0.035
	13132	Fabric finishing industry	0.382	11.128	0.033
	13923	Pillows and similar products industry	0.293	8.883	0.388
	13912	Embroidered fabric industry	0.292	8.629	0.105
⋮	⋮	⋮	⋮	⋮	⋮
	13131	Yarn finishing industry	0.209	11.000	0.083
	13994	Tire fabric industry	0.201	9.582	0.478
	13929	Other textile products industry	0.200	10.155	0.141
	13941	Rope industry	0.194	9.769	0.204
	13922	Embroidered textile products industry	0.182	10.679	0.446
2020–2021	13929	Other textile products industry	0.402	10.089	0.281
	13925	Jute sack industry	0.380	10.364	0.532
	13913	Imitation fur knitting industry	0.368	11.041	1.000
	13996	Tulle and net fabric industry	0.355	9.976	0.368
	13993	Non-woven fabric industry	0.351	12.302	0.200
⋮	⋮	⋮	⋮	⋮	⋮
	13113	Sewing thread spinning industry	0.188	11.480	0.305
	13999	Other textile industry	0.185	10.944	0.390
	13926	Non-jute sack industry	0.184	13.166	0.329
	13930	Carpet and rug industry	0.179	10.341	0.058
	13995	Kapok industry	0.167	9.135	0.074

Source(s): authors’ calculation.

shows that five subsectors with the highest average PCM had a range of PCM from 0.292 to 0.452. In Table 3, there are five subsectors with the highest PCM, which are products made from rope industry (13942), the household textile products industry (13921), the fabric finishing industry (13132), the pillows and similar products industry (13923), and the embroidered fabric industry (13912), with values of PCM being 0.452, 0.395, 0.382, 0.293, and 0.292, respectively. However, the subsectors with the five highest PCMs during period 2020–2021 were different from those during period 2028–2019. In 2020–2021, the five subsectors with the highest PCMs included other textile products (13929), jute sack (13925), imitation fur knitting (13913), tulle and net fabric (13996), as well as non-woven fabric (13993) industries, with PCM values of 0.402, 0.380, 0.368, 0.355, and 0.351, respectively. The same condition also arose in the five subsectors with the lowest average PCM, where the average APCMs during the period 2018–2019 were different from those in 2020–2021. For example, the subsectors with the five lowest average PCMs during the period 2018–2019 included yarn finishing (13131), tire fabric (13994), other textile products (13929), rope (13941) and embroidered textile products (13922) industries. Those subsectors were not listed with the lowest average PCMs during the period 2020–2021. This would be an indication that the COVID-19 pandemic significantly hit the TPT industry, causing dynamic changes in the performances of the firms during the COVID-19 pandemic.

Based on Table 3, the subsectors classified into the five highest average PCMs were not the subsectors with the greatest sizes. For example, only the subsector of the fabric finishing industry (13132), which was classified into the five highest average PCMs during the period 2018–2019, possessed high firm size (>11). Additionally, the subsectors classified into the five lowest average PCMs were not the subsectors with low size. For instance, only the subsector of kapok (13995), which was classified into the group of the five lowest average PCMs during 2020–2021, had no high size (<10). The five subsectors with the highest and lowest average sizes were also not the same between the period before the COVID-19 pandemic (2018–2019) and the period after the COVID-19 pandemic (2020–2021). This shows that the COVID-19 pandemic significantly affected firm size in the TPT industry.

The same condition also occurred in HHI, where the five subsectors with highest and lowest values did not correspond with higher or lower average PCMs. For example, the subsector of the fabric finishing industry (13132), which was classified into one of the five subsectors with the highest average PCM during 2018–2019, operated with low average HHI. Furthermore, the subsector of the sewing thread spinning industry (13113), which was classified into five subsectors with the lowest average PCMs during 2020–2021, operated in the industry with high HHI. In this context, there was a change in the order of the subsectors with the five highest HHIs, where the subsectors with the highest HHIs during 2018–2019 were imitation fur knitting (13913), imitation fur weaving (13123), jute sack (13925), tire fabric (13994), and embroidered textile products (13922) industries. The subsectors with the five highest HHIs during 2020–2021 were the imitation fur knitting industry, the jute sack industry, the pillows and similar products industry, the tire fabric industry, and the other textiles industry. This also could indicate that the COVID-19 pandemic significantly affected the competition amongst firms in the TPT industry.

Table 4. Average variables for the five biggest and lowest average PCMs based on export activity, 2018–2021.

Period	ISIC	Names of the Subsectors	Average PCM for Non-Exporting Firms	Average PCM for Exporting Firms
2018–2019	13942	Products made from rope industry	0.459	0.346
	13123	Imitation fur weaving industry	0.407	0.093
	13921	Household textile products industry	0.398	0.292
	13132	Fabric finishing industry	0.384	0.331
	13912	Embroidered fabric industry	0.300	0.067
⋮	⋮	⋮	⋮	⋮
	13131	Yarn finishing industry	0.208	0.229
	13995	Kapok industry	0.200	0.433
	13929	Other textile products industry	0.198	0.254
	13941	Rope industry	0.194	0.205
	13922	Embroidered textile products industry	0.181	0.211
2020–2021	13913	Imitation fur knitting industry	0.758	−0.022
	13929	Other textile products industry	0.398	0.472
	13993	Non-woven fabric industry	0.367	0.239
	13131	Yarn finishing industry	0.335	0.221
	13924	Knitted and embroidered textile products industry	0.331	0.259
⋮	⋮	⋮	⋮	⋮
	13113	Sewing thread spinning industry	0.186	0.201
	13930	Carpet and rug industry	0.181	0.158
	13999	Other textile industry	0.178	0.251
	13926	Non-jute sack industry	0.178	0.257
	13995	Kapok industry	0.159	0.246

Source(s): authors’ calculation.

shows the average PCMs classified the firms with and without export activities. Generally, Table 4 shows that the average PCM for non-exporting firms was higher than its exporting counterpart for both the 2018–2019 and 2020–2021 periods. For example, in the period 2018–2019, the subsectors of the products made from rope industry, the household textile products industry, the fabric finishing industry, the pillows and similar products industry, and the embroidered fabric industry possessed the average PCMs for firms with non-exporting activity, at 0.459, 0.407, 0.398, 0.384, and 0.300, respectively. The average PCMs for firms with exporting activity in the same subsectors reached only 0.346, 0.093, 0.292, 0.331, and 0.067 during 2018–2019, respectively, which also applied during the period 2020–2021.

Table 5. Average variables for the five highest and lowest average PCMs based on ownership, 2018–2021.

Period	ISIC	Names of the Subsectors	Average PCM for Domestic-Owned Firms	Average PCM for Foreign-Owned Firms
2018–2019	13942	Products made from rope industry	0.437	0.528
	13123	Imitation fur weaving industry	0.407	0.093
	13921	Household textile products industry	0.396	0.356
	13132	Fabric finishing industry	0.383	0.371
	13923	Pillows and similar products industry	0.293	0.290
⋮	⋮	⋮	⋮	⋮
	13999	Other textile industry	0.209	0.284
	13131	Yarn finishing industry	0.206	0.222
	13929	Other textile products industry	0.205	0.149
	13941	Rope industry	0.195	0.186
	13922	Embroidered textile products industry	0.176	0.210
2020–2021	13929	Other textile products industry	0.443	0.290
	13925	Jute sack industry	0.403	0.165
	13996	Tulle and net fabric industry	0.392	0.319
	13924	Knitted and embroidered textile products industry	0.388	−0.035
	13993	Non-woven fabric industry	0.376	0.311
⋮	⋮	⋮	⋮	⋮
	13926	Non-jute sack industry	0.199	0.108
	13992	Industrial fabric manufacturing industry	0.193	0.174
	13930	Carpet and rug industry	0.185	0.143
	13999	Other textile industry	0.167	0.254
	13113	Sewing thread spinning industry	0.136	0.263

Source(s): authors’ calculation.

shows the average PCMs classified by firms with domestic ownership and those with foreign ownership. Table 5 shows that the average PCMs for firms with domestic ownership were higher than those for the counterparts with foreign ownership for both 2018–2019 and 2020–2021. For example, in the period 2020–2021, the subsectors of the other textile products industry, the jute sack industry, the tulle and net fabric industry, the knitted and embroidered textile products industry, and the non-woven fabric industry possessed average PCMs for domestic firms of 0.443, 0.403, 0.392, 0.388, and 0.376, respectively. The average PCMs for foreign firms in the same subsectors reached only 0.290, 0.165, 0.319, −0.035, and 0.311 during 2020–2021, respectively. The same condition also applied during the period 2018–2019, which could be an indication that firms with domestic ownership achieved better performance compared to their counterparts with foreign ownership in the TPT industry during 2018–2021. Furthermore, the order of the five subsectors with the highest average PCMs and with domestic and foreign ownership were also different between the 2018–2019 and 2020–2021 periods. This could also be an indication that the COVID-19 pandemic affected the firm performances of firms with domestic and foreign ownership.

Table 6. Regression of determinants of the price–cost margin.

Independent Variable	Dependent Variable: PCM
	Coefficients
Intercept	0.199 (0.197)
Size	0.013 *** (0.002)
HHI	−0.007 (0.027)
Export	−0.027 *** (0.010)
Foreign	−4.339 × 10−4 *** (9.75 × 10−5)
Dcov	−0.011 * (0.007)
R2	0.100
F-statistics	19.51 ***

Source(s): authors’ calculation. Note(s): values of SE are given within parentheses; * denotes that the test statistic is significant at the 10% level; *** denotes that the test statistic is significant at the 1% level.

provides an estimation of Equation (1), relating the PCM as a representative of firm performance with determinants including firm size, Herfindahl–Hirschman index (HHI), export orientation (Export), share of foreign ownership, and COVID-19 pandemic. Using the White test for heteroscedasticity, the null hypothesis of the absence was rejected at the 5% critical level. Additionally, the Durbin–Watson test suggests that there was no autocorrelation problem in the model. To address the problem of heteroscedasticity, this research applied the (White) heteroscedasticity robust standard errors to estimate the model.

From Table 6, we can see that all variables had significant effects on the PCM except for HHI. The firm size possessed a positive effect on PCM with a coefficient of 0.013, suggesting that every 1% increase in the size enhanced the PCM by 0.013 units, ceteris paribus. The size significantly affected the PCM at the 1% critical level.

The HHI apparently did not have a significant influence on PCM. The variable HHI had a coefficient of −0.007 and did not have a significant effect on PCM at the 10% critical level.

The export activity of the firm possessed a negative effect on PCM, with a coefficient of −0.027, showing that the average PCM of the exporting firms was less by 0.027 units compared to the PCM of the non-exporting firms, ceteris paribus. The variable of export orientation also affected PCM significantly at the 1% critical level. This is supported by the data description in Table 4, where the average PCMs of firms with no export orientation were higher than the average PCMs with export orientation.

Foreign ownership also had a negative effect on PCM, with a coefficient of −4.339 × 10⁻⁴. The variable of foreign ownership had a significant effect on the PCM at the 1% critical level. This shows that an increase in foreign ownership by 1% decreased PCM by 4.339 × 10⁻⁴ units, ceteris paribus. The results correlate with the data provided in Table 5, where the average PCM of firms with domestic ownership was higher than the average PCM with foreign ownership.

Lastly, the COVID-19 pandemic affected PCM negatively. The dummy of COVID-19 pandemic had a coefficient of −0.011, and it had a significant effect on the PCM at the 10% critical level. This suggests that the average PCM was lower by about −0.011 units during the 2020–2021 period compared to the 2018–2019 period.

This research shows that firm size, export orientation, foreign ownership, and the COVID-19 pandemic played important roles in determining firm performance (PCM) in the TPT industry. However, the HHI did not significantly affect the firm performance. This research also provides new findings, wherein firms with export orientation had lower performance in terms of the PCM at the 10% critical level of markup. Furthermore, foreign ownership had negative effects on the firm performance in the TPT industry, which is an indication that the characteristics of the industry could influence how export and foreign ownership affect the firm performance.

Table 6 further presents the R-squared value, which indicates the proportion of variance in the dependent variable explained by the independent variables. Although a high R-squared value is generally desirable, a low R-squared does not necessarily signify a poor model in social science research. A model using pooled or panel data possesses a low coefficient of R² with high variability of the data. According to Ozili (2023), an R-squared value of a minimum of 0.1 (or 10%) can be considered acceptable in social science research, provided that some or most of the predictors or explanatory variables are statistically significant. Therefore, the research implies that the R-squared value reported is acceptable.

This research investigated firm performance and its determinants in the Indonesian TPT industry pre- and post-COVID-19 pandemic. It further addressed the gaps in the previous research, as discussed in the previous section. However, the research also possesses certain limitations regarding the variables used, as it did not include technological advancements and input prices as explanatory variables, despite these factors potentially impacting the PCM. This research could not address leverage, age of firm, risk, non-debt tax shields, and tax variables, which might be relevant variables. The exclusions are due to the unavailability of these data from published sources. Therefore, the research recommends that future publications should explore the inclusion of these variables to strengthen the analysis of the relationship with PCM during the pre- and post-COVID-19 pandemic, which could further improve the model’s performance.

6. Discussion

Regarding firm performance in the Indonesian TPT industry pre- and post-COVID-19 pandemic, this research shows that there are differences in dominant subsectors classified by the highest average PCM, average Size, and average HHI between the 2018–2019 and the 2020–2021 periods. This shows that there are changes in economic and market conditions caused by the COVID-19 pandemic. The COVID-19 pandemic has affected subsectors differently, with some subsectors being more able to adapt. Changes in corporate strategy and government policy have also contributed to this shift, which emphasizes the importance of adaptation and strategic response to changing market dynamics.

The results of this research show that, for both the 2018–2019 and 2020–2021 periods, the average PCMs for non-exporting enterprises were greater than those for exporting firms. This could be an indication that firms with a non-exporting orientation achieved better performance compared to firms with an export orientation in the TPT industry during 2018–2021. In addition, the orders of the five subsectors with the highest average PCMs of non-exporting and exporting firms were also different between the 2018–2019 and 2020–2021 periods. This could also be an indication that the COVID-19 pandemic affected the performance of exporting and non-exporting firms.

The most important findings of this research relate to firm performance and its determinants in the Indonesian TPT industry pre- and post-COVID-19 pandemic. The results effectively address the research objectives, showing that several variables influenced firm performance. First, the PCM was significantly affected by firm size at the 1% critical level. Larger firms are better positioned to achieve economies of scale, thereby reducing input costs relative to outputs. The results are consistent with those of Setiawan and Effendi (2016), who also concluded that larger firms tend to perform better. However, large firms in the industry may also exploit market power, allowing them to set higher prices while sustaining significant profit margins.

Second, PCM is negatively impacted by the firm’s export activities, which finding supports those of Pagoulatos and Sorensen (1981) as well as Wang and Wang (2008). This is due to the higher international competition, which forces firms to keep prices competitive in the global market. Firms active in exports should compete with many foreign firms, which often have lower production costs or access to wider markets, setting lower prices to remain competitive. Therefore, Chou (1986) stated that PCM could influence export incentives, which depend on the differences between prices and costs. When domestic prices are raised above producers’ costs, foreign producers feel an incentive to export. Consequently, higher PCMs lead to greater increases in competition from foreign producers. Domestic producers who enjoy market power tend to also avoid exporting so as to not attract the attention of foreign producers, leading to PCM and exports having a negative relationship.

Third, foreign ownership negatively impacts PCM, whereby firms with foreign ownership may have higher efficiency and access to better technology, thus setting lower prices. Foreign ownership can allow for increased efficiency, thereby reducing costs and enabling more competitive prices. Djankov and Hoekman (2000) as well as Rambe and Khaola (2022) stated that technology transfer would enhance productivity, and used total factor productivity as an estimate for technology transfer, further supporting the potential for foreign ownership to improve efficiency. Foreign ownership not only improves efficiency, but also promotes innovation, which is a key driver of firm productivity. Boubakri et al. (2013) found that firms with higher foreign ownership invested more in R&D, while Ayyagari et al. (2011) and Luong et al. (2017) showed a positive correlation between foreign ownership and innovation output. Additionally, Guadalupe et al. (2012) showed that foreign owners transfer innovation-related knowledge, and Wellalage and Locke (2020) found that foreign ownership increases the probability of both product and process innovation. These factors create a more competitive framework, allowing firms to lower costs and leading to more competitive prices in the market.

Fourth, the COVID-19 pandemic had a negative effect on PCM7, because COVID-19 provided uncertain economic conditions that may have caused a decline in consumer purchasing power. Therefore, there was also a possibility that firms had to lower their prices in order to maintain market share. The decline in demand and pressure to offer more affordable prices during the global economic crisis implies that firms are unable to set high prices, even though market power is held.

Lastly, the HHI did not have a significant effect on PCM at a critical level of 10%, suggesting that the HHI index did not lead to an increase in PCM, especially when there was competitive pressure (not oligopoly or monopoly), as seen in the Indonesian TPT industry. Additionally, HHI did not have a correlation with PCM, which may be due to the presence of other factors that are more dominant in determining the profitability of the firm. Based on the results of this research, firm size, export, foreign ownership, and the impact of the COVID-19 pandemic can influence PCM because these variables are more directly related to operational efficiency, economies of scale, and the firm’s business strategy. This is supported by previous research; Schmalensee (1989) found that the relation between seller concentration and profitability was statistically weak, and the estimated concentration effect was usually small. Based on their reports, Jacquemin et al. (1980) stated that concentration ratios were not the sole reason for monopoly power, and a refined analysis incorporating the various aspects of market structure and behavior is necessary in providing an appraisal of the existence of a degree of monopoly.

These results imply that Indonesian policymakers should provide access to firms to increase their size, such as supports to widen the market and easy access to funding. Additionally, the government should facilitate the competitiveness of exports to improve the performance of firm export. Considering that foreign ownership has a negative effect on PCM, foreign investment regulations should be designed to protect national interests while taking advantage of the technology and efficiency brought in by foreign investors. The government also needs to support for firms affected by the COVID-19 pandemic through tax incentives and economic stimulus programs.

7. Conclusions

In conclusion, this research addressed a significant gap in previous publications, which were limited by data sample constraints. None of these studies used PCM as a proxy for firm performance, or examined the productivity and its determinants, in the Indonesian TPT industry pre- and post-COVID-19 pandemic. This research used data from the manufacturing survey sourced from the Indonesian Bureau of Central Statistics (BPS) and the fixed-effect model on the subsectors applying least-square dummy variables.

The results show that all variables had a significant effect on PCM except for HHI. Firm size exhibited a positive effect on PCM, while exports, foreign ownership, and the COVID-19 pandemic showed negative effects. The lack of influence of HHI on PCM found here differs from the findings of most previous research, although certain publications have shown similar results. This could be due to other factors that were more dominant in influencing PCM, or the high level of competition in the market, which could neutralize the effect of market concentration on PCM.

Based on the results, the research suggests that policymakers must design appropriate programs to improve the performance of the Indonesian TPT industry. First, policymakers motivate firm growth through economies of scale. Second, policymakers could support the diversification of export markets and enhance the competitiveness of domestic products to help firms compete in the global market. This could be achieved through export subsidies, innovation incentives, and quality improvement programs. Third, we must ensure that foreign investment carries tangible benefits such as technology transfer and the creation of quality jobs. Regulations regarding foreign ownership need to be designed to remain attractive to investors while also ensuring that the economic benefits are felt more domestically. Fourth, policymakers need to develop strategies to mitigate the COVID-19 pandemic’s impact on businesses, such as financial support via working capital financing and policies to facilitate businesses’ adaptation to the pandemic’s effects, including digital transformation.