52.Current situation of Processing and Preserving Aquatic Products industry in Vietnam.... 144.1.2.Index analysis of frozen aquatic products processing and preservationindustry code 1020
Introduction
The fisheries and aquaculture sectors play a vital role in enhancing global food security and nutrition in the twenty-first century From 1961 to 2019, global consumption of aquatic foods grew at an average annual rate of 3.0 percent, nearly double the world population growth rate of 1.6 percent during the same period Understanding the processing and preservation of aquatic products is crucial for ensuring the quality and safety of these products for both domestic and international markets Consequently, we focus on the "Processing and Preserving Aquatic Products" industry, which significantly contributes to Vietnam's economic development.
This research analyzes concentration indices, including the Herfindahl-Hirschman Index (HHI) and the CR4 Index, to assess competition levels in Vietnam's Processing and Preserving Aquatic Products industry from 2015 to 2017 The study aims to enhance understanding of the industry's dynamics and offers strategic recommendations for its growth and development.
To achieve our objective, we will analyze a data set from the General Statistics Office of Vietnam (GSO) that encompasses the concentration and operational efficiency indicators of businesses in the Processing and Preserving Aquatic Products industry This comprehensive data, collected over three years from 2015 to 2017, includes all businesses in the sector to ensure accurate and conclusive results.
To meet the established objectives, this research employs key methods including data collection from the dataset provided by the General Statistics Office (GSO) and relevant research documents available online The study utilizes quantitative research techniques, specifically the Random Effect Model (REM), analyzed with STATA software, to examine concentration indices and develop a Cobb-Douglas production function.
The study is organized as follows:
Current situation of Processing and Preserving Aquatic Products industry in Vietnam
Definition
Vietnam Standard Industrial Classification System (Issued together with the Prime Minister's Decision No 10/2007/QD-TTg dated January 23, 2007) classify Processing and Preserving Aquatic Products industry as following:
- Sector code 102 - 1020: Industry code for processing and preserving aquatic products This group includes:
Processing and preserving fish, shrimp, shellfish and molluscs; chilled, dried, smoked, salted, in brine, packed
Producing fish, shrimp and crab products and molluscs; cooked fish, chunks, fried fish, caviar, caviar by-products
Producing food for humans or animals from fish;
Producing food from fish and other aquatic animals not for human consumption.
Operations of ships engaged in fish processing and preservation;
Processing of whales on land or on specialized ships, which is classified as
1010 (Processing and preserving of meat and meat products);
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Manufacture of fats and oils from aquatic materials, which is classified as
10401 (Manufacture of animal oils and fats);
Manufacture of ready-made seafood dishes, which is classified as 10752 (Manufacture of ready-made seafood dishes and foods);
Manufacture of fish soup, which is classified as 10790 (Manufacture of other food not elsewhere classified).
Classification
The industry is classified as 3 ways as following:
- 10202: Processing and preserving of frozen seafood:
This group includes: Processing mainly frozen seafood and Preservation of aquatic products is mainly by freezing method.
- 10203: Processing and preserving of dried aquatic products:
This group includes: Processing food mainly dried aquatic products and Preservation of aquatic products is mainly by drying, smoking, salting and canning methods.
- 10204: Processing and preserving fish sauce:
This class includes: Processing and preservation of fish sauce and other aquatic animals.
- 10209: Processing and preserving other aquatic products
This class includes: Activities of processing and preserving other fishery products, which have not been where to divide.
According to the data set collected from GSO, our study summarized the following table by running:
- State owned enterprises: if types of business are smaller than 6
- Private enterprises: if types of business are bigger than 5 and smaller than 11
- Foreign enterprises: if types of business are bigger than 10.
According to the data set collected from GSO, our study summarized the following table: loaihinh
According to the Government's Decree 39/2018/ND-CP, the criteria for evaluating micro, small and medium-sized enterprises include the average number of employees participating in social insurance per year.
- Micro enterprises: less than 10 persons employed
- Medium-sized enterprises: 100-200 persons employed
- Large enterprises: more than 200 persons employed.
According to the data set collected from GSO, our study summarized the following table: quymo
Current situation of Processing and Preserving Aquatic Products industry
The seafood processing and preserving industry is crucial for adding value within Vietnam's seafood production chain, which has seen rapid growth in recent years Between 2010 and 2020, seafood export turnover increased by an average of 5.3% annually, reaching $8.89 billion in 2021 despite the challenges posed by the Covid-19 pandemic As a prominent seafood exporter in the region, Vietnam is gaining recognition in the global market The government's approval of the "Project to develop the seafood processing industry in the 2021-2030 period" aims to establish Vietnam as a modern and safe seafood processing hub This expansion into international markets and trade organizations will enhance the country's economy while providing numerous job opportunities for local workers.
The seafood processing and preserving industry faces significant challenges, particularly regarding the inconsistent quality and quantity of input materials, which hampers production efficiency Currently, over 50% of seafood exports consist of low-value, simple preliminary and semi-finished products, as noted by Dr Dao Trong Hieu from the Ministry of Agriculture and Rural Development In 2020, only 22% of the total seafood export value came from processed products with added value, highlighting the industry's reliance on basic exports, with shrimp at 41.7%, pangasius at 2.7%, tuna at 52.1%, and squid and octopus products at 10.5%.
The current export statistics indicate that we primarily sell aquatic products as raw, semi-processed, or unfinished goods, lacking deep processing to enhance quality This approach results in low added value and diminished economic efficiency.
The seafood processing industry continues to rely significantly on abundant resources and inexpensive, low-skilled labor Over 90% of processing facilities are small to medium-sized, and there has been minimal investment in advanced technology This heavy dependence on unskilled workers contributes to low labor productivity within the sector.
Theoretical framework
Overview of Concentration Indices
Concentration indices are employed to measure the level of competition in an industry, often to examine whether concerns for dominant position creation exist
10 in the case of mergers and acquisitions Two major indices are the Herfindahl- Hirschman Index (HHI) and the CR4 index.
The CR4 index, which measures the concentration of the top four firms in a market, has historically been a key metric for assessing market concentration prior to the introduction of the Herfindahl-Hirschman Index (HHI) It is calculated by summing the market shares of the four largest companies in the industry.
: The market share of firm i
The CR4 ratio indicates the market share held by the four largest companies in an industry, with a higher ratio signifying greater market concentration Conversely, a low concentration ratio suggests a competitive market with no single entity dominating the landscape.
0 - 40 Effective Competition or Monopolistic Competition
40 - 60 Loose Oligopoly or Monopolistic Competition
> 60 Tight Oligopoly or Dominant Firm with a Competitive Fringe
3.1.2.The Herfindahl–Hirschman Index (HHI)
The Herfindahl-Hirschman owes its name to the two economists who developed it, though independently Albert O Hirschman proposed the index in 1945, while Orris
C Herfindahl presented it in 1950 in his unpublished doctoral dissertation at ColumbiaUniversity.
The Herfindahl-Hirschman Index (HHI) serves as an alternative metric for assessing market concentration In a market with n competing companies, the HHI is calculated by summing the squares of each company's market share, denoted as si This index provides a comprehensive understanding of market competitiveness by quantifying the distribution of market shares among the firms.
If the market shares are expressed as fractions of the whole market (i.e., 0 < ≤ 1, i), then we have 0 < HHI ≤ 1
The Herfindahl-Hirschman Index (HHI) offers a significant advantage over the concentration ratio by providing insights into the distribution of market share across all firms within an industry, rather than focusing solely on the largest companies (Arnold, 1989).
Theory of the model
A production function in economics is a mathematical formula that determines the output generated from specific inputs, mainly capital and labor.
The Cobb-Douglas production function, created in the 1920s by economist Paul Douglas and mathematician Charles Cobb, illustrates the relationship between output quantity and two key production factors: physical capital and labor.
= total production (the real value of all goods produced in a year) = labor input (person-hours worked in a year)
= capital input (a measure of all machinery, equipment, and buildings; the value of capital input divided by the price of capital)
= total factor productivity and are the output elasticities of capital and labor, respectively These values are constants determined by available technology.
This is a homogeneous function of degree α+β because when L and K are multiplied by a constant k, the output will increase by kα+β
If α+β = 1, the production function has a constant return to scale It means that the increase of 1 unit in labor and capital will lead to the increase of 1 unit in output
When the sum of α and β is less than 1, the production function exhibits decreasing returns to scale, indicating that an increase of one unit in both labor and capital results in an output increase of less than one unit.
When the sum of α and β exceeds 1, the production function exhibits increasing returns to scale, indicating that an increase of one unit in both labor and capital results in a more than proportional increase in output.
In the case of a perfect competition market, α and β can be seen as the proportions of labor and capital's contribution to output.
Index analysis and model estimation
Index analysis
4.1.1 Index analysis of aquatic products processing and canning industry (code 10201):
Utilizing STATA to compute the Concentration Ratio (CR4) and the Herfindahl-Hirschman Index (HHI) from the sales values of firms within the General Statistics Office's enterprise survey dataset yields significant insights into market concentration The resulting table illustrates the calculated metrics, providing a clear overview of the competitive landscape in the analyzed sector.
Table 4 1: Indexes of aquatic products processing and canning industry in
Year Number of firms CR4 HHI
Between 2015 and 2017, the CR4 index consistently exceeded 0.85, indicating a high concentration level in the aquatic products processing and canning industry Notably, in 2017, the CR4 reached 1, signifying a monopoly within the sector This period saw a rapid increase in the CR4 from 0.853 to 1, highlighting a significant decline in competition in the industry.
Between 2015 and 2017, the Herfindahl-Hirschman Index (HHI) for the aquatic products processing and canning industry ranged from 0.2 to 0.5, indicating a high market concentration and an oligopolistic trend This gradual increase in the HHI during the period suggests a decline in competition within the industry over time.
4.1.2 Index analysis of frozen aquatic products processing and preservation industry (code 10202):
Using STATA to calculate CR4 and HHI, we have the following table:
Table 4 2 Indexes of frozen aquatic products processing and preservation industry in 2015 - 2017
Year Number of firms CR4 HHI
The CR4 index indicates a low level of competitiveness, categorizing the industry as upper-middle level From 2015 to 2017, there was a slight increase in the CR4, followed by a decrease, suggesting that industry concentration remained relatively stable over this period.
From 2015 to 2017, the Herfindahl-Hirschman Index (HHI) ranged from 0.05 to 0.06, indicating a low level of competitiveness within the industry The gradual increase in HHI during this period signifies a rising concentration in the market.
4.1.3 Index analysis of dried aquatic products processing and preservation industry (code 10203):
Using STATA to calculate CR4 and HHI, we have the following table:
Table 4 3: Indexes of dried aquatic products processing and preservation industry in
Year Number of firms CR4 HHI
From 2015 to 2017, the CR4 index exhibited significant volatility in the dried aquatic products processing and preservation industry In 2015, the industry was characterized by high concentration, transitioning to a more competitive landscape in 2016 However, by 2017, the market returned to a concentrated state, with the CR4 index nearing 0.9.
The HHI values during this period indicate significant fluctuations in industry concentration Initially, the industry exhibited a high concentration level, transitioning to a competitive landscape in 2016 However, this competitive state was short-lived, as by 2017, the HHI value dropped to 0.3, reflecting a return to strong concentration, surpassing the levels seen in 2015.
4.1.4 Index analysis of fish sauce processing and preservation industry (code 10204):
Using STATA to calculate CR4 and HHI, we have the following table:
Table 4 4: Indexes of fish sauce processing and preservation industry in 2015 - 2017
Year Number of firms CR4 HHI
From 2015 to 2017, the CR4 index consistently exceeded 0.8, indicating a significant concentration level in the industry During this timeframe, the CR4 index showed a gradual increase, reflecting a steady decline in competition within the sector.
Between 2015 and 2017, the Herfindahl-Hirschman Index (HHI) remained stable, fluctuating between 0.21 and 0.23, indicating a high level of industry concentration Throughout this period, the HHI experienced a gradual and consistent increase, reflecting a steady rise in concentration levels within the industry.
4.1.5 Index analysis of processing and preserving aquatic and other products industry (code 10209):
Using STATA to calculate CR4 and HHI, we have the following table:
Table 4 5: Indexes of processing and preserving aquatic and other products industry in 2015 - 2017
Year Number of firms CR4 HHI
Between 2015 and 2017, the CR4 index consistently exceeded 0.8, indicating a high concentration within the industry This data reveals a gradual increase in the CR4 index during this period, signifying a steady decline in competition levels within the sector.
Between 2015 and 2017, the Herfindahl-Hirschman Index (HHI) initially rose, indicating a high level of industry concentration, before experiencing a decline in 2017 This fluctuation highlights a slight change in market concentration levels over the period.
Data description and the correlation between variables
Our research utilizes secondary panel data sourced from the Government Statistics Office's Enterprise survey dataset We analyzed 376 observations from firms operating in the Processing and Preserving Aquatic Products Industry (code 10) over the period from 2015 to 2017.
The study approaches the determinants of sales value of processing and preserving aquatic products firms, from 2015 to 2017, using a random effect model (REM) by STATA.
We suggest the following population regression model lnsales = β + β lnL + β lnK + β i.loaihinh + β i.nganh_kd + β 0 1 2 3i 4i 5i i.quymo+ u it
Where: lnsales: Sales of products in the period This variable is taken logarithmically. lnL: Average total number of employees in the period ((beginning + ending)/2).
The logarithmic transformation is applied to the variable lnK, which represents the average total assets calculated as the mean of the beginning and ending values over a specified period Additionally, the control variable i.loaihinh categorizes firms into three types: state-owned enterprises (i=1), private enterprises (i=2), and foreign-invested enterprises (i=3).
The variable "i.nganh_kd" serves as a control variable indicating the specific industries in which firms operate, with "i" representing the industry code Additionally, "i.quymo" is a control variable that categorizes firms based on their size, ranging from micro enterprises (i=1) to large enterprises (i=4) Lastly, "u it" denotes the random error term in the analysis.
By using the command ‘sum’ in STATA, we have the following data description table:
VARIABLES N mean sd min max nganh_kd 376 10,203 1.818 10,201 10,209 sales 376 547,488 1.194e+06 235.1 1.027e+07
By using command ‘corr’ in STATA, we have the following result:
The analysis reveals a strong correlation between lnK and lnL, as well as between quymo and lnL, indicating a potential multicollinearity issue To address this, our team utilized the 'collin' command in STATA to assess the extent of multicollinearity present in the data.
Variable VIF lnsales 4.20 lnL 8.06 lnK 3.72 loaihinh 1.05 nganh_kd 1.19
Considering VIF values are all under 10, we decide not to omit variables in the model.
Model estimation results and validity testing
Use for random effects to choose between OLS model and REM, with the following hypotheses:
H0: The model has no random effects
H1: The model has random effects Result: chibar2(01) = 113.70 Prob > chibar2 = 0.0000 p-value < 0.05 => Reject H0
Therefore, we choose REM rather than OLS model.
Use to choose between Fixed Effects Model (FEM) and Random Effect Model (REM), with the following hypotheses:
H0: Difference in coefficients is not systematic
H1: Difference in coefficients is systematic chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Therefore, we choose REM to identify the relationship between variables.
Use technique to fix the problems of the Random Effect Model (REM)
The final result is in the following table:
Table 4 9: Estimation result lnsales Coef St.Err t-value p-value [95% Conf Interval] Sig lnL 766 19 4.03 0 393 1.139 *** lnK 33 113 2.93 003 109 55 ***
Mean dependent var 11.784 SD dependent var 2.022
Overall r-squared 0.759 Number of obs 376
Group variable ma_thue Number of groups 147
The data set has 376 observations, divided into 147 groups, which is equivalent to
147 firms From the result, we find out that only capital, labor and the type of firm can significantly affect the sales of processing and preserving aquatic products firms from
2015 to 2017 Meanwhile, the industries which the firms are in and the size of firms do not have an impact on sales
Capital, labor, and the type of firm significantly influence sales performance An increase in capital or labor within firms correlates with a rise in sales value Furthermore, the findings indicate that foreign-invested enterprises achieve higher sales volumes compared to private firms.
The overall R of the model is 0.759, which represents the fit of the model 2 Specifically, the change in the independent variables can explain 75.9% the change in the dependent variable.
Case study of a business game
In 2007, Vietnam's instant noodle market experienced significant growth but lacked clear segmentation Noodle manufacturers, including Acecook Vietnam, Asia Food, and Vifon, adopted a strategy of introducing multiple brands to enhance consumer choice, resulting in numerous noodle varieties with diverse flavors However, this approach led to similar market shares among brands from the same company, shortening brand life cycles and intensifying price competition.
In the face of soaring raw material prices, businesses are struggling to maintain profitability while remaining competitive Many manufacturing companies have resorted to accepting lower profit margins, yet these reductions often fail to offset rising costs To navigate this challenge, firms have strategically opted to gradually decrease product weight, a tactic designed to minimize customer dissatisfaction while managing expenses.
Previously, the package of noodles was 1,500 VND/pack, with a net weight of
Many instant noodle packages have reduced their weight from 100 grams to just 70 grams while maintaining the same selling price, leading consumers to feel they are still getting the same value In 2017, Masan Food addressed this concern by launching Omachi instant noodles at a premium-mass price of 3,000 VND per pack, catering to consumers' longstanding worries about product value.
Eating noodles can create a warming sensation, but Omachi offers a unique twist with its slogan: "Omachi noodles made from potato fibers, very delicious without fear of heat." Since its launch nearly three years ago, Omachi has successfully captured 1.6% of the market share, outperforming established brands like Vifon and strong competitors such as Asia Food.
2012, the market share of Masan was 16.5%, far ahead of the third company, Asia Foods with 12.1% market share.
Masan aims to establish favorable conditions for its products to tap into substantial market opportunities and potential sizes by focusing on brand building and differentiation from competitors.
Conclusion and implications
Our report has examined the impact of 4 factors on the output of firms in the processing and preserving aquatic products industry from 2015 to 2017 The research
24 result reinforces the positive effect of average capital, average labor, the ownership and size of firms on average output of the industry.
The output of the aquatic products processing and preservation industry is significantly influenced by three key factors: capital, labor, and firm ownership Among these, labor plays the most crucial role in determining the overall production levels.
Vietnam boasts significant advantages, including a wealth of raw materials, competitive labor costs, numerous trade agreements, improved manufacturing capabilities, and a large workforce Despite these strengths, challenges remain in effectively utilizing these resources.
Our study highlights the significant positive impact of labor on increasing output in the aquatic products processing and preservation industry However, the quality of labor is crucial, as a survey from the Department of Agriculture and Rural Development in Ca Mau province reveals that approximately 60% of employees lack technical expertise, with untrained workers comprising 80% of the workforce Consequently, implementing training programs to enhance worker qualifications is essential for the industry's growth and development.
According to the General Statistics Office, most enterprises in the aquatic products industry are small to medium-sized, facing significant challenges such as a lack of modern technology The Food and Agriculture Organization reports that approximately 45% of global agricultural harvests are lost before reaching consumers, primarily due to pests and inadequate storage In our country, the processing and preservation of aquatic products also suffer from substantial post-harvest losses, resulting in low product value, with only 1-5% meeting international quality standards Therefore, the development of advanced technology for preserving and processing aquatic products is crucial in the era of Industry 4.0.
At the 10% level of significance, the ownership of firms is a significant factor that affects the amount of output in the processing and preserving aquatic products industry.
However, changing the type of business is quite difficult and requires a lot of time. Therefore, we should prioritize improving other factors.
In conclusion, the aquatic products processing and preserving industry represents a significant advantage for our country, with substantial potential for growth Investing in technological expertise and modern technology is essential to enhance output and increase the value of these products, paving the way for more positive contributions from businesses in this sector.
Total factor productivity (TFP) measures the efficiency of all inputs to a production process by comparing total outputs to total inputs It indicates TFP growth through changes in production and input volumes over a specified period, with the TFP index defined as the ratio of an Output Index to an Input Index An increase in TFP signifies a rise in output that does not stem from increased input use, highlighting contributions from technological advancements, efficiency improvements, and better management practices Different analytical forms of TFP are derived using various average types, such as Laspeyres and Paasche indices, with the Fischer index providing optimal statistical properties.
%20defined%20as,factors%20used%20to%20produce%20them (Accessed: March
Bài viết "Giải Bài Toán Nhân Lực ngành Thủy Sản" trên Báo Nhân Dân điện tử đề cập đến những thách thức và giải pháp trong việc quản lý nguồn nhân lực trong ngành thủy sản Tác giả phân tích các yếu tố ảnh hưởng đến hiệu quả lao động và đề xuất các biện pháp cải thiện Bài viết nhấn mạnh tầm quan trọng của đào tạo và phát triển kỹ năng cho người lao động để nâng cao năng suất và chất lượng sản phẩm thủy sản Để xem chi tiết, bài viết có thể được truy cập tại https://nhandan.vn/giai-bai-toan-nhan-luc-nganh-thuy-san-tiep-theo-va-het-post339803.html.
McKenzie, T (2020) Cobb-Douglas production function, INOMICS Available at: https://inomics.com/terms/cobb-douglas-production-function-1456726 (Accessed: March 22, 2023)
Naldi and Flamini (2014) conducted an empirical comparison of the CR4 index and the interval estimation of the Herfindahl-Hirschman Index, highlighting their methodologies and findings Their research is accessible through SSRN, providing valuable insights into market concentration measures For further details, the full paper can be found at the provided SSRN link.
Nhân lực ngành thủy sản hiện đang đối mặt với thách thức về chất lượng, không đáp ứng được nhu cầu ngày càng cao của thị trường Sự thiếu hụt kỹ năng và đào tạo chuyên sâu đã dẫn đến việc khó khăn trong việc phát triển bền vững ngành này Để cải thiện tình hình, cần có sự đầu tư mạnh mẽ vào giáo dục và đào tạo nghề, nhằm nâng cao trình độ cho người lao động Điều này không chỉ giúp tăng cường năng lực cạnh tranh cho ngành thủy sản mà còn đảm bảo nguồn nhân lực chất lượng trong tương lai.
Việt Nam đang phấn đấu trở thành trung tâm chế biến thủy sản toàn cầu, với mục tiêu nâng cao chất lượng sản phẩm và mở rộng thị trường xuất khẩu Chính phủ và các doanh nghiệp địa phương đang đầu tư mạnh mẽ vào công nghệ hiện đại và phát triển nguồn nhân lực, nhằm đáp ứng tiêu chuẩn quốc tế Sự hợp tác giữa các bên liên quan sẽ thúc đẩy ngành thủy sản phát triển bền vững, tạo ra nhiều cơ hội việc làm và tăng thu nhập cho người dân Các chính sách hỗ trợ từ nhà nước cũng đóng vai trò quan trọng trong việc xây dựng thương hiệu thủy sản Việt Nam trên thị trường quốc tế.
The Cobb-Douglas production function (2016) Economics Discussion Available at:https://www.economicsdiscussion.net/production-function/the-cobb-douglas- production-function/18519 (Accessed: March 22, 2023)
The CR4 index and the interval estimation of the Herfindahl-Hirschman (no date) Available at: https://hal.science/hal-01008144/document (Accessed: March
The article discusses the trends in the development of seafood processing technology, highlighting its significance in enhancing the quality and sustainability of seafood production It emphasizes the importance of innovation and modernization in the industry to meet consumer demands and environmental standards The piece also explores the impact of technological advancements on the efficiency and profitability of seafood processing operations Overall, it underscores the vital role that technology plays in shaping the future of the seafood industry.
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