Abstract
Kosovo is one of the poorest countries in Europe, despite the various poverty alleviation programs implemented by the authorities and the international funding community. This study aims to analyze income distribution inequality and factors behind rural households' poverty in Kosovo. Data on farm income, nonfarm income, unearned income, and socio-economic characteristics were collected using a semi-structured questionnaire from 203 randomly selected households in Kosovo. Linear regression, one-way ANOVA, and different versions of poverty indexes were used to examine the data. One-quarter of households' income comes from nonfarm activities. The middle-income households had the highest potential to find alternative employment in the nonfarm sector. Years of education, household size, number of family members above the age of 18, and total income had a positive impact on nonfarm revenues. The poorest rural households had the highest share of income from farm activities (77.52%). Nonfarm revenues have a positive impact on poverty alleviation; thus, the study suggests adopting suitable rural policies to enhance nonfarm employment for vulnerable rural households. The agro-tourism sector and circular economy approaches in agriculture with the focus on renewable energy can be considered as potential sources of nonfarm income, which could lead to sustainable poverty reduction.
1 Introduction
Understanding household livelihood strategies is pivotal in minimizing rural poverty in least developed countries (Paudel Khatiwada et al. 2017). Almost two-thirds of the world's poor reside in the rural areas of low-income countries, mainly depending on subsistence farming and other natural resources for their livelihood (World Banks and IMF 2014). In addition, rural populations experience the highs and lows of a global economy, for if the price of their crop drops, then their sustainability is affected (McCatty 2004). Although poverty is a multi-dimensional issue, it is directly associated with household incomes, asset holding, and other economic activities that together determine a household's livelihood strategy and outcomes (World Banks and IMF 2014). Widespread poverty is present in volatile Balkan states (Bezemer 2006), Kosovo among them (Mazrekaj 2016). Despite considerable economic growth (3.9% GDP growth in 2019), Kosovo has faced a slow job creation process, and its employment rate remains one of the lowest in Europe. Particularly the long-term unemployment rate is high: 25.9% in 2019 compared to 6.2% in the EU-27 in the same year (Trading Economics 2020a, 2020b) and the rates of extreme poverty are considered to be higher in rural settlements (ASK 2019).
As agriculture remains a major source of income and employment, agricultural growth is likely to remain crucial for alleviating poverty, in particular in the poorest countries (Macours − Swinnen 2006). On the other hand, migration is known as a strategy that can compensate low employment rates in the country (Corbanese − Rosas 2007) or allow people to flee violent conflict (Tétényi et al. 2019). In Kosovo, most of the migration happened during the war and was directed towards different countries. Rural-urban migration, on the other hand, is a result of the destruction of agriculture and property. Nonetheless, migration persists due to the overall high unemployment rate, and both migration outside of Kosovo and from rural to urban areas within Kosovo, partly explain Kosovo's low self-sufficiency rate for agricultural products (Haxhikadrija 2009; Gollopeni 2015; World Bank 2017; MAFRD 2018).
Kosovo's high poverty and unemployment rates, as well as migration trends, highlight the importance of investing in nonfarm activities. According to Bytyçi and Gjergjizi 2015, to increase farmers' incomes, rural families are often forced to engage in work outside of their farms. Different authors discussed how to avoid poverty in rural areas (Kabir et al. 2019; Cho et al. 2019; Lyu et al. 2019). As agricultural employment is generally associated with an elevated poverty risk, rural nonfarm income may offer a new opportunity in this situation (Kabir et al. 2019). Knowing that poverty is positively and significantly associated with income inequality (Ravallion − Datt 2002; Araujo 2004), researchers differ on how farmers can best generate off-farm income. The first approach is that off-farm work can reduce short and long-run income inequality and allow poor farmers to increase their capital stock (Kimhi 2009; Möllers – Buchenrieder 2011). The second approach adopted by other researchers (Iqbal et al. 2018; Woldehanna − Oskam 2000; Mat et al. 2012; Kmoch et al. 2018) found that off-farm income itself can aggravate income inequality among farm households in rural areas as in many cases wealthy farmers tend to dominate the most lucrative and risky nonfarm activity.
Nonetheless, for Kosovo and other European Union candidate countries, empirical evidence regarding nonfarm income is still patchy. In order to promote broader concepts of rural development,1 a better understanding of the levels of nonfarm income and their implications for poverty and inequality is essential. This paper aims to analyze (1) how important nonfarm income is for small households and how small farms can manage the ongoing structural changes; (2) how nonfarm income affects welfare and inter-household income inequality; and (3) whether nonfarm employment affects only income poverty.
2 The importance of rural nonfarm employment and its effect on poverty alleviation
To deal with different challenges concerning agriculture in Europe like decreasing farm revenues, price fluctuation, low productivity growth rates, migration, etc., it is appropriate to diversify both agronomic and nonagricultural activities in rural areas by adopting new entrepreneurial competencies (Stanovčić et al. 2018). In doing this, the competitive ability of the agricultural sector and its sustainability would be increased (Stanovčić et al. 2018). Different authors suggest that off-farm income can reduce rural to urban migration rates, which are believed to be caused by rural unemployment (and underemployment) (Cho et al. 2019). Lyu et al. (2019) support the policy interventions that focus on generating employment opportunities in rural areas to reduce migration to urban areas.
Farm household income can be categorized as an earned off-farm income (wages and salaries), unearned off-farm income (remittances, social security, pensions, and investments), and farm net cash income (Möllers −Buchenrieder 2011; Mat et al. 2012). According to Lyu et al. (2019), rural-urban migration comes 2–4 years following an increase in rural unemployment. The rural population takes a couple of years to internalize a shock in employment opportunities before migrating to cities. According to different studies, nonfarm activities can increase agricultural investments (Marenya, 2003). However, as poverty is related to reduced levels of education and low overall work competencies, poor farmers tend to be reluctant to migrate to urban areas (Haan − Rogaly 2002).
Thus, the government needs to support the development and funding of small and medium scale rural enterprises, and agriculture (Okhankhuele − Opafunso 2013). The World Bank (2017) suggested an integrated approach of combining farming with tourism, which has the potential to improve the livelihoods of rural populations by reducing unemployment, rural migration and overall poverty (Jȩczmyk et al. 2015; Ciani et al. 2016; Hüller et al. 2017; Nunkoo − Gursoy 2012; Iqbal et al. 2018). Furthermore, nonfarm incomes are correlated with reduced pressure on natural resources (Barrett et al. 2002). In the case of circular food supply systems, the production and consumption of resources happen in the same place (Jurgilevich et al. 2016). Many economic activities in lower-income countries revolve around sorting and reusing waste. However, higher-value, employment-generating opportunities for reuse, and remanufacturing of waste are yet to be captured (Preston − Lehne 2017). Another nonfarm income generating activity is installing solar panels in villages or using residues of biomass for energy which can lead to increased crop yields, and besides reducing poverty it is also environmentally friendly (Kurowska et al. 2014; Rozbicka − Szent-Iványi 2020; Ymeri et al. 2020). Funding for environmentally friendly causes is in line with European non-governmental development organizations' positions, who stress that poverty reduction needs to be ecologically sustainable (Rozbicka − Szent-Iványi 2020). In many developing nations, off-farm income has become an essential element of living standard schemes among rural people (Babatunde − Fakayode 2010; Mat et al. 2012), and it is widely associated with poverty reduction (Haggblade et al. 2010; Mat et al. 2012). For example, in Latin America, the share of nonfarm income in total income is around 40%, while in Ecuador, it is 36% (Köbrich – Dirven 2007). In India, on the one hand, in the early 90s the percentage of income from nonfarm activities was 34% (Lanjouw − Shariff 2004) while in Kedah, Malaysia, it was 32.35% (Mat et al. 2012) in Croatia 31.7% (Möllers − Buchenrieder 2011).
Basically, nonfarm income is more profitable than all forms of farm labor, which shows its potential for reducing rural poverty (Vasco − Tamayo 2017). Nonetheless, these transformations on the farm level require substantial investments of EU funds. In the case of transition economies, Davis and Pearce (2000) suggest deeper analysis of the labor force in agricultural households by studying additional parameters or variables. The individual's decision on continuation or cessation of farm work can depend on adaptation to the favorable situation on the (off-farm) labor markets (demand-pull factors). On the other hand, a continuation of (low-paid) farm work can also be an individual's survival strategy in the case of rigidity on the off-farm labor markets (distress-push factors).
Even though the importance of off-farm incomes is increasing in rural areas, it has been difficult to see the effect of nonagricultural rural employment on poverty reduction. The results of Kimhi (2009) show that nonfarm income is an equalizing source of income in Georgia, Korea, Ethiopia, and Croatia (Möllers − Buchenrieder 2011). On the other hand, findings of different authors (Iqbal et al. 2018; Mat et al. 2012; Kmoch et al. 2018; Woldehanna − Oskam 2000) show that off-farm income is one of the sources of income inequality among farm households in rural areas because relatively wealthy farmers dominate the most lucrative and risky nonfarm activity such as masonry, carpentry, and trading. In addition to this, if there are entry barriers to and rationing in the labor market, diversifying income into off-farm activities will be more difficult for the poor than it is for wealthier farm households (Woldehanna − Oskam 2000; De Janvry − Sadoulet 2001). Accordingly, our first hypothesis assumes a linear decreasing curve of the share of nonfarm incomes from the poorer to the better-off families (MacDonald 2006). In contrast, the second hypothesis assumes an increasing curve. According to Lanjouw and Lanjouw (1997), it is mainly the poorest and the richest households that are engaged in rural nonfarm employment, implying a u-shaped relationship between nonfarm incomes and total incomes.
3 Methodology
3.1 Sampling procedure and sample size
Our research investigates the effect of nonfarm incomes on poverty and the inequality of household incomes. The study was conducted during spring 2017. Kosovo is divided into seven regions and 38 municipalities (Latifi-Pupovci et al. 2020). Our sample area consisted of five regions and seven municipalities within those regions, and it was chosen based on the willingness of farmers to cooperate. The sample sizes of the five regions were: 31, 38, 57, 51 and 26, based on the number of questionnaires distributed. In total, the survey covered 203 farm households. Farmers engaged in the cultivation of various vegetables and small fruits were chosen by using a random sampling technique.
Due to the absence of knowledge in using the Internet among farmers, the questionnaires were filled out by hand. The researcher collected primary data through face-to-face interviews by making personal visits to rural areas, both at home and in the workplace of the respondents. Before beginning the interview, each respondent was given a brief idea about the nature and purpose of the study, explaining that it will be used solely for academic research purposes. Both closed and open questions were included in the interviews, and all answers were recorded carefully. The applied structured questionnaire contained several customized modules capturing, among other things, farming activities, all sources of income, and driving forces of income diversification. The questionnaires were first pre-tested with a sample size of 10. It is important to note that the study sample can be considered statistically representative at the national level because of the data collection methods used. The sample adequacy test showed that the sample chosen for the study is adequate at a 95% confidence level with a margin of error of 6.8%.
3.2 Data analysis
To further analyze the inequalities or differences within our sample between the poorest and the richest, the total sample was separated into three income classes (tertiles) based on respondents' incomes. To determine the significance level, these three tertiles are compared with each other in terms of socio-economic factors, using one-way ANOVA (Tukey method) in Minitab 17. A similar methodology for analyzing the significant differences between three variables was used in different studies elsewhere (Möllers − Buchenrieder 2011; Ymeri et al. 2017). Furthermore, to better understand the link between the level of farmers' engagement with agriculture and their income, farms were divided based on farm type. According to Möllers and Buchenrieder (2011), full-time farms are characterized by only 10% income coming from nonfarm sources, the second type of farms (complemented part-time farming) have a share of nonfarm incomes between 10% and 50% and the third type of farms, with more than 50% of income from nonfarm sources are considered as subsidiary part-time farming. These three types of farms are considered as independent variables. As the income per ha for full-time farms was quite low and not as expected, we decided to analyze the distribution of main crops and their selling price according to farm type.
According to Shkolnikov et al. (2003), the widely known and used measure of inequality is the Gini coefficient, which relies on the Lorenz curve; this curve represents a cumulative frequency curve. It compares the share of a specific variable (for example, income) over the population to show inequality. The Gini coefficient lies between 0 and 1, with 0 signifying absolute equality and 1 meaning absolute inequality (World Bank 2000; Möllers − Buchenrieder 2011).
4 Results and discussion
With per capita GDP estimates of close to €3,000, Kosovo is one of the poorest countries in Europe. Average per capita income is about one-tenth of EU levels, and the incidence of poverty remains high. No significant differences exist between urban and rural poverty, but there are some notable regional differences (World Bank 2016). However, from Table 1, it is clear that rural incomes are considerably lower than this national average (Table 1). The income portfolio of family farms contains three main categories, the largest of which, at 73.57%, is farming income, while nonfarm income makes up a share of 21.29%. Remaining income refers to so-called unearned income, and consists of cases of social transfers, remittances, and pensions (5.14%). We can find similar results from Demissie and Legesse (2013), who showed that agricultural activities contribute 77% of total household income and 23% from nonagricultural operations in Ethiopia.
Income of farm households in Kosovo, 2017
| Total average | |
| Per capita income (€) | 1,868.80 |
| Per capita income, equivalent scale (€) | 4,409.81 |
| Household income (€) | 12,362.78 |
| • Farm income (%) | 73.57 |
| • Nonfarm income (%) | 21.29 |
| • Unearned income (%) | 5.14 |
Notes: N = 203 farm households. The average household size in the sample is 7.8 persons.
Source: authors.
Table 2 shows that only two variables are significant: farm incomes and total incomes. Farm income has a negative relationship with nonfarm incomes, which means that if farm income decreases, nonfarm income will increase, while total income is positively correlated with nonfarm income, which means that if total income goes up, so will nonfarm income. The remaining variables are not significant at standard levels of significance, which may be due to the small sample size. Education is positively related to nonfarm incomes. When it comes to age, it is inversely associated with nonfarm income, which means that if the age decreases, then nonfarm income will increase. Nonfarm income also increases with the increase in the household size and the number of household members above 18 years old.
Socio-economic impact on nonfarm incomes in Kosovo, 2017
| Model | Unstandardized coefficients | Standardized coefficients | T | Sig. | 95.0% Confidence interval for B | |||
| B | Std. error | Beta | Lower bound | Upper bound | ||||
| 1 | (Constant) | 84.371 | 174.831 | 0.483 | 0.630 | −260.432 | 429.175 | |
| Years of education | 0.661 | 9.966 | 0.000 | −0.066 | 0.947 | −17.317 | 18.994 | |
| Age of household | −1.496 | 2.162 | −0.004 | −0.692 | 0.490 | −5.761 | 2.769 | |
| Household size | 0.903 | 11.748 | 0.001 | 0.077 | 0.939 | −22.267 | 24.073 | |
| Above 18 years | 1.502 | 17.302 | 0.001 | 0.087 | 0.931 | −32.621 | 35.624 | |
| Farm incomes | −0.993 | 0.006 | −1.790 | −162.710 | 0.000 | −1.005 | −0.981 | |
| Farm size/ha | −0.933 | 2.287 | −0.002 | −0.408 | 0.684 | −5.442 | 3.577 | |
| Total incomes | 0.994 | 0.005 | 2.138 | 196.289 | 0.000 | 0.984 | 1.004 | |
Linear regression. Dependent variable: Nonfarm incomes. Source: author's calculations.
Table 3 compares income groups (tertiles) from the poorest to the wealthiest as well as their socio-economic characteristics. From the table, we can find that nonfarm income and per hectare income make a difference in terms of overall economic well-being. The poorest group had the highest share of farm income (77.52%) and the lowest share of other income sources (22.48%) giving nonfarm income and unearned income a p-value below 0.05. The highest farming income per hectare is present in case of the wealthiest group (P < 0.05), their farm income share is 74.73%, and income from other sources is 25.27%. Overall, these structural differences in income shares between the wealthiest and the poorest group are significant. The wealthiest tertiles (2 and 3) are characterized by a higher share of education, household size, off-farm income, and younger households compared to the first group. The results of Demissie and Legesse (2013) showed that the number of children, number of economically active family members, gender, education, and age of household heads are strongly associated with nonfarm income employment decisions.
Socio-economic characteristics according to income classes, 2017
| Income class (tertile) | All households | p-value | |||
| 1 | 2 | 3 | |||
| Household income (€) | 3,726.47c | 8,477.17b | 24,827.54a | 12,362.78 | 0.000 |
| Per capita income, equivalent scale (€) | 1,467.72c | 3,166.92b | 8,576.51a | 4,409.81 | 0.000 |
| Median of per capita income (€), equivalent scale | 1,432.51 | 2,989.54 | 7,310.15 | 3,910.74 | |
| Share in all household incomes (%) | 11.15 | 23.70 | 65.15 | 100 | |
| Income shares (%) | |||||
| • Farm income | 77.52c | 68.36b | 74.73a | 73.57 | 0.000 |
| • Nonfarm income | 20.50b | 26.04b | 19.81a | 21.29 | 0.000 |
| • Unearned income | 1.98b | 5.60ab | 5.46a | 5.14 | 0.026 |
| • Nonfarm + unearned income | 22.48 | 31.64 | 25.27 | ||
| Farmland (ha) | 4.30b | 4.38b | 7.72a | 5.47 | 0.000 |
| Farm income per ha average(€/ha) | 1,363.01c | 2,742.81b | 3,756.62a | 2,620.21 | 0.000 |
| The education level of household head (%) | |||||
| Lower than elementary | 1.47 | 1.49 | 0.00 | 0.99 | 0.607 |
| Elementary school | 26.47 | 20.90 | 14.71 | 20.69 | 0.319 |
| Agricultural high school | 2.94 | 8.96 | 2.94 | 4.93 | 0.202 |
| Other secondary schools | 55.88 | 46.27 | 58.82 | 53.69 | 0.541 |
| University | 13.24 | 22.39 | 23.53 | 19.70 | 0.341 |
| Age of household head | 49.31a | 45.01a | 45.91a | 46.75 | 0.109 |
| Household size | 7.12a | 7.75a | 8.54a | 7.80 | 0.155 |
| Children under 18 | 2.19a | 2.55a | 2.60a | 2.45 | 0.541 |
Note: means that do not share a letter are significantly different.
Source: authors' calculations.
Fig. 1 shows the relationship between income level and distribution of off-farm income sources in total household income. Contrary to the expected decreasing or u-shaped curves, the higher level of nonfarm incomes in the middle-income class leads to an inversely shaped u-curve. The poorest group, with around 22%, is characterized by a moderately low degree of nonfarm income. Generally, poorer households have high motivation, but low ability to be involved in the nonfarm sector, as shown by Barrett et al. (2001). We can also see the percentage of migration, which is the lowest in the poorer households and is in line with different studies (Mendola 2008; McKenzie 2017). Again, if we analyze the middle-income group, we see that nonfarm income sources can significantly increase incomes. Therefore, the reason for the inverse u-shaped relationship might be a distress-push situation. In this situation, access to nonfarm employment is either easier or more difficult for certain parts of the population. Simultaneously, farming is seen as the most profitable solution for rural households compared to all other sources of income. The middle-income households appear to be defined by their potential and skills to find other options in nonfarm activities, enabling them to increase their total income and compensate for low farming incomes.
Income groups and share of nonfarm incomes (%). Source: authors' calculations
Citation: Society and Economy SocEc 42, 4; 10.1556/204.2020.00021
Incomes depending on farm types, are given in Table 4. According to Möllers and Buchenrieder (2011) from the total incomes of full times farms, only 10% are from nonfarm sources, while the second type of farms (complemented part-time farming) have a share of nonfarm incomes between 10% and 50%. The final type, is as a typical subsidiary farm where the head of the household gives priority to working outside the farm sector and as a result, nonfarm income is greater than farm income (part-time farms, subsidiary). However, complemented part-time farming (type 2) fares better in a comparison of per capita incomes (P > 0.05). This difference may be explained by the fact that farm income per hectare of land on type 2 is the highest. Nonetheless, this farm income per land on type 2 farms is not significant, and another explanation may be due to the high share of non-farming income compared to full-time farms (P < 0.05). Full-time farms are the poorest based on per capita income, even though they have higher income per hectare compared to subsidiary farms. The disadvantage of full-time farms is also shown by the lowest share of nonfarm income (P < 0.05), thus nonfarm income sources are essential, and it seems that alternative employment can increase incomes.
Incomes according to farm type classes in Kosovo, 2017
| Farm type (N) | Per capita income, equivalent scale (€) | Farm income per ha of land (€/ha) | Nonfarm income per capita eq. scale (€) | Farm share in total income (%) |
| Full-time farms | 3,997a | 2,601.71a | 29.7c | 99.2 |
| Complemented part-time farming | 5,165a | 3,001.98a | 1,923b | 62.32 |
| Subsidiary part-time farming | 4,223a | 2,055.17a | 2,909a | 31.59 |
| p-value | 0.182 | 0.167 | 0.000 | 0.456 |
Note: means that do not share a letter are significantly different.
Source: authors' calculations.
In the study of Möllers and Buchenrieder (2011), full-time farms had a share in total income around 68.1%, and per capita incomes of full-time farms were the highest when compared with the other two types of farms. Möllers and Buchenrieder (2011) stated that this could be a result of a higher share of farming income and higher productivity. In our case, farmers usually had a larger than 10% share of non-farm incomes, or no a share at all. As a result, full-time farms were fully engaged or had a farm share in total income of 99.2%, with significantly low nonfarm income. In these conditions, full time farms showed the lowest income per capita, as well as lower-income per hectares compared to complemented part-time farming; for this reason, we extended our results by analyzing the distribution of main crops and their selling price, which are presented in Table 5. From the analysis, we can see that planting crops with higher selling price depends on farm type classes (P < 0.05). Complemented part-time farmers and subsidiary part-time farmers cultivated crops with higher selling prices compared with full-time farms. This may be because full-time farms could grow crops that are sold to the industry, which requires less investment, while crops that are sold at a higher price require higher investments too. From Table 5, we can observe that on full-time farms crops dominate with lower selling price: 75.75% compared to 29.69% on complemented part-time farms and 27.5% on subsidiary part-time farms. Beyond low nonfarm income, distribution of main crops and their selling price can be another reason why full-time farms have lower incomes per capita and per hectare.
The distribution of main crops according to farm type classes
| The sale price of crops €/kg | Full-time farms (99) | Complemented part-time farming (63) | Subsidiary part-time farming (40) |
| (1.69–1.85)a | 8.08% | 29.69% | 32.5% |
| (1.06–1.20)b | 16.16% | 40.63% | 40.0% |
| (0.22–0.35)c | 40.40% | 21.88% | 7.5% |
| (0.11–0.26)c | 35.35% | 7.81% | 20.0% |
| P-value 0.000 | Pearson Chi-Square = 50.135, p-value = 0.000 | ||
Source: authors.
The incidence of poverty and income distribution are shown in Table 6. The headcount index calculated based at a USD 4.30 poverty line (Jimeno et al. 2000), results in 20% of the farm households in the sample facing absolute poverty. We find that slightly less than one quarter (24%) of the households falls below 60% of the average income. The poverty severity indicator shows relatively low figures for the sample households, meaning that there is no considerable inequality in income distribution amongst the poor. The poverty gap index measures the total difference between the actual incomes of poor households and the poverty line. This index shows how much money should be transferred to the poor to lift them out of poverty (Reinert 2017). The poverty deficit, defined as the average distance of the poor to the relative poverty line, is rather low at 9%. In our sample, a household can be lifted above the relative poverty line with an additional 489.79€ per year, and above the absolute poverty line with 316.42€. According to the impact that nonfarm income and unearned income have, nonfarm income lifts 16% of households out of poverty. The effect of unearned income is lower by about 0.03–3.45%.
Poverty incidence and income distribution
| Household annual income (€) | Headcount-index | Poverty severity | Poverty deficit (gap) | Share of households shifted above poverty line due to | ||
| Non-farm income | Unearned income | |||||
| Absolute poverty line ($4.30 USD; 1 USD = 0.94€) | 3,255.59 | 0.20 | 0.03 | 0.07 | 16% | 0.03% |
| Relative poverty line (60% of median income) | 3,886.41 | 0.24 | 0.04 | 0.09 | 16% | 3.45% |
Source: authors.
Table 7 shows the distribution of total household income in the sample based on the Gini coefficient. The income distribution was calculated excluding nonfarm incomes too. The Gini coefficient of 0.488 for the farm households in the sample indicates that income distribution is unequal. The Gini coefficient calculated without considering nonfarm incomes shows a notable increase to 0.699. This implies that nonfarm income contributes to more equal income distribution in rural areas. The examination of partial coefficients calculated on the basis of decomposed Gini coefficients confirms that all of the household income sources reduce inequality, especially farm income. For the calculation of Gini coefficients, all households in the sample were considered, including those with no share in the respective income source (Möllers − Buchenrieder 2011).
Income distribution and nonfarm incomes
| Gini coefficient | ||
| on the basis of adjusted per capita incomes, (equivalent scale) | 0.488 | |
| nonfarm incomes excluded | 0.699 | |
| Decomposed Gini coefficients | ||
| on the basis of farm incomes | 0.496 | (−0.252) |
| on the basis of nonfarm incomes | 0.754 | (−0.0451) |
| on the basis of unearned incomes | 0.94 | (−0.0024) |
| Gini total | 0.452 | |
Source: authors.
4 Conclusion
Based on the analysis in this paper, we conclude that nonfarm incomes play an important role in total household income in Kosovo, as one-quarter of households' incomes comes from nonfarm activities. The wealthiest groups are characterized by higher education levels, household size, off-farm, and farm income. Middle-income households seem to be characterized by their ability to find alternative employment in the nonfarm sector, thus enabling them to compensate for low farming incomes. We conclude that nonfarm incomes contribute to more equal income distribution and reduce poverty in rural areas. Hence, support to improve the farm production and income of farmers through the provision of extension services are needed. The agro-tourism sector would provide several educational, economic, and social benefits to producers, tourists, and communities and ensure motivation for producers to continue working in agriculture. The circular economy in agriculture with the focus on renewable energy can be considered as a potential source of nonfarm income by recycling or reusing agricultural wastes, which could lead to sustainable poverty reduction. Thus, we suggest future studies to compare the poverty and nonfarm income between farmers relative to the specific products they produce (fruits, vegetables, grapes, dairy, etc.) as well as to analyze the link between nonfarm income and the willingness of farmers adopt the principles of circular economy in agriculture. The research is limited due to its sample size. Data collection focused on from farmers who had different cultures (vegetables, small fruits), which lead to heterogeneous overall results. We found that part-time farmers had a significantly higher income per hectare compared to full-time farmers thus, analyzing this difference in incomes per hectare based on farm type needs further research and analysis.
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Such as the call for reorientating the Common Agricultural Policy from a product-centered focus towards more entrepreneurial modes of agriculture by diversifying both agronomic and non-agricultural activities (Stanovčić et al. 2018).
After having used the “old OECD scale” in the 1980s and the earlier 1990s, the Statistical Office of the European Union (EUROSTAT) adopted in the late 1990s the so-called “OECD-modified equivalence scale.” This scale, first proposed by Haagenars et al. (1994), assigns a value of 1 to the household head, 0.5 to each additional adult member and 0.3 to each child (OECD 2015).
