Does Inward Foreign Direct Investments Affect Export Performance of Micro Small and Medium Enterprises in India? An Empirical Analysis

Abstract

This article examines the effect of inward foreign direct investments (FDI) on the export performance of micro, small & medium enterprises (MSMEs) in India, and investigates the spillover impact and absorption capacity of the MSMEs sector. For the first time, the researchers applied the intersectoral linkage approach to investigate the matter and used a panel dataset between 2006 and 2017. The coefficients of forward and backward linkages are estimated by using the Rasmussen method, the study employs a basic linear panel data model, followed by various diagnostic tests to identify the problem of heteroscedasticity, autocorrelation / serial correlation, cross-sectional dependencies, multicollinearity, time-individual specific tests, and unobserved effects. The PCSE model was applied for robust standard error and the Hausman-Taylor IV model to check the robustness of the result generated in the linear panel data model. Despite the high prevalence of forward and backward intersectoral connections and the Lack of absorption capacity of local firms, the results show that FDI has little of an impact on the export performance of micro, small, and medium-sized businesses in India. This study adds to the existing literature on determining local firms' spillover effect and absorption capacity in response to inward FDI.

1. Introduction

The economic growth of any country depends upon trade, especially export, and India imports goods and services more than exports. The export performance of India is improving, in FY 2020–21, the government of India set the merchandised export target of $400 billion and service export of $240 billion for FY 21–22. India achieved those targets with $420 billion in merchandised exports and $250 billion in service export despite COVID-19 pandemic and the Russia-Ukraine war, and the economic instability of many countries. Now India set the export of $800 billion merchandised $500 billion and $300 billion) the threshold for FY 2022–23. India’s micro small and medium enterprises play a significant role in achieving those targets. The Prime Minister of India Shri Narendra Modi appreciated the contribution of MSME to the overall export performance of India, MSME contributes 48% of the total exports. Foreign investments and affiliates play a major role in accelerating exports of the host economy based on the linkage established by FDI and foreign affiliates in India. To determine the type of linkages developing in the Indian economy, the researcher used the input-output table to find out the Leontief coefficient and forward-backward linkages. The interaction between FDI and the formation of linkages will help policymakers to understand the nature of goods and services exporting and the place of India in the global value supply chain more critically.

2. Theoretical Framework and Literature Review

There are many theories of international trade developed by economists, but the most popular is Modern trade theory also known as Firm-based trade theory. This theory was developed after World War II, it was influenced due to the growth of multinational companies (MNCs) across the world. The limitation of the country-based trade theory necessitated the development of the modern trade theory. In this theory, the economist, apart from the country-based theory, considered other factors of production and service, such as customer loyalty, brand loyalty, the technology used, quality of tangible and intangible products, and so on.

In modern trade theory, the most effective theory is Porter’s National Competitive Advantage (PNCA) theory. In this theory, Michael stated the importance of the competitive advantage of local firms to accelerate the growth of exports in the international market. This theory was developed by Michael Porter in1990, he stated four different factors that represent the nation’s competitive advantage in certain industries. The factors are local firm character, local market demand, local suppliers & complementary industry, and local market resources and capabilities (Figure 1).

Figure 1: Factors Affecting Exports

For sustainable competitive advantage, derived a new list of advanced factors, a combination of natural resources extracted from the factor proportions theory and basic factors, such as skilled labor, investments in education, technology drive, infrastructure, utilities, etc.

The domestic market is the functional unit of macroeconomic activities, and the nature of the local market gives birth to inventions, discoveries, innovations, and creativities like features in smartphones, smart watches, AI-featured gadgets, and so on. The nature of the local market must be sophisticated, trendsetting, and demanding for the continuous development of products and technologies to ensure a sustainable competitive advantage over other countries (Kutan & Vukšić, 2007). According to Porter, the local supplier and complementary industries’ availability are basic determinants of identifying the sustainable competitive advantage. These factors are directly proportional to the competitive advantage. When a business runs under the blue ocean category, it mandates continuous innovation and creativity in production for a competitive advantage. A firm’s strategy, structure, and nature of competition decide the spirit of innovation and creativity. Healthy competition between the industries generates positive results in innovation and upgrades the industries.

2.1. The Theoretical Framework of FDI

There are a series of theories of FDI developed by economists to investigate the reason or determinants of foreign investments. Most of the theories are based on the nature of linkage, the motive behind the investments, and the nature of control over a firm’s assets. The ultimate reason behind the movement of foreign capital is the search for the demand for manufacturing products, resource-seeking, market-seeking, and efficiency-seeking. The present study focused on the industrialization theory of FDI and the spillover effects. This theory was developed by Haymer (1976); according to this theory, the role of multinational companies (MNCs) is neglected, which plays a major role in the transfer of capital stock and other intangible skills to international production. FDI is a combo of capital transfer, technology transfer, knowledge transfer, and management skill transfer. In other words, it is an extended version of industrial production internationalization. This theory was further modified by a number of economists, and stated that any deviation of a firm’s behaviour from the perfect market competition is a possible reason for FDI. To undertake FDI, the firm must have some competitive advantages over domestic competitors like technological superiority. Another dimension of FDI theory is the spillover effect, also known as the growth theory framework (ADB, WP, 2002), FDI through spillover leads to increasing return to scale in domestic production and promotes long-run growth in the host economy, knowledge transfer of spillover through foreign affiliates promotes research and development, innovation and relativity in the host economy.

2.2. Linkage of FDI in Host Economy with Local Firms

Foreign direct investments and exports, a theoretical framework of FDI and exports consider the same determinants such as innovation and creativity, research & development of the market (both domestic and foreign), domestic production capabilities and productivities, and local market resources and capabilities. According to the UNCTAD report (2002), the major benefit generates by the foreign affiliates in the host economy when it successfully established a productive linkage with the domestic firms, more particularly backward linkages, provides immense opportunities to the present and potential entrepreneurs to grow and pave the way towards sustainable economic growth. Forward linkage is good but not effective enough like backward linkages, their scopes are limited and the growth of the economy is non-sustainable (Figure 2).

Figure 2: Linkage of FDI in Host Economy with Local Firms

2.3. Literature Review

The existing literature relevant to the present study in Table 1 shows that FDI positively impacts the host economy’s export performance, whether it is a developed economy, developing economy, or emerging market economy. The above studies made it clear that the nature of linkage and spillover impact of FDI played a significant role in the improvement of the export performance of the host economy, these studies are conducted for the regional group of economies like OECD, EU, etc., as sectoral analysis of a country employing time-series data, and cross-sectional data. For analysis of the study authors employed linear panel data models, GMM, 2SLS, 3SLS, and time series models to analyze the data set. The main literature gap in these studies is there are no such studies based on linkages (forward-backward). The interdependence of industry and pattern of inward FDI in those economies and their respective impact on export performance of micro small and medium enterprises of India.

Table 1: Summary of Literature Review

2.4. The Objective of the Study

The primary objective of the study is to investigate whether or not inward FDI has an impact on the export performance of Micro small and medium enterprises in India. The study empirically investigated the panel data of MSMEs industry level export performance from the period 2006–2017. The applied basic panel data model, PCSE estimator, and the Hausman-Taylor instrument variable model for testing the robustness of the basic panel data models. For this study coefficient of forward and backward linkages are calculated and applied in a panel regression model to investigate the relationship between change in the coefficient of forward and backward linkage of each industry with inward FDI and its impact on the export performance of the MSME sector. For calculation of the coefficient of forward and backward linkages, used input-output table and supply-use table, and the Rasmussen method and Leontief inverse matrix model were used. The table of the coefficient is given at end of the paper.

The present study investigated the impact of FDI on the export performance of the MSMEs of India. Most of the studies investigated the FDI and export performance relationship either at the cross-country or sectoral level of the economy by using panel data and time series data as well. This study considers the importance of the relationship between MSMEs’ export performance and inward FDI, most of the studies ignored the FDI and its linkages in the host economy, this study considers the forward and backward linkages established by the foreign affiliates and how these forward and backward linkage plays role in export performance. Since MSMEs of India plays a vital role in economic development and 48% of export value generates from this sector only. Understanding the relationship will help policymakers in making policies to improve export performance to achieve the export target of $1 Trillion. For this study, the selected variables are gross fixed capital formation, gross domestic product, the workforce of the MSME sector, coefficient of forward and backward linkage of each industry, and sector-wise inward FDI.

3. Data and Methodology

The data for the present study is collected from the annual report of MSMEs, the Department of Industrial Policies & Promotions (DIPP), CSO India (input-output table), Asian Development Bank, and some data are the author’s calculations. The major variables of this study are inward FDI sector-wise, exports of MSMEs of India sector-wise, GDP of MSMEs sector-wise, GFCF, coefficient of forwarding, and backward linkages in the input-output table. The data is collected for the year 2006–2017 (due to unavailability of required data), and the nature of the dataset is a panel dataset. For the establishment of the relationship between FDI and export performance of MSME, sectors author used a basic panel data model (whether the model is fixed random), PCSE model, and the Hausman-Taylor instrument variable model (to correct if any standard error/heterogeneity in the model, cross-sectional dependence and autocorrelation problem).

The above figures (Figures 3, 4, 5, & 6) indicate the heterogeneity of export performance and inflows of foreign direct investments across upstream, downstream, key & weakly linked sectors of the economy. Figure 3 suggests the export performance of individual industries in the economy, and Figure 4, indicates the upward trend of export performance of micro, small & medium enterprises in India concerning the overall export value of the economy. There is no indication of a decline in export performance. Figures 5 & 6 indicate the indirect effect of FDI in the MSME sector since there is no provision of direct investment of foreign affiliates in the MSME sector, hence investigating the spillover effects of foreign affiliates, as well as the absorption capacity of local firms to absorb the positive externalities through indirect linkages, represent the sky-blue, and purple box representing the export performance of the downstream sector, upstream sector, key sector and weakly linked sector of the MSME respectively. According to the UNCTAD report (2002), the upstream (strongly backward linked) sectors have a higher possibility of positive spillover effects due to the presence of input supply link with foreign affiliates as well as domestic firms, and this linkage depends upon absorption capacity of local firms and skilled workforce. From the graph, it is clear that the aggregate export of the weakly linked sector is higher than the aggregate export of the overall MSME sector & the aggregate export performance of the downstream sector is lower than the aggregate of MSME as well other key sectors & upstream sectors. The performance of the backward linked upstream sector is not satisfactory and graph 5 indicates the inflow of FDI is higher in the key sector, followed by weakly linked sectors, downstream and upstream sectors, respectively. Therefore, we can conclude that despite the large concentration of industries in upstream sectors, the inflow of FDI and export performance is very poor, indicating the poor spillover effect and absorption capacity of local firms in the MSME sector in India.

Figure 3: Industries & Respective Export Performance

Figure 4: Total Exports and Exports of the MSME Sector Over the Year 2000–2019

Figure 5: Absorption Capacity & Spillover Effects

The mean value of the exports and inward FDI is not homogeneous; it’s changing across firms over time. In this condition, the application of simple OLS can create the problem of heteroskedasticity since it will create the correlation between the error term and the independent variable. To solve this problem, we applied an appropriate panel data model. Figure 6 shows that FDI inflowed in the economy’s key industries like chemical and chemical products, electricity, water, steam, basic metal, fabricated metal products, etc.

Figure 6: Heterogeneity of FDI in Upstream, Downstream, Key & Weakly Linked Sector

4. Analysis and Results

4.1. Coefficients of Forward and Backward Linkages

Mathematical calculation of the coefficient of forward and backward linkages:

Leontief Demand-Driven Model: this model was developed by Leontief in 1936, to identify the intersectoral interdependence on each other. Mathematically, this model is expressed in linear equation form, intersectoral are monetary and include flows of both goods and services.

Mathematical expression,

X_i = ∑a_ijx_j + F_i, i = 1, 2, 3…. n       (1)

Where X_i = Total Output of sector I,

F_i = Final Demand, and

a_ij = Technical Coefficient (input requirement of the sector i from sector j)

a_ij is calculated as a_ij = X_ij/X_j.

Final Demand includes private final consumption expenditure (PFCE), government final consumption expenditure (GFCE), change in stock (CIS), exports (EXP), and imports (IMP).

The above equation (1), in matrix notation, can be written as,

X = (I – A) – 1 * F       (2)

Rasmussen’s Model:

The limitations of other models created the need for the development of the Rasmussen model. The Rasmussen method helps in identifying the total impact on sector i due to unit change in demand of sector all sectors.

∑_iU_ij = 1/n * ∑_iB_ij/1/n² * ∑_ijB_ij       (3)

∑_iU_ij = 1/n * ∑_jB_ij/1/n² * ∑_ijB_ij       (4)

Results of the unit root test are reported in Table 2.

Table 2: Unit Root Test of Variables

4.2. Linear Panel Data Model

The mathematical expression of the fixed effects model:

lexp_it = β₁ lfdi_it + β₂ blij_it + β₃ flij_it + β₄ lgfcfi_t + β₅ lgdp_it + β₆ emp_it + dummy_linkages + α_i + μ_it

Here, lgdp = log form of the gross domestic product of the MSME sector.

lfdi = log form of foreign direct investment industry-wise,

blij = coefficient of backward linkage of industry-wise,

flij = coefficient of forward linkage of industry-wise.

lemp = log form of employment opportunities industry-wise MSME sector

The original value of the variables is transformed to log form since the original data is highly skewed and normally not distributed

Panel Corrected Standard Error Model:

pcse = (X^TX)^–1X^TΩ^X(X^TX)^–1

Ω = block diagonal matrix

The dataset of the present study is a short panel, where the number of time periods is less than the number of cross-sections (N > T), and the most effective tool for estimating the robust standard error of the micro panel is PCSE (Beck & Katz, 1995). PCSE estimator provides accurate standard error estimation with little loss in its efficiency (Bailey & Katz, 2011) since feasible general least square (FGLS) model performs inefficiently when the number of periods is less than cross-sections (Hoechle, 2007) (Reed & Webb, 2010).

4.3. The Hausman-Taylor Instrument Variable Model

The Hausman-Taylor Instrument Variable (1981), instrumental variable model is a hybrid of consistency of within effect model and efficiency and applicability of the random model. Since the one-way random effect model assumes the homogeneity of the independent variable (error terms are not correlated with the independent variable), independent of cross-sectional and observation level errors. This model is also known as the two-stage least square regression model. The researcher used this model to check the robustness of the output of the basic panel data model

4.4. Mathematical Expression of the HT-IV Model

Y_it = β₀ + X_1itβ₁ + X_2itβ₂ + Z_1itδ₁ + Z_2it + δ₂ + μ_i + ϑ_it

X_1it/Z_1it = exogenous time-varying variables uncorrelated with µ_i and ϑ_it,

X_2it/Z_2it = endogenous time-varying variable correlated with µ_i and ϑ_it.

Cov[ε_it,u_i | X_lit,Z_li,X_2it,Z_2i] = 0,

var[u_i | X_lit,Z_li,X_2it,Z_2i] = σ_u²,

Corr[ε_it + u_i,ε_is + u_i | X_lit,Z_li,X_2it,Z_2i] = ρ = σ_u²/σ²,

var[ε_it,u_i | X_lit,Z_li,X_2it,Z_2i] = σ² = σ_ε²,

E[X_lit,Z_li] = 0, though E = [u_i | X_2it,Z_2i] ≠ 0

X_lit = [lfdi], X_2it = [lgdp, lemp, lgfcf], Z_li,t = [blij, flij],

Z_2i,t = [Linkage]

4.4. Empirical Results

The results of the linear panel data model (Tables 3, 4, 5, & 6) of current panel data indicate that there is no significant relationship between inward FDI and export performance of the MSME sector; the most positive & significant variables are gross fixed capital formation, gross domestic products, and employment generation (0.235***, 1.383*** & 3.441*** at 1% level of significance)) in the MSME sector of India. In the case of coefficient of linkages, the backward linkage is negative but insignificant, and the forward linkage is negative as well as significant (–0.34**, at a 5% level of significance).

Table 3: Test/Diagnosis of Linear Panel Regression Model

Table 4: Consolidated Table Coefficients of Fixed Effect, Random Effect, PCSE & the Hausman Taylor Model

Note: **p < 0.1; **p < 0.05; ***p < 0.001.

Table 5: Coefficient of Models Applied

Table 6: t-value & Z-value of Respective Models

Note: **p < 0.1; **p < 0.05; ***p < 0.001.

Table 4 indicates the diagnostic test of the linear panel dataset and employed further models based on problems identified in the test. The Hausman test selected the fixed model as an efficient & effective model over pooled OLS & random effect model for the present dataset. Further diagnosis indicates the problem of first-order autocorrelation AR (1), heteroscedasticity across cross-sections, and the application of OLS, other linear model estimates, and standard errors (Bailey & Katz, 2011). Since our dataset is a short panel & fixed effect model showing the problem of AR (1), cross-sectional dependence, and endogeneity, we employed the PCSE model to estimate robust standard error and control the problems to derive batter inferences with a minimum dilution of efficiency.

The PCSE estimation (Tables 5 & 6) indicates that significant variables in fixed effect models are still significant in PCSE under robust standard error. The result confirms that inward FDI volume does not decide the quantum of exports in the MSME sector; rather a negative relationship of coefficient of forward linkages adversely affects the export performance of MSME, indicating the indirect impact of negative spillover of FDI. The Hausman Taylor instrument variable model, a hybrid of the fixed and random effect models, was used to address the unobserved effects in the linear (fixed effect) model. The result indicates the same if the estimated coefficients of a variable are close to the fixed model, which implies the validity of the instrument used. Here estimated coefficients of inward FDI, GDP, GFCF, and coefficient of backward linkages are almost the same in the fixed and HT IV model but employment and coefficient of forward linkages are invalid due to differences in their estimated coefficients. Table 6 represents the t-value and z-value of the respective model indicating the low reliability of the coefficient of inward FDI, key sector, weak sector, & forward linkage as predictive value whereas GDP, GFCF, EMP, and forward linkage t-value is high indicating the high reliability on coefficients for prediction.

5. Discussion

The study is based on a simple design of the relationship between the export performance of MSMEs in India having marked forward and backward linkages with Indian industries having FDI inflows to it following the liberalization of the Indian economy. Results show that despite having forward and backward linkages of MSMEs with FDI conceived industries failed to show perceivable growth in export. It demands a closer examination of the data to find out possible reasons for this paltry manifest.

Looking at backward linkages during the 2006–17 time periods, it is found that linkages are near to unity but mostly less than unity. It is consistently remaining so over the period. It indicates that FDI-attracting industries have produced below-average externalities for MSMEs’ export performance. However, MSME’s forward linkages or positive externalities are consistently more than unity over the same time span. Therefore, Indian industries have benefited from this sector but could not materialize similarly for the MSMEs sector’s performance on the export front. Reasons could be:

1. MSMEs sector could not expand its production capacity to create enough surpluses for input supply & export;

2. Industries might be exploring overseas markets or import for their consumables against the opportunity thrown by liberalized external sector,

3. Govt. failed to take policy intervention in the MSME sector to expand capacity building and modernize production and distribution technology; reforms in labour laws to make this sector progressively competitive within and outside India.

Therefore, it appears that the MSME sector was endorsed to the open and harsh realities of the competitive world of business and commerce in the post-liberalized economic world order without having any massive makeover plan for production and distributional technology, workforce training for the adoption of new machine and management technology. It was a lost occasion for a strategically important sector of the Indian economy in the era of rising opportunity and challenges

6. Conclusion and Suggestions

The insignificant relationship between inward FDI and export performance of the MSME sector indicates the Lack of substantial linkages between local firms, especially vertical industries, and foreign affiliates in the economy. The export performance of local firms is independent of the quantity of inward FDI in India. There are a few possible reasons for this condition:1. Lack of absorption capacity or Lack of capability of local firms in quality input supply to the foreign affiliates, 2. The motive of FDI either FDI is market seeking, resource seeking, and 3. Foreign affiliates have created their own source of input in the host country or imported from other countries like China, Korea, Taiwan, etc. Figure 3 indicates that the concentration of FDI is higher in the key sector and lower in upstream & downstream sectors and similarly export performance of the key sector is higher and lower in downstream and upstream respectively. Since there is no evidence of the negative performance of exports in local firms, we can conclude that there is the possibility of growth in export performance and improvement of intersectoral linkages if we improve the absorption capability of local firms through skilled manpower, advanced technology, infrastructural development, quality assurance of inputs and so on to ensure the positive spillover effects of FDI. The negative relation of forward linkages and the insignificant role of backward linkages represent the insignificant role of MSME in the global value supply chain due to its low productivity and poor quality of products unable to compete in the global market.

The policymakers and economic planning commission of India must consider this issue in their planning to achieve the predetermined set target of exports for FY–2022–23. The long-term benefits and sustainability in export performance are very important to take edge over other competitive emerging market economies and participate in the global value supply chain. There should be minimum government intervention and maximum governance involvement, there are a few suggestions for the current issue:

1. The government should liberalize the MSME sector, opening up foreign investment to improve the competitiveness of the MSME sectors.

2. The government should encourage the FDI in the manufacturing sector, condition of the manufacturing sector of India is very poor it needs some revolution to increase its production capabilities and productivity to promote exports of merchandise goods.

3. FDI should focus more on increasing production on the supply side of the host economy. the government should provide proper information to the foreign affiliates about available suppliers.

4. Shifting interest in foreign investments from key sectors to backward linkages will reduce the dependence on other foreign affiliates for input supply in the manufacturing sector and remove the uncertainties of supply chain disruption and ensure smooth manufacturing activities.

5. The focus should be more on export-oriented and efficiency-seeking FDI rather than market-seeking, resource-seeking FDI.