Role of Financial Development in Total Factor Productivity

THE ROLE OF FINANCIAL DEVELOPMENT IN TOTAL FACTOR PRODUCTIVITY OF VIETNAM’S MANUFACTURING SECTOR

Thesis Research Design

Instructors: Team of MDE Lectures

Student:Hồ Bảo Trân

1. Problem statement

There is a large literature about the link between financial development and economic growth. Many studies indentify that productivity as one channel through which finance affects growth. The importance of financial development in raising productivity and promoting economic growth has been discussed in many reports (e.g., Goldsmith 1969; McKinnon 1973; Shaw 1973; Greenwood and Jovanovic 1990; Bencivenga and Smith 1991). The financial system pressure handicaps financial development and results in misallocation of resources, then reduce productivity and economic growth. As the financial systems develop well, overall economic productivity will be improved through the efficient reallocation of resources. At firm level, financial development of a firm allows a firm to appropriate new business opportunities, conduct investment and research activities, make a defense against financial and non-financial shocks and achieve higher productivity. More importantly, firm productivity is an essential indicator in transforming financial market development to economic growth at macro level. There are also many empirical studies about the impact of financial development on productivity efficiency. Levine (2005), Beck et al. (2005) point out that financial constraints, including low liquid and limited access to financial resources, make the growth prospect of firms worse.

In Vietnam, manufacturing firms play an important role in the decade of economic growth. In 2013, there are 60700 businesses closed. There are many reasons such as long loss profit, management capacity, operating restrictions and lack of funds business… In which, financial market development offers a crucial impetus for enhancing firm competitiveness and catalyzing industrialization.

In Vietnam the scientific research using a panel data to find out the role of financial development in total factor productivity of Vietnam’s manufacturing sector is limited. This study will present the evidence of this linkage using panel data for manufacturing firms from 2003 to 2009.

2. Research objective

2.1. Research objectives

• To estimate the TFP growth rate of manufacturing sector
• To find the role of financial development in total factor productivity of Vietnam’s Manufacturing Sector.
• To give policy implication for improving firm productivity

2.2. Scope of the study

The study will examine the role of financial development in total factor productivity of Vietnam’s Manufacturing Sector using the panel data of 2003, 2005, 2007, and 2009.

3. Literature review

1. Productivity: Concepts and measurements

Productivity is commonly defined as a ratio of a volume measure of output to a volume measure of input use (Schreyer and Pilat 2001) or in other words, how much of output which is obtained from a given set of inputs (Syverson 2010).

Productivity = Quantity of outputs produced/ Quantity of inputs consumed

Productivity measurement efforts to point out improvements in using the capital resources, that is, to motivate and evaluate efforts to produce more inputs with fewer inputs while maintaining quality.

TFP is used to measure the firm productivity. It captures the growth, which could not be explained by changes in production inputs, thus it can serve as a traditional proxy of productivity improvement. However, there is a correlation between unobservable productivity shocks and optimal choices of input. There are at least two approaches to measure the TFP, which account for the sensitive of optimal input levels to the productivity shocks. First approach is the Olley-Pakes TFP measurement which uses investment as a proxy for productivity shocks (Olley and Pakes 1996). Another approach is the Levinsohn-Petrin TFP measurement which uses intermediate inputs as a proxy for productivity shocks (Levinsohn and Petrin 2003).

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In this study, I will use the Levinsohn-Petrin TFP measurement to control for the unobservable productivity shocks because of three reasons. Firstly, the dataset of firm investment is not available, thus the the Olley-Pakes TFP measurement will not be feasible. Secondly, even if the investment data is available, the estimation can be suffered from the truncated report “zero” investment of firms. Finally, the Levinsohn-Petrin TFP measurement is more acceptable than the Olley-Pakes TFP measurement because of it is built from production theory.

Assuming a Cobb-Douglas function, TFP is often computed by estimating the equation:

y_it = β₀ + β_ll_it + β_kk_it + ω_{it +} ε_it (1)

Where y_it is firm’s output, l_it is firm’s labor, k_it is firm’s capital; y, l, k are all in the nature logarithm forms. i and t denote firm and time. ω_it is a state variable, which measures productivity. ε_it is an error or a random productivity shock. Both ω_it and ε_it are unobservable variables.

According to Olley and Pakes (1996), the OLS estimates of (1) can be biased because choice of variable inputs can be depended on the firms’ beliefs about the state variable ω_i. If there is serial correlation in ω_i, the variable inputs such as labor, materials, etc will be positively correlated with ω_i. In order to control the bias in OLS estimates, they suggest the following partially linear model:

y_it = β₀ + β_ll_it + β_kk_it + h_t(i_it, a_it, k_it) +ε_it (2)

in which ω_it =h_t(i_it, a_it, k_it), a_it is age of firm i at time t, i_it is investment of firm i at time t. The unobservable variable ω_it can be expressed by a function of observable variables.

Levinson and Petrin (2003) point out that investment data at firm level is very lumpy (there are considerable adjustment costs). If it is true, the investment proxy will not smoothly respond to productivity shock. It violates the consistency condition. They propose the following modified model:

y_it = β₀ + β_ll_it + β_kk_it + β_mm_it + ω_{it +} ε_it (3)

m_it is the logarithm of the intermediate inputs such as raw material, power, and electricity expenditures. Its input demand m_it depends on the capital variable k_it and productivity ω_it

m_it = m_t (k_it, ω_it)

The demand of intermediate inputs m_it is assumed to be monotonously increasing with ω_it. It can be used as a proxy for unobservable variables. Therefore, the unobservable productivity ω_it is expressed by the function of two variables k_it and m_it.

ω_it = m_t (k_it, m_it)

Substituting above equation to equation (3), the production function can be estimated.

The coefficients of k_it and m_it can be recovered by GMM method with assumption that productivity is controlled by a first-order Markov process.

ω_it = E[ω_it/ω_it-1] + η_it

η_it is an innovation to productivity which can be correlated with labor but uncorrelated with capital.

The logarithm TFP of firm can be obtained from the residual of actual output and predicted output.

2. Financial development and firm productivity

The important role of financial development in raising productivity and promoting economic growth has been mentioned in many public researches. In traditional growth theories, the role of financial development in growth is through factor accumulations, which are regarded as the main force behind economic growth. Financial development can contribute to total factor productivity growth by increasing marginal productivity of capital (Goldsmith 1969). According to McKinnon and Shaw (1973), financial development also can improve efficiency of capital allocation so as to increase the aggregate saving rate and investment level. However, in traditional growth model, the impact of financial development on growth is limited due to diminishing return to scale of capital.

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Some recent researches also highlight the relationship between financial development and productivity growth. Jeanneney, Hua and Liang (2006) analyze data from 29 Chinese provinces and find out that financial development has contributed significantly to TFP growth through its positive effect on efficiency in the period from 1993 to 2001. Dabla-Norris, Kersting and Verdier (2010) use firm level dataset and find out that the positive effect of innovation on firm productivity is contributed from financial system. Firms enjoy the maximum benefits from innovation in countries with well-developed financial system. Minjia Chen (2012) examine a panel dataset of Chinese manufacturing firm from 1998 to 2007 also show that financial factors are highly essential to firms’ total factor productivity growth.

At micro level, financial development affects productivity growth by various channels.

A firm with better financial health tends to bring higher productivity level. Beck et al (2005) suggest that firm with high liquidity is expected to be resilient to financial and non-financial shocks. Similarly, entrance to large external finance can help a company reduce the level of credit constraints, therefore increase their ability and existence in the market (Aghion et al 2007; Levine 2005).

4. Overview of Vietnam SMEs

In Decree 90/2001 ND-CP from 2001 to 2009, SME is defined as an independent business establishment which registered capital not exceeding 10 billion VND or annual average number of workers not exceeding 300. From 2009 up to now, SME is defined as: Small and medium enterprises are business establishment which have registered their business according to the law and are divided into three levels of size: very small, small and medium depend on the sizes of their total capital or their average number of workers (total capital is the priority standard).

In term of size of employees, nearly 98.34% of firms are small or medium enterprises in 2012. These firms contributed to 40-60% GDP of Vietnam and over a half of total labors in 2011. SME has important role in Vietnam economy.

The SME survey has been conducted by the Central Institute for Economic Management (CIEM), the Institute of Labour Science and Social Affairs (ILSSA) under the Ministry of Labour, Invalids and Social Affairs of Vietnam (MoLISA) and the Development Economics Research Group (DERG) of the University of Copenhagen. The research group selects ten provinces including: Hanoi, Hai Phong, Ho Chi Minh City (HCMC), Ha Tay, Phu Tho, Nghe An, Quang Nam, Khanh Hoa, Lam Dong and Long An. The sample covers around a third of manufacturing firms in Vietnam and seems to be the best quality to represent the characteristics of SMEs in Vietnam.

5. Methodology

5.1. Model

Model:

TFP_it = α₀ + α₁ TFP_i,t-1 + α₂ ln LIQUIDITY_it + α₃ ln LEVERAGE_it + α₄ ln SIZE_it + α₅ ln AGE_it + α₆ ln COM_it + α₇ ln HUMANK_it + α₈ ln FOWN_it + u_it

Where i and t are index firm and time, respectively, u_it is the stochastic error term.

The lagged TFP variable is important due to Levinson and Petrin (2003) estimation method. The authors assume that firm productivity follows a first-order Markov process. Therefore, the lagged TFP variable must be taken in the model for controlling the serial correlation.

About the variables of financial development at firm level, my econometric specification contains two proxies of a firm’s financial quality: liquidity (LIQUIDITY_it) and leverage ratio (LEVERAGE_it). A firm with better financial health tends to bring higher productivity level. Beck et al (2005) suggest that firm with high liquidity is expected to be resilient to financial and non-financial shocks. Similarly, entrance to large external finance can help a company reduce the level of credit constraints, therefore increase their ability and existence in the market (Aghion et al 2007; Levine 2005).

Besides, we also control for several firm-specific characteristics to examine firm productivity performance. Firms’ size (SIZE_it) intends to control for the impacts of economies of scale on firm productivity (Balk 2001). Firms’ size (SIZE_it) and Firms’ age (AGE_it) have been found to be linked to firms’ productivity (e.g. Palangkaraya, Stierwald and Yong, 2009) and are used widely in analyzing firm productivity.

Oliner and Sichel (1994; 2000) indicate high-tech capital intensity (COM_it) as another determinant of TFP. High-tech capital accumulation has been proven to be an essential factor for raising productivity by improving operational efficiency, profits and, finally, productivity growth (Siegel and Griliches 1992). Another determinant should be considered that is human capital intensity. Since the 1990s, developing countries in Asia, including Vietnam, have been focused on investing human capital, e.g education and training, to achieve higher economic growth. Therefore, it may be interest to estimate the extent of human capital intensity account for TFP.

The last important determinant of firm productivity is mentioned widely in many researches of Arnold and Javorcik (2009), Benfratello and Sembenelli (2006), Germa et al. (2004) and Griffith (1999) is foreign ownership (FOWN_it). These researches suggest that foreign owned firms are potential to have superior managers, information network and ease to connect international market. Therefore, foreign firms are supposed to get higher productivity than domestic firms.

5.2. Research hypotheses

Hypothesis H1: Financial quality (liquidity ratio) will have positive relationship with firm productivity. A firm with high liquidity tends to bring higher productivity level.

Hypothesis H2: Financial quality (leverage ratio) will have positive influence on firm productivity.

5.3. Data sources

All data sources are available at the Survey of Small and Medium scale manufacturing enterprises (SMEs) in Vietnam, from 2003 to 2009. It gives data about production and financial characteristics of firms such as gross revenue, capital, number of labors, materials, profits, liquid assets, fixed assets, liabilities, equity…

The measurement of TFP: requires information on a firm’s gross output and production inputs. Net output is measured by net sales of manufactured goods. There are three inputs in the empirical model: labor, capital and intermediate materials. Labor is the number of employees working for a company. Intermediate materials include components used in the manufacturing process. Capital is measured by the value of land, building as well as machinery, equipment, excluding the depreciation of assets.

Financial development variables: First variable is liquidity, which is measured by the ratio of liquid assets to total assets. Second variable is leverage ratio that is measured by the ratio of liabilities to equity.

Control variables: Firm size is measured by the total sales of a firm. Firm age is measured by the number of years a firm in operating. If a firm goes in an industry for less than one year, it is set up for zero. High-tech capital accumulation is measured by the number of computers used for each worker. The proportion of skilled labor to total workers of a firm serves as the proxy of human capital investment. Finally, the ratio of investment capital undertaken by foreign parties relative to the total registered capital is used as a proxy of foreign ownership. Finally, the ratio of actual investment capital of foreign parties compared to the total registered capital is served as a proxy of foreign ownership.

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