CN114358954A - Employee medical insurance fund collection and payment data prediction method, device, medium and equipment - Google Patents
Employee medical insurance fund collection and payment data prediction method, device, medium and equipment Download PDFInfo
- Publication number
- CN114358954A CN114358954A CN202111106428.0A CN202111106428A CN114358954A CN 114358954 A CN114358954 A CN 114358954A CN 202111106428 A CN202111106428 A CN 202111106428A CN 114358954 A CN114358954 A CN 114358954A
- Authority
- CN
- China
- Prior art keywords
- medical insurance
- fund
- unit
- prediction
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to the technical field of data prediction, in particular to a method, a device medium and equipment for predicting staff medical insurance fund collection and payment data, which comprises the steps of dividing collection and payment condition information of staff medical insurance fund into a plurality of units; acquiring a data time sequence of a prediction object and factors influencing the prediction object in each unit in the cycle time of a target area, and analyzing and processing the data time sequence; and carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund. The prediction method provided by the invention predicts each unit node of the employee medical insurance fund by using the time series model and combines the unit node with the system dynamics model, can effectively predict the future trend of the fund in the field of employee medical insurance, solves the problem of error accumulation caused by inaccurate single-node prediction in the traditional method, and further improves the accuracy of predicting the balance data of the employee medical insurance fund.
Description
Technical Field
The invention relates to the technical field of data prediction, in particular to a method, a device medium and equipment for predicting employee medical insurance fund collection and payment data.
Background
Medical insurance is an important component of social insurance, and the medical insurance fund balance forecasting can timely adjust the medical insurance fund balance policy so as to avoid the risk of medical insurance fund vacancy and ensure the medical insurance fund balance. Particularly, in the case of public health emergencies, namely sudden occurrence, serious epidemic situations of infectious diseases, group unknown diseases, serious food and occupational poisoning and other incidents which seriously affect public health, which cause or possibly cause serious damage to social public health, the operation condition of the medical insurance fund is influenced by certain conditions, and the condition of 'not paying in or not paying out' may occur in the area with lower chargeable month number of rolling over part of the fund, so that the fund gap is continuously enlarged.
The traditional medical insurance fund collection and payment simulation prediction method generally adopts a system dynamics tool for modeling, but the method has certain defects, and certain measurement and calculation deviation is generated under the emergency background of public health events, which is specifically as follows:
(1) the node prediction is not accurate. According to the feedback characteristic that internal components of the system are causal, the system dynamics method searches the root cause of the problem from the internal structure of the system, and has strong dependence on the time sequence relation inside the data and the causal relation among the data, but the existing research method usually adopts regression analysis to predict the result of each sub-node, and further constructs a structural equation, so that the processing mode does not fully excavate the data characteristics, errors are gradually accumulated due to the fact that part of the nodes are not predicted accurately, and the final fund balance prediction is not accurate due to the fact that a chain reaction is generated. (2) There is a lack of measures for the impact of emergent public health emergencies on fund operation. For example, under a new crown epidemic situation, the existing research rarely has fund conditions oriented to such emergent public health emergency events, influence factors of fund operation under such background are not fully excavated, and great deviation is generated on the prediction of short-medium term and fund long-term lag change conditions.
Disclosure of Invention
In order to solve the technical problem that the node prediction of the traditional system dynamics model is inaccurate, the invention provides a prediction method of employee medical insurance fund collection and payment data, which comprises the following steps:
dividing the income and expenditure information of the medical insurance fund of the employee into a plurality of units;
acquiring a data time sequence of nodes required by a prediction object in each unit within the period time of a target area, and analyzing and processing the data time sequence;
and carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
Further, the acquired data time series are analyzed by an ARIMA model, a GM (1,1) gray model and/or a lunar phase averaging method.
Further, the unit comprises a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events, wherein the medical insurance fund intervention unit under the influence of the public health events comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit.
Further, the population prediction unit predicts the number of the population participating in the profession and the number of the population retiring under the influence of public health events;
the needed nodes of the prediction object are the number of the population of the employee and the GDP change, and the ratio of the incremental change rate of the number of the employee to the incremental change rate of the GDP is calculated by acquiring the number of the population of the employee and the time sequence data of the GDP within the period time of the target area; respectively measuring the number of the medical insurance participants of the town under the influence of public health events according to the GDP change condition;
the number of retired people is analyzed and processed through three time series models, namely an ARIMA model, a GM (1,1) model and a lunar phase averaging method, and the model with the best precision evaluation is selected as a prediction result.
Further, the abnormal medical insurance fund income reducing unit is used for measuring and calculating social flat wages under the influence of public health events and the medical insurance fund income changes when the number of the employee insurance population is reduced; the method comprises the following steps:
the required nodes of the prediction object are community-level pay and GDP change, the ratio of the community-level pay increment change rate to the GDP increment change rate is measured and calculated by acquiring time series data of the community-level pay and the GDP within a certain period of time in a target area, and the community-level pay change rate is measured and calculated according to a time series model of the GDP change rate, so that the community-level pay is further measured and calculated;
calculating the reduction amount of the occupational insurance population under the influence of public health events through a time series model;
obtaining medical insurance rates by obtaining policy parameters within the target area prediction cycle time;
obtaining a fund income adjusting coefficient by calculating the ratio of the average payment base number of the medical insurance fund in the last period time of the target area to the social flat wage in the last period time of the target area specified by the policy;
the system dynamics structure equation of the abnormal medical insurance fund income reduction unit is set as follows: the abnormal medical insurance fund income reduction amount is the amount of reduction of the employee insurance population under the influence of public health events, the social flat work fund and the medical insurance rate;
through the construction of the structural equation, the specific amount of reduction of the whole number of the participating insurance people to the income reduction of the medical insurance fund is calculated.
Further, the abnormal special outpatient behavior fund expenditure unit is used for measuring and calculating the expenditure amount of special outpatient behavior medical insurance fund under the condition of chronic disease increase; the method comprises the following steps:
sampling and investigating the conditions of chronic diseases and treatment intentions of the attending and insurance personnel;
counting the probability of the visit intention of the attending insured staff to suffer from chronic diseases and go to a special outpatient service;
combining the chronic diseases of the staffs in the office with the exercise condition, and respectively counting the number of people suffering from the chronic diseases and having a large amount of exercise, the number of people suffering from the chronic diseases and having a small amount of exercise and the number of people suffering from the chronic diseases and having no exercise when sitting still;
calculating the special outpatient service increase coefficient under the public health event through the counted number of people;
the system dynamics structural equation of the abnormal special outpatient service behavior fund expenditure unit is set as follows: the expenditure amount of the abnormal special outpatient service behavior fund is (the total amount of the special outpatient service behaviors of employees + the total amount of the special outpatient service behaviors of retirees) and the probability of the special outpatient service intention of the special outpatient service under the influence of public health events;
through the construction of the structural equation, the fund expenditure amount of the special outpatient behavior under the influence of the public health event is calculated.
Further, the unit further comprises a calculation unit for the payable monthly numbers of the medical insurance pool fund, and the calculation unit for the payable monthly numbers of the medical insurance pool fund comprises the following calculation steps:
calculating the balance of the medical insurance fund overall planning part in the target area prediction cycle time based on the balance calculation of the medical insurance fund overall planning part in the target area prediction cycle time and the accumulated balance amount of the medical insurance aggregate overall planning fund in the previous cycle time;
calculating the fund payment amount under the influence of public health events in the target area prediction period time through system dynamics simulation, and calculating the corresponding monthly payment amount;
and finally, the payable month number of the medical insurance overall fund in the target area prediction cycle time is calculated through the balance of the medical insurance overall fund and the average monthly expenditure.
The invention also provides a device for predicting the employee medical insurance fund collection and payment data, which comprises
The classification module is used for dividing the collection and payment condition information of the employee medical insurance fund into a plurality of units, namely a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events, wherein the medical insurance fund intervention unit under the influence of the public health events comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit;
the acquisition module is used for acquiring a data time sequence of nodes required by the prediction object in each unit within the period time of the target area and analyzing and processing the data time sequence;
and the prediction module is used for carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
The present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method of predicting employee medical insurance fund reimbursement data as set forth in any one of the above.
The present invention also provides an electronic device comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the processor to perform a method for employee medical insurance fund collection data prediction as described in any one of the above.
Compared with the prior art, the employee medical insurance fund collection data prediction method provided by the invention has the following advantages: by predicting each unit node of the employee medical insurance fund by using the time series model and combining the unit node with the system dynamics model, the future trend of the fund in the field of employee medical insurance can be effectively predicted, the problem of error accumulation caused by inaccurate single-node prediction in the traditional method is solved, and the accuracy of predicting the balance data of the employee medical insurance fund is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting employee medical insurance fund reimbursement data in accordance with the present invention;
FIG. 2 is a diagram showing the relationship between the units in the medical insurance fund collection and payment status;
FIG. 3 is a causal diagram of a population prediction unit;
FIG. 4 is a causal graph of the predicted amount reflecting status of each unit in the medical insurance fund reimbursement status;
FIG. 5 is a causal graph of a normal medical insurance fund revenue unit;
FIG. 6 is a causal graph of a normal outpatient behavior fund payout subunit;
FIG. 7 is a causality chart of the normal hospitalization behavior fund expenditure subunit;
FIG. 8 is a causal graph of an abnormal medical insurance fund income reduction unit;
FIG. 9 is a causal graph of an abnormal special clinic behavior fund payout unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, the technical features designed in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Medical insurance is an important component of social insurance, and the medical insurance fund balance forecasting can timely adjust the medical insurance fund balance policy so as to avoid the risk of medical insurance fund vacancy and ensure the medical insurance fund balance. However, the traditional medical insurance fund collection and payment simulation prediction method usually adopts a system dynamics tool for modeling, and adopts regression analysis to predict the result of each node, so as to construct a structural equation, so that the defect of inaccurate node prediction exists, and finally, the problem of inaccurate fund data collection and payment prediction is caused by error accumulation.
Therefore, the invention provides an improved employee medical insurance fund collection data prediction method, a time series model is introduced to predict system dynamics nodes, data time sequence characteristics can be fully mined, and node change trends can be accurately predicted, as shown in fig. 1, the method comprises the following steps: dividing the income and expenditure information of the medical insurance fund of the employee into a plurality of units; acquiring a data time sequence of nodes required by a prediction object in each unit within the period time of a target area, and analyzing and processing the data time sequence; and carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
When the system is specifically implemented, the collection and payment condition information of the medical insurance fund of the employee is divided into a plurality of units, wherein the units comprise a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events; the population prediction unit is mainly used for counting the number of persons who work and participate in the insurance and the number of persons who retire and participate in the insurance so as to prepare for subsequent data prediction; the normal medical insurance fund income unit is mainly used for paying medical social insurance amount for the staff; the normal medical insurance fund expenditure unit mainly comes from reimbursement expenditure amount of outpatient services and hospitalization services, and therefore can comprise an outpatient service action fund expenditure subunit and an hospitalization action fund expenditure subunit; in addition, in order to fully incorporate the occurrence of public health incidents such as the "new crown" epidemic situation into the prediction of employee medical insurance fund payment data so as to deal with the problem that the model prediction precision deviates from the real scene in a short time period under the scenes, a medical insurance fund intervention unit under the influence of the public health incidents is further divided, wherein the medical insurance fund intervention unit mainly comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit.
It should be noted that the normal medical insurance fund and the abnormal medical insurance fund in the present invention refer to the medical insurance fund without the influence of the public health event and the medical insurance fund with the influence of the public health event, respectively.
Then, a data time sequence of each node of the prediction object in each unit within the cycle time of the target area is obtained, it should be noted that the target area can be divided into provinces and cities according to different policies due to differences of medical insurance policies of each area, wherein the cycle time can be several years, one year, half a year, one season, one month or the like, and each node of the prediction object is mainly an influence factor in the unit which influences the condition of the medical insurance fund collection and payment of the unit.
And analyzing and processing the acquired data time sequence, wherein the time sequence node is predicted by preferably using an ARIMA model, a GM (1,1) gray model and a lunar phase averaging method. The method comprises the following steps of establishing pipelining processing on an ARIMA model fitting and predicting process from an engineering implementation angle, and automatically performing model parameter optimization, model fitting and predicting, wherein the specific process is as follows: firstly, ADF (automatic document feeder) inspection is carried out on a preprocessed time sequence, when a unit detection statistic p-value is larger than 0.05, the statistical significance is achieved, and an ARIMA model difference parameter d is fixed; secondly, performing AR and MA model parameter order determination, setting algorithm parameter configuration information, preferentially estimating and determining other parameters of the ARIMA model according to MSE, AIC criterion and BIC criterion, and fitting a prediction model; finally, performing iterative training and rolling prediction, in order to avoid the situations that the ARIMA model is under-fitted and the accuracy of the prediction model is insufficient, processing the time sequence data in the form of iterative training and rolling prediction, setting the prediction step length of the ARIMA model to be 1, adding the prediction value back to the training data to re-fit the model after the result of t +1 moment is predicted by each iteration training of the algorithm, and predicting the result of t +2 moment; the processing mode can reduce the model under-fitting error of the ARIMA model caused by too short data time period and too long prediction step length.
The calculation procedure for establishing the GM (1,1) gray model for the time series data is as follows: let the total N-period time sequence data be x(0)＝{x(0)(1),x(0)(2),…,x(0)(N)}；
Firstly, an accumulation sequence is constructed once based on a formula
Constructing a primary accumulation sequence of the original sequence as x (1) ═ x11, x12, … and x 1N;
secondly, a matrix of differential equations is constructed, in particular, the sequence x is set(0)And x(1)Satisfying the equation Y ═ B × U, wherein:
a is the developed ash number, and u is the endogenous control ash number;
then estimating parameters by least squares
And
the formula is as follows:
and solve for time responseThe equation should be:
finally, a time series prediction is performed, in particular, x can be calculated by a time response equation(1)(k) Fitting value of
And by the formula:
inverse solving the original sequence x(0)(k) Fitting value of
And (6) performing prediction.
The time series processing procedure for the lunar phase averaging method is as follows: and drawing a time sequence variation trend curve graph based on the preprocessed time sequence data, analyzing the time sequence variation trend curve graph, and calculating the predicted value of the corresponding month of the prediction period category by averaging the data with the significant change rule of the same-period moon phase.
Based on the method for analyzing and processing the time sequence of the required node data, the invention is configured according to the related measurement and calculation formula or policy parameter presetting, the unit data is subjected to system dynamics modeling, and the medical insurance fund payment data is predicted according to the obtained unit models.
In summary, the main design concept of the invention is that after the income and expenditure situation of the employee medical insurance fund is divided into a plurality of units, each unit node is configured through a time series model, formula combination and policy parameter presetting, and then is combined with a system dynamics model to predict the income and expenditure situation of the simulated medical insurance fund, so that the model prediction precision is improved. Based on the above main design concept, the present invention provides an embodiment of a specific data prediction method for each unit in detail. FIG. 2 is a diagram showing the relationship between the units in the medical insurance fund payment status, and FIG. 4 is a diagram showing the cause and effect relationship between the predicted amount of money reflected by the units in the medical insurance fund payment status.
In one embodiment, the population prediction unit is mainly set up for calculating data of each unit in the following process as an important basis, and mainly comprises the steps of predicting the number of the employee insured population and the number of the retirement population under the influence of public health events; considering that the economic development of a region can have strong correlation with the changing trend of the employment population, and the changing trend of the economic development and the employment population does not keep constant relationship for a long time. Firstly, establishing nodes, namely a causal relationship graph of a population prediction unit as shown in fig. 3, selecting nodes required by a prediction object as the number of the working insured population, calculating the ratio of the incremental change rate of the number of the working insured population to the incremental change rate of GDP (general data processing) by acquiring time sequence data of the number of the working insured population within the period time of a target area, and respectively calculating the number of the urban staff insured population under the influence of public health events according to a time sequence model of the GDP change rate if the number of the working insured population and the GDP change meet the trend of the ratio within the prediction period time; the GDP change status is based on data provided by the national institute of statistics, measurement, and research reports of the digital chinese institute. The number of retired people is analyzed and processed according to the three time series model methods of the ARIMA model, the GM (1,1) model and the lunar phase averaging method, and the model with the best precision evaluation is selected as a prediction result.
In one embodiment, as shown in fig. 5, the normal medical insurance fund income unit is mainly related to the medical insurance amount paid by the number of the participating insurance population, so the required nodes of the prediction object include the number of the participating insurance population, the social level fund of the previous period of time and the fund income adjustment coefficient; the specific prediction process is as follows:
(1) calculating the population number of the insured population, and obtaining the population number through the population prediction unit; acquiring medical insurance rates, and acquiring policy parameters in a period time of a target area;
(2) the method comprises the steps of measuring and calculating the social flat wages (the social flat wages are short for average wages of social workers, and generally refer to the average wages obtained by dividing the total amount of all the social workers in a certain period of a certain region or country by the number of workers in the period), wherein the ratio of the social flat wages change rate to the GDP change rate is measured by obtaining time sequence data of the social flat wages amount and the GDP in a target region period time because the regional economic development and the change trend of the social flat wages have strong correlation, and if the social flat wages change and the GDP change in the target region period time are consistent with the trend of the ratio, the social flat wages change rate can be calculated based on the GDP change rate, and the social flat wages can be further measured and calculated;
(3) calculating a fund adjustment coefficient, and predicting a fund income adjustment coefficient in the period time of the target area by calculating the proportion of an actual monthly payment base number in the last period time of the target area to the social flat pay base number in the last period time; taking the fund adjustment coefficient of the target region in 2020 as an example, assuming that the upper limit and the lower limit of the monthly payment base number of the target region are calculated based on the flat wages of the society in the previous year, firstly, the ratio of the monthly average payment base number of the target region in 2019 to the flat wages of the society in 2018 is calculated, and then the actual monthly per-capita payment base number of the target region in 2020 is the product of the ratio and the monthly average of the flat wages of the society in 2019 predicted by the step (1), so that the fund income adjustment coefficient is equal to the monthly average payment base number of the target region in 2019/the flat wages of the society in 2018.
The specific formula is as follows:
(4) the system dynamics structural equation of the normal medical insurance fund income unit is set as follows: the total amount of the normal medical insurance fund income is the social flat wage, the ginseng insurance population number, the medical insurance rate and the fund income adjusting coefficient;
through the construction of the structural equation, the total amount of the normal medical insurance fund income without the influence of public health events can be calculated and predicted.
In one embodiment, the normal medical insurance fund payment unit is mainly used for medical expenditure reimbursement generated by the employment and hospitalization of the insured staff, and therefore, the normal medical insurance fund payment unit comprises a normal outpatient service behavior payment subunit and a normal hospitalization behavior payment subunit, and the normal medical insurance fund payment amount within the period time of the target area can be predicted by adding the predicted payment amounts of the normal outpatient service behavior payment subunit and the normal hospitalization behavior payment subunit.
As shown in fig. 6, for the normal outpatient behavior fund expenditure subunit, the outpatient behaviors can be divided into two categories, namely special outpatient behaviors and ordinary outpatient behaviors, and the visitors can be divided into two categories, namely on-duty staff and retirement staff, wherein the visitors are all the insured staff. The total fund expenditure amount of the normal outpatient behavior is obtained by respectively measuring and calculating the total monthly counting times of the ordinary outpatient behavior and the special outpatient behavior of the on-duty personnel and the retirement personnel and the overall fund expenditure amount of each average time. Specifically, the system dynamics structural equation of each main node is set as:
the total number of the ordinary outpatient behaviors of the employees is the average number of times of the ordinary outpatients of the insurance personnel and the total number of the population of the insurance personnel, the total number of the special outpatients of the employees is the average number of times of the special outpatients of the employees and the total number of the ordinary outpatients of the retired personnel, the total number of the ordinary outpatients of the retired personnel and the total number of the special outpatients of the retired personnel and the average number of times of the special outpatients of the retired personnel, the total sum of the ordinary outpatients of the employees is the average number of times of the ordinary outpatients of the employees and the total sum of the ordinary outpatients of the retired personnel and the total sum of the average sum of the total sum of the ordinary outpatients of the retired personnel and the total sum of the general refunds of the general retired personnel and the total sum of the ordinary outpatients of the retired behaviors of the retired personnel and the average number of the outpatients of the total sum of the general refundsed personnel, and the total expenditure of the normal outpatient behavior fund is the total sum of the general outpatient behaviors of the employees, the total sum of the special outpatient behaviors of the employees, the total sum of the general outpatient behaviors of the retirees and the total sum of the special outpatient behaviors of the retirees.
Through the construction of the structural equation, three time series models of ARIMA, GM (1,1) and lunar phase averaging are used for predicting each node in the normal outpatient behavior expenditure subunit, and the method with the best precision evaluation is selected to serve as the prediction result of the normal outpatient behavior expenditure subunit.
As shown in fig. 7, for the normal hospitalization activity fund expenditure subunit, the hospitalization activities in the subunit can be classified into primary, secondary and tertiary according to the hospital grade, and the hospitalization agents with participation insurance can be classified into two categories, namely, the on-duty personnel and the retirement personnel. The total fund expenditure amount of the normal hospitalization behavior is obtained by testing the expenditure amounts of the first-level, second-level and third-level hospitalization behaviors of the working personnel and the retirement personnel respectively.
Specifically, the system dynamics structural equation of each main node is set as:
the total number of primary hospitalization activities of the employees is the average number of primary hospitalizations of the employees and the total number of the population of the employees, the total number of secondary hospitalization activities of the employees is the average number of secondary hospitalization times of the employees and the total number of the population of the employees, the total number of tertiary hospitalization activities of the employees and the total number of the population of the employees, the total number of secondary hospitalization activities of the retired employees and the total number of the secondary hospitalization activities of the retired employees and the total number of tertiary hospitalization activities of the retired employees and the total number of the primary hospitalization activities of the retired employees and the total number of primary hospitalization activities of the employees and the total number of the primary hospitalization activities of the retired employees and the total number of the primary hospitalization activities of the employees and the total number of the secondary hospitalization activities of the total number of the primary hospitalization activities of the total number of the populations of the residents and the secondary hospitalization activities of the employees, the total sum of the three-level hospitalization activities of the employees is equal to the average sum of the three-level hospitalization of the employees and the total sum of the population of the employees, the total sum of the first-level hospitalization activities of the retirees is equal to the average sum of the first-level hospitalization of the retirees and the total sum of the second-level hospitalization activities of the retirees is equal to the average sum of the second-level hospitalization of the retirees and the total sum of the three-level hospitalization activities of the retirees and the total sum of the normal hospitalization activities fund is equal to the total sum of the first-level hospitalization activities of the employees, the total sum of the second-level hospitalization activities of the employees, the total sum of the three-level hospitalization activities of the retirees, the total sum of the first-level hospitalization activities of the retirees and the total sum of the second-level hospitalization activities of the retirees and the total sum of the three-level hospitalization activities of the retirees.
Similarly, through the construction of the structural equation, each node in the normal hospitalization behavior expenditure subunit is predicted by using three time series models of ARIMA, GM (1,1) and lunar phase averaging, and the method with the best accuracy evaluation is selected as the prediction result of the normal hospitalization behavior expenditure subunit.
In summary, the total expenditure amount of the normal medical insurance fund expenditure unit should be the sum of the calculated total expenditure amount of the normal outpatient behavior fund and the calculated total expenditure amount of the normal hospitalization behavior fund; it should be noted that the total expenditure amount of the normal medical insurance fund expenditure unit provided by the invention is not limited to two most major expenditure sources including the total expenditure amount of the normal outpatient behavior fund and the total expenditure amount of the normal hospitalization behavior fund, and also includes other expenditures such as the total expenditure amount allocated to the personal account of the retirement staff, and the other expenditure sources occupy less percentage, and are not repeated herein; the person skilled in the art can also predict other expenditure amounts by using the prediction method proposed by the inventive concept, thereby obtaining more comprehensive expenditure conditions of the normal medical insurance fund expenditure unit.
In order to solve the problem of inaccurate medical insurance fund income and expenditure prediction in a short time during a public health accident, the invention adds a medical insurance fund intervention unit under the influence of the public health accident on the basis of an original normal medical insurance fund data prediction unit, and obtains the main factors of inaccurate medical insurance fund income and expenditure prediction caused by the interference of the public health accident through research, wherein the main factors are the reduction of the whole number of insured people to reduce fund income, the increase of the morbidity probability of chronic diseases to increase fund expenditure, the reduction of fund income caused by the policy preferential factors issued by the public health accident and the like. Therefore, the medical insurance fund intervention unit is divided into two main influencing factors, namely an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit.
In one embodiment, a certain impact is brought to the economy due to an emergent public health event, such as a 'new crown' epidemic situation, particularly for small and medium-sized micro-enterprises, the cash flow pressure of the enterprises is high, and the national GDP has downlink risks, so that the recruitment demand of the enterprises is reduced, even an officer or a fellow person is caused easily, and the reduction of the population of the insurance of the enterprise is influenced; therefore, as shown in fig. 8, the influence of the reduction of the population of the occupational insurance fund is required to be predicted in the abnormal medical insurance fund income reduction unit, which mainly comprises the measurement of the social flat fund under the influence of the public health event and the change of the medical insurance fund income when the population of the occupational insurance fund is reduced; the method comprises the following specific steps:
firstly, the required nodes of the prediction object are the social flat wages and the GDP change, the ratio of the social flat wage increment change rate to the GDP increment change rate is measured and calculated by acquiring time sequence data of the social flat wages and the GDP in the target area cycle time, and if the social flat wages and the GDP change in the prediction cycle time both accord with the trend of the ratio, the social flat wage change rate can be measured and calculated according to a time sequence model of the GDP change rate, and then the social flat wages are measured and calculated; the social flat wages are average wages of social employees, and the data is from reports provided by local agency and statistical bureau. And secondly, calculating the number of the occupational insurance population under the influence of the public health events through a time series model, and subtracting the number of the occupational insurance population under the influence of the public health events from the total number of the occupational insurance population to obtain the reduction amount of the occupational insurance population under the influence of the public health events. Then, the policy parameters within the target area prediction cycle time are obtained to obtain the medical insurance rates.
Then, a fund income adjusting coefficient is obtained by calculating the ratio of the average monthly payment base number of the medical insurance fund in the last period time of the target area to the social flat wages in the last period time of the target area specified by the policy; the specific calculation method takes the prediction cycle time of 2020 as an example, supposing that the upper limit and the lower limit of the monthly payment base number of each target area are calculated based on the monthly social flat pay of 2019, which is the last cycle time, firstly, the proportion of the monthly average payment base number of 2019 of the target area to the 2018 social flat pay is calculated, then the actual monthly average payment base number of 2020 of the target area is the product of the proportion and the monthly average value of the 2019 social flat pay predicted according to the time series, and then the formula of the fund income adjustment coefficient is as follows: and the fund income adjustment coefficient is equal to the average payment base number of the target area 2019 year and month/the average payroll of the target area 2018 society.
The specific formula is as follows:
and finally, setting a system dynamic structure equation of the abnormal medical insurance fund income reduction unit as follows: abnormal medical insurance fund income reduction amount is the reduction amount of the professional insurance population under the influence of public health events
Social capital, medical insurance rate, fund income adjustment factor. Through the construction of the structural equation, the specific amount of the reduction of the overall number of the participating insurance people to the abnormal medical insurance fund income can be calculated.
In summary, the total amount of the abnormal medical insurance fund income is equal to the sum of the normal medical insurance fund income and the abnormal medical insurance fund income reduction amount subtracted from the predicted normal medical insurance fund income, and the total amount of the medical insurance fund income under the influence of the public health event is predicted.
As a preferable scheme, specific policy factors under the influence of public health events in the target region can be brought into the measurement and calculation scope of the medical insurance fund income reduction unit, for example, during the new crown epidemic situation, a related medical insurance expense reduction policy appears, so that the medical insurance fund comprehensive rate under the policy in each region is revised and brought into the measurement and calculation scope.
The medical cost of the chronic disease is extremely high, the treatment period is long, once the chronic disease is not prevented and treated, the chronic disease can be repeatedly attacked and can not be cured, so that the economic and life hazards are caused, and for the chronic disease, various policy subsidies and medical reimbursement are carried out on medical insurance workers with the chronic disease in China, so that the expense cost of the medical insurance fund for the special outpatient service is very necessary. In one embodiment, due to a sudden public health incident, the chronic diseases of the employees have certain probability of being increased due to various factors; thus, the inclusion of increased incidence of chronic disease as an abnormal special outpatient behaviour fund expenditure unit into a medical fund reimbursement situation under the influence of public health events will further improve the accuracy of its prediction. The unit introduces a special outpatient service increase coefficient and special outpatient service intention probability under the influence of public health events based on the shadow variable node of the special outpatient service behaviors to measure the fund expenditure increment of the special outpatient service behaviors under the influence of public health events, such as a causal relationship diagram shown in fig. 9, and specifically comprises the following steps:
sampling and investigating the conditions of chronic diseases and treatment intentions of the attending and insurance personnel; counting the probability of the visit intention of the attending insured staff to suffer from chronic diseases and go to a special outpatient service; combining the chronic diseases of the staffs in the office with the exercise condition, and respectively counting the number of people suffering from the chronic diseases and having a large amount of exercise, the number of people suffering from the chronic diseases and having a small amount of exercise and the number of people suffering from the chronic diseases and having no exercise when sitting still; calculating the special outpatient service increase coefficient under the public health event through the counted number of people; specifically, if the number of people with chronic diseases and a large amount of exercise is a, the number of people with chronic diseases and a small amount of exercise is b, and the number of people with chronic diseases and no exercise while sitting is c, the prevalence coefficient under the influence of public health events is (b + c)/2, the prevalence coefficient under the influence of no public health events is (a + b)/2, and the ratio of the two is (b + c)/(a + b) as the special outpatient increase coefficient m. And finally, setting a system dynamics structural equation of the abnormal special outpatient service behavior medical insurance fund expenditure unit as follows: the total amount of the special outpatient behaviors of the employees and the total amount of the special outpatient behaviors of the retirees in the structural equation are the total amount of the special outpatient behaviors of the employees and the total amount of the special outpatient behaviors of the retirees measured in the normal hospitalization behavior fund expenditure subunit; through the construction of the structural equation, the fund expenditure increment amount of special outpatient service behaviors under the influence of public health events can be calculated.
In summary, the total expenditure amount of the abnormal medical insurance fund is equal to the sum of the predicted total expenditure amount of the normal medical insurance fund and the increment amount of the abnormal medical insurance fund expenditure, namely the total expenditure amount of the medical insurance fund under the influence of the public health event is predicted.
It should be noted that the plurality of units provided by the patent of the present invention for predicting and dividing the medical insurance fund income and expenditure data state cover most medical insurance fund income and expenditure sources, but do not exclude that other few medical insurance fund income and expenditure sources exist and are not mentioned by the present invention, the prediction method of the present invention can be used for data prediction, which is not described herein in detail, and those skilled in the art can add other medical insurance fund income and expenditure sources on the basis of the prediction method provided by the present invention to further improve the prediction data.
In an embodiment, based on the system dynamics simulation model and the pre-measured data result of each unit, the payable monthly data calculating unit of the medical insurance overall fund can be used for predicting the balance and balance conditions of the medical insurance fund within the prediction period time of the target area under the influence of public health events, and the payable monthly data of the medical insurance overall fund of the target area is calculated by combining the balance data rolled in the target area. The payable month number aims to calculate the payable month number of the accumulated balance of the medical insurance fund under the condition of zero income of the medical insurance fund system, and the specific calculation process is as follows: calculating the balance of the medical insurance fund overall planning part in the target area prediction cycle time based on the balance calculation of the medical insurance fund overall planning part in the target area prediction cycle time and the accumulated balance amount of the medical insurance overall planning fund in the previous cycle time; calculating the fund payment amount under the influence of public health events in the target region prediction cycle time through system dynamics simulation, and calculating the corresponding monthly payment amount; and finally, the payable month number of the medical insurance overall fund in the target region prediction cycle time can be obtained through the balance of the medical insurance overall fund and the average monthly payment amount.
In specific implementation, the 'new crown' epidemic situation is taken as the background of occurrence of public health events, and the number of months payable by the medical insurance pool fund at the end of 2020 is calculated. The specific measurement and calculation process is as follows:
(1) accumulating the balance amount of the town medical insurance fund by 2019; the following formula is used to obtain: the accumulated balance amount of the town medical insurance fund at the end of 2019 is k, and the accumulated balance amount of the city medical insurance fund at the end of 2018; and k is the increase rate of the accumulated balance amount of the urban employee medical insurance fund in the target area in 2019, and is obtained based on the accumulated balance amount of the urban employee medical insurance fund in each area in 2018 and 2017 published by the national statistical bureau according to the ratio of the two.
(2) The general planning part of the medical insurance fund of the city position at the end of 2019 accumulates the balance amount; the following formula is used to obtain: the accumulated balance amount of the general fund of the city job in the end of 2019 is b, and the accumulated balance amount of the general fund of the city job in the end of 2019 is b; and b, solving the ratio of the accumulated balance of the overall account to the total balance as b according to the operation condition analysis report of the medical insurance fund in each region in 2019.
(3) Comprehensively settling part of medical insurance funds in each region at the end of 2020; based on the surplus calculation of the medical insurance fund overall planning part in 2020 areas of the whole country and the accumulated surplus of the medical insurance fund at the end of 2019 of the previous calculation, the surplus of the medical insurance fund overall planning part in the areas at the end of 2020 is calculated.
(4) The medical insurance pool fund can pay the number of months at the end of 2020; based on system dynamics simulation, the fund payment amount of each month in 2020 of each region in China under the influence of epidemic situations is measured and calculated, the corresponding month average payment amount is calculated, and the monthly payable number of the 2020 end medical insurance overall fund can be calculated by combining the accumulated balance of the final medical insurance overall fund in 2020 of the last step of measurement and calculation.
The invention also provides a device for predicting the employee medical insurance fund collection and payment data, which comprises a classification module, an acquisition module and a prediction module, wherein the acquisition module and the prediction module can realize the method for predicting the employee medical insurance fund collection and payment data in the embodiment.
In specific implementation, the classification module divides the collection and payment condition information of the employee medical insurance fund into a plurality of units, wherein the units comprise a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events, and the medical insurance fund intervention unit under the influence of the public health events comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit; the acquisition module is used for acquiring a data time sequence of nodes required by the prediction object in each unit within the period time of the target area and analyzing and processing the data time sequence; and the prediction module is used for carrying out system dynamics modeling on each unit data according to the analyzed and processed data time sequence and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
The present invention also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method of predicting employee medical insurance fund reimbursement data as set forth in any one of the above.
In specific implementation, the computer-readable storage medium is a magnetic Disk, an optical Disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid-State Drive (SSD), or the like; the computer readable storage medium may also include a combination of memories of the above kinds.
The present invention also provides an electronic device comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the processor to perform the method for employee medical insurance fund balance data prediction as recited in any one of the above.
In particular, the number of processors may be one or more, and the processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be communicatively coupled to the processor via a bus or otherwise, the memory storing instructions executable by the at least one processor to cause the processor to perform a method of employee medical insurance fund balance data prediction as described in any of the above embodiments.
Compared with the prior art, the method, the device, the medium and the equipment for predicting the balance data of the medical insurance fund for the employees, provided by the invention, have the advantages that the time sequence model is utilized to predict each unit node of the medical insurance fund for the employees, and the unit nodes are combined with the system dynamics model, so that the future trend of the fund in the medical insurance field of the employees can be effectively predicted, the problem of error accumulation caused by inaccurate single-node prediction in the traditional method is solved, and the precision of predicting the balance data of the medical insurance fund for the employees is improved. Meanwhile, the influence on the medical insurance fund under the background of the sudden public health event is added, the key nodes are respectively predicted from four influenced important units, namely a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of the public health event, the construction of a system dynamics simulation model is completed, the prediction accuracy of the future trend of the employee medical insurance fund income and expenditure data is effectively improved, and the accuracy and the effectiveness of risk management on the medical insurance fund are further improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting employee medical insurance fund collection and payment data is characterized by comprising the following steps:
dividing the income and expenditure information of the medical insurance fund of the employee into a plurality of units;
acquiring a data time sequence of nodes required by a prediction object in each unit within the period time of a target area, and analyzing and processing the data time sequence;
and carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
2. The employee medical insurance fund reimbursement data prediction method according to claim 1, wherein: the acquired data time series are analyzed by an ARIMA model, a GM (1,1) grey model and/or a lunar phase averaging method.
3. The employee medical insurance fund reimbursement data prediction method according to any one of claims 1-2, wherein: the unit comprises a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events, wherein the medical insurance fund intervention unit under the influence of the public health events comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit.
4. The employee medical insurance fund reimbursement data prediction method according to claim 3, wherein: the population prediction unit predicts the number of the population participating in the insurance and the number of the retirement population under the influence of public health events;
the needed nodes of the prediction object are the number of the population of the employee and the GDP change, and the ratio of the incremental change rate of the number of the employee to the incremental change rate of the GDP is calculated by acquiring the number of the population of the employee and the time sequence data of the GDP within the period time of the target area; respectively measuring the number of the medical insurance participants of the town under the influence of public health events according to the GDP change condition;
the number of retired people is analyzed and processed through three time series models, namely an ARIMA model, a GM (1,1) model and a lunar phase averaging method, and the model with the best precision evaluation is selected as a prediction result.
5. The employee medical insurance fund reimbursement data prediction method according to claim 4, wherein: the abnormal medical insurance fund income reducing unit is used for measuring and calculating the social security fund affected by the public health event and the medical insurance fund income change when the number of the participating insurance population is reduced; the method comprises the following steps:
the required nodes of the prediction object are community-level pay and GDP change, the ratio of the community-level pay increment change rate to the GDP increment change rate is measured and calculated by acquiring time sequence data of the community-level pay and the GDP within the target region cycle time, and the community-level pay change rate is measured and calculated according to a time sequence model of the GDP change rate, so that the community-level pay is further measured and calculated;
calculating the reduction amount of the occupational insurance population under the influence of public health events through a time series model;
obtaining medical insurance rates by obtaining policy parameters within the target area prediction cycle time;
obtaining a fund income adjusting coefficient by calculating the ratio of the average payment base number of the medical insurance fund in the last period time of the target area to the social flat wage in the last period time of the target area specified by the policy;
the system dynamics structure equation of the abnormal medical insurance fund income reduction unit is set as follows: the abnormal medical insurance fund income reduction amount is the amount of reduction of the employee insurance population under the influence of public health events, the social flat work fund and the medical insurance rate;
through the construction of the structural equation, the specific amount of reduction of the whole number of the participating insurance people to the income reduction of the medical insurance fund is calculated.
6. The employee medical insurance fund reimbursement data prediction method according to claim 3, wherein: the abnormal special outpatient behavior fund expenditure unit is used for measuring and calculating the expenditure amount of special outpatient behavior medical insurance fund under the condition of increasing the incidence of chronic diseases; the method comprises the following steps:
sampling and investigating the conditions of chronic diseases and treatment intentions of the attending and insurance personnel;
counting the probability of the visit intention of the attending insured staff to suffer from chronic diseases and go to a special outpatient service;
combining the chronic diseases of the staffs in the office with the exercise condition, and respectively counting the number of people suffering from the chronic diseases and having a large amount of exercise, the number of people suffering from the chronic diseases and having a small amount of exercise and the number of people suffering from the chronic diseases and having no exercise when sitting still;
calculating the special outpatient service increase coefficient under the public health event through the counted number of people;
the system dynamics structural equation of the abnormal special outpatient service behavior fund expenditure unit is set as follows: the expenditure amount of the abnormal special outpatient service behavior fund is (the total amount of the special outpatient service behaviors of employees + the total amount of the special outpatient service behaviors of retirees) and the probability of the special outpatient service intention of the special outpatient service under the influence of public health events;
through the construction of the structural equation, the fund expenditure amount of the special outpatient behavior under the influence of the public health event is calculated.
7. The employee medical insurance fund reimbursement data prediction method according to claim 3, wherein: the unit further comprises a calculation unit for the payable monthly numbers of the medical insurance pool fund, and the calculation unit for the payable monthly numbers of the medical insurance pool fund comprises the following calculation steps:
calculating the balance of the medical insurance fund overall planning part in the target area prediction cycle time based on the balance calculation of the medical insurance fund overall planning part in the target area prediction cycle time and the accumulated balance amount of the medical insurance aggregate overall planning fund in the previous cycle time;
calculating the fund payment amount under the influence of public health events in the target area prediction period time through system dynamics simulation, and calculating the corresponding monthly payment amount;
and finally, the payable month number of the medical insurance overall fund in the target area prediction cycle time is calculated through the balance of the medical insurance overall fund and the average monthly expenditure.
8. An employee medical insurance fund collection data prediction apparatus, comprising:
the classification module is used for dividing the collection and payment information of the employee medical insurance fund into a plurality of units, wherein the units comprise a population prediction unit, a normal medical insurance fund income unit, a normal medical insurance fund expenditure unit and a medical insurance fund intervention unit under the influence of public health events, and the medical insurance fund intervention unit under the influence of the public health events comprises an abnormal medical insurance fund income reduction unit and an abnormal special outpatient behavior fund expenditure unit;
the acquisition module is used for acquiring a data time sequence of nodes required by the prediction object in each unit within the period time of the target area and analyzing and processing the data time sequence;
and the prediction module is used for carrying out system dynamics modeling on each unit data according to the analyzed and processed data time series and a related measuring and calculating formula so as to predict the balance data of the medical insurance fund.
9. A computer-readable storage medium characterized by: the computer readable storage medium storing computer instructions which, when executed by a processor, implement the method of employee medical insurance fund reimbursement data prediction according to any one of claims 1-7.
10. An electronic device, characterized in that: comprising at least one processor, and a memory communicatively coupled to the processor, wherein the memory stores instructions executable by the at least one processor to cause the processor to perform the method for employee medical insurance fund payment data prediction according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111106428.0A CN114358954A (en) | 2021-09-22 | 2021-09-22 | Employee medical insurance fund collection and payment data prediction method, device, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111106428.0A CN114358954A (en) | 2021-09-22 | 2021-09-22 | Employee medical insurance fund collection and payment data prediction method, device, medium and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114358954A true CN114358954A (en) | 2022-04-15 |
Family
ID=81096302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111106428.0A Pending CN114358954A (en) | 2021-09-22 | 2021-09-22 | Employee medical insurance fund collection and payment data prediction method, device, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114358954A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115760440A (en) * | 2022-11-02 | 2023-03-07 | 广东迪浪科技股份有限公司 | Medical insurance collection and payment forecasting method, system, device and storage medium thereof |
-
2021
- 2021-09-22 CN CN202111106428.0A patent/CN114358954A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115760440A (en) * | 2022-11-02 | 2023-03-07 | 广东迪浪科技股份有限公司 | Medical insurance collection and payment forecasting method, system, device and storage medium thereof |
| CN115760440B (en) * | 2022-11-02 | 2023-08-08 | 广东迪浪科技股份有限公司 | Medical insurance expense prediction method, system, device and storage medium thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Garrib et al. | An evaluation of the district health information system in rural South Africa | |
| Khomenko et al. | Impact of road traffic noise on annoyance and preventable mortality in European cities: A health impact assessment | |
| Gaver et al. | The influence of large clients on office-level auditor oversight: Evidence from the property-casualty insurance industry | |
| Zhang et al. | A Bayesian non-linear model for forecasting insurance loss payments | |
| US20100082382A1 (en) | Forecasting discovery costs based on interpolation of historic event patterns | |
| Arslan-Ayaydin et al. | Avoiding interest-based revenues while constructing Shariah-compliant portfolios: false negatives and false positives | |
| Urban | Tax wedge on labour income in Croatia and the European Union: Preface to the special issue of Financial Theory and Practice | |
| CN114358954A (en) | Employee medical insurance fund collection and payment data prediction method, device, medium and equipment | |
| Song et al. | An intraday-return-based Value-at-Risk model driven by dynamic conditional score with censored generalized Pareto distribution | |
| Kim et al. | A Bayesian estimation of child labour in India | |
| Weiss | Two measures of lifetime resources for Europe using SHARELIFE | |
| CN113327160A (en) | Bank post-loan risk prediction method and system based on enterprise financial and capital big data | |
| Immervoll et al. | How reliable are social safety nets? Value and accessibility in situations of acute economic need | |
| CN113837887A (en) | Method, device, medium and equipment for accurately predicting pension data of endowment insurance fund | |
| Zhang et al. | Corporate endowment insurance fee reduction and employee wages: evidence from China | |
| Shivaram et al. | Measuring audit quality | |
| Blau | Pensions, household saving, and welfare: A dynamic analysis | |
| JP2002074241A (en) | System and method for processing information for medical insurance and recording medium with information processing program to be operated in computer recorded thereon | |
| Feng et al. | Hukou Matters: The heterogeneous local labor market effects of export expansions in China | |
| Ibrahim et al. | Cross sectional estimation of loss reserve for Egyptian fire insurance market | |
| Khaled et al. | Modeling contractor’s cash-in-flow in public school building projects in Karbala | |
| Rong | Forecasting Model of Microsoft’s Daily Return and Its Volatility | |
| Yang et al. | Research on the Contribution Ratio of Long-term Care Insurance for Urban Workers in Shandong Province Based on the ILO Model | |
| Supartha et al. | Turnover Intention Antecedents: Study on Credit People Banks (BPR) in Gianyar Regency-Bali | |
| CN115731051A (en) | Method and device for processing energy type bond data, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |