INTRODUCTION
Problem statement
Recent discussions among economists and policymakers have highlighted the significant link between institutional frameworks and economic performance This growing interest stems from a consensus that institutions play a vital role in influencing economic outcomes The exploration of this relationship gained momentum with the emergence of New Institutional Economics, rooted in Coase's 1937 work, which underscored the importance of institutions in contexts characterized by high transaction costs, stating, "When it is costly to transact, institutions matter."
Since 1995, research has increasingly integrated institutions—defined as the formal and informal "rules of the game" and their enforcement (North, 1994)—into economic growth theories The core argument is that without efficient institutions, standard production factors struggle to foster rapid growth, particularly in transition economies (Eicher, Garcia-Penalosa & Teksoz, 2006) Existing literature indicates that institutions can influence economic outcomes both directly, by affecting costs, and indirectly, by shaping investment incentives Despite numerous studies exploring the institutions-growth relationship, institutional analysis remains underdeveloped (Brousseau & Glachant, 2008; Chang, 2006), highlighting the need for further research in this area.
“before the institutional perspective can be fully operationalised” (Pelikan, 2003; Rodrik, Subramanian & Trebbi, 2004)
Vietnam, as a transition economy undergoing recent institutional reforms, provides a crucial empirical context for examining the challenges faced by its non-state sector, which has significantly expanded since the 1986 "Doi moi" economic reform and the introduction of the Enterprise Law in 2000 Despite this legal framework, the sector encounters numerous obstacles related to the implementation of central policies by local governments, which vary widely across provinces These discrepancies stem from the complexity of laws that necessitate numerous sub-law gazettes for enforcement, leading to significant reliance on local officials' interpretations, even when regulations are clear Consequently, local authorities in Vietnam possess a high degree of discretion, distinguishing the country’s institutional reforms from those in other developing nations.
Vietnam's provinces exhibit significant disparities in economic performance, particularly between 2007 and 2011, with provincial GDP growth rates ranging from a low of 2.92% to a remarkable 29.52% The output levels also show considerable variation, with Ba Ria-Vung Tau recording the highest GDP of approximately VND122,000 billion in 2007 and VND170,000 billion in 2008 Ho Chi Minh City followed closely, achieving GDP figures of VND131,000 billion, VND151,000 billion, and VND158,000 billion from 2009 to 2011 In contrast, Bac Can and Lai Chau reported GDPs below VND3,000 billion during the same periods This stark contrast raises important questions about the role of institutions in influencing the economic outcomes across Vietnam's provinces.
The concept of institutions varies among scholars, complicating the measurement of this multi-dimensional notion (Nelson & Sampat, 2001) The Provincial Competitiveness Index (PCI), launched in 2005 through a partnership between the Vietnamese Chamber of Commerce and Industry (VCCI) and USAID/VNCI, serves as an annual composite index reflecting the local business community's perspective on governance quality across Vietnam's provinces It evaluates nine governance aspects, including entry costs, land access, transparency, regulatory compliance, informal charges, provincial leadership proactivity, business support services, labor and training, and legal institutions The PCI is a vital tool for assessing and monitoring the local regulatory environment, highlighting the private sector's crucial role in provincial economic reforms Schmitz et al emphasize that while there is no formal public-private coalition, a proactive government seeks private sector input, fostering responsive governance.
In recent years, provinces in Vietnam have engaged in a competitive effort to enhance their Provincial Competitiveness Index (PCI) scores, aiming to foster a more conducive environment for economic growth, as highlighted by data from VCCI (www.pcivietnam.org) This trend prompts a critical inquiry into whether improved PCI scores correlate with enhanced economic performance across the provinces This study utilizes PCI and its sub-indices as indicators of institutional effectiveness to analyze their impact on the economic performance of Vietnam's provinces during the 2007-2011 period.
Research objective and research questions
This thesis aims to investigate the contribution of institutional factors to Vietnam’s provincial economic performance through answering the following research question:
- How much of the variation in cross-provincial GDP of Vietnam could be explained by institutional quality, both in a broad sense and as specific factors of economic governance?
This thesis investigates the economic disparities among Vietnam's provinces by analyzing the impact of institutional factors, with a focus on the Provincial Competitiveness Index (PCI) and its sub-indices Key elements examined include Entry Costs, Time Costs, Regulatory Compliance, Informal Charges, Land Access and Tenure Security, Legal Institutions, Transparency and Access to Information, Labor Policy and Training, Business Support Services, and the Proactivity of Provincial Leadership.
Contribution of the study
This study enhances the existing literature by providing the first empirical evidence on the impact of institutional factors on Vietnam's provincial economic outcomes Unlike previous research that focused on firm-level analysis within a single year, this thesis examines provincial-level data over a five-year period, utilizing the Provincial Competitiveness Index (PCI) as a proxy for institutional quality Additionally, it addresses the de facto overstatement of growth data by incorporating it into estimation models, which improves the reliability of the regression results for analysis.
Organization of the study
The thesis is organized into several key chapters: the second chapter examines the theoretical and empirical literature on the relationship between institutions and economic growth Chapter 3 outlines the data, research methodology, and empirical models used to assess the effects of provincial institutional quality on economic performance Chapter 4 presents and analyzes the regression results, while the final chapter concludes the study by discussing policy implications, limitations, and suggestions for future research.
LITERATURE REVIEW
Theoretical literature: The New Institutional Economics
In recent decades, there has been a renewed focus on institutional literature, particularly through the lens of New Institutional Economics (NIE) This approach integrates institutional perspectives into economic analysis and addresses a diverse array of institutional issues The expanding body of literature highlights the significant effects of institutional quality on economic performance.
2.1.1 A renewed interest – some distinct features
Interest in institutions in economics, which dates back to Adam Smith, resurfaced in the 1990s due to the shortcomings of neoclassical economics in addressing growth and development, particularly highlighted by the economic struggles of ex-Soviet regimes during their transition (Nye, 2008; North, 1994) The rise of globalization, characterized by technology diffusion and capital mobility, has further exacerbated inter-country inequality, prompting the World Bank to emphasize that effective market-supporting institutions are crucial for development policy in the early twenty-first century (World Bank, 2002).
New Institutional Economics (NIE) builds upon the foundational analyses of Williamson (1985) and North (1990), defining institutions as “governing structures” and “rules of the game.” NIE posits that individuals operate with incomplete information and limited rationality, leading to uncertainty and transaction costs (Menard & Shirley, 2008) Consequently, humans establish both formal institutions, represented in written forms, and informal institutions, encompassing norms and beliefs Additionally, they create organizational arrangements to facilitate transactions and cooperation in production and exchange, as noted by Brousseau and Glachant.
Since 2008, significant research has focused on the interaction between institutions and organizational arrangements, exploring how these arrangements subsequently influence and alter the established rules of the game (Menard & Shirley, 2008, p.12).
New Institutional Economics (NIE) diverges from its predecessor, Traditional Institutional Economics, primarily in two ways Firstly, NIE is grounded in the neo-classical principles of scarcity and competition Secondly, while Traditional Institutional Economics lacked systematic theoretical foundations and empirical analysis, NIE is characterized by significant original contributions and extensive empirical studies, as noted by Menard and Mary (2008).
New Institutional Economics (NIE) is not a unified theory but rather a synthesis of diverse concepts from various traditions (Brousseau & Glachant, 2008, p.2) Its development has been shaped by an evolutionary process, allowing for contributions from scholars across multiple social and scientific disciplines This openness fosters an evolutionary perspective in NIE and accounts for the diverse range of academic insights within the field.
This article examines how institutional factors influence economic variations, building on the neoclassical tradition while recognizing its strengths and weaknesses It utilizes established methodologies to tackle a wider range of pertinent issues in the field.
2.1.2 Institutions matter for economic performance
Institutional factors play a crucial role in explaining both cross-country economic growth disparities and regional differences within nations (Smith, 1976) As noted by North (1990), institutions serve as fundamental determinants of long-term economic performance, prompting a growing body of literature to explore the question: "How do institutions impact economic outcomes?"
The research of institutional issues within New Institutional Economics (NIE) can be categorized into three main theoretical approaches: the Historical Perspective Approach, Comparative Institutional Analysis, and Imperfect Information Theory.
The Historical Perspective Approach, introduced by North in 1990, emphasizes the significance of analyzing institutions within their historical context Recognizing that institutions are shaped by their specific historical circumstances, it becomes crucial to consider this context when addressing institutional change, as highlighted by Alston in 1996.
In his influential 1990 book "Institutions, Institutional Change and Economic Performance," the author integrates theories of human behavior and transaction costs, emphasizing that institutions play a crucial role in economic performance By reducing uncertainty and minimizing transaction costs in social interactions, effective institutions significantly contribute to enhanced economic growth.
The second strand of the theoretical framework, known as "Comparative Institutional Analysis," is primarily developed by Aoki (2001) This approach integrates both Evolutionary and Repeated game theory, defining institutions as "self-sustaining systems of shared beliefs" that alter the incentive structures of games Consequently, this influences the strategic choices and interactions among agents.
A key aspect of New Institutional Economics (NIE) is the "Imperfect Information Theory," which posits that institutions arise from strategic behaviors influenced by asymmetric information among involved parties (Bardhan, 2000) This framework highlights how information and enforcement costs can lead to the absence of certain markets and the lack of competitiveness in others Consequently, institutions serve as a solution to address missing markets and help mitigate information-related issues in existing ones (Hoff, Braverman, Stiglitz, & Arnott, 1993).
The primary distinction among the Historical Perspective, Comparative Institutional approach, and Imperfect Information framework lies in their analytical tools for investigation The Historical Perspective seeks to integrate economic theory with economic history, while the Comparative Institutional approach effectively utilizes game theory alongside historical data In contrast, the Imperfect Information framework is the most mathematically focused of the three methodologies (Nabli & Nugent, 1989).
2.1.2.2 Mechanism and channels of influence
The above-mentioned theoretical approaches lay fundamental analytical frameworks for analyzing the “institutions matter” issue, specifically the institutional mechanism and channels of influence on economic performance
Institutions that establish property rights and enforce contracts are crucial for economic performance As Coase (1937) noted, well-defined property rights can help resolve prisoners’ dilemmas and mitigate collective action failures These institutions create norms and rules that govern the control of returns on investments and outline procedures for exchanges, fostering internal security and trust By reducing the risk of expropriation, they influence economic agents' decisions regarding savings and investments in both physical and human capital Consequently, a lower risk of expropriation and a higher level of security lead to increased investment and economic growth, assuming other factors remain constant.
Theoretical model: the aggregate production functions
2.2.1 History of the production functions
The production function is widely recognized by scholars as a vital analytical tool in neoclassical economics Schumpeter (1954) traces its origins to 1767, when Turgot, in his Observations on a Paper by Saint-Peravy, implicitly formulated the production function by examining how variations in the proportions of standard production factors influence marginal productivity Subsequently, Philip Wicksteed further developed this concept.
In 1894, an economist is credited with being the first to algebraically express the input-output relationship as P = f(x₁, x₂, , xₘ) However, evidence presented by Humphrey in 1997 suggests that Johann von Then may have originated the concept of the production function in the 1840s.
Between the 1950s and 1970s, the production function garnered significant attention from economists, leading to various analyses of input-output relationships and their implications (Mishra, n.d) Stigler (1952) highlighted that the logarithmic production function was first introduced by Malthus, while Humphrey (1997) revealed that the Cobb-Douglas function appeared in "disguised form" in Von Then's "The Isolated State, vol – II." Additionally, Velupillai (1973) pointed out that Wicksell formulated a production function identical to Cobb-Douglas in his work from 1900-1901, and further elaborated on this in his 1923 review of Gustaf Akerman’s doctoral dissertation.
The production function can be expressed as P = AL^αC^β, where α + β = 1 This formulation is reminiscent of the Leontief production function, which was developed collaboratively by economists Jevons, Menger, and Leon Walras It is important to recognize that significant academic contributions are often the result of collective research efforts over time, rather than the achievements of a single individual (Russel, 1984).
The Constant Elasticity of Substitution (CES) production function, formulated by Arrow et al (1961), allows for a fixed elasticity of substitution between capital and labor, ranging from zero to infinity, along isoquants, making it a generalization of production functions like Cobb-Douglas, Leontief, and linear forms Classical production functions assume Hicks-neutral technological progress, meaning the marginal rate of substitution between production factors remains unchanged by technical advancements or output levels Brown and Cani (1963) expanded the CES framework to include non-constant returns to scale, while Uzawa (1962) and Kazuo Sato (1967) contributed to incorporating multiple production factors Additionally, Nervole (1963) enhanced the Cobb-Douglas function with variable returns to scale, further explored by Ringstad (1967) Diewert (1971) introduced a production function accommodating variable elasticities of substitution, completing the generalization of the Cobb-Douglas and CES functions by the mid-1970s.
Research in the economics of production focuses significantly on empirically estimating the aggregate production function, a crucial analytical tool in macroeconomics According to Felipe and Fisher (2003), two main areas of literature challenge the concept of an aggregate production function: the Cambridge (UK)-Cambridge (USA) capital controversy and the aggregation literature The former questions the measurement units for capital goods as production factors, while the latter investigates the conditions under which neoclassical micro production functions can be aggregated into a neoclassical aggregate function Given the notable discrepancies between firm characteristics and those of industries, as well as the broader economy, caution is essential when attempting to identify production functions at the macroeconomic level.
Knut Wicksell (1923) supported the use of aggregate linearly homogeneous functions by demonstrating that non-homogeneous production functions at the firm level can align with a linear homogeneous function for the entire industry (Humphrey, 1997) This research trajectory advanced significantly with Koopmans' activity analysis (1979) and Georgescu-Roegen's aggregate linear production function.
The separation theorems and the generalization of Von Neumann’s model from 1951, along with Nikaido's robust proofs from 1968, significantly reinforced the foundation for employing the aggregate production function in economic analysis.
The Cobb-Douglas production function, as noted by Felipe and Adams (2005), remains a fundamental model in both theoretical and empirical studies of growth and productivity Its origins trace back to the groundbreaking work of Cobb and Douglas in 1928, which marked the first econometric estimation of an aggregate production function using data from the U.S manufacturing sector spanning 1899 to 1922, subsequently shared with contemporary economists (Cobb & Douglas, 1928).
2.2.2 Properties of the production function
The production function reflects the physical relationship between inputs and outputs, which can be mathematically defined as
The production function has the following properties
There is no such thing as a "free lunch," indicating that resources are always limited Additionally, the production function is characterized by monotonicity, which necessitates that all inputs yield positive marginal products in a single-output production scenario.
Thirdly, the production function is quasi-concave Notably, the monotonicity and quasi- concavity property of the production function cannot be imposed globally
Finally, for non-negative and finite x, f x( ) is, finite, continuous, non-negative and single-valued
Various functional forms are utilized in modeling production functions, with the Cobb-Douglas and Translog functions being the most prominent The Cobb-Douglas function is characterized by its linearity in logs, ease of estimation, and interpretation, along with the assumption of constant proportionate returns to scale In this model, the elasticity of substitution between capital and labor is fixed at unity, meaning a one percent change in one input results in a one percent change in the other Conversely, the Translog function serves as a generalization of the Cobb-Douglas function, offering a more flexible approach with a second-order approximation This flexibility allows for fewer restrictions on production and substitution elasticities, making the Translog function more widely adopted in economic analysis.
Empirical literature
Over the past ten years, numerous empirical studies have utilized various forms of the production function to explore the relationship between institutions and economic growth across different countries This approach primarily focuses on the application of the production function within the context of institutional frameworks.
The "extended production function" integrates institutional proxies with traditional growth factors, as noted by Glaeser et al (2004) This approach encompasses various determinants of growth, including trade openness, geography, and macroeconomic policy, which influence institutional effects on economic performance (Jacob & Osang, 2007) The choice of dependent variables introduces further heterogeneity, categorized into output-level and output-growth related variables Eicher and Leukert (2006) found that output levels exhibit more robust empirical effects than output growth Additionally, the selection of institutional proxies and their potential endogeneity significantly contribute to this heterogeneity Institutional factors are often represented by aggregated indices or national survey-based indicators However, the multi-dimensional nature and variability of these proxies raise questions about their validity in representing institutional concepts (Aidis et al., 2009; Shirley, 2008) Overall, the substantial heterogeneities in empirical institutional research complicate the ability to derive a consistent empirical impact across the literature (Efendic et al., 2011).
Dias and Tebaldi (2012) emphasize the crucial role of the interactions between institutions and human capital accumulation in driving economic growth Utilizing panel OLS and GMM dynamic panel estimation techniques, the study analyzes data from 61 countries spanning the years 1965 to 2005 The researchers employ democracy and autocracy as indicators of political institutions, while the share of educated labor in the economy serves as a measure of structural institutions These institutional variables are integrated into a formal growth model alongside a human capital variable developed based on Hall and others.
Jones (1999) introduced a piecewise function, while Easterly and Levine (2001) developed the Perpetual Inventory Method for calculating capital stock Empirical evidence supports the idea that strong structural institutions enhance productivity and contribute to sustained economic growth.
Siddiqui and Ahmed (2013) examine the influence of institutional factors on economic performance across 84 countries from 2002 to 2006, utilizing the Institutionalized Social Technologies (IIST) index and its sub-indices, including Institutional and Policy Rents, Political Rent, and Risk Reducing Technologies Their extended growth model incorporates human capital, savings, and trade openness, analyzed through OLS and GMM methods The introduction of interactive variables reveals significant complementarities between institutions that safeguard property rights and political institutions Ultimately, the study concludes that robust institutions play a crucial role in enhancing economic outcomes.
Atul and Sal (2012) utilize Solow's growth accounting model alongside World Bank regulation indicators to examine the influence of regulatory frameworks on economic performance By applying fixed and random effects estimation to data from 23 OECD countries between 2002 and 2008, the study reveals that the quality of regulation positively impacts economic growth by enhancing total factor productivity.
Gagliardi (2008) examines the key theoretical and empirical literature on how institutional frameworks impact economic performance The study highlights various theoretical approaches from New Institutional Economics, including historical perspectives, comparative analysis, and imperfect information theory It emphasizes the concept of institutional complementarities, which has significant policy implications Despite challenges in measuring institutions, empirical evidence supports the idea that a country's institutional framework plays a crucial role in its economic development.
Sobel (2008) utilizes state-level cross-sectional data from the U.S and OLS regression to explore the connection between institutional quality and entrepreneurial productivity, measured through the economic freedom index and net entrepreneurial productivity index The study incorporates geographic and demographic controls, including median age, population density, male population percentage, and the proportion of college-educated individuals The findings provide the first empirical support for Baumol’s theory, demonstrating that strong institutions facilitate productive entrepreneurship and highlighting that variations in entrepreneurial productivity significantly account for economic performance disparities among states.
Hasan, Wachtel, and Zhou (2007) explore the relationship between China's institutional development and economic growth, focusing on three key aspects: financial deepening, legal institution development, and political pluralism They utilize proxy variables such as the size of the private sector, the rule of law, and awareness of property rights, measured through the ratio of private sector fixed investment to total fixed investment, the number of lawyers per 10,000 people, and the ratio of domestic trademark applications to the number of firms, respectively Their analysis employs Barro (1991)’s growth equation and utilizes an annual dataset from 31 provinces covering the period from 1986 to 2002, applying OLS, annual system GMM, and three-year average system GMM estimation methods The findings indicate that institutional development significantly contributes to explaining the differences in economic growth across provinces.
Jalilian, Kirkpatrick and Parker (2007) investigate the role of regulatory framework on economic growth in developing countries Data set covers 117 countries for the 1980 –
Between 1980 and 2000, a study analyzed data from 96 countries using cross-section and panel regression methods to estimate the impact of regulatory quality and government effectiveness on GDP per capita growth The analysis controlled for various factors, including initial GDP per capita, gross capital formation, education, trade, inflation, and government expenditure The findings reinforce the theoretical literature, indicating a strong association between regulatory frameworks and economic growth.
Using Vietnam’s firm-level data in 2005 and the Provincial Competitiveness Index
In their 2009 study, Tran, Grafton, and Kompas utilize 2006 as a proxy variable to examine the effects of Vietnam's 2000 institutional reforms on the economic performance of the non-state sector Their model incorporates control variables, such as human capital and market size, which influence firm performance based on provincial initial endowments Additionally, the study introduces dummy variables to account for firm-specific characteristics, including size, age, capital intensity, and ownership type The findings indicate that enhanced institutional quality, particularly in market information, land tenure security, and labor training, significantly boosts labor productivity among firms.
Some studies challenge the idea that institutions drive economic growth Glaeser et al (2004) reexamined this proposition using cross-country data from 1960 to 2000 and found no supporting evidence They also noted that existing literature on the relationship between institutions and growth often suffers from inadequate measurement of institutional variables and the use of irrelevant instrumental variables.
In her 2007 study, Jenny Minier challenges the common assumption of parameter invariance in empirical research by utilizing panel data from 70 countries spanning 1960 to 2000 to explore the indirect effects of institutions on economic growth through parameter heterogeneity The study employs executive constraint as a proxy for institutions, alongside control variables such as physical capital accumulation, education, geography, and initial income By implementing the endogenous sample splitting technique proposed by Hansen (2000), Minier aims to assess the varying parameters of growth determinants in light of potential thresholds The findings indicate minimal support for the threshold effect of institutions on production factors through parameter heterogeneity; instead, they suggest that institutions significantly influence the marginal effects of policy variables on economic growth.
DATA AND RESEARCH METHODOLOGY
Data and variable measurement
This study utilizes secondary data from various published sources, focusing on different variables Specifically, investment and GDP data are sourced from the Provincial Statistics Year Book for the periods 2005-2009 and 2010-2011 Labor force data is obtained from the national General Statistics Office (GSO) at www.gso.gov.vn Additionally, scores for the Provincial Competitiveness Index (PCI) and its sub-indices are derived from PCI Annual Reports spanning 2007 to 2011, along with information from their official website.
The Provincial Competitiveness Index (PCI) is developed through a collaborative effort between the Vietnam Chamber of Commerce and Industry (VCCI) and the United States Agency for International Development (USAID), managed by Development Alternatives, Inc (DAI) The PCI creation process involves three key steps: collecting survey-based and hard data, constructing sub-indices, and calibrating these indices to accurately reflect their impact on private sector development It utilizes both perception indicators from surveys, which may be influenced by biases, and hard data from published sources, which can limit the scope of information By integrating these two types of data, the PCI research team aims to optimize the analysis while minimizing the limitations associated with each data source.
The collection and processing of "soft" data utilize a stratified sampling method at the provincial level, focusing on business age, sector, and legal form to ensure a representative sample of the local business community with a 3% sampling error The sample size varies between 7,000 and 10,000 private domestic firms due to significant fluctuations in response rates over the years Notably, since 2010, the mail-out survey has expanded to include over 1,000 foreign-invested firms Additionally, the survey questions, tailored for both domestic and foreign-invested firms, are regularly updated to reflect key changes in the provincial business and regulatory landscape.
It is worth noticing that alterations were implemented to PCI methodology in 2009 Complying with the principle set up from the outset that construction measures could be
The 2009 PCI Annual Report highlights that while minor adjustments were made, significant methodological changes should be approached with caution to maintain consistent comparisons over time Key revisions included the removal of the SOE bias sub-index amid extensive equitization of local state-owned enterprises, minor modifications to indicators within sub-indices, and a new method for calculating sub-index weights to prevent accidental correlations Consequently, subsequent data analysis and estimation model development will focus on identifying the significant impacts of these methodological changes on provincial economic performance.
This study utilizes annual data from Vietnam's 58 provinces for the period of 2007 to 2011 While the PCI index data is available from 2005 to 2011, the years 2005 and 2006 were excluded due to inadequate provincial statistics for key variables like GDP and investment Additionally, six provinces—Ha Tay, Cao Bang, Hung Yen, Thanh Hoa, Long An, and Ninh Binh—were removed from the analysis because of insufficient data for the relevant variables.
Real Gross Domestic Product (RGDP) is a key economic indicator that represents the annual outcomes of production and business activities within a province It is calculated at constant prices by adjusting nominal GDP figures with the provincial annual Consumer Price Index (CPI), using 2005 as the base year RGDP is expressed in millions of VND.
Due to the unavailability of annual provincial Consumer Price Indices (CPIs) for five out of 58 provinces—specifically Dac Nong, Hau Giang, Hoa Binh, Lang Son, and Phu Yen—national CPIs are utilized as a substitute for calculating real GDP and investment in these regions.
LABFO, or labor force, refers to individuals aged 15 and older who actively engage in business and production activities within the province This metric is quantified in thousands of people, reflecting the size of the workforce contributing to the local economy.
CAP denotes capital input variable Applying the Unified approach of the Perpetual
The Inventory Method (PIM) introduced by Berlemann and Wesselhửft (2012) utilizes provincial nominal investment values to generate the capital input variable As defined by the General Statistics Office (GSO), investment refers to the expenditure aimed at enhancing and preserving material assets over a specific timeframe, typically one year This investment can be quantified through various projects and national programs designed to augment both fixed and liquid assets.
In this study, real values of investment are obtained by deflating nominal values using provincial annual CPIs, with 2005 as the base year
The net capital stock at the beginning of period t, let sayK t , could be mathematically expressed as
Where K t 1 is the net capital stock at the beginning of the previous period t1
D t is the consumption of fixed capital of the previous period
I t is gross investment in the current period
For the sake of simplicity, depreciation is assumed to be at a constant rate δ of 5% and then the capital stock could be rewritten as
Repeatedly substituting this equation for the capital stock at the beginning of period t1,
To determine the initial value of the capital stock (K t-1) for the base year, we utilize Harberger's steady-state approach from 1978 This method operates under the assumption that the economy is in a steady-state, allowing us to calculate the GDP growth rate effectively.
Thus making it possible to derive the function of K t 1 as
It is worth-noticing that in computingg GDP , we use average GDP growth rate with the omission of g GDP outliers
In our analysis of the initial value of investment \( I_{t-1} \), we utilize the method proposed by Nehru and Dhareshwar (1993) through their application of the Steady State Approach This approach involves regressing the time-series of logarithmic investments on time using Ordinary Least Squares (OLS), represented by the equation \( \ln I_{it} = \alpha + \beta_i + i_t + \epsilon_{it} \).
And then the fitted value for the first period investment is obtained
By applying the exponential function to the fitted values, we obtain the complete time series of investment values needed for capital stock calculation The initial fitted value from this investment time series is utilized to determine the capital stock for the base year, following the specified formula (3.5).
This procedure offers a significant advantage over the traditional steady-state approach by generating the initial value of the investment time series without depending solely on the investment figure from a single year, resulting in a more reliable initial value.
For all the institution variables, relevant information is obtained via PCI Annual Report from
The Provincial Competitiveness Index (PCI) is a comprehensive 100-point scale that evaluates and ranks the economic governance quality of Vietnam's provinces Developed through the "Three Cs" method—Collection, Construction, and Calibration—the PCI begins by adjusting survey data with verified statistics to mitigate perception bias This refined data is then transformed into ten-point sub-indices In the final calibration phase, these sub-indices are categorized into three weight classes: high (20%), medium (10% and 15%), and low (5%), reflecting their impact on sector growth, investment, and profitability For example, the 2009 PCI report assigns the highest weight of 20% to the Transparency & Access to Information and Labor Policy sub-indices, highlighting their significance for business outcomes Conversely, the Land Access, Legal Institution, and Private Sector Development sub-indices receive the lowest weight of 5% due to minimal score variation across provinces The Time Cost of Regulatory Compliance sub-index is notable with a 15% weight, while other sub-indices in the medium category are assigned 10%.
A higher score in the Provincial Competitiveness Index (PCI) and its sub-indices indicates improved governance quality, with a higher labor policy sub-index reflecting better evaluations of provincial labor policies and a lower informal charge sub-index signifying reduced unofficial fees for business operations Significant changes to the PCI methodology were introduced in its fifth iteration in 2009, including adjustments to the number and weighting of sub-indices to better reflect the evolving provincial economic landscape Notably, the State-owned enterprise (SOE) bias index was removed due to the extensive equitization of local SOEs, eliminating a major barrier to private sector development previously caused by perceived favoritism towards SOEs.
Research methodology
Choosing the right functional form is a crucial step in developing an estimation model According to Coelli et al (2005), an effective functional form should be flexible, linear in parameters, regular, and parsimonious A regular functional form adheres to the economic regularity properties of production functions, either inherently or through simple restrictions The two most widely used production functions are Cobb-Douglas and Translog While the Cobb-Douglas function is parsimonious, it lacks flexibility; conversely, the Translog function offers flexibility but is not parsimonious A parsimonious function conserves degrees of freedom and represents the simplest solution to a problem, whereas a flexible function imposes fewer assumptions or restrictions on the production function's properties.
The Cobb-Douglas production function is generally expressed as follows: i i i y x (3.8)
The Cobb-Douglas functional form is characterized by constant proportionate returns to scale and constant elasticity of factor substitution, with the assumption that all pairs of inputs are complementary These features contribute to its restrictive nature.
The Translog production function is a generalization of the CD function It is a flexible functional form providing a second order approximation, which is generally expressed as below:
ln i ln i 0.5 ij ln i ln j i i j y x x x
The Translog production function is notable for its unrestricted monotonicity, factor dependence, and lack of constant proportionate return to scale, allowing for a more realistic representation of production dynamics compared to the Cobb-Douglas function However, it may experience curvature violations, as its concavity cannot be globally imposed While the Translog form enhances the testability of production function properties, it introduces complexity through cross and squared terms, leading to a higher number of parameters This can result in potential correlations among these parameters and a reduction in degrees of freedom, which is particularly critical when dealing with a limited number of observations.
Applying the Cobb-Douglas and Translog functional form to model the institutional effects on GDP, the model specification is constructed as
2009*ln it it it it it it
Y cap labfo INSTITUTION t dum INSTITUTION
(3.10) for the Cobb-Douglas functional form and
11 ln ln ln ln 2009*ln
0.5 ln( ) 0.5 ln( ) 0.5 ln( ) 0.5 ( ) 0.5 ln *ln
0.5 ln *ln it it it it it it it it it it it it
Y cap labfo INSTITUTION t dum INSTITUTION cap labfo INSTITUTION t cap labfo cap INSTITUTION
0.5 ln * 0.5 ln * it it it it it it cap t labfol INSTITUTION labfo t INSTITUTION t
The INSTITUTION variable encompasses PCI and its nine sub-indices: Encost, Timeco, Informal charges, Landacc, Leinsti, Infoacc, Pridevelop, Proact, and Labpol, which are integrated into both models Additionally, the variable 't' is employed to represent technological change.
The variable Dum2009 is defined as a dummy variable, assigned a value of 1 for the year 2009 and subsequent years, while it is coded as 0 for all other years To account for a potential structural break in the relationship between institutions and growth, the interaction term Dum2009*lnINSTITUTION is incorporated into both models.
In the context that provincial growth data have been so far assumed to be target-driven, we conduct a check for such a de facto overstatement as illustrated in Table 3.4
Table 3.4: Overstatement of provincial growth data
Source: Calculated from Provincial Statistical Yearbooks
We calculated the GDP growth rate for each province by using the proportion of provincial GDP to national GDP as weights and compared these figures with those obtained from GSO The findings in Table 3.3 indicate a significant discrepancy, highlighting the overstatement of provincial data Consequently, we employed various estimation methods to examine the institutional impacts on provincial GDP, acknowledging the established overstatement of growth data.
In our analysis, we initially utilize Ordinary Least Squares (OLS) regression for panel data, ensuring the data is cleaned through outlier detection Additionally, we implement the Stochastic Frontier Analysis model, which leverages its component error terms to effectively address both statistical noise and data overstatement.
3.2.2.1 Data cleaning – OLS regression for panel data
We initiate our data cleaning process by utilizing graphs, which are essential for gaining a comprehensive understanding of the data According to Asterious & Hall (2007), any robust empirical analysis should start with a graphical examination of the data Consequently, we focus on detecting outliers in our growth data through various types of graphs.
We analyzed individual time-series plots of real GDP for 58 provinces over five years to identify significant jumps in values This analysis revealed four within-variation outliers, specifically from Dien Bien, Hai Duong, and Phu Tho in 2011, as well as Hoa Binh in 2010.
In analyzing provincial real GDP per labor, five graphs were created to compare annual means and identify any outlier variations Can Tho and Vung Tau displayed significant data anomalies, leading to their exclusion from the sample for a more accurate assessment.
In the final step of our data cleaning process, we applied the 3σ rule to eliminate any data points that exceeded their mean by more than three standard deviations This resulted in the removal of three additional data points from Dien Bien (2011), Hai Duong (2011), and Quang Ngai (2010), bringing the total number of excluded observations to 15.
Then, we employ panel data models of either Fixed Effect Model (FEM) or Random Effect Model (REM) and run OLS regression afterwards
The FEM is generally expressed as
The fixed-effects model (FEM), also known as the least-squares dummy variables (LSDV) model, permits each cross-sectional unit to possess a unique intercept value, denoted as β1i While this intercept can vary across different entities, it remains constant over time, which is why the model is referred to as fixed-effects Importantly, fixed-effects estimators are consistently reliable.
Meanwhile, in the random-effects model, the intercept 1i is assumed to be a random variable with mean value of 1 and could be expressed as
The random error term, denoted as εi, has a mean of zero and a variance of σ², reflecting the unique variations in the intercept values for each entity This term is commonly known as the cross-section or individual-specific error component, highlighting the individual differences in the data.
The model could be re-written as
The Random Effects Model (REM) assumes no correlation between the error term and the explanatory variables; if this assumption is violated, it leads to inconsistent regression coefficient estimates The choice between Fixed Effects Model (FEM) and REM hinges on the correlation between the cross-sectional specific error component and the explanatory variables When they are uncorrelated, REM is the more efficient option The Hausman test helps identify the appropriate model by assessing whether the error term is correlated with the explanatory variables in a given regression analysis.
Null hypothesis: FEM and REM estimators do not differ significantly
The test statistic follows an asymptotic chi-square distribution When the calculated chi-square value surpasses the critical threshold, it leads to the rejection of the null hypothesis, indicating a preference for the Fixed Effects Model (FEM) over the Random Effects Model (REM).
3.2.2.2 Modeling the overstatement of growth data – The Stochastic Frontier Analysis (SFA)
Where y it = provincial GDP that we aim to measure x it = controlling variables, including standard factors of production and institutional elements