American Journal of Business Management, Economics and Banking ISSN (E): 2832-8078 Volume 34, March - 2025 P a g e | 91 www.americanjournal.org STATISTICAL ASSESSMENT OF THE EFECTIVA EMPLOYMENT OF THE POPULATION IN THE REPUBLIC OF UZBEKISTAN Akbarova Zaripa Associate Professor of the Department of Economic Statistics, TSUE E-mail: z.akbarova@tsue.uz +998 98 360 00 08 A B S T R A C T K E Y W O R D S When analyzing the labor market, it is important to quantify the relationship between the employed population and general indicators that reflect the development of the economy. To do this, the number of people employed in the economy and the factors affecting this indicator, as well as forecast values, were determined. In this regard, it is important to create multifactorial econometric models based on the study of the labor market of our country, taking into account the factors affecting the employment rate. Labor market, employment, unemployment, average nominal wages, sums, volume of capital investments. Introduction The development of market relations in the labor sphere requires new approaches to studying employment and its structure. An independent approach is necessary to solve employment problems and review the unemployment situation, as well as to revise the duration and scope of this status. The need to transition from full employment to market relations was determined by the Law of the Republic of Uzbekistan on Employment of the Population, approved by the State Senate on August 7, 2020. Article 4 stipulates that employment is an activity of citizens not prohibited by law, related to satisfying their personal and social needs, and bringing them income (labor income). The following citizens are considered employed: persons working under an employment contract for remuneration; persons temporarily absent from their workplace; persons elected or appointed to a paid position; persons serving; persons who are self-employed; Persons temporarily engaged in labor activity outside the Republic of Uzbekistan; Persons working in non-governmental non-profit organizations, including religious organizations that carry out their activities in accordance with the legislation. The basis of the study is the basic foundations of statistical theory, the works of representatives of world economic thought, methodological developments of scientists in the field, the study of American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 92 www.americanjournal.org quantitative aspects of unemployment and employment, development forecasting and modeling, as well as methodological recommendations and official data of state statistics and its territorial divisions, scientific periodicals devoted to the problems of statistical research of the main indicators of labor market development. LITERATURE ANALYSIS In the International Covenant on Social, Economic and Cultural Rights, employment is understood as the free and socially necessary activity of a person. This concept is also characteristic of economists such as V.G. Kastakov, A.L. Nikifirov, Ye.I. Ruzavina, who understand the process of labor activity by employment. According to A.E. Kotlyar, employment is not an activity, but social relations between people. The scientific research of T.N. Agapova, S.A. Ayvazyan, V.F. Voronin, E.K. Vasileva, G.L. Gromyko, M.R. Efimova, A.V. Kuznesov, B.T. Ryabushkin, N.A. Sadovnikova and M.M. Yuzbashev studied the issues of forming and improving methodological tools of statistical research. The issues of analyzing the structural structures of the labor market and its development are studied in the works of S.D. Ilenkova, I.A. Polyakova, V.A. Sivelkin, N.V. Savchuk and others. The problems of statistical research of employment and unemployment are analyzed in the works of B.D. Breev, V.S. Bulanov, A.N. Vorobev, I.I. Eliseeva, M.G. Nazarov, S.Yu. Rochin, N.V. Stroykina, V.N. Salin and others. Employment is understood as providing a person with work, that is, income-generating labor activity. With this approach, employment represents the level of involvement of the population in the labor process, the employment of the population, and the conscious participation of people in the production of spiritual and material benefits. According to A.L. Orlova, such an interpretation of this category is rather limited, since it is incorrect to equate such concepts as “work”, “employment” and “labor activity”. N.N. Danilenko, V.G. Kostakov, A.L. Nikiforova, Ye.I. Ruzavina defined employment as a process of labor activity. Employment is a set of various economic, administrative and legal relations associated with the involvement of employees in the labor process. In this regard, A.E. Kotlyar emphasizes that employment is not an activity. At the same time, in accordance with current legislation, those who are not directly involved in production can also be considered employed. In order to be considered employed, a person must have a specific job, labor and employment represent a single system. In this case, it is employment that is primary. From the above it follows that employment is a certain socio-economic relationship aimed at satisfying both social and personal needs through the participation of the able-bodied population in socially useful work that generates income. The process of regulating employment is a mechanism for the development of the national economy. The specific features of providing employment to the population are determined by the current situation in the national market of commercial products, along with the development of production, which, in turn, is determined by the objective and subjective factors of the activity of the sectoral complexes of the country's economy. This makes it possible to scientifically substantiate the basic principles of the functioning of the national labor market in the context of economic reforms and further development of human capital. American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 93 www.americanjournal.org RESEARCH METHODOLOGY In recent years, changes have been taking place in the labor market of our republic. As a result of the creation of new jobs, the employment rate is increasing, and the number of unemployed is decreasing. As a result of the increase in wages and the benefits provided to entrepreneurial activity, the income of the population is also increasing. It is important to build multifactor econometric models based on the factors affecting the employment rate based on the study of the labor market of our country. ANALYSIS AND RESULTS The analysis of labor market development trends and their determinants involves the study of the dynamics and structure of labor market performance indicators using a system of statistical indicators representing the labor market, as well as the determination of the dependence of the number of employed people in the economy on a number of factors. The following factors were selected for the multifactor econometric model: as a result factor - the number of employed people in the Republic of Uzbekistan, thousand people (Y), and as influencing factors - the number of unemployed people in the Republic of Uzbekistan, thousand people (X1), the average nominal wage in the Republic of Uzbekistan, soums (X2) and the volume of investments in fixed capital, billion soums (X3). The data involved in econometric modeling were obtained for 2000- 2022 (since the units of measurement of the data are different, we use their logarithmic values).The results of descriptive statistics on factors affecting the number of employed people in the Republic of Uzbekistan are presented in Table 1 below. Table 1 Descriptive statistics on factors affecting the number of employed persons in the Republic of Uzbekistan lnY lnX1 lnX2 lnX3 Mean 9.341869 2.496740 13.54911 9.762960 Median 9.385897 2.332144 13.54674 9.878170 Maximum 9.545912 4.592085 14.90993 12.45435 Minimum 8.985370 0.993252 12.13605 6.612713 Std. Dev. 0.173093 0.929049 0.764659 1.817153 Skewness -0.697262 0.708719 -0.022857 -0.067843 Kurtosis 2.212918 2.840273 2.345998 1.837696 Jarque-Bera 2.457352 1.949865 0.411899 1.312304 Probability 0.292680 0.377218 0.813874 0.518844 Sum 214.8630 57.42502 311.6296 224.5481 SumSq. Dev. 0.659145 18.98892 12.86347 72.64496 Observations 23 23 23 23 1. Mean lnY (Highly Volatile Variable): The mean value is 9.341869. This variable represents the average value relative to the spread of the data. lnX1 (First Variable): The mean value is 2.496740. The mean value of this variable indicates the average level of variability among the data. lnX2 (Second Variable): The American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 94 www.americanjournal.org mean value is 13.54911. This variable represents the average value that makes up the majority of the data. lnX3 (Third Variable): The mean value is 9.762960. The mean value of this variable indicates the average level of the data. 2. Median lnY: The median is 9.385897. The median value of this variable gives information about the central point of the data. lnX1: Median 2.332144. The median value represents the point that is evenly distributed among the values. lnX2: Median 13.54674. This value represents the average value located in the center of the data. lnX3: Median 9.878170. The median value of this variable indicates high variability. 3. Maximum and Minimum lnY: The maximum value is 9.545912, and the minimum value is 8.985370. This variable indicates the small and large limits of variation. lnX1: The maximum value is 4.592085, and the minimum value is 0.993252. This variable is the maximum and minimum points between the values that are widely distributed. lnX2: The maximum value is 14.90993, and the minimum value is 12.13605. These values show a wide variation between the high and low points. lnX3: The maximum value is 12.45435, and the minimum value is 6.612713. This variable shows that the values have a large range. 4. Standard Deviation lnY: The standard deviation is 0.173093, which means that the values of this variable differ very little from each other. lnX1: The standard deviation is 0.929049. This shows that the values of this variable vary significantly. lnX2: The standard deviation is 0.764659. This value is also relatively variable, but the difference of this variable is smaller. lnX3: The standard deviation is 1.817153. This indicates that the variable has high variability. 5. Skewness lnY: Skewness -0.697262. This indicates that the variable is skewed to the left, meaning there are more low values. lnX1: Skewness 0.708719. This value is skewed to the right, meaning there are more high values. lnX2: Skewness -0.022857. This indicates that the values of the variable are well distributed and there is almost no asymmetry. lnX3: Skewness -0.067843. This variable is relatively well distributed, meaning the values are almost symmetrical. 6. Kurtosis lnY: Kurtosis 2.212918. This variable has a skewed distribution, showing high peaks along the mean. lnX1: Kurtosis 2.840273. The values of this variable are well distributed and show high concentration. lnX2: Kurtosis 2.345998. This variable has moderate kurtosis, showing a good distribution of values. lnX3: Kurtosis 1.837696. The values of this variable are relatively flat and have low kurtosis. 7. Jarque-Bera Test lnY: Jarque-Bera test is 2.457352, indicating a closeness to a normal distribution (probability 0.292680, indicating that a normal distribution is acceptable). lnX1: Jarque-Bera test 1.949865, which also shows that it is close to the normal distribution (probability 0.377218). lnX2: Jarque-Bera test 0.411899, American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 95 www.americanjournal.org which shows that this value is very close to the normal distribution (probability 0.813874). lnX3: Jarque-Bera test 1.312304, which also shows that it is close to the normal distribution (probability 0.518844). 8. Sum (Amount) lnY: Amount 214.8630. lnX1: Amount 57.42502. lnX2: Amount 311.6296. lnX3: Amount 224.5481. 9. Sum of Squared Deviations lnY: SumSq. Dev. 0.659145. lnX1: SumSq. Dev. 18.98892. lnX2: SumSq. Dev. 12.86347. lnX3: SumSq. Dev. 72.64496. 10. Observations lnY: 23 observations. lnX1: 23 observations. lnX2: 23 observations. lnX3: 23 observations. The distribution of data across multiple variables can be inferred from measures such as skewness and kurtosis. The values of skewness and kurtosis indicate that the distribution of some variables is skewed or uncertain, but some are very close to a normal distribution. A high standard deviation means that the data is widely distributed and some variables show large variations. From the data in Table 1 above, it can be seen that the asymmetry coefficient of the unemployment rate (lnX1) factor in the Republic of Uzbekistan has positive values. The graphs of the distribution functions of all factors are presented in Figure 3 below. 0.0 0.5 1.0 1.5 2.0 2.5 8.7 8.8 8.9 9.0 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 De ns ity lnY .0 .1 .2 .3 .4 .5 .6 0 1 2 3 4 5 6 De ns ity lnX1 .0 .1 .2 .3 .4 .5 .6 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 De ns ity lnX2 .00 .05 .10 .15 .20 .25 4 5 6 7 8 9 10 11 12 13 14 15 Kernel Normal De ns ity lnX3 Figure 1. Graphs of distribution functions of factors From Figure 1, it can be seen that all factors included in the multifactor econometric model obey the law of normal distribution. The distribution graphs of the factors calculated from the theoretical values of the normal distribution graph are located asymmetrically, that is, shifted. These shifts mainly indicate changes in the dynamics of the factors under study. In some years, some factors had a sharp increase, while in others there were no significant changes. “Anomalous” values were observed in the dynamics of some factors. The histogram graph of the normal distribution of the resulting factor is presented in Figure 4 below. American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 96 www.americanjournal.org Figure 2. Checking the compliance of the resulting factor with the law of normal distribution To check the compliance of the resulting factor (lnY) with the law of normal distribution, the Jacques- Bera criterion is used. This criterion is a statistical criterion that checks the errors of observations with the moments of normal distribution with the third moment (skew) and the fourth moment (excess) with a normal distribution and and . From Figure 4, it can be clearly seen that the resulting factor obeys a normal distribution. This is confirmed by the calculated parameters and criteria, namely, the calculated Jacques-Bera coefficient is equal to 10.0994 and its probability is less than 0.05 (prob=0.0015). To select factors for a multifactor econometric model based on factors affecting the number of jobs in the Republic of Uzbekistan, it is necessary to analyze the relationships between factors. For this, a correlation analysis is carried out between the factors, that is, the private and pair correlation coefficients between the factors are calculated. The matrix of private and pair correlation coefficients between the factors is presented in Table 2 below. From Table 2, it can be seen that the private correlation coefficients indicate the density of connections between the resulting factor (lnY) and the factors influencing it (lnXi). Thus, the private correlation coefficients indicate the existence of various connections between the resulting factor - the number of employed persons in the Republic of Uzbekistan (lnY) and the influencing factors. 2-jadval Omillar o‘rtasida xususiy va juft korrelyatsiya koeffitsientlari matritsasi lnY lnX1 lnX2 lnX3 lnY 1.000000 lnX1 0.379643 1.000000 1.334737 ----- 0.1963 ----- lnX2 0.946901 0.531602 1.000000 13.49581 2.876179 ----- 0.0000 0.0590 ----- lnX3 0.960936 0.494837 0.485406 1.000000 15.91058 2.609512 2.52879 ----- 0.0000 0.0664 0.0612 ----- Thus, the density of the relationship between the number of employed persons in the Republic of Uzbekistan (lnY) and the number of unemployed persons in the Republic of Uzbekistan (lnX1) is 0.3796. This indicates that there is a weak relationship between the number of employed persons in the Republic (lnY) and the number of unemployed persons in the Republic (lnX1). The density of the relationship between the number of employed persons in the Republic of Uzbekistan (lnY) and the American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 97 www.americanjournal.org average nominal wage in the Republic of Uzbekistan (lnX2) is 0.9469, which also indicates the existence of a strong relationship between these two factors. There is a strong relationship between the number of employed persons in the Republic of Uzbekistan (lnY) and the volume of investments in fixed capital (lnX3), that is, the private correlation coefficient between them is 0.9609. In addition, Table 2 also presents pairwise correlation coefficients between the influencing factors. These coefficients determine multicollinearity between factors. If the calculated value of the pairwise correlation coefficient between the influencing factors is greater than 0.7, then multicollinearity between the factors is said to exist. From Table 2, where the matrix of private and pairwise correlation coefficients between the factors is calculated, it can be seen that the calculated value of the pairwise correlation coefficient between the factors affecting the number of jobs in the Republic of Uzbekistan is not greater than 0.8. This, in turn, allows all the selected factors to be included in the multifactor econometric model. Table 2 also calculates the coefficients to determine the reliability and probability of the correlation coefficients (the values in the rows below the calculated correlation coefficients). At the bottom of each correlation coefficient, its calculated value and probability by Student's t-test are presented. The condition is that the calculated probability between the factors should not be greater than 0.05. For example, the specific correlation coefficient between the number of employed persons in the Republic of Uzbekistan (lnY) and the average nominal wage in the Republic of Uzbekistan (lnX2) is equal to , and . This indicates that there is a strong connection between these two factors, that the specific correlation coefficient is reliable and that there is a positive high correlation between the two factors with a 95% accuracy. In order to verify the above, that is, to determine the densities and forms of connections between factors, we will consider their dot plots to determine the relationship of each factor with the resulting indicator (lnY) (Figure 3). 0 1 2 3 4 5 8.8 9.0 9.2 9.4 9.6 lnY ln X 1 12.0 12.5 13.0 13.5 14.0 14.5 15.0 8.8 9.0 9.2 9.4 9.6 lnY ln X 2 6 8 10 12 14 8.8 9.0 9.2 9.4 9.6 lnY ln X 3 Figure 3. View of the relationship between the number of employed persons (lnY) and the factors influencing it (lnXi) in the Republic of Uzbekistan In the graphs in Figure 3, it can be said visually that there are correct and dense connections between the resulting factor (lnY) and the influencing factors (lnXi). Thus, the correlation coefficients between the factors included in the multifactor econometric model for the number of jobs in the Republic (lnY) fully meet the requirements for the calculated value and probability of the Student's t-test. This indicates that we include all the factors affecting the number of jobs in the Republic (lnY) in the multifactor econometric model. In general, the multifactor econometric model looks like this:  +++++= nn xxxy ...22110 , (1) where y is the resulting factor, - influencing factors, - random error. American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 98 www.americanjournal.org The "least squares method" was used to determine the values of the unknown parameters in the multivariate econometric model (1). The results are presented in Table 3 below. Table 3 Parameters of a multifactor econometric model calculated for the number of employed persons in the Republic and the factors affecting it Variable Coefficient Std. Error t-Statistic Prob. LNX1 -0.053008 0.007444 -7.120463 0.0000 LNX2 0.097680 0.046175 2.115436 0.0478 LNX3 0.064440 0.018939 3.402571 0.0030 C 7.521603 0.439759 17.10392 0.0000 R-squared 0.979119 Mean dependent var 9.341869 Adjusted R-squared 0.975822 S.D. dependent var 0.173093 S.E. of regression 0.026915 Akaike info criterion -4.235519 Sum squared resid 0.013764 Schwarz criterion -4.038042 Log likelihood 52.70847 Hannan-Quinn criter. -4.185854 F-statistic 296.9729 Durbin-Watson stat 1.901030 Prob(F-statistic) 0.000000 Using the data in Table 3 above, we express in analytical form the multifactor econometric model for the number of employed persons (lnY) in the Republic: 321 ln0644,0ln0977,0ln0530,05216,7ˆln XXXY ++−= , (2) The multifactor econometric model calculated for the number of employed persons in the Republic of Uzbekistan shows that if the number of unemployed persons in the Republic of Uzbekistan (lnX1) increases by an average of one percent, the number of employed persons in the Republic of Uzbekistan (lnY) decreases by an average of 0.05 percent. If the average nominal wage in the Republic of Uzbekistan (lnX2) increases by an average of one percent, the number of employed persons in the Republic of Uzbekistan (lnY) increases by an average of 0.09 percent. An increase in the volume of investments in fixed capital in the Republic (lnX3) by an average of one percent leads to an increase in the number of employed persons in the Republic of Uzbekistan (lnY) by an average of 0.06 percent. To check the quality of the multifactor econometric model (2) constructed for the number of employed persons in the Republic of Uzbekistan, we check the coefficient of determination. The coefficient of determination shows how much the resulting factor is composed of the factors included in the model. The calculated coefficient of determination (R2 - R-squared (Table 3)) is equal to 0.9791. This indicates that 97.91 percent (2) of the number of employed persons in the Republic of Uzbekistan (lnY) is composed of the factors included in the multifactor econometric model. The remaining 2.09 percent (100.0-97.91) is the influence of factors not taken into account. To check the statistical significance or adequacy (fitness) of the multifactor econometric model (2) constructed on the number of employed persons in the Republic of Uzbekistan (lnY) to the process under study, Fisher's F-test is used. The calculated value of Fisher's F-test is compared with its value in the table. If Fcount>Ftable, then the multivariate econometric model (2) is said to be statistically significant and can be used to forecast the resulting indicator - the number of employed persons in the Republic of Uzbekistan (lnY) for future periods. Now, to check the statistical significance of the multivariate econometric model (2) constructed on the number of employed persons in the Republic of Uzbekistan, we find the tabular value of the F-criterion. To do this, we calculate the values of the degrees of freedom and and the significance level. Based on American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 99 www.americanjournal.org the significance level and the degrees of freedom and, the tabular value of the F-criterion is equal to . Based on the fact that the calculated value of the F-criterion is Fcount=296.97 and the table value Ftable=3.13, and since the condition Fcount>Ftable is met, the multifactor econometric model (2) can be considered statistically significant and can be used to forecast the number of employed persons (lnY) in the Republic of Uzbekistan for future periods. The Student's t-test is used to check the reliability of the calculated parameters of the multivariate econometric model (2) based on the number of employed persons in the Republic of Uzbekistan (lnY). By comparing the calculated (thisob) and tabular (ttabval) values of the Student's t-test, we accept or reject the hypothesis N0. To do this, we find the tabular value of the t-test based on the selected reliability probability ( ) and degree of freedom ( ). Here - the number of observations, - the number of factors. When the reliability probability and degree of freedom are, the tabular value of the t-test is equal to . From the calculations carried out on the construction of the multivariate econometric model, it can be seen that the calculated values of the t-test for the independent variable and all factors included in the multivariate econometric model are significantly greater than the tabular value (Table 3). This indicates that all factors are reliable and allows these factors to participate in the multivariate econometric model. The calculated DW value is compared with the DWL and DWU in the table. If DWcalcDWU is greater than, then the residuals of the resulting factor are said to have no autocorrelation. The lower limit of the Durbin-Watson criterion is equal to DWL=1.08 and the upper limit is equal to DWU=1.66. DWcalc=1.9010. Therefore, since DWcount>DWU, there is no autocorrelation in the residuals of the resulting factor (number of employed persons in the Republic of Uzbekistan (lnY)). The absence of autocorrelation in the residuals of the resulting factor also indicates that the multifactor econometric model (2) presented above can be used in forecasting. The actual (Actual), estimated (Fitted) values of the multifactor econometric model (2) and the differences between them (Residual) are presented in Figure 6 below. -.06 -.04 -.02 .00 .02 .04 .06 8.8 9.0 9.2 9.4 9.6 00 02 04 06 08 10 12 14 16 18 20 22 Residual Actual Fitted Figure 4. Graph of the actual (Actual), estimated (Fitted) values of the number of employed people (lnY) in the Republic of Uzbekistan and the differences between them (Residual) It can be seen from Figure 4 that the graph of the calculated values of the number of employed persons in the Republic of Uzbekistan according to the multifactor econometric model (2) is very close to the graph of its actual values, and the differences between them are not very large. This is another proof American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 100 www.americanjournal.org that the multifactor econometric model (2) can be used to forecast the size of the bank's net profit for future periods. To do this, first of all, we will build trend models for each of the influencing factors. A trend model is a time-dependent function of an influencing factor and generally has the following form:  ++= tX i 10 (3) Initially, the trend model for the number of unemployed people in the Republic of Uzbekistan, per thousand people (lnX1), looks like this: tX += 0659,07054,1ˆln 1 (4) 6317,02 =R , 3346,6хисоб =F , 5168,2хисоб =t The trend model for the average nominal wage in the Republic of Uzbekistan, soums (lnX2) looks like this: tX += 1110,02167,12ˆln 2 (5) 9699,02 =R , 12,677хисоб =F , 021,26хисоб =t The trend model for the volume of investments in fixed capital, billion soums (lnX3) looks like this: tX += 2669,0559,6ˆln 3 (6) 9930,02 =R , 98,2997хисоб =F , 7538,54хисоб =t The analysis of the trend models constructed between the influencing factors (lnXi) and the time factor (t) shows that the statistical significance of all the calculated coefficients in the trend models (4) – (6) and the reliability of their parameters have been determined. So, we calculate the trend models (4) – (6) and, having put their calculated values into the multifactor econometric model (2), first calculate the forecast values of the influencing factors, and then the forecast values of the resulting factor. We exponentiate the forecast values to free them from the logarithm. As a result, we get the values of the variables included in the multifactor econometric model constructed by the number of employees in the Republic of Uzbekistan in the forecast period (Table 4) Table 4 The number of employed people in the Republic of Uzbekistan and the forecast values of the factors influencing it for 2025-2030 Years Number of posts, thousand people, Y Number of unemployed, thousand people, X1 Nominal wage, soums, X2 Investments in fixed assets, billion soums, X3 2000 7985,4 14,2 186475,3 744,5 2001 8365,2 12,1 216547,1 1320,9 2002 8634,7 8,6 269874,4 1526,6 2003 9145,7 9,3 316524,3 1978,1 2004 9547,8 10,4 376478,4 2629,0 2005 9845,5 7,3 463578,8 3165,2 2006 10456,7 7,2 534213,7 4041,0 2007 10865,2 6,5 593642,1 5903,5 2008 11124,6 8,9 635472,6 9555,9 2009 11423,7 10,3 665247,9 12531,9 2010 11628,4 16,2 718364,6 16463,7 2011 11919,1 12,9 764321,8 19500,0 2012 12223,8 6,0 834576,2 24455,3 2013 12523,3 5,4 896412,5 30490,1 American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 101 www.americanjournal.org 2014 12818,4 3,4 942124,3 37646,2 2015 13058,3 2,7 986514,2 44810,4 2016 13298,4 5,0 1064214,6 51232,0 2017 13520,3 14,4 1251538,0 72155,2 2018 13273,1 32,3 1542598,3 124231,3 2019 13541,1 57,9 1946778,3 195927,3 2020 13236,4 37,1 2227141,2 210195,1 2021 13538,9 98,7 2662002,8 239552,6 2022 13987,4 63,4 2987452,2 256364,2 2023 15314,2 26,8 2903757,1 427988,7 2024 15694,9 28,6 3244753,7 558965,5 2025* 16085,0 30,6 3625794,5 730025,0 2026* 16484,9 32,7 4051582,1 953433,5 2027* 16894,7 34,9 4527371,1 1245211,6 2028* 17314,7 37,3 5059033,4 1626282,0 2029* 17745,1 39,8 5653130,4 2123971,0 2030* 18186,3 42,5 6316993,9 2773967,0 Note - *- forecast period In conclusion, it can be said that the forecast calculations based on a multifactor econometric model representing the number of employed persons in the Republic of Uzbekistan show that the factors affecting the number of employed persons have a tendency to increase during the forecast period. However, it would be advisable to regulate and control some of the influencing factors. CONCLUSIONS AND SUGGESTIONS The problem of unemployment is a very important and urgent problem that needs to be solved in all countries of the world, especially in our republic. Currently, in Uzbekistan, when the scope of informal employment is expanding, it is difficult to completely control unemployment. The main labor resources are engaged in seasonal work. Also, the increase in youth unemployment is becoming a big problem in Uzbekistan. Unemployment is not only a social and economic problem, but also a political problem that every country can face. This situation can have a detrimental effect on the entire economic life of society and lead to significant socio-economic losses. Unemployment is one of the issues without which it is impossible to conduct effective economic activity. In conclusion, labor market statistics are faced with the tasks of improving the methodology for determining statistical indicators based on the interpretation of the terms labor resources, economically active and inactive population, employment of the population, unemployment, studying the number, composition, and productivity of employees in enterprises and organizations. In addition, familiarizing the population with the state of the labor market and providing management bodies with information is also one of the most important tasks of statistical bodies. In our opinion, it is precisely a comprehensive approach to the system of ensuring employment of the population and managing social protection that reduces difficulties in labor relations and ensures an increase in the quality of work activities of the population. American Journal of Business Management, Economics and Banking Volume 34 March - 2025 P a g e | 102 www.americanjournal.org References 1. Maslova I.S. Effektivnaya zanyatost i rynok labor force / I.S. Maslova // Vestnik statistician. – 2008. – No. 12. – P.8–19. 2. Roshchin, S. Yu. Gendernoe ravenstvo i rasshirenie prav i vozmojnostey genshchin v Rossii v kontekte seley razvitiya tysyacheletiya / S.Yu. Roshchin, N.V. Zubarevich. - M.: Information center OON, 2005. - 73 3. Sarukhanov, E.R. Upravlenie zanyatostyu naseleniya / E.R. Sarukhanov. - SPb.: SPbUEF, 1993. - 164p. 4. Shuvaeva, Ye.V. Zanyatost kak vajneyshiy element trudovykh otnosheniy / Ye.V. Shuvaeva // Aktualnye voprosy ekonomicheskikh nauk. - Ufa: Leto, 2011. - P.96-98. 5. Maslova I.S. Effektivnaya zanyatost i rynok labor force / I.S. Maslova // Vestnik statistician. – 2008. – No. 12. – P.8–19. 6. Yefimova, M.R. Osnovnye tendendii i osobennosti migratsii naseleniya v regionakh Rossiyskoy Federatsii / M.R. Yefimova, S.G. Bychkova // Rol statistiki v razvitii obshchestva. Istoricheskiy opyt. Dostigenia. Perspective. - Kostroma: Kostroma State University, 2015. - P. 205-210 7. Yeliseeva, I.I. Econometricheskie zavizimosti: principy i metody postroeniya / I.I. Yeliseeva, G.B. Kleiner, S.A. Smolyak // Ekonomika i matematicheskie metody. - 2001. - T. 37. - No. 1. - P. 124-132. 8. I.I. Eseleeva. Econometrics: uchebnoe posobie - M.: Finance and statistics, 2001. pp. 41-87. 9. Soatov N.M, Ayubjonov A.Kh. /Theory of statistics/. Textbook.-T.: Teacher, 2020 - 647 pages. 10. Umarova M.A. Economic and statistical analysis of factors of the international labor market // International Journal of Research in Management & Business Studies ((IJRMBS 2019) ISSN: 2348- 65033(Online)) Vol. 6, Issue 2 April - June 2019 (#5, GIF 0.705).