Global Production System,Textile Industry and Wage Disparity: Evidence from South India

Abstract

This article discusses the root causes of wage disparity in the textile industry. The study argues that wage disparity arises through direct and indirect approaches. Both create pressure on suppliers and leads to low wage payment, depriving workers of social security benefits, unpaid holidays and leaves. Hyper-consumerism, free on board (FoB) price, and flexibility have created competition among suppliers and other stakeholders. Suppliers flexibilise rules and re-organise work arrangements to meet on-time production by increasing working hours, introducing wage penalty, strict supervising, and increasing surveillance.

JEL Codes: J3, J31, J41, J46, J81, J82, J83

Keywords

Wage disparity global production system (GPS)labour market institutions free on board (FoB) price textile industry Tiruppur

I. Introduction

This study argues that wage disparity cannot happen in a vacuum but through structural conditions, demand-led indicators, and supply-led factors. Both structural conditions and demand-led indicators are macro factors, while supply-led determinants are micro factors. Thus, wage disparity can arise owing to macro and micro factors, which requires an in-depth exploration. The main components at the macro-level are flexible production¹, regulations², global governance, national labour market institution³ and enterprises⁴ (Gürtler et al., 2015; Gotte et al., 2020; Locke, 2013). The contributing factors at the micro-level are skills, gender, age, labour migration and employment (Amo-Agyei, 2020; Krishna & Paul 2013; Srivastava & Srivastava, 2010). In addition, hyper-consumerism⁵ and FoB (free on board) price systems which are part of the macro debate, determine the wage gap (Anner, 2018). As a result, buyers shift the risk to suppliers, which further passes onto the workers. It results in lowering pay, reducing employment protection, increasing working hours and dismantling workers’ rights. Studies show that wage disparity depends on the sector and value chain (Gürtler et al., 2015). Further, studies suggest that a coordinated and collaborative effort is necessary by applying short-term and long-term practices using a sector-wide approach to gauge wage differentials (Gürtler et al., 2015). Human development indicators and heterogeneities in the labour market explain wage pay (Becker, 1962; Groshen, 1990; Livingstone, 1987; Mincer, 1974; Schultz, 1961). However, the importance of institution, regulation, governance and production strategies remains under explained at both macro and micro levels. Therefore, the empirical investigation fails to explain why workers’ features are crucial to wage disparity (Amo-Agyei, 2020). Studies in global value chains estimate that institutionalising effective wage floor through tripartite agreements and collective bargaining can enable workers to bridge the gap (Crane et al., 2019; Gürtler et al., 2015). Since the agreements are not legally binding, suppliers can often ignore wages emerging from these labour market institutions. It reflects flaws in the state labour market institutions, regulations and global governance. It also means that demand-led indicators, which is part of the macro framework, production strategies—hyper-consumerism, flexibility and FoB pricing mechanism—overpowered the institutional (regulations and global governance) macro-structures. Thus, leading to a decent work deficit in the textile industry (Crane et al., 2019). One of the sources of the shortfall is wage deprivation.

Indeed, the incapability of the macro factors (direct and indirect) impacts the wage disparity. For instance, regulations do not enforce written contracts to combat outsourcing and subcontracting, resulting in flexible production. The FoB pricing system plays a significant role in the production system and labour at global and local levels. Buyers use the window-shopping mechanism to gauge the FoB prices, resulting in buyers consistently placing orders with the lowest bidders, leading to fluctuating orders. There are asymmetries in the macro-structure of the production system that impact the suppliers’ behaviour. At the micro-level, suppliers devise various strategies (formal and informal). This article critically examines them and explore the root causes of wage disparity.

II. Data, Methods and Analytical Framework

This study is based on primary data collected from 25 companies across various sizes (large, medium, small, micro and power-table) in Tiruppur to understand wage disparity. The study uses a four-stage sampling technique. In the first stage, the selection of Tiruppur as a site of research was based on purposive sampling. The second stage applied stratified sampling to select large, medium, small, micro and power-table units. In stage three, the companies are stratified based on the first-tier and extended suppliers network (ESN). Since the units are externally homogeneous and internally heterogeneous, the enterprises are divided into multiple groups using cluster sampling. Finally, in the fourth stage, workers’ are stratified based on skills, wage, migration status, gender, work experience, age and social group. The study applied quota sampling to select the respondents.

The researcher collected a sample of 600 during June–December 2019 from these enterprises (see Table 1). This study describes workers’ socioeconomic determinants in the Tiruppur textile industry. Out of the 600 samples, females are 37 per cent, males are 63 per cent, migrants are 74 per cent, whereas locals are 26 per cent. The age factor indicates that 84 per cent of them fall within 15 to 35 years, while 16 per cent are within the age group of 36 to 65 years. The sample data reflects that 49 per cent are unskilled, 37 per cent are skilled, 9 per cent are multiskilled, and 5 per cent are super-skilled workers.

Table 1.

Data Composition in the Textile Industry.

Unit Description	No of Units	Workers Sample
Large	2	230
Medium	4	120
Small	4	120
Micro	10	80
Power table	5	50
Total	25	600

Source: Based on primary survey.

The study applied the ordered logit regression (Agresti, 1996, 2002; Liao, 1994; Long & Freese, 2006). The dependent variable (W) is wage, which has four categories—low-wage, wage 2, wage 3, and highest wage (see Table 3). Explanatory variables have two categories—continuous (working hour, years of migration, level of education and years of work experience) and categorical (gender, skills, state of origin and activities) as mentioned in equation (1).

The equation for ordered logit regression:

\begin{array}{l} L_{i} = (\frac{P_{i}}{1} - P_{i}) = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + β_{4} X_{4} + β_{5} X_{5} + β_{6} X_{6} + β_{7} X_{7} + β_{8} X_{8} + β_{9} X_{9} + β_{10} X_{10} + β_{11} X_{11} + β_{12} X_{12} + \\ β_{13} X_{13} + β_{14} X_{14} + β_{15} X_{15} + β_{16} X_{16} + u_{i} \end{array}

(1)

Where, X₁ is working hours, X₂ is gender (female worker), X_{3 is} skill type (multiskilled worker), X₄ is low-skilled worker, X₅ is super-skilled worker, X₆ is age, X₇ is years of migration, X₈ is age square, X₉ is state (Uttar Pradesh), X₁₀ is state (West Bengal) + X₁₁ is state (Odisha), X₁₂ is suppliers activity (printing), X₁₃ is suppliers activity (knitting), is X₁₄ is suppliers activity (Spinning), X₁₅ is level of education, and X₁₆ years of work experience.

Factory A is a vertically integrated company, whereas factories B, C and D are specialised garment units. Factory A and B started as an own-brand unit, partnered with international brands and transformed into an export unit. However, factory C and D started as an export garment unit and later catered to domestic demand. The shift is because of the FoB price, as highlighted in Table 2. It foregrounds the per-unit price of a T-shirt in various factories. The FoB prices from Table 2 indicate that as the supplier status diminishes from being a first-tier supplier to an extended supplier network, there is a decline in FOB prices. Therefore, the first-tier suppliers always subcontracted production to extended suppliers network to take advantage of FoB prices.

Table 2.

Overview of Factory Characteristics.

Factory Characteristics	Factory A	Factory B	Factory C	Factory D
Production	Vertically integrated	Specialized	Specialized	Specialized
Operation	Garment
In-house production	1%	10%	10%	10%
Outsourced production	99%	90%	90%	90%
Employees	4,000	300	150	30
Company location	Tiruppur, Tamil Nadu
Years in operation	41	71	15	21
FOB price (per unit of T-shirt) international buyer	₹275.00	₹225.00	₹175.00	₹160.00
FOB price (per unit of T-shirt) domestic buyer	₹325.00	₹315.00	₹225.00	₹225.00
Making price (per unit of T-shirt) supplier for international and domestic buyer	₹125.00	₹140.00	₹130.00	₹130.00

Source: Computed by author.

III. Findings and Discussion

This section discusses the impact of gender, skills, migrant state, work experience, age and suppliers unit activity on causing wage disparity. The analysis explores the relationship between gender and across wage categories. In the low wage category, the males’ predicted probability is 8.94 per cent, and it is 19.37 per cent for females. It means that females have a higher likelihood of entering the low wage compared to males. Suppliers prefer to employ female workers because of their docile nature, and they are new entrants to the labour market. Therefore, they accept low wages. Even male migrants are not willing to work in low wage jobs, as they directly enter into the wage 2 category. Table 3 indicates that females have a higher predicted probability of 41.84 per cent, and for males, it is 30.27 per cent in wage 2 category. It is because most of the females are locals, and their population surpasses the male migrants. It is essential to note that young local female workers do not engage in low wage activity. In the high wage group, males’ predicted probability is 17.15 per cent and 7.80 per cent for females. Males can quickly graduate because of networking, gendered notions of employment, and perception about females as not suited for supervisory roles. Thus, the analysis shows that males dominate the higher wage category while females occupy the low-wage group. A study by Amo-Agyei (2020) notes that the labour market is unfavourable for women migrants, as they face a double wage penalty with a pay gap of 20.9 per cent between local males and migrant females (Amo-Agyei, 2020). This sort of comparison is crucial when discussing equal pay between the gender.

Table 3.

Ordered Logit Regression Model Wage Disparity.

Description		Predicted probability for categorical variables
Variable	Category	Low Wage (5,000–7,500)	Wage2 (7,501–8,500)	Wage3 (8,501–12,220)	High Wage (12,221–40,000)
Gender	Male	0.0894484***	0.3027666***	0.4362422***	0.1715428***
Gender	Female	0.1937854***	0.4184644***	0.3097276***	0.0780226***
Skill type I	Other skill	0.1306718***	0.3661646***	0.38397***	0.1191936***
Skill type I	Multi-skilled	0.0475994***	0.199566***	0.4635694***	0.2892652***
Skill type II	Other skill	0.0409137***	0.1779776***	0.4582367***	0.3228721***
Skill type II	Low-skilled	0.320837***	0.4354537***	0.2024282***	0.0412811***
Skill type III	Other skill	0.1490361***	0.3859557***	0.3609514***	0.1040568***
Skill type III	Super-skilled	0.0016068	0.0088549	0.0628588	0.9266795***
State I	Other states	0.1019746***	0.3252712***	0.4208375***	0.1519167***
State I	UP	0.5532407***	0.3372878***	0.093311***	0.0161605**
State II	Other states	0.1142817***	0.3444735***	0.405065***	0.1361799***
State II	WB	0.3691425***	0.4244111***	0.1728519***	0.0335944**
State III	Other states	0.1066908***	0.33295***	0.4148322***	0.1455269***
State III	Odisha	0.1470298***	0.3840023***	0.3634186***	0.1055493***
Supplier unit activity I	Other activity	0.1433109***	0.3802514***	0.3680254***	0.1084123***
Supplier unit activity I	Printing	0.0102198**	0.0532999***	0.2731804***	0.6632998***
Supplier unit activity II	Other activity	0.1254769***	0.3597325***	0.3906253***	0.1241653***
Supplier unit activity II	Knitting	0.0407242**	0.177341***	0.4580041***	0.3239306***
Supplier unit activity III	Other activity	0.1155144***	0.346252***	0.4034734***	0.1347602***
Supplier unit activity III	Spinning	0.2424127***	0.4352127***	0.2626047 ***	0.0597698**

Source: Computed by author based on primary survey.

Note: *** p < .01, **p < .05, *p < .1 significance level.

The study discusses the relationship between skills and wage categories. For a multiskilled worker, the predicted probability of wage 3 is 46.35 per cent. Male migrants are the significant group this category. If a worker is super skilled, the predicted probability of earning the highest wage is 92.66 per cent. However, local workers hegemonize this category. For low-skilled workers, the predicted probability of wage 2 is 43.54 per cent. The analysis confirms that skill mobility causes wage progression, and wage disparity at the same time. There is a problem with this analysis: they do not discuss skill with the type of employment. With improved skills, social security, employment benefits and workers’ rights should be accessible, but this is not the case with workers in the global production system (GPS). However, low-skill workers gain spot-contract employment, skilled and multiskilled workers get a short-term contract, and super skilled workers obtain power-table⁶ employment. Thus, the study outlines that, higher the skills higher the deterioration of employment.

Suppliers unit activity examines that employment in printing activity can lead to the highest wage, and employment in spinning activity means low wages. It means that not all activities can lead to skill mobility and higher wages. The overall analysis on migrant state indicates that migrants receive low wages and locals receive the highest wage. It is evident from analysing states such as Uttar Pradesh, West Bengal and Odisha. Incidentally, there are other factors that explore the cause of wage disparity. For instance, at the time of employment, both males and females enter the labour market as low-skilled workers. The work experience trajectories are different for males and females. Males can work continuously for 30 years and achieve desired skill mobility. Females work intermittently for 33 years, which is higher than males, but do not reach the desired skill mobility. Females in their early years (16 to 26) move from low skill to skilled and multiskilled. When they re-enter the labour market at 36 years, there is skill reversal, and this is due to socioeconomic constraints such as marriage, child-bearing and poor health. It is challenging because they have to compete with the younger migrant workforce. Under such circumstances, older female workers fit into low skill jobs such as packing and trimming, and there is no scope for upward mobility.

Anner (2018) argues that since the FoB prices declined by 7.79 per cent, it eventually impacted suppliers profit margin (Staritz, 2011). Taplin (2014) further argues that buyers seek to develop hyper-consumerism to push for a shorter lead time. In 2011, the lead-time was 93.4 days, and in 2016, it reduced to 85.83 days—such squeezing practices impact wages and workers’ rights (Anner, 2018). Flexibility in FoB price affected suppliers’ profitability, forcing them to move from export to domestic market. Despite, suppliers behaviour does not change towards workers well-being or protection. As long as macro factors are skewed, the suppliers’ can appropriate workers (see Table 4). It means workers cannot leverage their socioeconomic characteristics in the global production system (GPS).

Table 4.

Workers Socio-Economic Conditions.

S. No	Socio-Economic Inputs	Units in Textile Industry
S. No	Socio-Economic Inputs	Large	Medium	Small	Micro
1	Employment	Casual Contract Power-table Spot-contract	Casual Contract Power-table	Casual Contract Power-table	Contract Power-table
2	Provident Fund (12%)	Irregular	No	No	No
3	ESI (3.25%)	No	No	No	No
4	Bonus (8.33%)	Sporadic	No	No	No
5	Social security	• One employee’s socioeconomic yearly appropriation is ₹22,008 ($314.14).
6	Working days	338	345	350	350
7	Labour minutes	98,640	138,000	177,600	177,600

Source: Computed by author.

IV. Conclusion

Wage disparity exists among workers and various factors are responsible for this. The root cause of wage disparity is economic pressures in the global competitive markets in the textile industry. Evidently, social upgradation for workers is not taking place along with economic upgradation in the value chain of the textile industry. Workers are trapped in various decisions and the hyper-competitive environment through shorter-lead time in the contract system between suppliers and buyers, introduction of piece-rated payment system for workers, hyper-consumerism, and FoB pricing system. Skill-upgradation will lead to a rise in the cost of production for buyers. In order to reduce the cost of production, the burden is shifted to the workers. Therefore, suppliers’ proactive involvement and intervention in conducting higher-value activities such as branding and product differentiation can avoid rights violations for the workers in the production systems.

Footnotes

Acknowledgements

I thank Tata Institute of Social Sciences, Guwahati Campus for resources. I express my deep gratitude to my PhD supervisor Dr. Debdulal Saha. I thank Dr. Rajdeep Singha and Dr. Jagannath Ambagudia for their valuable feedback and comments when I presented findings. I am indebted to Dr. Chitrasen Bhue for his inputs in the statistical model. Special thanks to two anonymous referees of the journal for their important and comments that help me in shaping this article.

Declaration of Conflicting Interests

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iD

Nandini Ramamurthy

References

Amo-Agyei

(2020). The migrant pay gap: Understanding the differences between migrants and nationals. International Labour Organisation (ILO).

Agresti

(1996). An introduction to categorical data analysis. John Wiley & Sons, Inc

Agresti

(2002). Categorical data analysis (2nd ed). John Wiley & Sons, Inc.

Anner

(2018). Binding power: The sourcing squeeze, workers’ rights, and building safety in Bangladesh since Rana Plaza. Research Report. PennState, Center for Global Workers’ Rights (CGWR).

Becker

G. S.

(1962). Investment in human capital: A theoretical analysis. Journal of Political Economy , 70(5), 9–49.

Crane

, Soundarajan

, V. , Bloomfield

M. J.

, Spence

, & Le Baron

(2019). Decent work and economic growth in the South Indian garment industry. University of Bath, Royal Holloway University, The University of Sheffield.

Groshen

E. L.

(1990, February 15). How are wages determined? Federal Reserve Bank of Cleveland, economics commentary.

Gürtler

, McAllister

, & Bondevik

P. N.

(2015). Living wages in global supply chains a new business agenda. Ethical Training Initiative.

Gotte

, Iossa

, Schneider

, Selberg

, Siegmann

K. A.

, & Williams

(2020). Are agri-food workers only exploited in Southern Europe? Case studies on migrant labour in Germany, The Netherlands, and Sweden. Open Society European Policy Institute.

10.

Krishna

, & Bino

P. G. D.

(2013). What explains wage in India? Indian Journal of Industrial Relations , 48(3), 487–499.

11.

Liao

T. F.

(1994). Interpreting probability models: Logit, Probit, and other generalized linear models. SAGE Publications.

12.

Livingstone

D. W.

(1997). capital theory. In The knowledge economy and lifelong learning (Vol. 4). Sense Publishers.

13.

Locke

R. M.

(2013). The promise and limits of private power. Cambridge University Press.

14.

Long

J. S.

, & Freese

(2006). Regression models for categorical and limited dependent variables using stata (2nd ed). Stata Press.

15.

Mincer

(1974). Schooling, experience, and earnings. Columbia University Press.

16.

Schultz

T. W.

(1961). Investment in human capital. American Economic Review , 51(1), 1–17.

17.

Srivastava

, & Srivastava

(2010). Women, work and employment outcomes in rural India. Economic and Political Weekly , 45(28), 49–63.

18.

Staritz

(2011). Making the cut? Low-income countries and the global clothing value chain in a post-quota and post-crisis world. The World Bank.

19.

Taplin

I. M.

(2014). Who is to blame? A re-examination of fast fashion after the 2013 factory disaster in Bangladesh. Critical Perspectives on International Business , 10(1/2), 72–83.