Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach

Alamneh, Tadele; Mada, Melkamu; Abebe, Tora Article Impact of Urban expansion on income of evicted farmers in the periUrban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Alamneh, Tadele; Mada, Melkamu; Abebe, Tora (2023) : Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-20, https://doi.org/10.1080/23322039.2023.2199596 This Version is available at: https://hdl.handle.net/10419/304049 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach Tadele Alamneh, Melkamu Mada & Tora Abebe To cite this article: Tadele Alamneh, Melkamu Mada & Tora Abebe (2023) Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach, Cogent Economics & Finance, 11:1, 2199596, DOI: 10.1080/23322039.2023.2199596 To link to this article: https://doi.org/10.1080/23322039.2023.2199596 © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 12 Apr 2023. Submit your article to this journal Article views: 3030 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach Tadele Alamneh 1 *, Melkamu Mada 1 and Tora Abebe 1 Abstract: The rate of urbanization in the Amhara region has apparently increased over the past three decades and is now higher than that of any other region in Ethiopia. As cities expand, peri-urban farmers in the region are being compelled to abandon their landholdings, which is their life-long asset. Therefore, the aim of this study is to investigate the impact of urban expansion on the income of evicted periurban farmers in Injibara, Burie and Gish Abay cities of the Amhara regional state, Ethiopia. Using stratified sampling technique, primary data was collected from 393 households (197 evicted and 196 non-evicted). The endogenous switching regression model was employed to examine the impact of urbanization on the income of evicted farmers. The model’s output of ATT revealed that evicted farmers had a substantial reduction in yearly income of 9202.36 Ethiopian Birr as compared to Tadele Alamneh ABOUT THE AUTHORS Tadele Alamneh is a PhD Scholar in Development Economics at Arba Minch University, Ethiopia. His areas of research interest include macro-economic analysis, efficiency analysis, livelihood analysis, impact analysis, and choice experiment. He has published many manuscripts in internationally reputable journals and presented his papers in more than 10 national and international conferences. Melkamu Mada is Associate professor of Economics, PhD, in the department of economics at Arba Minch University. He has published more than 25 articles in internationally reputable journals. His research interests include livelihood analysis, impact analysis, environmental valuation method analysis, and poverty analysis. Tora Abebe has PhD in economics and serves as a lecturer for various economics courses and is supervisor of many post-graduate students at Arba Minch University, Ethiopia. His research interests include impact analysis, poverty analysis, and efficiency analysis. He has published several articles in internationally reputable journals. PUBLIC INTEREST STATEMENT The majority of the world’s population has continued to live in cities, and there has been a rapid urbanization process during the previous seven decades. In 1950, less than one-third of people lived in urban areas, and more than 70% of people lived in rural areas. 54% of people on the planet lived in urban areas in 2014. The urban population growth is anticipated to grow. The degree of urbanization varies widely between regions. Latin America and North America had high rates of urbanization in 2014 (at or over 80%), while Africa were largely rural, with urbanization rates of 40%. From 1950 to 2005, urbanization in Africa and Ethiopia increased from 14.5 to 37.9 and 4.6 to 16.1, respectively. Urbanization in Ethiopia is very low compared to Africa, but the annual rate of change within this period is 1.72% and 2.32%, respectively. Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 1 of 20 Received: 13 February 2023 Accepted: 03 April 2023 *Corresponding author: Tadele Alamneh, Department of Economics, College of Business and Economics, Arba Minch University, Arba Minch, Ethiopia E-mail: [email protected] Reviewing editor: Antonella Francesca Cicchiello, Catholic University of the Sacred Heart: Universita Cattolica del Sacro Cuore, Italy Additional information is available at the end of the article © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. their counterfactuals. In other words, eviction diminishes the annual income of evicted farmers by 26.73%. Furthermore, ATU results also confirmed that nonevicted farmers’ income would have decreased by 4666.78 Birr if they had chosen to be evicted, proving that eviction might have a negative impact on farmers’ income at any circumstance. Therefore, this study suggests that the government conduct a comprehensive socioeconomic and demographic evaluation before evicting indigenous farmers and that an alternative strategy be developed to mitigate the negative effects of eviction on peri-urban farmers. Subjects: Agriculture & Environmental Sciences; Political Economy; Economics; Finance; Industry & Industrial Studies; Urban Studies Keywords: Evicted; impact; non-evicted; peri-urban; Ethiopia 1. Introduction In 1900, just 15% of the world’s population lived in urban areas. However, the picture transformed after 1950, when the pace of urban population growth accelerated rapidly, and by the year 2009, half of the world’s population lived in urban areas (Annez & Buckley, 2008). In 2016, an estimated 54.5% of the world’s population lived in urban settlements, and by 2030 urban areas are projected to house 60% of the population globally. Understanding the important urbanization trends that will emerge in the next few years is critical for implementing the 2030 Agenda for Sustainable Development and attempts to construct a new urban development framework (Bravo & Francia, 2018). Urban population in Africa is expected to triple in the next 50 years, changing the profile of the region, and challenging policy makers to harness urbanization for sustainable and inclusive growth (Freire et al., 2014). These data indicate that African countries are urbanizing drastically. Due to both rural-to-urban migration and natural growth, most African cities have grown many times over the past few decades. In several African nations, a sizeable fraction of the majority of small and medium-sized cities 1 have expanded more quickly than the larger cities, also as a result of planning decisions (Lwasa, 2014). Ethiopia has low level of urbanization even by African standard, with only about 19% of the people residing in urban areas, the country’s urban population is currently rising at a rate of roughly 4% per year, which is twice as fast as the rate for rural areas (HABITAT, 2017; Hamore, 2019; Mefekir Woldegebrel Tessema, 2017). According to United Nations (2015) some projections, Ethiopia’s urban population will triple over the course of the next 20+ years, growing at an extremely rapid rate of over 5% annually (HABITAT, 2011; Mefekir Woldegebrel Tessema, 2017). Urbanization in Amhara regional state is growing faster compared to the national growth rate (see Table 1). The region has towns, which have drastic urbanization, which is tremendous as compared with other towns even in the country (see Table 2). Based on the forecast from 1994 to 2007 of national census of Ethiopia, many of the towns in the regional state have a growth rate of more than 6% per year which is far above the national average of urbanization of 4% (Adam, 2014; Central Statistical Agency, 2013; Mohammed et al., 2020). Unprecedented urban population expansion in the region has led to a strong demand for urban land for construction of infrastructure, residential housing, and service provision. According to Amhara National Regional State Urban Development, Housing and Construction Bureau (2017), more than 15,000 peri-urban farmers had their agricultural land expropriated, and the land was then given to 129,594 urban people through the lease system. As a result, cities have grown Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 2 of 20 Table 1. Percentage of Urban population in 2007 and projected percentages, 2017–37 Region % of urban population Census projection Rate of urbanization in % 2007 2017 2037 From 1994 – 2007 Projected 2007 – 2037 Amhara 12.3 17.0 37.1 3.94 6.02 Afar 13.4 19.2 50.5 6.62 7.53 Tigray 19.5 26.1 50.5 4.52 5.17 Oromia 12.4 15.0 31.8 4.13 5.12 SNNP 10.3 14.0 24.6 6.04 4.77 Benishangul 14.6 21.2 43.4 7.69 5.94 Gambela 25.4 32.1 60.4 8.03 4.75 Somali 14.0 14.4 18.6 2.71 2.65 Harari 54.2 55.7 62.4 2.02 2.30 Dire Dawa 67.9 69.3 74.8 2.28 2.20 Addis Ababa 100 100 100 2.10 2.10 National average 16.1 20.4 31.1 3.77 3.86 Sources: CSA, 2007.Population projection of Ethiopia, 2007–2037; and Summary and Statistical Report of the 2007 population and housing census results, 2007 Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 3 of 20 Table 2. Population trend in large and medium towns of the regional state Towns Censuses Growth rate 1984 1994 2007 2017 1994 to 2007 projection 2007 to 2016 Gondar 80,886 112,249 207,044 341,991 6.50 6.518 Bahir Dar 54,800 96,140 155,428 297,794 4.74 9.160 Dessie 68,848 97,314 120,095 198,428 1.80 6.523 Debre Birhan 25,753 38,717 65,231 107,827 5.27 6.530 Debre Mark’os 39,808 49,297 62,497 103,263 2.06 6.523 Kombolcha 15,782 39,466 58,667 96,968 3.74 6.529 Debre Tabor NA NA 55,596 91,968 NA 6.542 Woldiya 15,690 24,533 46,139 76,331 6.77 6.544 Mota NA NA 26,177 43,322 NA 6.550 Finote Selam NA NA 25,913 42,882 NA 6.548 Dangila NA NA 24,827 41,067 NA 6.541 Injibara NA NA 22,466 45,874 NA 7.41 Burie NA NA 25,971 62,049 NA 9.01 Gish Abay NA NA 6,772 15,240 NA 8.45 NAno available data. Sources: CSA, 2007.Population projection of Ethiopia, 2007–2037; and Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 4 of 20 horizontally as a result of these demographic pressures. This will have far-reaching ecological, socio-economic and environmental consequences, especially to the urban peripheral areas. Particularly, the main challenge of the urbanization process is the rapid conversion of a large amount of prime agricultural land to urban land uses in the peri-urban areas. This trend will exacerbate further expropriation of farm households and may lead to food insecurity and social instability in the surrounding areas although the problem is worse in small and medium-sized cities. Injibara, Burie and Gish Abay are medium-sized cities where urbanization is expanding more rapidly than the bigger metropolitan and capital cities where urbanization in the study areas is in a condition of fast horizontal expansion (Lwasa, 2014). Currently, these cities are expanding dramatically, and the demand of land for urban development program increases rapidly with non-existent urbanization process. In response to these, the government is taking large tracts from peri-urban areas. As a result, large numbers of local landholders who mainly engage in agricultural activity for their livelihood have been forced to lose their land rights (Achamyeleh, 2014). However, there is no sufficient empirical evidence that can address the problem related to urbanization and its impact on the evicted farmers. Despite the existence of few literatures in this regard (Wogedie, 2018; Gebeyehu et al., 2022; Mohammed et al., 2020), many scholars and researchers have a bias toward studying metropolitan areas, while neglecting or underestimating the impact of urban expansion on lowincome households in small and medium-sized cities became more prevalent. Owing to bias and inadequate analysis of the problem, there is dearth of appropriate empirical evidence to address the issue of urbanization and its impact on the income of evicted farmers. So, there is a knowledge gap in academia on the current crisis and its magnitude that evictees are experiencing. This suggests that decision-makers tend to prioritize metropolitan cities over small and medium-sized urban outskirts. Yet, policies ensuring adequate and equitable compensation for these expropriated farmers remain critical (Z. Gebeyehu et al., 2022; Mohammed et al., 2020; Muluwork, 2014). Moreover, this study also contributes in filling methodological gaps in impact analysis. The majority of existing research studies employed propensity score matching (PSM) to examine how evicted peri-urban farmers were affected by urban expansion. However, the PSM technique has drawbacks, including the fact that it only takes into account an observable characteristic of the household, which can cause an endogeneity problem and also more likely to be affected by selectivity bias. These constraints may lead to over or under-estimations of the outcome variable. Hence, an endogenous switching regression model estimate can resolve every one of these issues and generate reliable results (Abdulai & Huffman, 2014 T. Hasebe, 2020). Therefore, a better understanding of the spatial and temporal dynamics of urban expansion and its impact on evicted smallscale farmer’s income with reliable estimation technique in the study area became essential. Hence, this research finding will considerably contribute as empirical evidence to the existing literature and academia when it comes to arguing the pros and cons of urban expansion, particularly in small and medium cities where its rate is higher compared to the larger cities. Empirical findings from this analysis are also helpful for other comparable towns in the country in general and in the region in particular due to homogeneity of towns in the region with similar policy intervention procedures. On top of that, this finding with other earlier ones, can serve as a starting point for future empirical research studies and also crucial for designing a win–win development strategy for the region’s urban development program as well as the sustainability of the evicted farmers. 2. Review of literature 2.1. Development Induced displacement Several studies have empirically evidenced that urban expansion is most likely associated with spatial patterns of land use change of the peri-urban agricultural land that eventually forces people to displace from their original environments is often a life changing event (Bereket, Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 5 of 20 2020). Urban expansion process as one of the developments induced displacement phenomena nowadays is increasing pressures on land use change for the acquisition of land by the government due to urban population increases, rapid economic development, and increasing infrastructure requirements especially in the fast-growing economy (Jiang et al., 2013). In many of the developing countries urban expansion is at the expense of productive and fertile agricultural farmland and forest (Ibid). 2.2. Impact of urban expansion An important feature of the global trend of urban expansion accompanied with rapid increase of population has negative and positive consequences. The positive perspectives given that when cities are properly managed, urban centers can play major role in providing employment, shelter and services as well as serving as centers of culture, learning and technological development, portals to the rest of the world, industrial centers for the processing of agricultural products and manufacturing, and places to generate income, increased peri-urban farm households’ income and consumption primarily through remittances, boost non-farm income for rural households (Debebe Belete, 2018; Leulsegged et al., 2012; Mohammed et al., 2020; Nguyen Thi & Philippe Lebailly, 2010). Through information exchanges and increased understanding of productionrelated skills, urban growth can have a favorable impact on the human capital building of rural areas (Haggblade et al., 2018). Its focus was the transfer of agricultural labor and growth of output and employment to the modern urban industrial sector through wages that is higher than subsistence agriculture. It also postulated that the city offers cost-reducing advantages in economic services. Contrarily, other studies have shown that, due to urban-driven development processes, some or all of peri-urban farmers’ land has been lost forever and causes displacement, dislocation and segregation that result in social fabrics disorder (DFID, 2013). Urban growth that results in agricultural land loss may have a detrimental effect on peri-urban farm households’ income (Muluwork, 2014; Satterthwaite et al., 2010; Tassie Wegedie & Duan, 2018). Continuous pressure is placed on native peri-urban areas by annual urban housing construction and rapid urban growth. People in the expanded urban areas “live partially rurally and in locations where many residents are living, they are not socially or economically integrated”. They usually do not participate in the planning and design of resettlement and dislocation options as well as the distribution of associated costs or benefits (Carter et al., 1995). 2.3. Empirical literatures Continuous urban expansion to its surrounding peripheral area consumes large amount of farmland throughout the world. For instance, due to rapid urbanization China loses close to one million hectares of cultivable farm land each year to accommodate various demands such as construction of roads, industrial buildings, and commercial centers and for residential purposes (Dayong N, 2004). This creates numerous challenges that may be beyond the capabilities of the economies of developing nations including the creation of job opportunities and provision of basic needs. Unlike the situation in the developed countries at comparable stages of development, the process of urbanization in the Third World Countries appears to be more a function of rural push factors than the urban pull factors (Adem, K. and Fenta, M., 2012). Urban expansion disproportionately affects the livelihood of poor people by diminishing the natural resource available to them. After the acquisition of farmland, agricultural households often have to give up farming partly or entirely depending on how much land they have left. It was estimated that a loss of one hectare farmland corresponds to an average job loss of 13 people in the case of Vietnam and of 20 in the case of Hanoi (). Urbanization in Ethiopia is neither participatory nor supportive of farmers who are forced to leave their land and property in periphery and thus has a negative impact on people livelihood Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 6 of 20 where women and youth are the major victims (Gebeyehu et al., 2022). According to Gebeyehu et al. (2022), the consumption expenditure of evicted households was decreased by 5936.92 Ethiopian Birr compared to non-evicted households. Besides the economic impacts, the displacement of suburban dwellers due to rapid expansion of urban centers also hassocial welfare and socio-cultural related effects on the life of the displaced people. Social welfare influences such as food insecurity and homelessness and socio-cultural influences like marginalization, the weakening of social bond between the displaced people which directly resulted from the dismantlement of the previously held social cohesion within the smallholders (Muluwork, 2014). Moreover, the nonfarm-based economic sector was not developed to absorb displaced farming communities. Therefore, the non-integrative type of urban development-induced displacement disturbs on those marginalized rural communities and forces them to live in poverty, food insecure and hopelessness (Mohammed et al., 2020). However, Wogedie (2018) found that using the PSM model of the nearest neighbor matching estimator result indicated that there is an insignificant difference in livelihood outcome of displaced farming community as compared to the non-displaced farming community. 2.4. Theoretical and Conceptual Framework of Urban expansion Many theoretical analyses have sought to understand the reasons behind and impacts of urban expansion, especially on peri-urban agricultural communities. The three urban growth theories proposed by Barnes et al. (2012) were useful for this study. According to the theory of population growth, an increase in the urban population either through natural growth or through rural-to-urban migration emerges to the city’s outskirts. According to the economic growth theory, increased per capita income and employment rates will raise demand for new housing units and related infrastructure. Also, the development of new industries on urban outskirts forced farmers to lost their land. The third factor is government development strategies that place an emphasis on using restrictive land use laws to promote urban development. This viewpoint contends that different approaches to managing land use and development may be impeding the economic and social phenomenon of urban periphery’s agricultural communities (Barness et al., 2001). In addition, the lack of appropriate planning policies and the failure to enforce such policies are the main causes of unrestrained urban expansion, which disturbs zoning structures (for residential, commercial, industrial, institutional and other land uses) and ultimately poses a threat to the agricultural populations of developing nations who live in the cities’ environs. The sociological urban expansion hypothesis, on the other hand, placed a greater emphasis on the role that human action, social networks and social relationships have had in bringing about a variety of changes, including social, political, economic and others (Briassoulis, 2002). The human ecology ideas, proposed by Robert Park and Ernest Burgess in 1925 and linking behavioral patterns to their physical and social environments in urban and rural locations, were closely related to this theory. To be more precise, it is vital to see the following conceptual Driving force Demographic change Economic growth Development policy Farm land Eviction Livelihood change Urban Expansion Programs Economic effect Production Physical Asset Livestock Social effect Social capital Social network unemployment Demand for New places Figure 1. Conceptual frame work for impacts of urban expansion. Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 7 of 20 education levels of evicted and non-evicted farmers were 2.94 and 4.18 years, respectively. The test statistics shows that there were significant differences between evicted and non-evicted farm households in terms of age and education level. The average family size of the sampled peri-urban farm households was 3.75 with dependency ratio of 0.93. In reference to the groups, the average family sizes were 3.88 and 3.62 for evicted and nonevicted farm households, respectively. The survey results showed that there was a statistically significant mean difference between the evicted and non-evicted groups in terms of family size and dependency ratio at 10% and 1% probability level, respectively. As shown in Table 5, the mean landholding sizes for the evicted and non-evicted sample households were found to be 0.16 and 0.92 ha per head, respectively. The maximum and minimum land-holding size of the respondents is 0 and 2 ha, respectively. The statistical analysis showed that there was a significant difference at the 1 percent probability level in the mean landholding size between evicted and non-eviced farm households. This indicates that evicted farm household’s landholding size has been significantly reduced as compared to non-evicted farm households as result of urban induced expansion. Similarly, the mean livestock holding of evicted and Table 5. Demographic and socio-economic characteristics of the households Explanatory variables Pooled (393) Evicted (197) Non-evicted (196) Sig. Age(year) 38.87(0.55) 40.4(0.84) 37.32(0.7) ** Distance from the market(km) 5.82(0.08) 6.02(0.12) 5.62(0.11) Land size(hectare) 0.59(0.02) 0.16(0.02) 0.92(0.03) *** Distance to credit institution(km) 5.31(0.04) 5.51(0.06) 5.1(0.05) *** Education (years of schooling) 3.56(0.18) 2.94(0.24) 4.18(0.26) *** Family size (number) 3.75(0.08) 3.88(0.1) 3.62(0.11) * Dependency ratio (number) 0.93(0.044) 1.1(0.06) 0.76(0.06) *** Total physical asset (Birr) 82621.22(5026.36) 74292.76(5079.47) 90992.17(8663.27) Tropical livestock unit (TLU, number) 2.11(0.08) 2.01(0.1) 2.22(0.12) ***, **, * Significant at 1%, 5% and 10%, respectively. Number in parenthesis are standard deviation. Source: Own survey computation 2022. Table 6. Descriptive statistics of mean annual income by eviction status variable Evicted (N=197) Non-evicted(N=196) t-value pooled (N=393) mean SD mean SD mean Std.Err Total annual income 28367.97 4892.93 34103.84 8580.19 8.145*** 31228.61 380.29 ***significant at 1% Source: Own survey computation, 2022. Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 14 of 20 non-evicted farm households in the study areas were 2.01 and 2.22 tropical livestock unit (TLU), respectively. The survey result demonstrated that the mean differences between livestock holding among evicted and non-evicted farmers were not statistically significant. The mean distance of the market place from the respondent’s residence is 5.82 km with a maximum and minimum distance of 3 and 9 km, respectively. The mean distance of market place for evicted and non-evicted farmers is 6.02 and 5.62 km, respectively. Moreover, the average distance of credit institutions from the respondents is 5.3 km with a maximum and minimum distance 3 and 7 km, respectively. The mean distance of credit institutions for evicted and non-evicted farmers is 5.51 and 5.1 km, respectively. Both variable, distance from the market place and distance to credit institutions have no statistically significance differences for evicted and non-evicted farmers. Finally, the average total physical asset for the respondents is 67,094.53 birr 3 with a maximum and minimum of 1,700 and 882,000 birr, respectively. The mean value for evicted and non-evicted farmers is 74,292.76 birr and 90,992.17 birr, respectively. There is no statistically significant difference between the evicted and non-evicted farmers in their mean of total physical asset value. Table 6 indicates that the average total annual income of the respondents is 31,228.61 birr with a maximum and minimum of 19,625 birr and 96,640 birr, respectively. The mean annual income of evicted and non-evicted farmers is 28,36797birr and 34,103.53 birr, respectively. The t-test statistics depicted that the mean difference between the evicted and non-evicted farmers in their mean annual income is significantly different at a 1 percent level of significance. 4.2. Impact of eviction on peri-urban farmers: ESR Result Table 7 portrays that the ESR model was estimated using the full information maximum likelihood (FIML) approach which derives both the selection and outcome equations jointly. The first stage of the estimation of ESR regression is presented in columns (1), while the second stage of the estimation, i.e. estimation of separate outcome equations for evicted and non-evicted farmers, is reported in columns (2) and (3). The exclusion restriction variable, proximity to urban, is statistically significant for the selection equation model and the negative coefficient (−0.2102) of the variable stated in Table 5 confirms the expectation that households whose agricultural land nearer to urban areas are less likely to be nonevicted, i.e. as the distance between agricultural land of the household increases by 1 km from the urban areas, the likelihood of the farmer to be evicted will decrease by 21.02% compared to their counterparts. Meanwhile, the second stage of the FIML shows that the estimated coefficients of the correlation between farmers eviction and household income are all negative, but statistically significant only for the correlation coefficient (ρ 2 ) between the outcome equation for non-evicted farmers and the selection equation, indicating that self-selection bias is occurred for non-eviction. Furthermore, the negative sign for ρ 2 shows a positive selection bias for non-eviction, implying that farmers with higher-than-average yearly incomes are more likely to prefer to not being evicted and the model suggests that individuals who are non-evicted earn higher mean annual income than a random individual from the sample would have earned. Thus, being non-evicted may not have the same effect on the evicted if they choose to be non-evicted (Lokshin & Sajaia, 2004). This means that the null hypothesis, which indicated that the model had no sample selectivity bias, may be rejected. Hence, the presence of sample selectivity bias implies that both observed and unobserved factors influence the decision to participate in farmer eviction. The statistically insignificant covariance estimate for evicted households (ρ 1 ) suggests that in the presence of eviction, there would be no significant difference in the average behavior of the two categories of farmers caused by unobservable factors. 4 Generally, due to urban expansion and other related development-induced interventions, evicted farmers who dispossessed their agricultural land will no longer be better off in their annual income earnings compared to their counterparts. This finding is consistent with other earlier studies by (Gebeyehu et al., 2022; Mohammed et al., 2020; Weldearegay et al., 2021). Outcome equations from the ESR regressions show that evicted farmers total annual income is significantly determined by the age, land size, education, family size of the household, and tropical Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 15 of 20 livestock unit (TLU). Results of the ESR also exhibit some differences in the determinants of household income for evictees and non-evictees. For evicted farmers, the main variables that affect significantly of their total annual income are age of the household head, land size of the Table 7. Endogenous switching regression model output of household income variable Selection equation (1) Outcome equation for Evicted (2) Outcome equation for non-evicted (3) Coefficient coefficient coefficient Age of the household head −0.115** (0.04) −371.305** (187.404) 1444.95** (482.12) Age2 0.001*** (0.0005) −4.093(2.1) 4.5(5.77) Distance from the market 0.077(0.043) −89.947(205.44) −199.62(396.84) Land size −0.844*** (0.178) 2597.67** (1263.93) 2955.22** (1507) Distance from credit institutions 0.28*** (0.08) 159.6(435.75) 140.96(775.56) Education status of the hh head −0.05** (0.02) 1209.24** (506.75) −176.02(194.71) Family size of the household −0.03(0.05) 1291.49*** (457.14) 1082.07** (536.34) Dependency ratio 0.301*** (0.09) 471.73(480.29) 717.6(925.88) Value of total physical asset −1.45e-06(7.5e07) −0.002(0.005) −0.005(0.005) Tropical livestock unit −0.06(0.045) 68.32(227.58) 1213.98** (364.28) Proximity to urban (Instrumental Variable) −0.21*** (0.046) δ 1 4719.2*** (246.29) δ 1 8439.22*** (471.25) ρ 1 −0.08(0.312) ρ 2 −0.285*** (0.144) loglikelihood −4212.071 Number of observations 393 197 196 Note: ***, **, * significant at 1%, 5% and 10% respectively. Source: Own computation, 2022. Table 8. Summary of predicted values for total income for evicted and non-evicted Mean outcome effectevicted Non-evicted 28368.12(91.058) 34099.32(156.58) ATT −9202.36*** (153.93) −26.73% ATU −4666.78*** (139.49) −11.8% TH −4535.58 −14.93% Results are expressed in Ethiopian birr. Standard errors in the parenthesis. *** Refers significant at the 1% level. Source: Own survey data, 2022 Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 16 of 20 household, family size of the household and education level. Meanwhile, the household income of non-evicted farmers is influenced by age, land size, education and tropical livestock unit (TLU). The ESR model produces mean outcomes on treated households and corresponding counterfactual outcomes, i.e. what would have been the outcome had the treated group not received the treatment. The average treatment effect on treated (ATT) is the net difference between these two outcomes. Similarly, the model also produces the mean outcome of the control group (nonevictees) and its counterfactual, i.e. what would have been the mean outcome had the control group received the treatment. The difference between these last two outcomes produces the average treatment effect on untreated (ATU). These average outcomes and the estimated ATT and ATU are presented in Table 8. The estimates portray that the treatment effect for evictees due to urban expansion on households’ total annual income is negative and significantly different from zero. The ATT is −9202.36 indicating that evicted farmers mean annual income had been declined by 9202.36 Ethiopian birr or by 26.73% due to horizontal urban expansion. However, had nonevicted decided to be evicted, their mean annual income would have been decreased by 4666.78 Ethiopian birr. This implies that had the non-evicted farmers decided to be evicted, their annual income would decline by 11.8% per annum. On top of these, the heterogeneous effect of urban expansion on evicted farmers can be estimated by taking into account all observable and unobservable household characteristics of the sample respondents. As a result, the transitional heterogeneous effect is defined as the difference between the ATT and ATU. According to this finding, the transitional heterogeneity of urban expansion on evicted farmers’ annual income is −4535.58 birr implying that being evicted can reduce annual income by 4535.58 birr, or 14.4%. This finding is in conformity with the findings of (Muluwork, 2014; Tassie Wegedie & Duan, 2018). Other researchers finding revealed that development induced urban expansion brings significant and negative impact on the displaced farm households’ welfare. For example, the study conducted by Gebeyehu et al. (2022) in Bahir Dar, Gondar and Dessie metropolitan cities of Ethiopia and analyzed using PSM method, they found that on average, the displaced farm households’ consumption expenditure per adult equivalent was reduced by 6688.03 Ethiopian Birr. Moreover, the findings of Dejene (2011) conducted in Sebata town of Ethiopia showed that urban expansion during the last few years made significant impacts on livelihood of farming community on peri-urban area: agricultural land fragmentation, land insecurity, and loss of farmer’s property on the land. Field research on expropriation in China documents that unemployment and low income are common among land-lost farmers, mainly due to their low educational level and their lack of experience in non-agricultural work. The studies give evidence that land expropriation led to the loss of subsistence, interruption of economic activities, psychological distress and land conflicts among farmers, whose land was expropriated (Jacoby et al., 2002). People are affected emotionally, culturally or spiritually by the loss of their land (Chi et al., 2013). 5. Conclusion and policy implications This paper used household-level data to examine the impact of urban expansion on evicted farmers' income levels in the peri-urban areas of Injibara, Burie and Gish Abay cities of Amhara region, Ethiopia. Comparisons of mean annual income between evicted and non-evicted farmers revealed some significant differences. However, knowledge of average differences is not sufficient to understand the eviction decisions across a sample of farmers, since they do not account for the effect of other characteristics of farmers. We therefore modeled eviction as a selection process, where the expected disadvantages to the treatment drive farmers’ decisions. Specifically, we employ an endogenous switching regression approach to account for selectivity bias, and to capture the differential impact of eviction on evicted and non-evicted farmers. The result showed that sample selection bias would result if the outcome equations (total annual income) were to be estimated without considering the eviction decision. Thus, eviction may not have the same effect on the non-evicted if they were evicted. Furthermore, a positive selection bias was observed for the non-evicted outcome variable, suggesting that peri-urban farmers tend to prefer to be nonevicted. Alamneh et al., Cogent Economics & Finance (2023), 11: 2199596 https://doi.org/10.1080/23322039.2023.2199596 Page 17 of 20 The study also revealed that the average treatment effect on the treated (ATT) is −9223.36 Birr, implying eviction resulted in a substantial reduction in mean annual income of about 26.37%, while the average treatment effect on the untreated is −4666.78 Birr, which depicts that, had the nonevicted farmers preferred to be evicted, their mean income would decline by 14.9%. Therefore, eviction has negative impact on the income of peri-urban farmers. According to this research, the income of evicted peri-urban farmers may be significantly impacted by development-induced urban expansion, which could result in a reduction in their livelihood and a move towards poverty. The causal effect of eviction which lowers the earnings of peri-urban farmers serves the potential consequence of worsening the rural–urban dichotomy and indirectly exacerbating rural poverty in urban fringes. In general, from the above conclusion we can draw the following policy implications for the concerned stakeholders. Urban development Programmes that have an impact on the adjacent farmers must be participatory, allowing them to participate and have a saying while ensuring shared benefits. Hence, the farming community whose land is to be expropriated and, as a result, whose livelihood is to be disrupted, needs to be compensated appropriately when it is executed. Moreover, the government must closely track the impacted community’s post-eviction existence and offer support in accordance with their capacity and interests so that their families can secure their livelihood. Therefore, strong institutional support could be offered to evicted households in order to encourage them to create job opportunities, train people in various skills and knowledge, and organize financial institutions for loan service in order to create sustainable livelihood. Eviction should only take place once this communities have received training on how to create urban enterprises and how to manage them under smaller and micro-industry entities where there is strong institutional follow-up. In addition, it is imperative to provide just compensation that is proportionate to the land they abandoned or the government should guarantee farmers the right to own land so that they can bargain and sell it at market value like they do for other assets. Abbreviations ESRM endogenous switching regression model FIML full information maximum likelihood ATT average treatment effect on treated ATU average treatment effect on the untreated TH transitional heterogeneity Acknowledgments The authors would like to thank, without implicating, the journal editor and two anonymous reviewers for valuable comments and suggestions that have substantially improved the paper. They would also like to thank Arba Minch University for funding this research for data collection. Author details Tadele Alamneh 1 E-mail: [email protected] Melkamu Mada 1 Tora Abebe 1 Antonella Francesca Cicchiello 1 Department of Economics, College of Business and Economics, Arba Minch University, Arba Minch, Ethiopia. Disclosure statement No potential conflict of interest was reported by the authors. Citation information Cite this article as: Impact of Urban expansion on income of evicted farmers in the peri-Urban areas of Amhara Regional State, Ethiopia: Endogenous switching regression approach, Tadele Alamneh, Melkamu Mada & Tora Abebe, Cogent Economics & Finance (2023), 11: 2199596. Notes 1. Small sized city is urban area which has a population of below 100 thousand and medium city is urban places having population size ranging from 100 thousand to 500 thousand” according to definition of OECD (2022), Urban population by city size (indicator). doi: 10.1787/ b4332f92en (Accessed on 28 February 2022). 2. Kebele is the smallest administrative unit in the country, Ethiopia. 3. Birr is the unit of currency in Ethiopia. It is subdivided into 100 santim (cents). 4. 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