Rural and urban household consumption patterns in Tanzania: A consumer theory analysis of determinants

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This study investigated the determinants of household consumption patterns using simultaneous quantile regression model. By integrating consumer’s theory with empirical analysis of determinants, this study contributes to the existing literature on households’ consumption economics and provides actionable insights for policymakers and stakeholders aiming at fostering equitable economic development. The findings indicate that, income positively influences consumption patterns across all quantiles, that is at 25 th , 50 th and 75 th quantile respectively in rural and urban areas. Notable differences emerge, with urban households displaying diverse consumption behaviors linked to higher incomes and greater access to goods, while rural households depend on agricultural production and spend a larger share of their income on food, facing constraints like limited savings and market access. Additionally, high utility costs negatively impact consumption patterns by about 74 percent subject to effect variation depending on the location (rural or urban). The study underscores the significance of socio-economic factors, such as gender and family structure, in household decision-making and highlights the influence of external factors like economic policies and infrastructure on consumption choices. It calls for targeted policies to improve market access, enhance consumer education, and support local production to bridge the consumption gap between urban and rural households, emphasizing the need for inclusive economic policies tailored to the diverse needs of Tanzanian households. Agricultural Economics & Policy Microeconomics Rural and urban Households Consumption patterns 1.0 Introduction The income disparity between rural and urban dwellers is the critical issue on micro economics policy planning for stimulating consumption in the economy (see Kuznets, 2019; Azizur and Carl, 2023). While consumption being one of the key drivers of the economic growth and development among household and the national at large, the income disparity is widening between rural and urban areas (Lu, 2018). Household consumption patterns are critical indicators of economic well-being and are influenced by a myriad of factors, including income levels, household size, education, and geographical location among others (see Yu et al , 2025; Grover et al , 2025). In this study, Consumption patterns refer to the typical behaviors and trends associated with how individuals or groups use goods and services (Mehta et al, 2020). These patterns are influenced by various factors, including cultural, economic, social, and psychological elements ( ibid) . in that regards, individual consumers (Households) can indicate preferences for certain products, the frequency of purchases, and the overall impact of consumption on the environment and society. Hence, understanding consumption patterns helps businesses tailor their offerings, policymakers assess economic health, and researchers analyze consumer behavior and its implications for sustainability and resource use (see Zhang and Chang, 2021; Varalakshmi, (2024). In Tanzania, the distinction between urban and rural consumption patterns is pronounced, reflecting differences in access to resources, infrastructure, and economic opportunities. Thus, again, understanding these patterns is essential for formulating effective policies aimed at poverty alleviation and economic development (Rodríguez et al , 2024). Besides, household consumption patterns are fundamental to understanding economic behavior and welfare in any society (Osolo et al , 2025). In Tanzania again, the distinction between urban and rural consumption patterns is particularly pronounced due to varying socio-economic conditions, access to resources, and cultural influences. Urban households typically exhibit different consumption behaviors compared to their rural counterparts, influenced by factors such as income levels, education, and market access (Qi et al , 2025). Thus, understanding these differences is crucial for policymakers aiming to address poverty and improve living standards across the country. In urban areas, households often have higher disposable incomes (Ma et al , 2018), which allows for greater expenditure on non-essential goods and services. This trend aligns with Engel's Law, which posits that, as household income increases, the proportion of income spent on food decreases, leading to a diversification of consumption towards luxury items and services (Rashid et al , 2024). Conversely rural households with lower income levels (Ranzani et al, 2022) tend to allocate larger shares of their budgets to basic necessities, that is, primarily food and shelter. This disparity in consumption patterns can exacerbate existing inequalities and hinder overall economic development (ibid). Education also plays a pivotal role in shaping consumption choices; households with higher educational attainment are more likely to make informed decisions regarding their consumption, leading to healthier dietary choices and better financial management (Chen and Antonelli,2020). In contrast, limited access to education in rural areas often results in lower consumption of diverse and nutritious foods, contributing to health disparities and food insecurity (Nyath et al , 2025). Moreover, access to markets and infrastructure significantly influences consumption patterns. Urban areas typically benefit from better infrastructure, including transportation and communication networks, which facilitate access to a wider variety of goods and services. In contrast, rural households often face challenges such as poor road conditions and limited market access, which restrict their consumption choices and contribute to higher levels of poverty (Riva, et al , 2018). The study was conducted in Tanzania due to the following fated reasons; First, Tanzania presents a unique economic landscape characterized by a significant divide between urban and rural areas. Understanding consumption patterns in this context can reveal how different economic conditions influence household decisions, contributing to more effective economic policies Secondly, with a substantial portion of the population living below the poverty line (Letta et al, 2018), understanding consumption patterns is essential for developing targeted poverty alleviation strategies. This research can provide insights into how consumption choices are made under financial constraints in both urban and rural settings. Third, Tanzania is experiencing rapid urbanization, leading to shifts in consumption behaviors (Sumari et al. , 2020). Studying these dynamics can inform policymakers about the changing needs and preferences of urban populations, which are crucial for sustainable urban planning and development. Fourth, food insecurity remains a pressing issue, particularly in rural areas (Kamat and Woo Kinshella, 2018). So, analyzing consumption patterns can help to identify gaps in food access and nutritional quality, guiding interventions aimed at improving food security and health outcomes. Fifth, the disparity in infrastructure and market access between urban and rural areas affects consumption choices and economic opportunities. Thus, investigating these effects can help to identify necessary improvements needed to support rural development and enhance market access. This study analyzed the determinants of household consumption patterns in urban and rural areas of Tanzania from a consumer theory perspective using [1]National Panel Survey 2020-21, Wave 5 obtained from World Bank dataset. By examining the interplay of consumption, income, utilities, family structures and gender leadership in households, this paper provides an insight into the consumption behaviors of different household types and the implications for policy interventions aimed at reducing poverty and enhancing economic stability. The purpose of this study was to investigate how variations in household income levels affect consumption choices and patterns in urban and rural settings while accounting for other variables such as utilities, family structure and gender (household size and female head). Besides, the paper was guided by the following two hypotheses H 1 : Income level has a positive effect on household consumption expenditure; H 2 : Urban households have lower consumption expenditures than rural households on food. Despite the fact that, there is a growing body of literature on consumption economics in various contexts, there is still limited research focusing specifically on households’ consumption patterns in Tanzania, especially from a consumer theory perspective using simultaneous quantile regression model. This study can fill this gap in the literature of household’s consumption economics and contribute to a potential academic discourse. Apart from that, the findings from this research can have direct implications for policymakers and the public. Additionally, insights into the determinants of household’s consumption can guide the design of social safety nets, subsidies, and programs aimed at enhancing household welfare and reducing inequality. The rest of this study is organized as follows: Section 2 includes a literature review, Section 3 details the data and methods, Section 4 reports the results and discussion, Section 5 provides the conclusion and policy implications. [1] Tanzania - National Panel Survey 2020-21, Wave 5 2.0 Literature review 2.1 Theoretical literature review This paper adopts the perspective of central ideas presented in the consumer theory as applied in micro economic issues and perverse. According to Geoffrey and Philip (2011), Consumer theory is a fundamental concept in economics that analyzes how individuals make decisions about the allocation of their limited resources (such as income) among various goods and services to maximize their utility (satisfaction or happiness) (see also Dubé, 2019). It provides a framework for understanding consumer behavior, preferences, and the factors that influence purchasing decisions (Fekete-Farkas et al, 2021). The key assumptions (Jehle and Reny, 2011 ) for this theory among others are; first, Rational Behavior (Utility Maximization)-Consumers are assumed to act rationally, seeking to maximize their utility (satisfaction) from consumption. They make choices that provide the highest level of satisfaction given their preferences and constraints. Secondly, completeness whereby a consumers can compare and rank all possible combinations of goods and services; they can determine which combination provides more utility. Third, transitivity-if a consumer prefers A to B and B to C, then they should also prefer A to C. This ensures consistent preferences. Fourth, non-satiation-more of a good is preferred to less, meaning consumers always want to increase their consumption as long as it leads to greater utility. The reasons for adopting this theory are; First, Consumer theory helps to explain how households prioritize their consumption based on preferences and utility maximization (Browning and Zupan, 2020). This is particularly relevant in contrasting urban and rural settings, where preferences may differ significantly due to cultural and socio-economic factors. Secondly, the theory emphasizes the role of budget constraints in consumption decisions (Kreps, 2020). In Tanzania, urban households may experience different constraints compared to rural households, influencing their purchasing behavior and consumption choices. Third, Consumer theory allows for the examination of how changes in income levels affect consumption patterns, particularly the concept of income elasticity of demand (Mohammadi and Rezvani,2019). This is crucial for understanding how urban and rural households respond to economic changes. Fourth, Consumer theory enables a systematic comparison of consumption behaviors between urban and rural households, shedding light on the socio-economic dynamics that influence these patterns (Alhadeff, 2024). To test this theory, we use two hypotheses namely; H 1 : Income level has a positive effect on household consumption expenditure; H 2 : Urban households have lower consumption expenditures than rural households on food. We test the hypothesis by using a simultaneous quantile regression model of estimation which the results are detailed in section 4 of this paper. 2.2 Empirical review There are many studies conducted related to determinants of household consumption patterns in urban and rural areas in various countries using different methodologies such as descriptive statistics, literature reviews, Data mining (DM) techniques, noise (DBSCAN) algorithm, Social Accounting Matrix (SAM) and online consumer survey (see Değirmenci and Özbakır, 2018; Wang et al, 2018; Chenarides et al , 2021). After thorough review, it was found that, previous studies have shown that, there is positive effect of income on consumption decision of the households and mostly urban households consume more on goods and services than that of rural areas (see Sauer et al , 2021; Ishengoma and Igangula, 2021; Wang et al , 2022). Understanding the determinants of household consumption patterns in urban versus rural areas of Tanzania is crucial for addressing economic disparities and improving living standards (Dzanku et al , 2024). Recent empirical studies have highlighted various factors influencing these consumption behaviors, drawing on consumer theory to analyze the underlying mechanisms (see Safari, et al, 2022; Stadlmayr et al , 2023; Wang et al , 2029). In most cases, these studies have used other methodological approach such as literature review, descriptive analysis from primary data survey, ten percent and double median and alike (see Awan and Bilgili, 2022; Han et al , 2018). 2 .2.1 Income and expenditure patterns Previous studies indicated that, household income significantly affects consumption patterns (see Swema and Mwinuka, 2021; Oyeniran and Isola, 2023). Urban households tend to have higher incomes, leading to greater expenditure on non-essential goods compared to rural households, which allocate a larger share of their budgets to basic necessities such as food and shelter (see Kuhe and Bisu, 2020; Swema and Mwinuka, 2021). For instance, urban dwellers spend approximately 63.2 per cent of their total expenditure on food, while rural counterparts spend about 55.2 per cent (Rashid et al , 2024). This aligns with Engel's Law, which posits that as income increases, the proportion of income spent on food decreases (Matabaro et al, 2024). 2.2.2 Household Utilities patterns Household utilities, encompassing water, electricity, and sanitation services which play a crucial role in shaping consumption patterns in both rural and urban settings. In Tanzania, access to these utilities varies widely, significantly influencing household strategies and overall quality of life (Li, et al, 2021). Urban households generally have better access to essential utilities compared to their rural counterparts, where infrastructural challenges persist. Studies have shown that, the availability and reliability of utilities affect consumption choices, with households that have consistent access to electricity, for instance, being more likely to invest in energy-intensive goods like refrigerators and televisions (Gou et al , 2018; Reddick et al, 2020). Understanding how these utilities are accessed and utilized provides valuable insights into the broader consumption patterns observed in Tanzanian households, highlighting the importance of infrastructure development in enhancing quality of life and economic resilience. 2.2.3 Household size patterns (Family structures) The size of household also has been found to have effect on consumption in both rural and urban settings (Siman et al, 2020). Household size plays a crucial role in shaping consumption patterns through various mechanisms, including economies of scale, diverse needs, income distribution, time constraints, social influences, and savings behavior (see Madudova and Corejova,2023). In larger households, income is often pooled, which can lead to increased overall consumption. However, the distribution of income within the household can also affect individual consumption choices. Moreover, larger households may face different budget constraints compared to smaller ones (Liu et al , 2018); they might prioritize essential goods (such as food, housing) over luxury items. 2.2.4 Gender Settings (Female-Headed Households) In recent years, research on female-headed households (FHHs) has gained prominence, particularly in the context of developing countries like Tanzania (see Lufuke and Tian, 2024; Mbunda and Ndunguru,2024). FHHs often face unique challenges compared to their male-headed counterparts due to socio-economic disparities and cultural norms. Studies indicate that, women in FHHs tend to have lower access to resources, education, and economic opportunities, which can adversely affect their household consumption patterns (Assenga and Kayunze, 2021). For example, women may prioritize expenditures on health and education for their children over other goods, leading to distinct consumption behaviors. Furthermore, the role of women in decision-making processes within the household can influence the allocation of resources, often reflecting their priorities and the socio-cultural dynamics at play (Ahinkorah et al , 2018). The intersection of gender, poverty, and household dynamics necessitates a nuanced understanding of how FHHs navigate consumption in both rural and urban settings, as their strategies may differ significantly based on local contexts. 2.2.5 Education and consumption choices In various studies consumption patterns and education, educational attainment plays a critical role in shaping consumption behaviors (see Steils, 2021; Lührmann et al, 2018). Households with higher education levels are more likely to make informed consumption choices, leading to healthier dietary patterns and better financial management (Laibson et al, 2024). Conversely, limited access to education in rural areas often results in lower consumption of diverse and nutritious foods, contributing to health disparities ( ibid ). Studies have shown that, education positively influences food expenditure, as it enhances awareness of food attributes and encourages the adoption of healthier consumption practices (Wijayaratne et al , 2028; Lusk and McCluskey, 2018; Lee et al, 2022). Therefore, given the fact that, there are many studies being conducted about the consumption patterns in rural and urban areas in different countries globally as demonstrated herein above review; but in most reviewed literatures ( see for example in Eger et al , 2021; Ismagilova et al, 2020; Martínez-Plumed et al, 2019; Fan et al, 2021; Aragie et al , 2021; Guthrie et al , 2021), they used different methodologies of which among others are; the descriptive statistics, literature reviews, Data mining (DM) techniques, noise (DBSCAN) algorithm, Social Accounting Matrix (SAM) and online consumer survey to study consumption patterns. They in fact used other data sources not from the World Bank for Tanzania. This creates literature and knowledge gap in the household’s consumption economics discourse in Tanzania and global at large. Therefore, this study aims to fill the gap in the existing literature using National Panel Survey data for the year 2020-21, Wave 5 by applying the Simultaneous Quantile regression model to analyze the determinants in households’ consumption as result of income level while accounting for other variables (utilities, household size and female head). A Simultaneous Quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) simultaneously in the distribution of the response variable. The analysis can help to see the consumption behaviors/patterns among the urban and rural dwellers on food and other services within the consumer theory’s perspective. 3.0 Data and methodology 3.1 Data source This study used data from the World Bank dataset specifically for Tanzania. The National Panel Survey data for the year 2020-21, Wave 5 were downloaded and analyzed in the STATA program. The sample size of the dataset was 4,709 households selected at random from the Tanzanian rural and urban areas including Zanzibar. Findings from the previous literatures have been used to supplement the argument on the presentation and justification of findings for this study especially in showing the disparities between rural and urban consumption patterns. 3.2 Hypotheses A hypothesis is a specific, testable prediction about the relationship between two or more variables. It serves as a foundational element in scientific research and experimentation, guiding the design of studies and the analysis of data (Scerbo et al , 2019). This study has two key hypotheses to test; H 1 : Income level has a positive effect on household consumption expenditure. H 2 : Urban households have lower consumption expenditures on food than rural households. 3.3 Estimation Model Based on the theoretical review (Randolph, 2009) and previous empirical studies; this study investigates the determinants of household consumption patterns in Urban and Rural areas of Tanzania using National Panel Survey data obtained from the World Bank for year 2020-21. In this study, the simultaneous quantile regression (SQR) model was employed to robustically estimate the relationship between variables (dependent and independent) in different percentile level across the distribution of responsible variables. This study uses simultaneous quantile regression because (Waldmann, 2018) it is a statistical technique that estimates the conditional quantiles of a response variable simultaneously ; unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable given the independent variables, quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) simultaneously in the distribution of the response variable ( ibid ). This is relevance model to this study given the fact that, the study is looking at the consumption patterns among households which is mostly affected by the income within the population in this case. Also, with SQR, “in cases where either the requirements for mean regression, such as homoscedasticity, are violated or interest lies in the outer regions of the conditional distribution, quantile regression can explain dependencies more accurately than classical methods’ (Staffa et al , 2019). In explaining the variables from the dataset, the dependent variable under this study is the consumption of food in house (FoodIN) which was regarded as the Households consumption per annum per household on basic households’ needs such as food, beverages, clothes, health costs, education cost transport and alike. The independent variables are total expenditure (which is HH income) annual amount per household; Utilities per anum per household; Household size and Female household. Table 1 shows the details of explanation of the key variables. In this study as indicated in Table 1 , total annual expenditure is used in place of annual income because; First, total expenditure provides a more comprehensive view of a household's economic well-being, as it captures not only regular income but also any additional financial resources that may not be reflected in income data, such as transfers, savings, or credit. Second, total expenditure can better reflect the actual consumption patterns and living standards of households. Since expenditures are recorded based on actual spending, they can offer a more accurate depiction of a household's financial situation at a given time. Besides, the study uses food consumption as a proxy for overall consumption because; food is a fundamental human need, and its consumption is directly linked to health and well-being. So, by considering much on food consumption in place of overall consumption, study can gain insights into the nutritional status and dietary diversity of households, which are critical indicators of quality of life. Utilities variable is used in this study because; first, the cost of utilities can significantly affect household budgets, particularly for lower-income families. High utility costs may force households to make trade-offs, impacting their spending on other essential goods and services. Second, understanding the role of utilities in budget constraints provides insights into the economic pressures faced by households and how these pressures shape consumption patterns. Households Size variable is used because; First, it is thought that, larger households may benefit from economies of scale, allowing them to purchase goods in bulk or share resources, which can affect their consumption behavior. Thus, by including household size, the study can examine how these economies impact spending patterns and whether larger households tend to have lower per capita consumption costs. Second, Household size directly influences the demand for goods and services; larger households typically require more food, clothing, and other essentials, which can lead to different consumption patterns compared to smaller households. Third, understanding how household size affects overall consumption helps in analyzing the resource allocation strategies employed by families. Female-headed households (FHHs) variable is used because; First, Female-headed household often exhibit different consumption behaviors compared to male-headed households. Women may prioritize spending on health, education, and nutrition, which can significantly influence overall household consumption patterns. Thus, understanding these differences can provide deeper insights into how gender dynamics shape economic decisions. Second, understanding the role of female headship in consumption patterns can inform policy-making and development programs. Third, recognizing the needs and challenges of FHHs can lead to targeted interventions that enhance their economic resilience and improve overall household welfare. This is particularly important in efforts to promote gender equality and empower women within the economic framework within the Country. Table 1 Variables, symbols, unit of measures and sources of data Variables Symbols Unit of measure Sources Consumption FoodIN annul consumption of individual household on food, beverage and alcohol at home World Bank (2020-21) Household income expm Total expenditure annually per individual household on things like health, education, transport World Bank (2020-21) Utilities nf_utilitiesR_pae Utilities per anum per household on water, lighting, kerosine, etc World Bank (2020-21) Household Size hhsize The size of household World Bank (2020-21) Female head femalehead Female headed houshold World Bank (2020-21) Source: World Bank (2020-21) 3.3.1 Empirical model The empirical model of estimation is given as follows; Sq c (τ | X) = β₀(τ) + β₁(τ)INC + β₂(τ)UT + β₃(τ)HHS + β₄(τ)FH+ 𝜇 Where; Sq c (τ∣X) is the conditional quantile of Consumption given Income, Utilities, Household size and Female head at 25th, 50th and 75th quantiles. Β 1,2,3,4 (τ) are the coefficients that vary with the quantile levels β 0 ​(τ)-Constant at Quantile level INC-Income UT-Utilities HHS-Household size FH-Female Head 𝜇- error term The possible bias in the estimation equation above is Unobserved Heterogeneity because if there are unobserved factors that affect both the independent variables and the dependent variable, this can lead to bias in the quantile estimates. But unlike OLS, quantile regression focuses on different points in the distribution, making it sensitive to such biases to appear. Simultaneous quantile regression (SQR) is useful for studying income or wealth inequality, as it can show how different factors affecting people at various income levels differently (Demir et al , 2022). In this study, it is useful since the study focus on the consumption pattern which has direct relationship with income levels among households in the population. The coefficients from quantile regression represent the change in the quantile of the dependent variable (consumption) for a one-unit change in the independent variables (income, utilities, households’ size and female head), holding other variables constant. 3.3.2 Reduced form estimation model equation The reduced model equation is given below; Sq c (τ | X) = γ₀(τ) + γ₁(τ)Z + ϵ Where: Z-is a vector of all the independent variables (INC, UT, HHS, FH) γ 0 (τ)-captures the intercept term γ 1 (τ)-represents the combined coefficients of the independent variables ϵ-is a new error term that encompasses the original error term and any omitted variables that may affect Sq c The reduced form of equation representation assumes that, the relationships between Sq c and the independent variables can be summarized in this reduced form while retaining the functional relationships implied in the original model. 4.0 Results and Discussion 4.1 Simultaneous Quantile Regression results for consumption patterns To find out the variation and determinants of consumption patterns between rural and urban population, the simultaneous quantile regression technique was employed under this study involving the maximum robust bootstrap replication with simulated dataset, which facilitates the evaluation of the responsible variable income within the consumption pattern distribution (Table 2 ). The findings revealed that, income has positive effect throughout the quantiles, this is due to fact that, income is the most critical factor when it comes to measures and determining consumption capabilities among households in both rural and urban areas. This study also found from other studies that, other factors such as level of education, households’ composition, gender, access to markets and transportation facilities do influence much on consumption patterns among the households in rural and urban areas (see Biresselioglu et al , 2023). Besides, there is much disparities in terms income and spending among the rural and urban households due to their inequalities in economic status; urban dwellers face higher income and consumption inequalities than of rural (Waryoba, 2023). Moreover, for the purpose of regional comparison, this study, used references from the previous related studies (Zehra and Singh, 2023) to provide the categorical comparison of the findings between rural and urban household’s’ consumption patterns and their consumption disparities. The study’s results from quantile regression analysis (Table 2 ) show that, there is high consumption variability between the households in rural and urban areas in the country. The quantile regression results indicates that, income has a positive effect on consumption pattern per households in all quantile levels (25th, 50th and 75th quantiles) of all regions under study (Dar es Salaam, Rest of the urban, rural areas and Zanzibar) as indicated in the Table 3. The results show that, income is the prevailing consumption pattern among others in both rural and urban areas. The income is statistically significant at 95% confidence interval (p < 0.05) across the distribution meaning that, it is actually has a direct positive effect on consumption pattern within the households; hence, we fail to reject the H 1 hypothesis in this study and so, we conclude that, income level has a positive effect on household consumption expenditures in house needs, food in particular. These findings reveal that, households with higher income consume less and save more and may have good nutrition status while those with low income consume more and save less from their income. This is justified in previous studies which revealed that, income has direct effects on consumption patterns in the population given its variability among individual expenditures in the household settings (see Bjelle et al , 2021; Nguye et al , 2021; Piyapromdee and Spittal, 2020). Also, Su, et al (2029) in their study in China found that, “the current expansion of income variance has a positive effect on consumption, which is likely to lead to the aggravation of consumption inequality in the country”. This poses the consideration in policy reforms to reduce income gap among households in the economies (rural vs urban) for sustainable development of all people. The results further indicate in the 75th quantile that, the households’ consumption share on food is about 74 per cent, this can vary depending on the location of the household (rural or urban). Previous studies indicates that, rural households spend more of their income share on households needs mostly food for their family members while those of urban areas have substantial high income with minimal consumption share on food. This is justified in the study by (see De Brauw and Giles, 2018) in China who found that, rural villages contribute to significant increases in the consumption of goods, and these effects are larger in magnitude for households that were relatively poor”. Given these findings, we fail to reject H 2 hypothesis and conclude that, urban households have lower consumption expenditures share of their income on food than rural households. This indicates that, urban consumers use part of their income on food consumption and the other part for saving and investment. The study further argues from the literatures that, the rural households have larger share of consumption in the economy than that of urban areas (Janssens et al , 2021; Kansiime et al , 2021). That means, they have low income earning which is mostly used for food consumption purpose in their daily livings with no surplus income for saving and investment, that’s why the rural communities are characterize by being poor communities in the most developing countries. While that hold, the utilities pattern is statistically significant at 75th quantile level with negative coefficient meaning that, it affects the income of the households in decreasing units for every unit of income. Overall, utilities spending in rural and urban areas affect negatively the consumption patterns of households, this may be due to high costs of utilities package of services for households given their level of income. The findings suggest for re-consideration of rural development programs to reducing the costs of utilities package such as electricity and solar energy as well as water access especially for the people in rural areas given their level of income is lower (Waryoba, 2023) compared to that of urban population From this study, it was discovered that, the household’s size has significant effect across the distribution between rural and urban areas (all quantile levels) with positive effect on consumption levels across the population. Households with higher number of members has obvious larger share of consumption as compared to that of households with less number. The difference is not that much between the two regions (rural vs urban) this may be due to the family planning awareness campaign that was introduced by the government for all Tanzanians to reduce unplanned birth in household. Besides, the study found that, the condition for being female head in the household has no direct effects on the consumption pattern in rural and urban areas. This observation may be due to the fact that, female headed households represent poor group in the population especially those in rural areas as (Nwosu and Ndinda, 2018) justifies this from their study in South Africa. Table 2 Simultaneous Quantile regression results for consumption patterns 25th Quantile Coefficient Standard Error T-statistic P-value Income .5960199 .0895455 6.66 0.000*** Utilities .0104345 .0675131 0.15 0.877 Household Size .0664169 .0167937 3.95 0.000*** Female head − .0711162 .0689316 -1.03 0.303 Constant 4.601602 .9087241 5.06 0.000 50th Quantile Income .6601132 .0530301 12.45 0.000*** Utilities − .033319 .0345942 -0.96 0.336 Household Size .0419974 .016536 2.54 0.011** Female head − .0715621 .058437 -1.22 0.221 Constant 4.559619 .5180807 8.80 0.000 75th Quantile Income .7460812 .0639724 11.66 0.000*** Utilities − .0635425 .0251954 -2.52 0.012** Household Size .0231008 .0091553 2.52 0.012** Female head − .001052 .032047 -0.03 0.974 Constant 3.902319 .705205 5.53 0.000 Source: World Bank (2020-21) Note : ***p < 0.01; **p < 0.05; *p < 0.1 5.0 Conclusion and policy implications This study investigates the effect of income on households’ consumption choice and patterns among households in urban and rural areas of Tanzania while accounting for utilities, households’ size and female head variables in a regression model by using National Panel Survey data obtained from the World Bank for the year 2020-21. To estimate the results, the study applied the quantile regression model which is robust in estimating the changes between responsible variables in different levels (quantiles) within the distribution. The results indicates that, income has a positive effect on consumption pattern per households in all quantile levels (25th, 50th and 75th quantiles) in both rural and urban areas in the country affecting much the consumer’s choice. Besides, the results further indicate in the 75th quantile, the rural and urban dwellers have high consumption share of about 74 per cent of their income on household needs mostly food for their family members, this percent can vary depending on the income status of the household and location. There is a notable consumption gap between the rural and urban population due income disparities. Also, utilities spending in rural and urban areas affect negatively the consumption patterns of households, this may be due to high costs of utilities package of services especially for rural households given their level of income. The findings of this study emphasize that, there is considerable variability in food consumption, income, and utility expenditures among households, indicating that, a one-size-fits-all approach may not be effective for policy or economic analysis in Tanzania; instead, a tailored or customized approach which involves creating solutions or strategies that, are specifically designed to meet the unique needs of individuals or particular groups is essential for policy and economic ratification. Furthermore, it emphasizes the impact of external factors, including economic policies and infrastructural development, on consumption choices. Overall, the findings of this study provide a significant call for policy makers to design a tailor-made economic program that helps to promote the rural households in terms of their income earnings and saving education as well improving their general wellbeing. This would help to reduce the income and consumption imbalance that exist between rural and urban households in Tanzania. Policy and decision makers should design distinct economic policies that address the specific consumption needs and behaviors of urban and rural households. For example, urban policies may focus on facilitating access to diverse goods, while rural policies might prioritize enhancing agricultural production, transport infrastructures and market access. The limitation of this study is that, income alone may not be sufficient factor that influence households’ consumption patterns in rural and urban areas, as other factors such as price, consumer preferences and test should be considered for further study on factors affecting consumption patterns and choice among households in rural and urban areas of Tanzania. Declarations Funding The study has no funding requirements. Declaration of Competing Interest The authors declare that, they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Ahinkorah, B. O., Dickson, K. S., & Seidu, A. A. (2018). Women decision-making capacity and intimate partner violence among women in sub-Saharan Africa. Archives of Public Health , 76 , 1-10. Alhadeff, D. A. (2024). Microeconomics and human behavior: Toward a new synthesis of economics and psychology . Univ of California Press. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6797273","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466384361,"identity":"23f810dd-be66-4f23-a370-851a042fc1d4","order_by":0,"name":"Jackson Bulili Machibya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACAwbmhgMPgCQDAxuILyEHIg88wKuFseFAAkKLhTFYSwIBLQwQBWAtFYkNIAqfFnOJxMYDCQUM7Pztx5I/fNwhkT4/7PBDoC12croN2LVYzkiEOEziTNoxyZlnJHI33k4zAGpJNjY7gMNhN6BaGG6wtzHztgG1zE4AaTmQuI2QFvkb7M2fgVrSDWenfyBOi8ENtgPSQC0J8tI5+G2x7HkI0iLBbHgmLU1yZpuE4QbpnAKgCG6/mLMnH/7w4Y9NstzxY8YfPrbVycvPTt/84UOFnRwuLVAgkYxwKlilAV7lYGAHZ8k3EFY9CkbBKBgFIwsAAJjoYsxyHam8AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4656-148X","institution":"INSTITUTE OF RURAL DEVELOPMENT PLANNING","correspondingAuthor":true,"prefix":"","firstName":"Jackson","middleName":"Bulili","lastName":"Machibya","suffix":""},{"id":466384508,"identity":"d87a32d0-8b37-44fa-b344-5e62fed62a37","order_by":1,"name":"Fulgence Dominick Waryoba","email":"","orcid":"https://orcid.org/0000-0001-9506-0839","institution":"St. Augustine University of Tanzania (SAUT)","correspondingAuthor":false,"prefix":"","firstName":"Fulgence","middleName":"Dominick","lastName":"Waryoba","suffix":""}],"badges":[],"createdAt":"2025-06-01 18:48:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6797273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6797273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83960911,"identity":"34deabee-3039-4ae6-91d3-1a37163241c3","added_by":"auto","created_at":"2025-06-05 05:10:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6797273/v1/5335ee41-d2d2-4916-b412-02bcdf5694c0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRural and urban household consumption patterns in Tanzania: A consumer theory analysis of determinants\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe income disparity between rural and urban dwellers is the critical issue on micro economics policy planning for stimulating consumption in the economy (see Kuznets, 2019; Azizur and Carl, 2023). While consumption being one of the key drivers of the economic growth and development among household and the national at large, the income disparity is widening between rural and urban areas (Lu, 2018). Household consumption patterns are critical indicators of economic well-being and are influenced by a myriad of factors, including income levels, household size, education, and geographical location among others (see Yu \u003cem\u003eet al\u003c/em\u003e, 2025; Grover \u003cem\u003eet al\u003c/em\u003e, 2025). In this study, Consumption patterns refer to the typical behaviors and trends associated with how individuals or groups use goods and services (Mehta \u003cem\u003eet al,\u003c/em\u003e 2020). These patterns are influenced by various factors, including cultural, economic, social, and psychological elements (\u003cem\u003eibid)\u003c/em\u003e. in that regards, individual consumers (Households) can indicate preferences for certain products, the frequency of purchases, and the overall impact of consumption on the environment and society. Hence, understanding consumption patterns helps businesses tailor their offerings, policymakers assess economic health, and researchers analyze consumer behavior and its implications for sustainability and resource use (see Zhang and Chang, 2021; Varalakshmi, (2024). In Tanzania, the distinction between urban and rural consumption patterns is pronounced, reflecting differences in access to resources, infrastructure, and economic opportunities. Thus, again, understanding these patterns is essential for formulating effective policies aimed at poverty alleviation and economic development (Rodr\u0026iacute;guez \u003cem\u003eet al\u003c/em\u003e, 2024). Besides, household consumption patterns are fundamental to understanding economic behavior and welfare in any society (Osolo \u003cem\u003eet al\u003c/em\u003e, 2025). In Tanzania again, the distinction between urban and rural consumption patterns is particularly pronounced due to varying socio-economic conditions, access to resources, and cultural influences. Urban households typically exhibit different consumption behaviors compared to their rural counterparts, influenced by factors such as income levels, education, and market access (Qi \u003cem\u003eet al\u003c/em\u003e, 2025). Thus, understanding these differences is crucial for policymakers aiming to address poverty and improve living standards across the country.\u003c/p\u003e\n\u003cp\u003eIn urban areas, households often have higher disposable incomes (Ma \u003cem\u003eet al\u003c/em\u003e, 2018), which allows for greater expenditure on non-essential goods and services. This trend aligns with Engel\u0026apos;s Law, which posits that, as household income increases, the proportion of income spent on food decreases, leading to a diversification of consumption towards luxury items and services (Rashid \u003cem\u003eet al\u003c/em\u003e, 2024). Conversely rural households with lower income levels (Ranzani \u003cem\u003eet al,\u003c/em\u003e 2022) tend to allocate larger shares of their budgets to basic necessities, that is, primarily food and shelter. This disparity in consumption patterns can exacerbate existing inequalities and hinder overall economic development (ibid). Education also plays a pivotal role in shaping consumption choices; households with higher educational attainment are more likely to make informed decisions regarding their consumption, leading to healthier dietary choices and better financial management (Chen and Antonelli,2020). In contrast, limited access to education in rural areas often results in lower consumption of diverse and nutritious foods, contributing to health disparities and food insecurity (Nyath \u003cem\u003eet al\u003c/em\u003e, 2025). Moreover, access to markets and infrastructure significantly influences consumption patterns. Urban areas typically benefit from better infrastructure, including transportation and communication networks, which facilitate access to a wider variety of goods and services. In contrast, rural households often face challenges such as poor road conditions and limited market access, which restrict their consumption choices and contribute to higher levels of poverty (Riva, \u003cem\u003eet al\u003c/em\u003e, 2018).\u003c/p\u003e\n\u003cp\u003eThe study was conducted in Tanzania due to the following fated reasons; First, Tanzania presents a unique economic landscape characterized by a significant divide between urban and rural areas. Understanding consumption patterns in this context can reveal how different economic conditions influence household decisions, contributing to more effective economic policies Secondly, with a substantial portion of the population living below the poverty line (Letta \u003cem\u003eet al,\u003c/em\u003e 2018), understanding consumption patterns is essential for developing targeted poverty alleviation strategies. This research can provide insights into how consumption choices are made under financial constraints in both urban and rural settings. Third, Tanzania is experiencing rapid urbanization, leading to shifts in consumption behaviors (Sumari \u003cem\u003eet al.\u003c/em\u003e, 2020). Studying these dynamics can inform policymakers about the changing needs and preferences of urban populations, which are crucial for sustainable urban planning and development. Fourth, food insecurity remains a pressing issue, particularly in rural areas (Kamat and Woo Kinshella, 2018). So, analyzing consumption patterns can help to identify gaps in food access and nutritional quality, guiding interventions aimed at improving food security and health outcomes. Fifth, the disparity in infrastructure and market access between urban and rural areas affects consumption choices and economic opportunities. Thus, investigating these effects can help to identify necessary improvements needed to support rural development and enhance market access.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study analyzed the determinants of household consumption patterns in urban and rural areas of Tanzania from a consumer theory perspective using [1]National Panel Survey 2020-21, Wave 5 obtained from World Bank dataset. By examining the interplay of consumption, income, utilities, family structures and gender leadership in households, this paper provides an insight into the consumption behaviors of different household types and the implications for policy interventions aimed at reducing poverty and enhancing economic stability. The purpose of this study was to investigate how variations in household income levels affect consumption choices and patterns in urban and rural settings while accounting for other variables such as utilities, family structure and gender (household size and female head). Besides, the paper was guided by the following two hypotheses H\u003csub\u003e1\u003c/sub\u003e: Income level has a positive effect on household consumption expenditure; H\u003csub\u003e2\u003c/sub\u003e: Urban households have lower consumption expenditures than rural households on food.\u003c/p\u003e\n\u003cp\u003eDespite the fact that, there is a growing body of literature on consumption economics in various contexts, there is still limited research focusing specifically on households\u0026rsquo; consumption patterns in Tanzania, especially from a consumer theory perspective using simultaneous quantile regression model. This study can fill this gap in the literature of household\u0026rsquo;s consumption economics and contribute to a potential academic discourse. Apart from that, the findings from this research can have direct implications for policymakers and the public. Additionally, insights into the determinants of household\u0026rsquo;s consumption can guide the design of social safety nets, subsidies, and programs aimed at enhancing household welfare and reducing inequality.\u003c/p\u003e\n\u003cp\u003eThe rest of this study is organized as follows: Section 2 includes a literature review, Section 3 details the data and methods, Section 4 reports the results and discussion, Section 5 provides the conclusion and policy implications.\u003c/p\u003e\n\u003cp\u003e[1] Tanzania - National Panel Survey 2020-21, Wave 5\u003c/p\u003e"},{"header":"2.0 Literature review","content":"\u003cp\u003e\u003cstrong\u003e2.1 Theoretical literature review\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper adopts the perspective of central ideas presented in the consumer theory as applied in micro economic issues and perverse. According to Geoffrey and Philip (2011), Consumer theory is a fundamental concept in economics that analyzes how individuals make decisions about the allocation of their limited resources (such as income) among various goods and services to maximize their utility (satisfaction or happiness) (see also Dubé, 2019). It provides a framework for understanding consumer behavior, preferences, and the factors that influence purchasing decisions (Fekete-Farkas \u003cem\u003eet al,\u003c/em\u003e 2021). The key assumptions (Jehle and Reny, 2011\u003cstrong\u003e)\u003c/strong\u003e for this theory among others are; first, Rational Behavior (Utility Maximization)-Consumers are assumed to act rationally, seeking to maximize their utility (satisfaction) from consumption. They make choices that provide the highest level of satisfaction given their preferences and constraints. Secondly, completeness whereby a consumers can compare and rank all possible combinations of goods and services; they can determine which combination provides more utility. Third, transitivity-if a consumer prefers A to B and B to C, then they should also prefer A to C. This ensures consistent preferences. Fourth, non-satiation-more of a good is preferred to less, meaning consumers always want to increase their consumption as long as it leads to greater utility. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reasons for adopting this theory are; First, Consumer theory helps to explain how households prioritize their consumption based on preferences and utility maximization (Browning and Zupan, 2020). This is particularly relevant in contrasting urban and rural settings, where preferences may differ significantly due to cultural and socio-economic factors. Secondly, the theory emphasizes the role of budget constraints in consumption decisions (Kreps, 2020). In Tanzania, urban households may experience different constraints compared to rural households, influencing their purchasing behavior and consumption choices. Third, Consumer theory allows for the examination of how changes in income levels affect consumption patterns, particularly the concept of income elasticity of demand (Mohammadi and Rezvani,2019). This is crucial for understanding how urban and rural households respond to economic changes. Fourth, Consumer theory enables a systematic comparison of consumption behaviors between urban and rural households, shedding light on the socio-economic dynamics that influence these patterns (Alhadeff, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test this theory, we use two hypotheses namely; H\u003csub\u003e1\u003c/sub\u003e: Income level has a positive effect on household consumption expenditure; H\u003csub\u003e2\u003c/sub\u003e: Urban households have lower consumption expenditures than rural households on food. We test the hypothesis by using a simultaneous quantile regression model of estimation which the results are detailed in section 4 of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Empirical review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are many studies conducted related to determinants of household consumption patterns in urban and rural areas in various countries using different methodologies such as descriptive statistics, literature reviews, Data mining (DM) techniques, noise (DBSCAN) algorithm, Social Accounting Matrix (SAM) and online consumer survey (see Değirmenci and Özbakır, 2018; Wang et al, 2018; Chenarides \u003cem\u003eet al\u003c/em\u003e, 2021). After thorough review, it was found that, previous studies have shown that, there is positive effect of income on consumption decision of the households and mostly urban households consume more on goods and services than that of rural areas (see Sauer \u003cem\u003eet al\u003c/em\u003e, 2021; Ishengoma and Igangula, 2021; Wang \u003cem\u003eet al\u003c/em\u003e, 2022). Understanding the determinants of household consumption patterns in urban versus rural areas of Tanzania is crucial for addressing economic disparities and improving living standards (Dzanku \u003cem\u003eet al\u003c/em\u003e, 2024). Recent empirical studies have highlighted various factors influencing these consumption behaviors, drawing on consumer theory to analyze the underlying mechanisms (see Safari, \u003cem\u003eet al,\u003c/em\u003e 2022; Stadlmayr \u003cem\u003eet al\u003c/em\u003e, 2023; Wang \u003cem\u003eet al\u003c/em\u003e, 2029). In most cases, these studies have used other methodological approach such as literature review, descriptive analysis from primary data survey, ten percent and double median and alike (see Awan and Bilgili, 2022; Han \u003cem\u003eet al\u003c/em\u003e, 2018).\u003c/p\u003e\n\u003cp\u003e2\u003cstrong\u003e.2.1 \u003cem\u003eIncome and expenditure patterns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies indicated that, household income significantly affects consumption patterns (see Swema and Mwinuka, 2021;\u0026nbsp;Oyeniran and Isola, 2023). Urban households tend to have higher incomes, leading to greater expenditure on non-essential goods compared to rural households, which allocate a larger share of their budgets to basic necessities such as food and shelter (see Kuhe and Bisu, 2020; Swema and Mwinuka, 2021). For instance, urban dwellers spend approximately 63.2 per cent of their total expenditure on food, while rural counterparts spend about 55.2 per cent (Rashid\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, 2024). This aligns with Engel's Law, which posits that as income increases, the proportion of income spent on food decreases (Matabaro \u003cem\u003eet al,\u003c/em\u003e 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 \u003cem\u003eHousehold Utilities patterns\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHousehold utilities, encompassing water, electricity, and sanitation services which play a crucial role in shaping consumption patterns in both rural and urban settings. In Tanzania, access to these utilities varies widely, significantly influencing household strategies and overall quality of life (Li, \u003cem\u003eet al,\u003c/em\u003e 2021). Urban households generally have better access to essential utilities compared to their rural counterparts, where infrastructural challenges persist. Studies have shown that, the availability and reliability of utilities affect consumption choices, with households that have consistent access to electricity, for instance, being more likely to invest in energy-intensive goods like refrigerators and televisions (Gou \u003cem\u003eet al\u003c/em\u003e, 2018; Reddick \u003cem\u003eet al,\u003c/em\u003e 2020). Understanding how these utilities are accessed and utilized provides valuable insights into the broader consumption patterns observed in Tanzanian households, highlighting the importance of infrastructure development in enhancing quality of life and economic resilience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 \u003cem\u003eHousehold size patterns (Family structures)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe size of household also has been found to have effect on consumption in both rural and urban settings (Siman \u003cem\u003eet al,\u003c/em\u003e 2020). Household size plays a crucial role in shaping consumption patterns through various mechanisms, including economies of scale, diverse needs, income distribution, time constraints, social influences, and savings behavior (see Madudova and Corejova,2023). In larger households, income is often pooled, which can lead to increased overall consumption. However, the distribution of income within the household can also affect individual consumption choices. Moreover, larger households may face different budget constraints compared to smaller ones (Liu \u003cem\u003eet al\u003c/em\u003e, 2018); they might prioritize essential goods (such as food, housing) over luxury items.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 \u003cem\u003eGender Settings (Female-Headed Households)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn recent years, research on female-headed households (FHHs) has gained prominence, particularly in the context of developing countries like Tanzania (see Lufuke and Tian, 2024; Mbunda and Ndunguru,2024). FHHs often face unique challenges compared to their male-headed counterparts due to socio-economic disparities and cultural norms. Studies indicate that, women in FHHs tend to have lower access to resources, education, and economic opportunities, which can adversely affect their household consumption patterns (Assenga and Kayunze, 2021). For example, women may prioritize expenditures on health and education for their children over other goods, leading to distinct consumption behaviors. Furthermore, the role of women in decision-making processes within the household can influence the allocation of resources, often reflecting their priorities and the socio-cultural dynamics at play (Ahinkorah \u003cem\u003eet al\u003c/em\u003e, 2018). The intersection of gender, poverty, and household dynamics necessitates a nuanced understanding of how FHHs navigate consumption in both rural and urban settings, as their strategies may differ significantly based on local contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.5 \u003cem\u003eEducation and consumption choices\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn various studies consumption patterns and education, educational attainment plays a critical role in shaping consumption behaviors (see Steils, 2021; Lührmann \u003cem\u003eet al,\u003c/em\u003e 2018). Households with higher education levels are more likely to make informed consumption choices, leading to healthier dietary patterns and better financial management (Laibson \u003cem\u003eet al,\u003c/em\u003e 2024). Conversely, limited access to education in rural areas often results in lower consumption of diverse and nutritious foods, contributing to health disparities (\u003cem\u003eibid\u003c/em\u003e). Studies have shown that, education positively influences food expenditure, as it enhances awareness of food attributes and encourages the adoption of healthier consumption practices (Wijayaratne \u003cem\u003eet al\u003c/em\u003e, 2028; Lusk and McCluskey, 2018; Lee \u003cem\u003eet al,\u003c/em\u003e2022).\u003c/p\u003e\n\u003cp\u003eTherefore, given the fact that, there are many studies being conducted about the consumption patterns in rural and urban areas in different countries globally as demonstrated herein above review; but in most reviewed literatures ( see for example in Eger \u003cem\u003eet al\u003c/em\u003e, 2021; Ismagilova \u003cem\u003eet al,\u003c/em\u003e 2020; Martínez-Plumed \u003cem\u003eet al,\u003c/em\u003e 2019; Fan \u003cem\u003eet al,\u003c/em\u003e 2021; Aragie \u003cem\u003eet al\u003c/em\u003e, 2021; Guthrie \u003cem\u003eet al\u003c/em\u003e, 2021), they used different \u0026nbsp;methodologies of which among others are; the descriptive statistics, literature reviews, Data mining (DM) techniques, noise (DBSCAN) algorithm, Social Accounting Matrix (SAM) and online consumer survey to study consumption patterns. They in fact used other data sources not from the World Bank for Tanzania. This creates literature and knowledge gap in the household’s consumption economics discourse in Tanzania and global at large. Therefore, this study aims to fill the gap in the existing literature using National Panel Survey data for the year 2020-21, Wave 5 by applying the Simultaneous Quantile regression model to analyze the determinants in households’ consumption as result of income level while accounting for other variables (utilities, household size and female head). A Simultaneous Quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) simultaneously in the distribution of the response variable. The analysis can help to see the consumption behaviors/patterns among the urban and rural dwellers on food and other services within the consumer theory’s perspective.\u0026nbsp;\u003c/p\u003e"},{"header":"3.0 Data and methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data source\u003c/h2\u003e \u003cp\u003eThis study used data from the World Bank dataset specifically for Tanzania. The National Panel Survey data for the year 2020-21, Wave 5 were downloaded and analyzed in the STATA program. The sample size of the dataset was 4,709 households selected at random from the Tanzanian rural and urban areas including Zanzibar.\u003c/p\u003e \u003cp\u003eFindings from the previous literatures have been used to supplement the argument on the presentation and justification of findings for this study especially in showing the disparities between rural and urban consumption patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hypotheses\u003c/h2\u003e \u003cp\u003eA hypothesis is a specific, testable prediction about the relationship between two or more variables. It serves as a foundational element in scientific research and experimentation, guiding the design of studies and the analysis of data (Scerbo \u003cem\u003eet al\u003c/em\u003e, 2019). This study has two key hypotheses to test;\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e: Income level has a positive effect on household consumption expenditure.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e: Urban households have lower consumption expenditures on food than rural households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Estimation Model\u003c/h2\u003e \u003cp\u003eBased on the theoretical review (Randolph, 2009) and previous empirical studies; this study investigates the determinants of household consumption patterns in Urban and Rural areas of Tanzania using National Panel Survey data obtained from the World Bank for year 2020-21. In this study, the simultaneous quantile regression (SQR) model was employed to robustically estimate the relationship between variables (dependent and independent) in different percentile level across the distribution of responsible variables. This study uses simultaneous quantile regression because (Waldmann, 2018) it is a statistical technique that estimates the conditional quantiles of a response variable simultaneously ; unlike ordinary least squares (OLS) regression, which estimates the mean of the dependent variable given the independent variables, quantile regression provides a more comprehensive view by estimating the effects of predictors at different points (quantiles) simultaneously in the distribution of the response variable (\u003cem\u003eibid\u003c/em\u003e). This is relevance model to this study given the fact that, the study is looking at the consumption patterns among households which is mostly affected by the income within the population in this case. Also, with SQR, \u0026ldquo;in cases where either the requirements for mean regression, such as homoscedasticity, are violated or interest lies in the outer regions of the conditional distribution, quantile regression can explain dependencies more accurately than classical methods\u0026rsquo; (Staffa \u003cem\u003eet al\u003c/em\u003e, 2019). In explaining the variables from the dataset, the dependent variable under this study is the consumption of food in house (FoodIN) which was regarded as the Households consumption per annum per household on basic households\u0026rsquo; needs such as food, beverages, clothes, health costs, education cost transport and alike. The independent variables are total expenditure (which is HH income) annual amount per household; Utilities per anum per household; Household size and Female household. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the details of explanation of the key variables.\u003c/p\u003e \u003cp\u003eIn this study as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, total annual expenditure is used in place of annual income because; First, total expenditure provides a more comprehensive view of a household's economic well-being, as it captures not only regular income but also any additional financial resources that may not be reflected in income data, such as transfers, savings, or credit. Second, total expenditure can better reflect the actual consumption patterns and living standards of households. Since expenditures are recorded based on actual spending, they can offer a more accurate depiction of a household's financial situation at a given time. Besides, the study uses food consumption as a proxy for overall consumption because; food is a fundamental human need, and its consumption is directly linked to health and well-being. So, by considering much on food consumption in place of overall consumption, study can gain insights into the nutritional status and dietary diversity of households, which are critical indicators of quality of life.\u003c/p\u003e \u003cp\u003eUtilities variable is used in this study because; first, the cost of utilities can significantly affect household budgets, particularly for lower-income families. High utility costs may force households to make trade-offs, impacting their spending on other essential goods and services. Second, understanding the role of utilities in budget constraints provides insights into the economic pressures faced by households and how these pressures shape consumption patterns.\u003c/p\u003e \u003cp\u003eHouseholds Size variable is used because; First, it is thought that, larger households may benefit from economies of scale, allowing them to purchase goods in bulk or share resources, which can affect their consumption behavior. Thus, by including household size, the study can examine how these economies impact spending patterns and whether larger households tend to have lower per capita consumption costs. Second, Household size directly influences the demand for goods and services; larger households typically require more food, clothing, and other essentials, which can lead to different consumption patterns compared to smaller households. Third, understanding how household size affects overall consumption helps in analyzing the resource allocation strategies employed by families.\u003c/p\u003e \u003cp\u003eFemale-headed households (FHHs) variable is used because; First, Female-headed household often exhibit different consumption behaviors compared to male-headed households. Women may prioritize spending on health, education, and nutrition, which can significantly influence overall household consumption patterns. Thus, understanding these differences can provide deeper insights into how gender dynamics shape economic decisions. Second, understanding the role of female headship in consumption patterns can inform policy-making and development programs. Third, recognizing the needs and challenges of FHHs can lead to targeted interventions that enhance their economic resilience and improve overall household welfare. This is particularly important in efforts to promote gender equality and empower women within the economic framework within the Country.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables, symbols, unit of measures and sources of data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbols\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit of measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFoodIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eannul consumption of individual household on food, beverage and alcohol at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (2020-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal expenditure annually per individual household on things like health, education, transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (2020-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enf_utilitiesR_pae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUtilities per anum per household on water, lighting, kerosine, etc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (2020-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehhsize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe size of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (2020-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemalehead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale headed houshold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (2020-21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: World Bank (2020-21)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Empirical model\u003c/h2\u003e \u003cp\u003eThe empirical model of estimation is given as follows;\u003c/p\u003e \u003cp\u003e \u003cem\u003eSq\u003c/em\u003e \u003csub\u003e \u003cem\u003ec\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(τ | X) = β₀(τ) + β₁(τ)INC\u0026thinsp;+\u0026thinsp;β₂(τ)UT\u0026thinsp;+\u0026thinsp;β₃(τ)HHS\u0026thinsp;+\u0026thinsp;β₄(τ)FH+ \u0026#120583;\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eSq\u003csub\u003ec\u003c/sub\u003e (τ∣X) is the conditional quantile of Consumption given Income, Utilities, Household size and Female head at 25th, 50th and 75th quantiles.\u003c/p\u003e \u003cp\u003eΒ\u003csub\u003e1,2,3,4\u003c/sub\u003e(τ) are the coefficients that vary with the quantile levels\u003c/p\u003e \u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e​(τ)-Constant at Quantile level\u003c/p\u003e \u003cp\u003eINC-Income\u003c/p\u003e \u003cp\u003eUT-Utilities\u003c/p\u003e \u003cp\u003eHHS-Household size\u003c/p\u003e \u003cp\u003eFH-Female Head\u003c/p\u003e \u003cp\u003e\u0026#120583;- error term\u003c/p\u003e \u003cp\u003eThe possible bias in the estimation equation above is Unobserved Heterogeneity because if there are unobserved factors that affect both the independent variables and the dependent variable, this can lead to bias in the quantile estimates. But unlike OLS, quantile regression focuses on different points in the distribution, making it sensitive to such biases to appear.\u003c/p\u003e \u003cp\u003eSimultaneous quantile regression (SQR) is useful for studying income or wealth inequality, as it can show how different factors affecting people at various income levels differently (Demir \u003cem\u003eet al\u003c/em\u003e, 2022). In this study, it is useful since the study focus on the consumption pattern which has direct relationship with income levels among households in the population. The coefficients from quantile regression represent the change in the quantile of the dependent variable (consumption) for a one-unit change in the independent variables (income, utilities, households\u0026rsquo; size and female head), holding other variables constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Reduced form estimation model equation\u003c/h2\u003e \u003cp\u003eThe reduced model equation is given below;\u003c/p\u003e \u003cp\u003eSq\u003csub\u003ec\u003c/sub\u003e(τ | X) = γ₀(τ) + γ₁(τ)Z + ϵ\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eZ-is a vector of all the independent variables (INC, UT, HHS, FH)\u003c/p\u003e \u003cp\u003eγ\u003csub\u003e0\u003c/sub\u003e(τ)-captures the intercept term\u003c/p\u003e \u003cp\u003eγ\u003csub\u003e1\u003c/sub\u003e(τ)-represents the combined coefficients of the independent variables\u003c/p\u003e \u003cp\u003eϵ-is a new error term that encompasses the original error term and any omitted variables that may affect Sq\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe reduced form of equation representation assumes that, the relationships between Sq\u003csub\u003ec\u003c/sub\u003e and the independent variables can be summarized in this reduced form while retaining the functional relationships implied in the original model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4.0 Results and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Simultaneous Quantile Regression results for consumption patterns\u003c/h2\u003e \u003cp\u003eTo find out the variation and determinants of consumption patterns between rural and urban population, the simultaneous quantile regression technique was employed under this study involving the maximum robust bootstrap replication with simulated dataset, which facilitates the evaluation of the responsible variable income within the consumption pattern distribution (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The findings revealed that, income has positive effect throughout the quantiles, this is due to fact that, income is the most critical factor when it comes to measures and determining consumption capabilities among households in both rural and urban areas. This study also found from other studies that, other factors such as level of education, households\u0026rsquo; composition, gender, access to markets and transportation facilities do influence much on consumption patterns among the households in rural and urban areas (see Biresselioglu \u003cem\u003eet al\u003c/em\u003e, 2023). Besides, there is much disparities in terms income and spending among the rural and urban households due to their inequalities in economic status; urban dwellers face higher income and consumption inequalities than of rural (Waryoba, 2023). Moreover, for the purpose of regional comparison, this study, used references from the previous related studies (Zehra and Singh, 2023) to provide the categorical comparison of the findings between rural and urban household\u0026rsquo;s\u0026rsquo; consumption patterns and their consumption disparities.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s results from quantile regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) show that, there is high consumption variability between the households in rural and urban areas in the country. The quantile regression results indicates that, income has a positive effect on consumption pattern per households in all quantile levels (25th, 50th and 75th quantiles) of all regions under study (Dar es Salaam, Rest of the urban, rural areas and Zanzibar) as indicated in the Table\u0026nbsp;3. The results show that, income is the prevailing consumption pattern among others in both rural and urban areas. The income is statistically significant at 95% confidence interval (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across the distribution meaning that, it is actually has a direct positive effect on consumption pattern within the households; hence, we fail to reject the H\u003csub\u003e1\u003c/sub\u003e hypothesis in this study and so, we conclude that, income level has a positive effect on household consumption expenditures in house needs, food in particular. These findings reveal that, households with higher income consume less and save more and may have good nutrition status while those with low income consume more and save less from their income. This is justified in previous studies which revealed that, income has direct effects on consumption patterns in the population given its variability among individual expenditures in the household settings (see Bjelle \u003cem\u003eet al\u003c/em\u003e, 2021; Nguye \u003cem\u003eet al\u003c/em\u003e, 2021; Piyapromdee and Spittal, 2020). Also, Su, \u003cem\u003eet al\u003c/em\u003e (2029) in their study in China found that, \u0026ldquo;the current expansion of income variance has a positive effect on consumption, which is likely to lead to the aggravation of consumption inequality in the country\u0026rdquo;. This poses the consideration in policy reforms to reduce income gap among households in the economies (rural vs urban) for sustainable development of all people. The results further indicate in the 75th quantile that, the households\u0026rsquo; consumption share on food is about 74 per cent, this can vary depending on the location of the household (rural or urban). Previous studies indicates that, rural households spend more of their income share on households needs mostly food for their family members while those of urban areas have substantial high income with minimal consumption share on food. This is justified in the study by (see De Brauw and Giles, 2018) in China who found that, rural villages contribute to significant increases in the consumption of goods, and these effects are larger in magnitude for households that were relatively poor\u0026rdquo;. Given these findings, we fail to reject H\u003csub\u003e2\u003c/sub\u003e hypothesis and conclude that, urban households have lower consumption expenditures share of their income on food than rural households. This indicates that, urban consumers use part of their income on food consumption and the other part for saving and investment. The study further argues from the literatures that, the rural households have larger share of consumption in the economy than that of urban areas (Janssens \u003cem\u003eet al\u003c/em\u003e, 2021; Kansiime \u003cem\u003eet al\u003c/em\u003e, 2021). That means, they have low income earning which is mostly used for food consumption purpose in their daily livings with no surplus income for saving and investment, that\u0026rsquo;s why the rural communities are characterize by being poor communities in the most developing countries.\u003c/p\u003e \u003cp\u003eWhile that hold, the utilities pattern is statistically significant at 75th quantile level with negative coefficient meaning that, it affects the income of the households in decreasing units for every unit of income. Overall, utilities spending in rural and urban areas affect negatively the consumption patterns of households, this may be due to high costs of utilities package of services for households given their level of income. The findings suggest for re-consideration of rural development programs to reducing the costs of utilities package such as electricity and solar energy as well as water access especially for the people in rural areas given their level of income is lower (Waryoba, 2023) compared to that of urban population\u003c/p\u003e \u003cp\u003eFrom this study, it was discovered that, the household\u0026rsquo;s size has significant effect across the distribution between rural and urban areas (all quantile levels) with positive effect on consumption levels across the population. Households with higher number of members has obvious larger share of consumption as compared to that of households with less number. The difference is not that much between the two regions (rural vs urban) this may be due to the family planning awareness campaign that was introduced by the government for all Tanzanians to reduce unplanned birth in household. Besides, the study found that, the condition for being female head in the household has no direct effects on the consumption pattern in rural and urban areas. This observation may be due to the fact that, female headed households represent poor group in the population especially those in rural areas as (Nwosu and Ndinda, 2018) justifies this from their study in South Africa.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimultaneous Quantile regression results for consumption patterns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th Quantile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5960199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0895455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0104345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0675131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0664169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0167937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0711162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0689316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.601602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9087241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e50th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.6601132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0530301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.033319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0345942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0419974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.016536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0715621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.058437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.559619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.5180807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e75th Quantile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.7460812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0639724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUtilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0635425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0251954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.0231008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0091553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.001052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.032047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.902319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.705205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: World Bank (2020-21) \u003cb\u003eNote\u003c/b\u003e: ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion and policy implications","content":"\u003cp\u003eThis study investigates the effect of income on households\u0026rsquo; consumption choice and patterns among households in urban and rural areas of Tanzania while accounting for utilities, households\u0026rsquo; size and female head variables in a regression model by using National Panel Survey data obtained from the World Bank for the year 2020-21. To estimate the results, the study applied the quantile regression model which is robust in estimating the changes between responsible variables in different levels (quantiles) within the distribution. The results indicates that, income has a positive effect on consumption pattern per households in all quantile levels (25th, 50th and 75th quantiles) in both rural and urban areas in the country affecting much the consumer\u0026rsquo;s choice. Besides, the results further indicate in the 75th quantile, the rural and urban dwellers have high consumption share of about 74 per cent of their income on household needs mostly food for their family members, this percent can vary depending on the income status of the household and location. There is a notable consumption gap between the rural and urban population due income disparities. Also, utilities spending in rural and urban areas affect negatively the consumption patterns of households, this may be due to high costs of utilities package of services especially for rural households given their level of income.\u003c/p\u003e \u003cp\u003eThe findings of this study emphasize that, there is considerable variability in food consumption, income, and utility expenditures among households, indicating that, a one-size-fits-all approach may not be effective for policy or economic analysis in Tanzania; instead, a tailored or customized approach which involves creating solutions or strategies that, are specifically designed to meet the unique needs of individuals or particular groups is essential for policy and economic ratification. Furthermore, it emphasizes the impact of external factors, including economic policies and infrastructural development, on consumption choices.\u003c/p\u003e \u003cp\u003eOverall, the findings of this study provide a significant call for policy makers to design a tailor-made economic program that helps to promote the rural households in terms of their income earnings and saving education as well improving their general wellbeing. This would help to reduce the income and consumption imbalance that exist between rural and urban households in Tanzania. Policy and decision makers should design distinct economic policies that address the specific consumption needs and behaviors of urban and rural households. For example, urban policies may focus on facilitating access to diverse goods, while rural policies might prioritize enhancing agricultural production, transport infrastructures and market access.\u003c/p\u003e \u003cp\u003eThe limitation of this study is that, income alone may not be sufficient factor that influence households\u0026rsquo; consumption patterns in rural and urban areas, as other factors such as price, consumer preferences and test should be considered for further study on factors affecting consumption patterns and choice among households in rural and urban areas of Tanzania.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe study has no funding requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003eThe authors declare that, they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhinkorah, B. 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Consumer dynamics: Theories, methods, and emerging directions. \u003cem\u003eJournal of the Academy of Marketing Science\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 166-196.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"No funding ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rural and urban, Households, Consumption patterns","lastPublishedDoi":"10.21203/rs.3.rs-6797273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6797273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs Tanzania experiences significant economic transformation, understanding household consumption behaviors is crucial for formulating effective micro-economic policies that enhance welfare and promote sustainable growth in both rural and urban areas. This study investigated the determinants of household consumption patterns using simultaneous quantile regression model. By integrating consumer’s theory with empirical analysis of determinants, this study contributes to the existing literature on households’ consumption economics and provides actionable insights for policymakers and stakeholders aiming at fostering equitable economic development. The findings indicate that, income positively influences consumption patterns across all quantiles, that is at 25\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e quantile respectively in rural and urban areas. Notable differences emerge, with urban households displaying diverse consumption behaviors linked to higher incomes and greater access to goods, while rural households depend on agricultural production and spend a larger share of their income on food, facing constraints like limited savings and market access. Additionally, high utility costs negatively impact consumption patterns by about 74 percent subject to effect variation depending on the location (rural or urban). The study underscores the significance of socio-economic factors, such as gender and family structure, in household decision-making and highlights the influence of external factors like economic policies and infrastructure on consumption choices. It calls for targeted policies to improve market access, enhance consumer education, and support local production to bridge the consumption gap between urban and rural households, emphasizing the need for inclusive economic policies tailored to the diverse needs of Tanzanian households.\u003c/p\u003e","manuscriptTitle":"Rural and urban household consumption patterns in Tanzania: A consumer theory analysis of determinants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 05:02:29","doi":"10.21203/rs.3.rs-6797273/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42d5ced6-2688-43c7-9542-4bc8d4539e36","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49514761,"name":"Agricultural Economics \u0026 Policy"},{"id":49514762,"name":"Microeconomics"}],"tags":[],"updatedAt":"2025-06-05T05:02:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 05:02:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6797273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6797273","identity":"rs-6797273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}