Swedish Short-Term Human Resource Forecasting Model

- Cobb-Douglas production function method

- RAS method

- MRP method

1.6.3. Sweden's experience in human resource forecasting

Short-term occupational forecasting in Sweden is based primarily on assessments by employment offices (public employment offices in Sweden). About 200 occupations are surveyed twice a year. Employment offices assess the demand for each occupation as: i) Increase (low increase or high increase), ii) No change or iii) Decrease (small or large decrease) in the coming year.

Assessment is carried out at the following levels: i) Major labour shortage,

ii) Small labor shortage, iii) Balance, iv) Small labor surplus and v) Large labor surplus.

There are about 400 employment offices in Sweden, each of which is responsible for assessing occupations for the following year. This means that about 400 occupations are listed on the National Labour Market Bulletin. A “shortage coefficient” is calculated based on these figures.

The calculation is performed at the following levels:

Labor demand status

Code
Missing a lot	5
Lack of a little	4
Enough	3
Little surplus	2
Surplus	1

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• Qos Assurance Methods for Multimedia Communications zt2i3t4l5ee zt2a3gs zt2a3ge zc2o3n4t5e6n7ts low. The EF PHB requires a sufficiently large number of output ports to provide low delay, low loss, and low jitter. EF PHBs can be implemented if the output port's bandwidth is sufficiently large, combined with small buffer sizes and other network resources dedicated to EF packets, to allow the router's service rate for EF packets on an output port to exceed the arrival rate λ of packets at that port. This means that packets with PHB EF are considered with a pre-allocated amount of output bandwidth and a priority that ensures minimum loss, minimum delay and minimum jitter before being put into operation. PHB EF is suitable for channel simulation, leased line simulation, and real-time services such as voice, video without compromising on high loss, delay and jitter values. Figure 2.10 Example of EF installation Figure 2.10 shows an example of an EF PHB implementation. This is a simple priority queue scheduling technique. At the edges of the DS domain, EF packet traffic is prioritized according to the values ​​agreed upon by the SLA. The EF queue in the figure needs to output packets at a rate higher than the packet arrival rate λ. To provide an EF PHB over an end-to-end DS domain, bandwidth at the output ports of the core routers needs to be allocated in advance to ensure the requirement μ > λ. This can be done by a pre-configured provisioning process. In the figure, EF packets are placed in the priority queue (the upper queue). With such a length, the queue can operate with μ > λ. Since EF was primarily used for real-time services such as voice and video, and since real-time services use UDP instead of TCP, RED is generally not suitable for EF queues because applications using UDP will not respond to random packet drop and RED will strip unnecessary packets. 2.2.4.2 Assured Forwarding (AF) PHB PHB AF is defined by RFC 2597. The purpose of PHB AF is to deliver packets reliably and therefore delay and jitter are considered less important than packet loss. PHB AF is suitable for non-real-time services such as applications using TCP. PHB AF first defines four classes: AF1, AF2, AF3, AF4. For each of these AF classes, packets are then classified into three subclasses with three distinct priority levels. Table 2.8 shows the four AF classes and 12 AF subclasses and the DSCP values ​​for the 12 AF subclasses defined by RFC 2597. RFC 2597 also allows for more than three separate priority levels to be added for internal use. However, these separate priority levels will only have internal significance. PHB Class PHB Subclass Package type DSCP AF4 AF41 Short 100010 AF42 Medium 100100 AF43 High 100110 AF3 AF31 Short 011010 AF32 Medium 011100 AF33 High 011110 AF2 AF21 Short 010010 AF22 Medium 010100 AF23 High 010110 AF1 AF11 Short 001010 AF12 Medium 001100 AF13 High 001110 Table 2.8 AF DSCPs The AF PHB ensures that packets are forwarded with a high probability of delivery to the destination within the bounds of the rate agreed upon in an SLA. If AF traffic at an ingress port exceeds the pre-priority rate, which is considered non-compliant or “out of profile”, the excess packets will not be delivered to the destination with the same probability as the packets belonging to the defined traffic or “in profile” packets. When there is network congestion, the out of profile packets are dropped before the in profile packets are dropped. When service levels are defined using AF classes, different quantity and quality between AF classes can be realized by allocating different amounts of bandwidth and buffer space to the four AF classes. Unlike EF, most AF traffic is non-real-time traffic using TCP, and the RED queue management strategy is an AQM (Adaptive Queue Management) strategy suitable for use in AF PHBs. The four AF PHB layers can be implemented as four separate queues. The output port bandwidth is divided into four AF queues. For each AF queue, packets are marked with three “colors” corresponding to three separate priority levels. In addition to the 32 DSCP 1 groups defined in Table 2.8, 21 DSCPs have been standardized as follows: one for PHB EF, 12 for PHB AF, and 8 for CSCP. There are 11 DSCP 1 groups still available for other standards. 2.2.5.Example of Differentiated Services We will look at an example of the Differentiated Service model and mechanism of operation. The architecture of Differentiated Service consists of two basic sets of functions: Edge functions: include packet classification and traffic conditioning. At the inbound edge of the network, incoming packets are marked. In particular, the DS field in the packet header is set to a certain value. For example, in Figure 2.12, packets sent from H1 to H3 are marked at R1, while packets from H2 to H4 are marked at R2. The labels on the received packets identify the service class to which they belong. Different traffic classes receive different services in the core network. The RFC definition uses the term behavior aggregate rather than the term traffic class. After being marked, a packet can be forwarded immediately into the network, delayed for a period of time before being forwarded, or dropped. We will see that there are many factors that affect how a packet is marked, and whether it is forwarded immediately, delayed, or dropped. Figure 2.12 DiffServ Example Core functionality: When a DS-marked packet arrives at a Diffservcapable router, the packet is forwarded to the next router based on Per-hop behavior is associated with packet classes. Per-hop behavior affects router buffers and the bandwidth shared between competing classes. An important principle of the Differentiated Service architecture is that a router's per-hop behavior is based only on the packet's marking or the class to which it belongs. Therefore, if packets sent from H1 to H3 as shown in the figure receive the same marking as packets from H2 to H4, then the network routers treat the packets exactly the same, regardless of whether the packet originated from H1 or H2. For example, R3 does not distinguish between packets from h1 and H2 when forwarding packets to R4. Therefore, the Differentiated Service architecture avoids the need to maintain router state about separate source-destination pairs, which is important for network scalability. Chapter Conclusion Chapter 2 has presented and clarified two main models of deploying and installing quality of service in IP networks. While the traditional best-effort model has many disadvantages, later models such as IntServ and DiffServ have partly solved the problems that best-effort could not solve. IntServ follows the direction of ensuring quality of service for each separate flow, it is built similar to the circuit switching model with the use of the RSVP resource reservation protocol. IntSer is suitable for services that require fixed bandwidth that is not shared such as VoIP services, multicast TV services. However, IntSer has disadvantages such as using a lot of network resources, low scalability and lack of flexibility. DiffServ was born with the idea of ​​solving the disadvantages of the IntServ model. DiffServ follows the direction of ensuring quality based on the principle of hop-by-hop behavior based on the priority of marked packets. The policy for different types of traffic is decided by the administrator and can be changed according to reality, so it is very flexible. DiffServ makes better use of network resources, avoiding idle bandwidth and processing capacity on routers. In addition, the DifServ model can be deployed on many independent domains, so the ability to expand the network becomes easy. Chapter 3: METHODS TO ENSURE QoS FOR MULTIMEDIA COMMUNICATIONS In packet-switched networks, different packet flows often have to share the transmission medium all the way to the destination station. To ensure the fair and efficient allocation of bandwidth to flows, appropriate serving mechanisms are required at network nodes, especially at gateways or routers, where many different data flows often pass through. The scheduler is responsible for serving packets of the selected flow and deciding which packet will be served next. Here, a flow is understood as a set of packets belonging to the same priority class, or originating from the same source, or having the same source and destination addresses, etc. In normal state when there is no congestion, packets will be sent as soon as they are delivered. In case of congestion, if QoS assurance methods are not applied, prolonged congestion can cause packet drops, affecting service quality. In some cases, congestion is prolonged and widespread in the network, which can easily lead to the network being "frozen", or many packets being dropped, seriously affecting service quality. Therefore, in this chapter, in sections 3.2 and 3.3, we introduce some typical network traffic load monitoring techniques to predict and prevent congestion before it occurs through the measure of dropping (removing) packets early when there are signs of impending congestion. 3.1. DropTail method DropTail is a simple, traditional queue management method based on FIFO mechanism. All incoming packets are placed in the queue, when the queue is full, the later packets are dropped. Due to its simplicity and ease of implementation, DropTail has been used for many years on Internet router systems. However, this algorithm has the following disadvantages: − Cannot avoid the phenomenon of “Lock out”: Occurs when 1 or several traffic streams monopolize the queue, making packets of other connections unable to pass through the router. This phenomenon greatly affects reliable transmission protocols such as TCP. According to the anti-congestion algorithm, when locked out, the TCP connection stream will reduce the window size and reduce the packet transmission speed exponentially. − Can cause Global Synchronization: This is the result of a severe “Lock out” phenomenon. Some neighboring routers have their queues monopolized by a number of connections, causing a series of other TCP connections to be unable to pass through and simultaneously reducing the transmission speed. After those monopolized connections are temporarily suspended, Once the queue is cleared, it takes a considerable amount of time for TCP connections to return to their original speed. − Full Queue phenomenon: Data transmitted on the Internet often has an explosion, packets arriving at the router are often in clusters rather than in turn. Therefore, the operating mechanism of DropTail makes the queue easily full for a long period of time, leading to the average delay time of large packets. To avoid this phenomenon, with DropTail, the only way is to increase the router's buffer, this method is very expensive and ineffective. − No QoS guarantee: With the DropTail mechanism, there is no way to prioritize important packets to be transmitted through the router earlier when all are in the queue. Meanwhile, with multimedia communication, ensuring connection and stable speed is extremely important and the DropTail algorithm cannot satisfy. The problem of choosing the buffer size of the routers in the network is to “absorb” short bursts of traffic without causing too much queuing delay. This is necessary in bursty data transmission. The queue size determines the size of the packet bursts (traffic spikes) that we want to be able to transmit without being dropped at the routers. In IP-based application networks, packet dropping is an important mechanism for indirectly reporting congestion to end stations. A solution that prevents router queues from filling up while reducing the packet drop rate is called dynamic queue management. 3.2. Random elimination method – RED 3.2.1 Overview RED (Random Early Detection of congestion; Random Early Drop) is one of the first AQM algorithms proposed in 1993 by Sally Floyd and Van Jacobson, two scientists at the Lawrence Berkeley Laboratory of the University of California, USA. Due to its outstanding advantages compared to previous queue management algorithms, RED has been widely installed and deployed on the Internet. The most fundamental point of their work is that the most effective place to detect congestion and react to it is at the gateway or router. Source entities (senders) can also do this by estimating end-to-end delay, throughput variability, or the rate of packet retransmissions due to drop. However, the sender and receiver view of a particular connection cannot tell which gateways on the network are congested, and cannot distinguish between propagation delay and queuing delay. Only the gateway has a true view of the state of the queue, the link share of the connections passing through it at any given time, and the quality of service requirements of the traffic flows. The RED gateway monitors the average queue length, which detects early signs of impending congestion (average queue length exceeding a predetermined threshold) and reacts appropriately in one of two ways: − Drop incoming packets with a certain probability, to indirectly inform the source of congestion, the source needs to reduce the transmission rate to keep the queue from filling up, maintaining the ability to absorb incoming traffic spikes. − Mark “congestion” with a certain probability in the ECN field in the header of TCP packets to notify the source (the receiving entity will copy this bit into the acknowledgement packet). Figure 3. 1 RED algorithm The main goal of RED is to avoid congestion by keeping the average queue size within a sufficiently small and stable region, which also means keeping the queuing delay sufficiently small and stable. Achieving this goal also helps: avoid global synchronization, not resist bursty traffic flows (i.e. flows with low average throughput but high volatility), and maintain an upper bound on the average queue size even in the absence of cooperation from transport layer protocols. To achieve the above goals, RED gateways must do the following: − The first is to detect congestion early and react appropriately to keep the average queue size small enough to keep the network operating in the low latency, high throughput region, while still allowing the queue size to fluctuate within a certain range to absorb short-term fluctuations. As discussed above, the gateway is the most appropriate place to detect congestion and is also the most appropriate place to decide which specific connection to report congestion to. − The second thing is to notify the source of congestion. This is done by marking and notifying the source to reduce traffic. Normally the RED gateway will randomly drop packets. However, if congestion If congestion is detected before the queue is full, it should be combined with packet marking to signal congestion. The RED gateway has two options: drop or mark; where marking is done by marking the ECN field of the packet with a certain probability, to signal the source to reduce the traffic entering the network. − An important goal that RED gateways need to achieve is to avoid global synchronization and not to resist traffic flows that have a sudden characteristic. Global synchronization occurs when all connections simultaneously reduce their transmission window size, leading to a severe drop in throughput at the same time. On the other hand, Drop Tail or Random Drop strategies are very sensitive to sudden flows; that is, the gateway queue will often overflow when packets from these flows arrive. To avoid these two phenomena, gateways can use special algorithms to detect congestion and decide which connections will be notified of congestion at the gateway. The RED gateway randomly selects incoming packets to mark; with this method, the probability of marking a packet from a particular connection is proportional to the connection's shared bandwidth at the gateway. − Another goal is to control the average queue size even without cooperation from the source entities. This can be done by dropping packets when the average size exceeds an upper threshold (instead of marking it). This approach is necessary in cases where most connections have transmission times that are less than the round-trip time, or where the source entities are not able to reduce traffic in response to marking or dropping packets (such as UDP flows). 3.2.2 Algorithm This section describes the algorithm for RED gateways. RED gateways calculate the average queue size using a low-pass filter. This average queue size is compared with two thresholds: minth and maxth. When the average queue size is less than the lower threshold, no incoming packets are marked or dropped; when the average queue size is greater than the upper threshold, all incoming packets are dropped. When the average queue size is between minth and maxth, each incoming packet is marked or dropped with a probability pa, where pa is a function of the average queue size avg; the probability of marking or dropping a packet for a particular connection is proportional to the bandwidth share of that connection at the gateway. The general algorithm for a RED gateway is described as follows: [5] For each packet arrival Caculate the average queue size avg If minth ≤ avg < maxth div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 15pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s3 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s7 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s8 { color: black; font-family:Arial, sans-serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 15pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 6pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s12 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s13 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-d
• Identify Rating Levels and Rating Scales zt2i3t4l5ee zt2a3gstourism,quan lan,quang ninh,ecology,ecotourism,minh chau,van don,geography,geographical basis,tourism development,science zt2a3ge zc2o3n4t5e6n7ts of the islanders. Therefore, this indicator will be divided into two sub-indicators: a1. Natural tourism attractiveness a2. Cultural tourism attractiveness b. Tourist capacity The two island communes in Quan Lan have different capacities to receive tourists. Minh Chau Commune is home to many standard hotels and resorts, attracting high-income domestic and international tourists. Meanwhile, Quan Lan Commune has many motels mainly built and operated by local people, so the scale and quality are not high, and will be suitable for ordinary tourists such as students. c. Time of exploitation of Quan Lan Island Commune: Quan Lan tourism is seasonal due to weather and climate conditions and festivals only take place on certain days of the year, specifically in spring. In Quan Lan commune, the period from April to June and from September to November is considered the best time to visit Quan Lan because the cultural tourism activities are mainly associated with festivals taking place during this time. Minh Chau island commune: Tourism exploitation time is all year round, because this is a place with a number of tourist attractions with diverse ecosystems such as Bai Tu Long National Park Research Center, Tram forest, Turtle Laying Beach, so besides coming to the beach for tourism and vacation in the summer, Minh Chau will attract research groups to come for tourism combined with research at other times of the year. d. Sustainability The sustainability of ecotourism sites in Quan Lan and Minh Chau communes depends on the sensitivity of the ecosystems to climate changes. landscape. In general, these tourist destinations have a fairly high level of sustainability, because they are natural ecosystems, planned and protected. However, if a large number of tourists gather at certain times, it can exceed the carrying capacity and affect the sustainability of the environment (polluted beaches, damaged trees, animals moving away from their habitats, etc.), then the sustainability of the above ecosystems (natural ecosystems, human ecosystems) will also be affected and become less sustainable. e. Location and accessibility Both island communes have ports to take tourists to visit from Van Don wharf: - Quan Lan – Van Don traffic route: Phuc Thinh – Viet Anh high-speed boat and Quang Minh high-speed boat, depart at 8am and 2pm from Van Don to Quan Lan, and at 7am and 1pm from Quan Lan to Van Don. There are also wooden boats departing at 7am and 1pm. - Van Don - Minh Chau traffic route: Chung Huong high-speed train, Minh Chau train, morning 7:30 and afternoon 13:30 from Van Don to Minh Chau, morning 6:30 and afternoon 13:00 from Minh Chau to Van Don. f. Infrastructure Despite receiving investment attention, the issue of infrastructure and technical facilities for tourism on Quan Lan Island is still an issue that needs to be resolved because it has a direct impact on the implementation of ecotourism activities. The minimum conditions for serving tourists such as accommodation, electricity, water, communication, especially medical services, and security work need to be given top priority. Ecotourism spots in Minh Chau commune are assessed to have better infrastructure and technical facilities for tourism because there are quite complete and synchronous conditions for serving tourists, meeting many needs of domestic and foreign tourists. 3.2.1.4. Determine assessment levels and assessment scales Corresponding to the levels of each criterion, the index is the score of those levels in the order of 4, 3, 2, 1 decreasing according to the standard of each level: very attractive (4), attractive (3), average (2), less attractive (1). 3.2.1.5. Determining the coefficients of the criteria For the assessment of DLST in the two communes of Quan Lan and Minh Chau islands, the students added evaluation coefficients to show the importance of the criteria and indicators as follows: Coefficient 3 with criteria: Attractiveness, Exploitation time. These are the 2 most important criteria for attracting tourists to tourism in general and eco-tourism in particular, so they have the highest coefficient. Coefficient 2 with criteria: Capacity, Infrastructure, Location and accessibility . Because the assessment area is an island commune of Van Don district, the above criteria are selected by the author with appropriate coefficients at the average level. Coefficient 1 with criteria: Sustainability. Quan Lan has natural and human-made ecotourism sites, with high biodiversity and little impact from local human factors. Most of the ecotourism sites are still wild, so they are highly sustainable. 3.2.1.6. Results of DLST assessment on Quan Lan island a. Assessment of the potential for natural tourism development  For Minh Chau commune: + Natural tourism attractiveness is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12. + Capacity is determined as average (2 points) and the coefficient is quite important (coefficient 2), then the score of Capacity criterion is 2 x 2 = 4. + Exploitation time is long (4 points), the most important coefficient (coefficient 3) so the score of the Exploitation time criterion is 4 x 3 = 12. + Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points + Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points. + Infrastructure is assessed as good (3 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 3 x 2 = 6 points. The total score for evaluating DLST in Minh Chau commune according to 6 evaluation criteria is determined as: 12 + 4 + 12 + 4 + 4 + 6 = 42 points Similar assessment for Quan Lan commune, we have the following table: Table 3.3: Assessment of the potential for natural ecotourism development in Quan Lan and Minh Chau communes Attractiveness of self-tourismof course Capacity Mining time Sustainability Location and accessibility Infrastructure Result Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti CommuneMinh Chau 12 12 4 8 12 12 4 4 4 8 6 8 42/52 Quan CommuneLan 6 12 6 8 9 12 4 4 4 8 4 8 33/52 b. Assessment of the potential for humanistic tourism development  For Quan Lan commune: + The attractiveness of human tourism is determined to be very attractive (4 points) and the most important coefficient (coefficient 3), so the score of the Attractiveness criterion is 4 x 3 = 12. + Capacity is determined to be large (3 points) and the coefficient is quite important (coefficient 2), then the score of the Capacity criterion is 3 x 2 = 6. + Mining time is average (3 points), the most important coefficient (coefficient 3) so the score of the Mining time criterion is 3 x 3 = 9. + Sustainability is determined as sustainable (4 points), the important coefficient is the average coefficient (coefficient 1), so the score of the Sustainability criterion is 4 x 1 = 4 points. + Location and accessibility are determined to be quite favorable (2 points), the coefficient is quite important (coefficient 2), the criterion score is 2 x 2 = 4 points. + Infrastructure is rated as average (2 points), the coefficient is quite important (coefficient 2), then the score of the Infrastructure criterion is 2 x 2 = 4 points. The total score for evaluating DLST in Quan Lan commune according to 6 evaluation criteria is determined as: 12 + 6 + 6 + 4 + 4 + 4 = 36 points. Similar assessment with Minh Chau commune we have the following table: Table 3.4: Assessment of the potential for developing humanistic eco-tourism in Quan Lan and Minh Chau communes Attractiveness of human tourismliterature Capacity Mining time Sustainability Location and accessibility Infrastructure Result Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Point DarkMulti Quan CommuneLan 12 12 6 8 9 12 4 4 4 8 4 8 39/52 Minh CommuneChau 6 12 4 8 12 12 4 4 4 8 6 8 36/52 Basically, both Minh Chau and Quan Lan localities have quite favorable conditions for developing ecotourism. However, Quan Lan commune has more advantages to develop ecotourism in a humanistic direction, because this is an area with many famous historical relics such as Quan Lan Communal House, Quan Lan Pagoda, Temple worshiping the hero Tran Khanh Du, ... along with local festivals held annually such as the wind praying ceremony (March 15), Quan Lan festival (June 10-19); due to its location near the port and long exploitation time, the beaches in Quan Lan commune (especially Quan Lan beach) are no longer hygienic and clean to ensure the needs of tourists coming to relax and swim; this is also an area with many beautiful landscapes such as Got Beo wind pass, Ong Phong head, Voi Voi cave, but the ability to access these places is still very limited (dirt hill road, lots of gravel and rocks), especially during rainy and windy times; In addition, other natural resources such as mangrove forests and sea worms have not been really exploited for tourism purposes and ecotourism development. On the contrary, Minh Chau commune has more advantages in developing ecotourism in the direction of natural tourism, this is an area with diverse ecosystems such as at Rua De Beach, Bai Tu Long National Park Conservation Center...; Minh Chau beach is highly appreciated for its natural beauty and cleanliness, ranked in the top ten most beautiful beaches in Vietnam; Minh Chau commune is also home to Tram forest with a large area and a purity of up to 90%, suitable for building bridges through the forest (a very effective type of natural ecotourism currently applied by many countries) for tourists to sightsee, as well as for the purpose of studying and researching. Figure 3.1: Thenmala Forest Bridge (India) Source: https://www.thenmalaecotourism.com/(August 21, 2019) 3.2.2. Using SWOT matrix to evaluate Quan Lan island tourism General assessment of current tourism activities of Quan Lan island is shown through the following SWOT matrix: Table 3.5: SWOT matrix evaluating tourism activities on Quan Lan island Internal agent Strengths- There is a lot of potential for tourism development, especially natural ecotourism and humanistic ecotourism.- The unskilled labor force is relatively abundant.- resource environmentunpolluted, still Weaknesses- Poorly developed infrastructure, especially traffic routes to tourist destinations on the island.- The team of professional staff is still weak.- Tourism products in general quite wild, originalintact general and DLST in particularalone is monotonous. External agents Opportunity- Tourism is a key industry in the socio-economic development strategy of the province and Van Don economic zone.- Quan Lan was selected as a pilot area for eco-tourism development within the framework of the green growth project between Quang Ninh province and the Japanese organization JICA.- The flow of tourists and especially ecotourism in the world tends toincreasing Challenge- Weather and climate change abnormally.- Competition in tourism products is increasingly fierce, especially with other localities in the province such as Ha Long, Mong Cai...- Awareness of tourists, especially domestic tourists, about ecotourism and nature conservation is not high. Through summary analysis using SWOT matrix we see that:  To exploit strengths and take advantage of opportunities, it is necessary to: - Diversify products and service types (build more tourism routes aimed at specific needs of tourists: experiential tourism immersed in nature, spiritual cultural tourism...) - Effective exploitation of resources and differentiated products (natural resources and human resources) div.maincontent .p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent p { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; margin:0pt; } div.maincontent .s1 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s2 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 13pt; } div.maincontent .s3 { color: #0D0D0D; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s4 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s5 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s6 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -3pt; } div.maincontent .s7 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -2pt; } div.maincontent .s8 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; vertical-align: -1pt; } div.maincontent .s9 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s10 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s11 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s12 { color: black; font-family:Symbol, serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s13 { color: black; font-family:Wingdings; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s14 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 5pt; } div.maincontent .s15 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 9pt; vertical-align: 5pt; } div.maincontent .s16 { color: black; font-family:Cambria, serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s17 { color: #080808; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 14pt; } div.maincontent .s18 { color: #080808; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s19 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s20 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 10pt; } div.maincontent .s21 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: bold; text-decoration: none; font-size: 11pt; } div.maincontent .s22 { color: black; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; text-decoration: none; font-size: 11pt; } div.maincontent .s23 { color: black; font-family:"Times New Roman", serif; font-style: italic; font-weight: normal; text-decoration: none; font-size: 14pt; } div.maincontent .s24 { color: #212121; font-family:"Times New Roman", serif; font-style: normal; font-weight: normal; tex

Figure 1.7. Sweden's short-term human resource forecasting model

Source: Nguyen The Ha (2011): "Forecasting Vietnam's labor market, models and forecast results to 2020"

All the data for each occupation are summed and averaged for the surveyed establishments (hereinafter referred to as enterprises). This average value reflects the general shortage of the whole country according to each occupational classification. Based on the need for additional labor, a “Job Chart” is built reflecting the distribution of labor by occupational group, or details by occupation within each group as well as changes in labor demand through a chart called “Occupational Barometer”. This “Occupational Chart” has been operating well due to its simplicity. This tool is the product of experts in the national labor market management system related to occupational aspects and is also a valuable tool for training in labor market planning.

However, a lot of information is needed to make occupational predictions, although “Occupational Charts” can provide valuable information and can be used to

for planning purposes. It is useful to know how many people are leaving their jobs for retirement or other reasons; and also how many people are currently training for a particular occupation. Many countries do not have such statistics, and even Sweden does not have sufficient data. Statistics on the number of workers in a particular occupation are needed, and these should be broken down by training sector (usually at one-year intervals). There should also be adequate training statistics by occupation, as well as unemployment statistics (including labour market policy programmes) for each occupation and vacancy statistics by occupation. These statistics are not always available, but if priority is given to the possibility of graphical representation of employment, unemployment and training for each occupation, the possibility of displaying them graphically should be considered.

Like economic forecasting, occupational forecasting needs to be closely linked to social partners. Employers’ organisations and trade unions have insights, not just on the occupational side. What is important is an understanding of the changing occupations and whether the occupational names are inappropriate in certain cases – that seems to be a bigger question than what skills are needed. However, there is a lot of interest in occupational forecasting from the public, journalists, education planners, etc.

Data conditions

Micro data: First of all, to be able to apply the Swedish model, it is necessary to have a database from annual or periodic surveys (every 2 years). The basic content of this survey is aimed at the labor force used by occupation (with a unified national occupational code in the long term) and the assessment of enterprises on the current labor balance with jobs (shortage, surplus). Depending on the division of sectors and occupations of each country, the occupations are

Divide into groups according to different criteria. The most important division is by training code.

In surveys, it is also necessary to clearly distinguish between training occupations and occupations according to work (in fact, Vietnam has two codes that are easily confusing). Whether or not labor is used in the right occupation must be assessed through the training objectives of each occupation (according to specific expertise and the level of that expertise). Therefore, people are also interested in the status of workers according to the division of functions in socio-economic activities. In addition, it is necessary to combine surveys with workers themselves about the needs for training work, career change, etc.

Macro data: In order for businesses to have a more complete reference to the environment, policies and strategies for socio-economic development in order to have a correct estimate of labor demand, it is necessary to provide macro data and data compiled from previous surveys. These data may be related to growth, state and private investment, import-export policies and the results of implementing plans of previous units. This data is needed to conduct interviews related to the need to add or reduce labor.

The research team of the National Center for Labor Market Forecasting and Information has used the Swedish forecasting model to test for Hai Duong, calculated and built a part of the output of the Swedish model according to the following principle diagram, however, it has only stopped at building the model and collecting data for the model:

	List of level 2 occupations

Information from business survey

Level 3 profession

Level 3 profession

Level 3 profession

+ Job description

+ Wind and rain give career

During the period

Next time

Total number of employees

Number of additional vacancies

Identify 14 occupational groups

Figure 1.8. Swedish human resource forecasting model (applied to Hai Duong province)

1.6.4. Human resource forecasting experience of some other countries

Some developed countries have agencies that carry out the function of forecasting education and human resource needs. Below is an overview of the experiences of some other countries in carrying out the work of forecasting education and human resource needs:

Alberta's labor supply and demand forecasting model (Canada) : The Alberta Department of Employment, Immigration and Industry annually develops the Occupational Demand Outlook Model. The development of this model began in 2000, and since 2003, the Occupational Supply Outlook Model has been developed. According to Le Dong Phuong [71], this occupational demand forecasting model predicts the development of 140 occupations in

14 economic sectors (industries) over a period of 5 years. Information is provided by occupation and by year, calculated as a proportion of the labour market. Information provided for each occupation includes: i) the number of people working in that industry; ii) the annual change rate; iii) the proportion of the labour market. Forecasts are calculated for the whole province of Alberta and 8 economic regions within the province. Based on the forecasts, some comments on the labour areas that may have labour shortages, creating a basis for learners to make appropriate choices about their majors, or employers to find measures to attract workers to their occupations.

After having forecasts on supply and demand, the ratio of the difference between supply and demand will be calculated to indicate the imbalance in the labor market. If the supply/demand ratio is less than 1, it may signal a labor shortage, and if it is greater than 1, it will indicate a surplus on the supply side. To make forecasts, the forecasting agency also makes its own comments on the general development of education. These comments are made on the basis of historical data series on education development.

In Australia , labour demand is mainly forecasted based on employer recruitment surveys and tertiary demand forecasts are based on analysis of enrolment data, factors influencing tertiary demand and demographics. Monash University uses a Consolidated General Equilibrium (CGE) labour market forecasting approach. The model represents the relationship between highly detailed macroeconomic variables of the Australian economy, which is used as the basis for the labour force forecasting process. The result is a forecast covering 112 sectors and 340 different occupations, conducted every 4 years.

In New Zealand , forecasts on labor demand and supply or human resources are based on standard occupational classification, classification of training sectors and specialties, economic classification... Forecasts on labor demand and labor training are conducted by state agencies and publicly announced for individuals and organizations to easily access and look up.

In Singapore , the demand for labor is forecasted based on the 2005 economic classification of Singapore and universities are not subject to the training quota division of the Singapore Ministry of Education but develop their own enrollment plans based on the school's training capacity and actual labor demand.

Currently, no ASEAN country has a complete national human resource forecasting model. In some countries such as the Philippines, Indonesia, Malaysia ... there are forecasting models, but they are not widely used as in developing countries.

1.6.5. Lessons for Vietnam's human resource forecasting

In forecasting labor supply and demand, the experience of other countries has shown that after forecasting the demand for jobs by economic sector and by occupation (based on the occupational list), an additional forecasting layer is often carried out: forecasting the labor market by level and training group, in which: (1) on the supply side is the number of workers (total and classified by level, training group) willing to work with a certain salary and working conditions; the labor force supply is the number of people who have graduated at different levels of training and are willing to work (looking for jobs); (2) on the demand side is the demand for employees and workers (can be collectively called workers) in a specific labor market (for example, construction or banking) and also needs to be classified by type of occupation, level and training sector. This is a very important step to have scientific bases in planning education and training policies in accordance with the needs of the labor market, avoiding unreasonable supply and demand, wasting both monetary costs and opportunity costs of society.

Depending on the purpose of using forecast results in policy making and the specific characteristics of socio-economic conditions, the organization and structure of the economy, etc., the models are built in different ways. However, we can find many similarities between the above forecast models on labor demand in general.

and the need for trained labor in particular. Common points that can be drawn include:

(1) In essence, the above models are all based on and provide forecasts of labor demand by economic sector and occupational field. The economic sector is an important starting point for forecasting labor demand for that sector and the basis for considering forecasts of demand for the entire economy;

(2) Economic development and labor demand are affected by many factors. All countries agree that it is impossible to include all factors in a specific model. In practice, solving this problem requires making assumptions and the construction of assumptions must be done carefully. It is best to build a forecasting model based on each country's own economy; in the case of "importing" a model from another economy, it is necessary to adjust and make it suitable for the economy of the country applying that model;

(3) There are many ways to calculate the employment coefficient, but the most common way that countries have used to calculate is through the production function. Of course, each country will choose the form of function to use;

(4) The employment matrix by economic sector and occupational type depends on the economic sector and occupational classification, and the level of occupational detail also depends on the available statistical data. Usually, models consider the additional costs of data collection and processing when opening up more levels of the matrix.

(5) For forecasting the demand for college and university-level human resources in Vietnam, it is possible to apply the implementation steps of the BLS model. However, because the BLS model only stops at forecasting the demand for jobs and jobs by industry, it is necessary to continue forecasting jobs by training level to determine the demand for human resources training. And so, the proposed steps for forecasting college and university-level human resources in Vietnam are as follows: