Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 17 Determinants of Earnings of Rural Households of Multan District (Pakistan) Rashid Ahmad a, Saba Rehman b a Assistant professor, School of Economics, Bahaudin Zakariya University Multan, Pakistan b M.Phil Scholar, School of Economics, Bahaudin Zakariya University Multan, Pakistan ARTICLE DETAILS ABSTRACT History: Accepted 28 January 2023 Available Online March 2023 This study focuses on exploring the factors affecting on urban Earnings of household of Multan district. Cross-sectional data was collected through questionnaire from household of District Multan's belonging to rural areas. About 300 respondents belonging to rural areas were randomly engaged for an interview in 2021. Mincerian earning function was used for analysis and its extension form was also analyzed. In this study it was found that Experience and Education positively impact on earning whereas experience square had a negative impact while evaluating Mincerian Earing function. In the Extended Mincerian Earing function, Education, experience, age, spouse involvement, marital status, and migration positively impact on earnings while age squared, experience squared, and employment have a negative impact on earnings of rural household of Multan district. Β© 2023 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Rural Household, Determinants of Earnings, Mincerian Earning Function JEL Classification: O18, D1 DOI: 10.47067/reads.v9i1.475 Corresponding author’s email address: rashidahmad@bzu.edu.pk 1. Introduction Agriculture is an important sector in Pakistan because it accounts for a substantial amount of the country's economy and is the primary source of employment for rural residents. Improvement of rural development and the agricultural sector are top priorities of the governments for eradicating poverty. So, the agriculture sector contributes 19.2 percent to the GDP of Pakistan and it offers employment opportunities to almost 38.5 percent labor force. Furthermore, agriculture provides a living and meets the fundamental necessities of 60 to 70 percent of Pakistan's population. (PBS,2021). In recent years, Climate change, water scarcity, and arable land reduction have slowed agricultural expansion and the majority of the population has shifted from a rural to an urban region which has a detrimental impact on the agriculture industry. There is a need for innovative technologies to boost agriculture's productivity because this industry contributes 19.2 percent of Pakistan's GDP and can be a major driver of economic growth (Gillani et al., 2013). Multan is an agricultural city, and more than 80 percent of residents depend on the agricultural sector for their livelihood. Mangoes production is famous in the district and is also exported to other Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 18 countries. Cotton is another product of the district and the major source of foreign exchange earnings in Pakistan. New cotton varieties announced by the Agriculture Ext. wing among the rural population is being discovered with the help of the Central Cotton Research Institute and Cotton Research Station in Multan. On the other hand, Mango Research Institute is actively involved in the development of new varieties of mangoes which is the primary source of livelihood for rural residents of the Multan area (Govt of Punjab, 2021). Despite its economic importance, Multan has about 2.28 percent of the country's population, with rural areas having a proportion of 56.62 percent of the Multan total population. People in rural areas of district Multan face severe poverty issues due to a lack of educational and health infrastructure, a lack of employment opportunities, resource inequality, and poor access to natural resources. In addition, rural areas of Multan have received little attention from policymakers, which has a negative impact on the rural people. Multan's rural inhabitants have very few employment facilities to make revenue for purchasing food and meet the need for the basic facility; as a result, they experience hunger problems during their unemployment period. Rural people rely heavily on agriculture for a living, thus they suffer from food insecurity and micronutrient deficiencies, which diminish productivity, work days lost, and different illnesses. So the primary goal of the research is to identify the elements that influence the Earnings of rural residents. 2. Literature Reviews Dayal Talukder (2014) used regression analysis to evaluate the income determinants of Bangladesh's rural inhabitants. The economic and non-economic factors were studied to determine their combined effect on family income. The findings of the linear regression demonstrated that family size was the only non-economic factor that was significant and positively affecting family income in both 1986 and 2005. In this period, the family size was the most important positive determinant of income, whereas the farmer was the most important negative determinant of income. The revenue share from the rice was a positive component but not significant in contrast to the current year endowment. The agriculture share was a positive factor of family income in both years, but it was insignificant in 2005. Memon et al. (2020) explored the factors of earning diversification of flood-prone rural areas of Pakistan. The primary data was used which was collected from 350 households in rural areas randomly and the linear regression method was used for the estimation of the data. The regression results revealed that households with more earnings persons and ruled by educated male earners had a diversified earning portfolio. Moreover, the moderation analysis indicated that a family ruled by an educated male head was more diversified than an uneducated person. Nzabakenga et al. (2013) showed the agrarian earning determinants of a small-holder farmer from the northern part of Burundi. To achieve the study objectives, primary data was obtained from 218 inhabitants, and the linear regression model was employed for analysis. The eight variables were selected for the analysis but just two of them have an effect i.e farm size and family size were the only significant variables among all variables. Parvin and Akteruzzaman (2012) studied the factors influencing the income of agricultural and Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 19 non-farm households in Bangladesh's Netrokona area. The descriptive analysis was performed to explain the socioeconomic features of the target sample and to determine the factors influencing the income of farm and non-form inhabitants According to the results of the estimated regression, family size, and farm size affect farm income positively, but non-farm income affects farm revenue negatively. Furthermore, the influence of family size on non-farm income was significant and positive, however, the effect of farm revenue on non-farm income is negative and significant. Odozi and Adeyonu (2021) investigated the factors that influence employment and wages in rural Nigeria. The panel data was used in this study and the logit model was applied for the estimation of income and employment determinants. According to the study, agricultural self-employment was the most common cause of employment in rural Nigeria between 2010 and 2015. However, it was two times the non-farm employment and five times as much as the wage employment. In addition, the wage employment was diminishing during the study period. 3. Data Source and Methodology This study was carried out in the rural region of Multan, where agriculture is the main source of income for the local population. Approximately 70% of them work in agriculture. This study is based on primary data that was gathered through a survey in rural areas during the year 2022. A questionnaire was developed according to the study's objective. A random sample of 300 rural household was drawn from the district. To describe the socio-economic characteristics of the head household descriptive statistic was used including maximum, minimum, mean, and standard deviation. For finding the degree of association among the variables the coefficient correlation was used. The Mincerian earning function was used in both strict and extended forms to estimate the determinants of rural household income. The econometrics form of the model is listed below: Firstly, the Mincerian earning function was considered to find the impact of the variable on income which is given below: Total income = f (Education, Experience, Square of Experience) ToT INC= f (EDU, EXP, EXPSQ) The regression form of the model is as given below 𝐿𝑛𝑇𝑂𝑇_𝑁𝐢 = [𝛽0 + 𝛽1πΈπ·π‘ˆ + 𝛽2𝐸𝑋𝑃 + 𝛽3𝐸𝑋𝑃𝑆𝑄 + ¡𝑖] A log-linear model was used in which total income was used as a regressand variable while the education, work experience, and experience square were used as independent variables. Secondly, the extended form of the MEF (Mincerian Earning Function) was used by incorporating the other variables which affect income along with the variable included in the strict model. Total income = f (Education, Experience, Age, Square of Age, Spouse Participation, Employment sector , Total acres of land ) Total income = f (Edu , Exp , Age, AGSQ , SP , EMP , LS ) Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 20 The econometric form of the model is given as [ 𝐿𝑁𝑇𝑂𝑇_𝐼𝑁𝐢 = 𝛽0 + 𝛽1πΈπ·π‘ˆ + 𝛽2𝐸𝑋𝑃 + 𝛽3𝐴𝐺𝐸 + 𝛽4𝐴𝐺𝑆𝑄 + 𝛽5𝑆𝑃 + 𝛽6𝐸𝑀𝑃 + 𝛽7𝐿𝑆 + ¡𝑖 ] A log-linear model was used in which total income was used as a dependent variable and education, work experience, experience square, age, age square, spouse participation, migration, employment, and land size are used as independent variables. Where Variables Description of variables Total_Inc Total household income from all sources EDU completed years of education of the household head EXP Years of work experience EXPSQ Years of work experience (Square) AGE Age of the household head in years AGESQ Age of the household head in years (Square) SP Spouse Participation ( Dummy variable by Yes= 1, No= 0) EMP Employment sector ( Dummy variable by informal= 1, formal=0) LS Total acres of land 4. Explanation of the Variables 4.1 Education Education is the most important human capital and investment in human capital in return increases the earnings of the family head. Educated persons are more productive, and skilled that's why the earnings opportunities are higher for them. So according to Schultz, we should use our resources on human capital which not only increases the earnings of the households but also make them able to take part in the economic growth of the country (Krasniki& Topxhiu, 2016). Education is expected to have a positive effect on the earnings of the family, the higher the education the higher will be the earnings. Household head education is measured in completed years of education. 4.2 Experience The experience of the household head is taken in the years. When the experience of the person increases due to training in a particular job their earnings start to rise because the experience people have more opportunities than inexperienced people. So it is expected to have a positive effect of experience on the earnings of the household, which means that when people gain experience in a particular job it improves their productivity which in turn increases the income of the family head. To capture the effect of experience over a longer period experience squarely is used in this study. Experience square showed that it affects the earnings negatively because nobody can work in the same position for a long-term period they start to get tired and want to take rest from their job that’s why their earnings start to decline. 4.3 Spouse Participation Spouse participation explains the involvement of both partners in different types of fields to earn income. If the spouse is participating financially with the partner earnings of the household heads will increase. Education is the most important indicator of the spouse’s participation because when the spouse is educated they can work in different fields which in turn increases their income (Pape & Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 21 Mistiaen, 2018). It is expected to have a positive effect of spouse participation on the earnings of the household head, so to capture the effect of spouse participation on earnings dummy variable is used. 4.4 Age of the household head The age of the household head is used as an explanatory variable. Based on literature, it is observed that household head earnings are higher at the young stage because they are more active and productive in the initial stage period. To capture the effect of old age households age squarely is used in this model it indicates that earnings at the initial stage of age increase but start to decline when we move towards old age so age square has a negative effect on the earnings of the family. The age of the household head is taken in the year (Khan. Z,2021). 4.5 Migration Migration is the movement of people from one region to the other region within the country, in looking for better employment opportunities and efficient allocation of resources. The statistics showed that migration to the urban area is much more in the last few decades, showing that the majority portion of the population has been facing problems in rural areas (Harris and Todaro, 1970). The expected income in urban areas is greater than in rural areas so it is expected to have a positive impact on migration on the earnings of the household. To find the effect of migration in our model a dummy variable is used. 4.6 Employment Status Employment status explains the economically active person concerning his or her employment. The status of the job indicates whether the respondent has a job or not and if the respondent has an occupation then the question arises in which job sector they engaged. The categorical variable is used for this, if the respondent belongs to the formal sector it will be represented by 0 otherwise by 1. Because this status of employment determines the earnings of an individual and people engaged as common laborers and professional workers will in general receive more payment than the junior team of workers and clerical staff. 4.7 Land Size Land size is an important indicator that affects the earnings of the household head. If the head of the household has the possession of one or more acres of land the income can be increased because the land can be utilized for different earning purposes. Higher will be the land size higher will be the earnings of the family which in turn decreases the earnings gap among the regions. So it is expected to have a positive effect on the land size on the earnings of the family. 5. Results and Discussion 5.1 Descriptive Statistics Descriptive analysis is the best method to analyze the data and to explain the minima, maxima, mean, and standard deviation. The results of the descriptive analysis of determinants of income are explained in the tables below. Table 1, shows that the average age of the family head of the rural population of district Multan is 59.43 years. The mean of the education years of the family head belonging to district Multan is 17.29 years, suggesting that on average education of the family head in district Multan is 17.29. The mean value of work experience is 21.71, implying that the average work experience of a rural household is 21.71. Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 22 Table: 1 Descriptive Statistics N Minimum Maximum Mean Std. Deviation Age 300 20 65 59.43 19.618 Education 300 1 18 17.29 2.701 Work Ex 300 3 36 21.71 9.252 Migration 300 0 1 .50 .519 SP 300 0 1 1.36 1.277 EMP_SEC 300 0 1 .79 .426 Land Size 300 1 3 1.50 .650 Total_INC 300 12000 110000 48571.43 29635.515 Source: Authors own calculation through SPSS The mean value of migration is 0.50, revealing that on average 0.50 people migrated to district Multan urban areas for income-earning. The mean value of spouse participation is 1.36, denoting that on average spouse participation is 1.36 in rural areas. The mean value of the employment status is .79, showing that 0.79 percent of the rural household in the district Multan belongs to the informal sector while the remaining belong to the formal sector. The mean value of land size is 1.50, revealing that an average rural household in the Multan district possesses 1.50 acres of land. The mean value of rural household head monthly income in district Multan is 48571.43, pointing out that the average income of the household in district Multan is 48571.43. The minimum earnings of the rural respondents from district Multan is 12000 rupees while the maximum earnings of the rural respondents in district Multan is 110000 rupees. The maximum variation is shown by the education variable and the least variation is shown by the variable migration. 5.2 Correlation Matrix The problem of multicollinearity is investigated by coefficient correlation, it explains the degree of association among the variables presented in the data. This table indicates the correlation between the different factors, however, the diagonal of the table shows the correlation of the factors with itself which always be 1. All variables are positively related to income except employment which is negatively associated with income. As can be seen from the table the value of correlation among all the variables is less than 0.70 so according to the rule of thumb, it can be concluded that there is not a high problem of multicollinearity in this study. Table. 2 Correlations District Multan S-PART AGE EDU MIG WORK EMP LAND TOT-INC S-PART 1 AGE .092 1 EDU .290 .227 1 MIG .124 .099 .274 1 WORK .004 .301 .160 .249 1 EMP -.140 -.034 -.174 -.258 -.251 1 LAND .024 .000 .114 .000 .268 .139 1 TOT-INC -.055 .239 .340 .253 .157 .069 .283 1 Source: Authors own calculation through SPSS Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 23 5.3 Econometrics Analysis 5.3.1 Strict Mincerian Earnings Function The outcomes are explained by the restricted Mincerian earnings function. Further the results of the R-Square value, explain that the model's explanatory variables were responsible for 59.1 percent of the variation in household income. Indicating a good fit model with an F-Value of (24.45), which was significant at a 1 percent level. The education variable is statistically significant at a 1% level of significance and it shows a direct association between earning and education in a rural area of district Multan. If education increases by a year it will increase the income of the household head by 0.023 percent. The result is consistent with the study of Khan, Z. (2021); Shabbir (1994); Teng et al. (2007); Su B & Heshmati A (2013); Rashid and Faridi (2014); Mincer (1974) and Chaudhry et al.(2011). The work experience is statistically significant at a 5% level of significance and explains a direct relationship between work experience and the income of the family head. If the experience of the family head increases by one year it will raise income by 0.0260 percent. This result of experience is also in line with the study of Khan, Z. (2021); Shabbir (1994); Lima et al.(2020); and Nasir (2000). 5.3.2 Extended Mincerian Earning Function In this, the results of the 'extended' MEF were showed which was obtained by extending the 'traditional' MEF by incorporating variables, age, age square, spouse participation, migration, employment status, and the size of landholding. The relationship between household education and the natural log of income is positive and significant at a 1% level of significance. The household head education variable indicated that if the education years of the household head increase by 1 year, it leads to a rise in the household income by 0.0202%. The result is consistent with the study of Khan, Z. (2021); Shabbir (1994); Teng et al. (2007); Su B & Heshmati A (2013); Rashid and Faridi (2014); Mincer (1974) and Chaudhry et al.(2011). Table: 3 Strict MEF: Rural District Multan Dependent Variable: Lnincome of the household Variable Coefficient Std. Error t-Statistic Prob. C 9.938897 0.101664 97.76178 0.0000 EDUCATION 0.023663 0.007123 9.321902 0.0015 WORK_EX 0.026056 0.011182 2.330173 0.0231 EXP2 -3.78E-05 0.000324 -0.116621 0.9075 R-squared 0.591576 Adjusted R-squared 0.571490 F-statistic 24.45152 Prob(F-statistic) 0.000000 Sample size (N) 300 Source: Authors Own calculation through E-Views The years of experience of the household head are positively related to income and are statistically significant at a 5% level of significance. It explains that the income of a household head increases by 0.0134 % if there is a rise in the experience of the family head by 1 year. This result of experience is also in line with the study of Khan, Z. (2021); Shabbir (1994); Lima et al.(2020), and Nasir (2000). The age of the household head is directly related to the monthly earnings of the family member and is significant at the one percent level of significance. Family monthly income rise by 0.0173 % as the family member’s age rises by 1 year. The reason is that as the family Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 24 member moved toward older age they become more hard workers, efficient, specialized, and responsible in their work so their earnings also rise as the age of individuals increases. The same result was in the study by Khan, Z. (2021). Chaudhry et al.(2011) Sun et al.(20110 and Lima et al.(2020). The results indicated an indirect relationship between income and age square variables and are significant at a 5 % level. The negative sign of the age square shows the nonlinearity of the age square, which indicates when the age of the household head reaches a certain level it will raise income by -0.00016 but this increase of income will be at a decreasing rate. This result of age square is also consistent with the study of Khan, Z. (2021) and Su, B., & Heshmati, A. (2013). Migration is directly related to the income of the household and is significant at a five percent level of significance. The monthly household income upsurged by 0.1740 % as family members migrated from rural to urban areas. This finding supports the study of Huhua Cao (2010) and Lima et al. (2020). The size of the landholding is included in the model and is statistically significant at a 1% level of significance. Further, the result proposes that if the head of the household has the possession of one or more acres of land the income can be increased because the land can be utilized for different earning purposes. So, The coefficient of landholding size is positively related to the monthly income of the household head, it shows that as household heads possess more land their income increase by 0.04 percent. This result is also in line with the study of Chaudhry et al. (2011). The results of the strict Mincerian function indicate that education and work experience has a positive effect on the earnings of the rural households in district Multan while experience square has a negative effect on the earnings due to the nonlinearity of the variable. The variables for the extended Mincerian function were Education work, experience, Exp2, Age, Age2, Spouse Participation, Migration, Employment status, and land size. Moreover, in the extended Mincerian function Table 4 Extended MEF: Rural District Multan Dependent Variable: Lnincome of the household Variable Coefficient Std. Error t-Statistic Prob. C 9.755284 0.251827 38.73799 0.0000 EDUCATION 0.020163 0.006903 2.920924 0.0051 WORK_EX 0.013467 0.005940 2.267108 0.0270 EXP2 0.000147 0.000432 0.339615 0.7354 AGE 0.017336 0.004406 3.934715 0.0002 AGE2 0.000162 6.77E-05 2.396513 0.0197 SPOUSE_PART -0.007350 0.068075 -0.107971 0.9144 MIGRATION 0.174054 0.084596 2.057480 0.0444 EMP_STAT -4.26E-05 0.000557 -0.076515 0.9393 LAND_SIZE 0.045667 0.013599 3.358029 0.0013 R-squared 0.434427 Adjusted R-squared 0.341879 F-statistic 4.694065 Prob(F-statistic) 0.000123 Sample size (N) 300 Source: Authors Own calculation through E-Views Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 25 6. Conclusion and Policy Recommendation This paper examined the determinants of income among rural households of district Multan. This study was based on primary data which was collected through a structured questionnaire during the period 2022. A sample of 300 households from rural areas was drawn randomly. To describe the socio-economic characteristics of the household head descriptive static was used. The coefficient correlation was used to find the degree of association among the variables. In estimating the determinants of rural household income Mincerian earning function was used both in strict and extended form. variables like education, work experience, age, spouse participation, migration, and land size has a positive effect on the earnings while work experience square, age square, and employment sector have a negative effect on income in district Multan. Furthermore, the value of the coefficient correlation was less than 0.30, revealing that there is no problem with multicollinearity in our model. 7. Recommendation β€’ Withholding taxes should be withdrawn by the Government from rural areas and must be exchanged with less taxation so that the indirect taxes on the less well-off is removed and decrease the income gap between rural and urban area. β€’ Policy formulation on investment in human capital is needed and its effective implementation is necessary for the well-being of society. β€’ The job formation for skillful labor in rural areas would be instrumental in tumbling the wage gaps and in the growth of rural workers. β€’ Programs should be introduced that benefit the rural areas and assist in enhancing the productivity of rural areas to stop the migration towards the urban areas. β€’ Women’s employment has dramatic effects on income. Fewer women involve in Working in rural areas as compared to urban. Women should be counseled in rural areas through awareness. References Ahmad, R., & Faridi, M. Z. (2020). Socio-Economic and Demographic Factors of Poverty Alleviation in Pakistan: A Case Study of Southern Punjab. Review of Economics and Development Studies, 6(2), 425-438. Akram, W., Naz, I., & Ali, S. (2011). An empirical analysis of household income in rural Pakistan: evidence from tehsil Samundri. Pakistan Economic and Social Review, 231-249. Chaudhry, D. I. S., Faridi, D. M. Z., & Bashir, F. (2020). Rural-Urban Saving Differentials in Pakistan: Investigation from Primary Data. South Asian Studies, 26(1). De Lima, C. F., Costa, E. M., Mariano, F. Z., Justo, W. R., & de Carvalho Castelar, P. U. (2020). Migration of labor: differential of income between rural and urban trade union workers in Brazil. Journal of Economic Studies. Fadipe, A. E. A., Adenuga, A. H., & Lawal, A. (2014). Analysis of income determinants among rural households in Kwara State, Nigeria. Trakia Journal of Sciences, 4, 400-404. Gillani, D. Q., Khan, R. E. A., & Zahir, M. (2013). Earning determinants and urban informal sector: Evidence from District Multan. Pakistan Vision, 14(2), 132-148. Harris, J. R., & Todaro, M. P. (1970). Migration, unemployment and development: a two-sector analysis. The American economic review, 60(1), 126-142. Huhua Cao (2010) Urban-Rural Income Disparity and Urbanization: What Is the Role of Spatial Distribution of Ethnic Groups? A Case Study of Xinjiang Uyghur Autonomous Region in Western China, Regional Studies, 44:8, 965-982. Jansen, H. G., Rodriguez, A., Damon, A., Pender, J., Chenier, J., & Schipper, R. (2006). Determinants of Review of Economics and Development Studies, Vol. 9 (1) 2023, 17-26 26 income-earning strategies and adoption of conservation practices in hillside communities in rural Honduras. Agricultural Systems, 88(1), 92-110. Khan, K., & Idrees, M. (2014). Determinants of Earnings: A District Wise Mapping of Pakistan. Forman Journal of Economic Studies, 10. Krasniqi, F. X., Topxhiu, R., & Rexha, D. (2016). The contribution of several Nobel Laureates in the development of the theory of general economic equilibrium. The Romanian Economic Journal, 19(62), 153-166. Mincer, J. A. (1974). The human capital earnings function. In Schooling, experience, and earnings (pp. 83-96). NBER. Nasir, Z. M. (2000). Earnings differential between public and private sectors in Pakistan. The Pakistan Development Review, 111-130. Nzabakenga, A., Feng, L. X., & Yaqin, H. (2013). Agricultural income determinants among smallholder farmers: Case of northern part of Burundi. Asian Journal of Agriculture and Rural Development, 3(393-2016-23853), 780-787. Nzabakenga on, M. H., Ali, M., & Khalil, S. (2020). Determinants of income diversification in flood- prone rural Pakistan. International Journal of Disaster Risk Reduction, 50, 101914. Odozi, J. C., & Adeyonu, A. G. (2021). Household-level determinants of employment and earnings in rural Nigeria. Cogent Economics & Finance, 9(1), 1982232. Parvin, M. T., & Akteruzzaman, M. (2012). Factors affecting farm and non-farm income of haor inhabitants of Bangladesh. Progressive Agriculture, 23(1-2), 143-150. Pape, U. J., & Mistiaen, J. A. (2018). Household expenditure and poverty measures in 60 minutes: a new approach with results from Mogadishu. World Bank Policy Research Working Paper, (8430). Shabbir, T. (1994). Mincerian earnings function for Pakistan. The Pakistan Development Review, 1-18. Sun, L., Chang, J., Liu, Y., & Yang, Z. (2011). The urban-rural disparities of the elderly labor supply and income in China. Procedia Engineering, 15, 5274-5278. Su, B., & Heshmati, A. (2013). Analysis of the determinants of income and income gap between urban and rural China. China Economic Policy Review, 2(01), 1350002. Sheikh, M. R., Akhtar, M. H., ASGHAR, M. M., & Abbas, A. (2020). Demographic and Economic Aspects of Poverty: A Case Study of Multan District, Pakistan. Pakistan Economic and Social Review, 58(1), 131. Teng, S., Yue, X., & Bjorn, G. (2007). The Urban-rural Income Gap and Inequality in China. Review of Income and Wealth, 53(5). Talukder, D. (2014). Assessing determinants of income of rural households in bangladesh: a regression analysis. Journal of Applied Economics and Business Research, 4(2), 80-106.