Research issue

One of the most important constraints hindering development in rural Egypt is the limited availability of irrigated land. The land in Egypt is fertile but scarce and densely populated, as it depends on the Nile River. With scarcely any rainfall and almost all of the population living along the Nile River, the population density in Egypt is one of the highest in the world. In addition, with continuing population growth combined with Islamic hereditary law, rural land in Egypt is increasingly fragmented.

One result is that land in rural Egypt continues to be the most influential determinant of the household income distribution (Adams, 2002)[i]. For example, Adams 2002) analyzed the household income distribution in rural  Egypt based on a nationwide household survey conducted by the International Food Policy Research Institute in 1997 and found that overall inequality in rural Egypt was because of high levels of income inequality in the agricultural sector, largely through land ownership.

However, we should understand the survival strategy of rural Egyptian households in relation to the ocioeconomic settings that differ within rural Egypt. In fact, there are substantial regional variations in poverty and opportunities between and within Lower and Upper Egypt (Ghanem, 2014; World Bank, 2006)[ii]. For instance, Lokshin et al. (2010) revealed significant differences in the rate of growth and poverty dynamics between the two regions. In Upper Egypt, where most of the country’s rural poor live, household expenditure did not grow, despite thesignificant increase in average household expenditures in Egypt as a whole (Lokshin et al., 2010).Thus, the question outstanding for the future development of rural Egypt is: what are the mechanisms underlying income generation in each of the different possible socioeconomic settings? Egyptian villages are under the same condition of being constrained by land and water. Nonetheless, as the Sustainable Rural Livelihoods Framework (Scoones, 1998; 2009) suggests, the endowments of assets (human, financial, and physical and social capital) upon which rural Egyptian households build their livelihoods may differ within rural Egypt. However, because the available data have been limited to official statistics derived from nationwide sample surveys or agglomerated data at the village level, this issue has not yet been the subject of attention[iii].

In this paper, we employ household survey data and examine the income distribution pattern in three villages as a case study to shed light on the factors that influence income generation in rural Egypt. First, we apply decomposition techniques to pinpoint the contribution of farm and nonfarm income to overall household inequality. This is useful to analyze the contribution of different income types to income inequality in each village. Second, we use regression analysis to identify the role of different factors in determining the level of farm and nonfarm income.

The three villages included in this study were part of a 2007 survey and joint research project between the Graduate School of Economics at Hitotsubashi University and the Central Agency for Public Mobilization and Statistics (CAPMAS).[iv] The villages were from different regions within the Nile Basin: Central (or Southern) Lower Egypt, Middle Egypt, and Upper Egypt[v].

 

In rural Egypt, nonfarm employment currently accounts for most employment, and nonfarm employment opportunities continue to increase. However, the empirical evidence on the effect of nonfarm income on rural income inequality is mixed. In fact, a study in a 2006 World Bank report provides contrasting findings to that of Adams (2002). Adams (2002) found that nonfarm income reduces poverty and improves income distribution, because in Egypt where land is highly productive, the poor lack access to land and are thus “pushed” to work in the nonfarm sector. In the 2006 World Bank report, on the other hand, El-Laithy et al. (2003) concluded that households in Upper Egypt that earned higher incomes from nonfarm employment belonged to high-income classes, whereas the poor remained in the agricultural sector.

This contradictory result may be because of the heterogeneity of the nonfarm sector. According to the World Bank, nonfarm opportunities in Upper Egypt are not only limited but also narrow,undiversified, and skewed toward microenterprises (World Bank, 2006, pp. 10-11). Thus, as Sato (2003) pointed out for China, analyzing aggregate nonfarm income at the national level may fail to reveal the differential income–inequality effects according to the different socioeconomic settings.

Studies on other developing countries have shown that nonfarm income affects the rural income distribution pattern differently because the nonfarm activities are diverse (Lanjouw and Feder, 2001). For instance, Zhu and Luo (2006) found that self-employment income in China worsens income inequality, while wage employment has an equalizing effect on income distribution. Adams (2002) obtained similar results for Egypt. Conversely, Senadza (2011) indicated the opposite for rural Ghana, suggesting that nonfarm income increased income inequality. Elsewhere, Stifel (2010) found mixed results for rural Madagascar, mainly because low-income households tended to be engaged in low-return nonfarm activities. As Senadza (2011) noted, these findings perhaps confirm the existence of entry barriers in nonfarm activities. That is, because the poor lack the necessary human or financial capital, they predominantly engage in lower-skill casual wage employment; hence the inequality-reducing effect of wage income.

As for migration, while it is also widely agreed to influence nonfarm activity, there is no consensus on its impact on income distribution. In the case of rural Egypt, labor migration to the oil-producing Arab countries in the 1980s particularly attracted the attention of researchers, with studies such as Adams (1986; 1991) and Richards and Martin (1983) confirming the large revenues derived from emigration as a factor underlying improvements in the consumption levels of rural inhabitants. However, the case studies in this area provide divergent findings and no guidance as to the effect on income distribution.

Thus, it is critical to identify the availability and type of nonfarm employment opportunities, along with the human, financial and other capital that households possess, in order to understand better the rural household strategies used for income generation. In this regard, the sustainable livelihoods framework is a useful framework for analysis (DFID, 1999). This assumes that people may have access to five categories of assets (human, financial, physical, social, and natural) and combine these to achieve their objectives through livelihood strategies. Complex socio-institutional webs as well as the politico-economic environment in which they operate influence these strategies.

In the case of rural Egypt, all villages share a common natural context in that they are all dependent on the Nile River. However, they differ in many aspects, and each has its own unique characteristics. In the case of our three study villages (Abu Senita, Homa, and Awlad Sheykh), although all are dependent on the Nile River and have fertile but scarce land, they differ in terms of market access, sociopolitical environment, and human capital endowment. For example,  Abu  Senita  has  better  human capital (in terms of the level of education) than the two other villages and has more nonfarm employment opportunities available in the locality or in nearby towns. Similar to Awlad Sheykh, Homa has a lower educational level but is located not far from Cairo. Finally, Awlad Sheykh is located in a remote area and has the least nonfarm employment opportunities nearby, but there are many village migrants abroad, as explained in detail later[i].

 

Study area and data collection

The data for the study are from a household survey that included three villages. As shown in Figure 1, Abu Senita is located about 60 kilometers north of Cairo in markaz Bagur, in the Menufiya governorate. Homa is 80 kilometers south of Cairo and 30 kilometers north of Beni Suef city in markaz Wasta, in the Beni Suef governorate[i]. Awlad Sheykh is about 550 kilometers south of Cairo in markaz Dar Salam, in the Sohag governorate.

 

Structure of household income

From a decomposition analysis based on the pseudo-Gini coefficient, the components of total income have the following characteristics (Table 4). To start with, according to the pseudo-Gini coefficient, farm income is unequally distributed in Abu Senita. The pseudo-Gini coefficient for other income (mainly retirement pensions) is also high. These values imply that farm income and social security allowances have much more influence than the other components on overall income inequality in Abu Senita.

 

 

Homa and Awlad Sheykh have contrasting income distribution patterns to Abu Senita. Their nonfarm wage incomes are extremely unequally distributed. Moreover, in Homa and especially Awlad Sheykh, the percentage contributions to overall income inequality are extremely high. This implies that overall income inequality is mainly the result of an unequal distribution of nonfarm wage income.

Level and structure of household income, excluding migrant households

Because emigration could affect the income distribution patterns described in the previous section, in this section, we exclude migrant households before comparing the household income level and structure across the three villages.

Migrant households constitute households in which one or more members work outside Egypt as wage earners. Wage earners working abroad account for 24.4% (Homa) and 64.1% (Awlad  Sheykh)  of  all wage earners, including agricultural wage laborers. The proportions of households with members working abroad are 0.3% in Abu Senita, 19.9% in Homa and 42.8% in Awlad Sheykh. The percentage shares of the wages of these migrant household members in household wage income are 1.5% in Abu Senita, 48.3%  in   Homa,   and   83.9%   in  Awlad  Sheykh. Therefore, emigration is a unique source of nonfarm wage employment and income, especially in Awlad Sheykh.

Table 5 reports the estimation of the income levels excluding migrant households. It shows that the level of household income decreases and the Gini coefficient falls when we exclude migrant households. This change is particularly remarkable in Awlad Sheykh. As a result, the household income level in Awlad Sheykh becomes much lower than in Abu Senita. Therefore, emigration contributes greatly to raising household income. We draw two findings from the Gini coefficients of total income when excluding  migrant  households.

 

 

One is that because the Gini coefficients for Homa and Awlad Sheykh decline, emigration contributes to increasing household income inequality. The second is that the Gini coefficient for Awlad Sheykh remains high when we exclude migrant households. This implies that Awlad Sheykh would have high-income inequality, even without emigration. By contrast, income inequality in Homa would be lower without emigration.

The decomposition analysis based on the pseudo-Gini coefficients reveals the following. As previously pointed out, nonfarm wage income is the single most important source of income contributing to overall income inequality. However, when excluding migrant households from the analysis, nonfarm wage income contributes to decreasing income inequality. By contrast, farm income contributes to increasing income inequality in Awlad Sheykh. The change  in   the  pseudo-Gini  coefficient  of  nonfarm  wage income for Homa is therefore not as marked as that for Awlad Sheykh.

As explained in the previous section, one reason for this difference between Homa and Awlad Sheykh is that migrant households tend to be landless in Awlad Sheykh and landowners in Homa. Hence, excluding migrant households could result in an overestimation of income inequality in Awlad Sheykh and an underestimation in Homa. However, judging from the pseudo-Gini coefficient of farm income, although its evaluation is difficult, we consider inequality in farm incomes as the most important contributor to overall income inequality in Awlad Sheykh. This is not the case for Homa.

This dissimilarity relates to the difference in the distribution of nonfarm wage incomes between the three villages. In fact, the pseudo-Gini coefficient of nonfarm wage  income   is  smaller  than  that  of  total  income  in  all three villages. However, closer examination of nonfarm wage income reveals considerable differences in the income structure in all three villages. The share of nonfarm wage income is largest in Abu Senita, but its pseudo-Gini coefficient is extremely small. By contrast, in the absence of migrant households, Awlad Sheykh has a low share of nonfarm wage income. Even when excluding migrant households, Homa has almost the same share as Abu Senita, but its pseudo-Gini coefficient is higher than that of Abu Senita.

Source of income from nonfarm employment, excluding migrant households

The question is: why is there more uneven distribution of nonfarm wage income in Homa and Awlad Sheykh than in Abu Senita, even when we exclude migrant households? One reason could be that the predominant form of nonfarm employment is government employment in Abu Senita but private sector employment in Homa and Awlad Sheykh, as explained in the previous section.

Table 6 details the farm and nonfarm wage distributions by income category, excluding migrant households. In Abu Senita, income from the government sector accounts for a large share of nonfarm wage income. Although this is also true of Homa and Awlad Sheykh, the share is particularly high in Abu Senita, especially among low-income households.

 

 

Another reason, specifically for Homa, is that even the households with higher incomes tend to rely on nonfarm activities as their main source of income. This is not the case for Abu Senita and Awlad Sheykh when excluding migrant households. In these two villages, households with higher incomes rely on farm income, whereas those with low incomes survive by undertaking nonfarm activities, such as government employees in Abu Senita and agricultural wageworkers or construction workers in Awlad Sheykh.

Results of the participation equation

In this subsection, we first examine the determinants of household participation in farm and nonfarm activities. We then turn our attention to the determinants of income in each of these activities. Tables 7 and 8 present the results of the household participation probit regressions.

 

 

 

We find that household land area plays a positive role in participation in farm activities, and a negative role in participation in nonfarm activities in all of the three villages. With a 1 feddan increase in land, the probability of participating in nonfarm activities decreases by 66.2% in Awlad Sheykh, 69.2% in Homa, and 146.2% in Awlad Sheykh. As the estimated coefficient for land is much larger than that for human capital and the labor force, it is clear that access to land is the most determinant factor for participation in farm activities and for encouraging households to look for alternatives to farming-only. It plays an especially strong role as a pull-and-push factor for  farm and nonfarm activities in Awlad Sheykh, as its coefficient is higher than the other two villages (390.5% higher for farm activities and 146.2% higher for nonfarm activities).

However, the estimated coefficient for the square of land area is significant and negative for participation in farm activities, and vice versa for nonfarm activities, inferring an inverted U-shaped relation between land area and farm activities. This means that households in the highest income category earn income both by renting out their land and by performing nonfarm activities.

We also observe that the coefficient for land area owned is lower in Homa than in the other two villages (140.4% for farm and 69.2% for nonfarm). This conforms to the observation in the previous section that households in Homa in the higher income categories earn nonfarm income. One possible explanation is the low return of land as shown later in the farm income equation. Because land in Homa generates relatively little farm income, Homa households have a relatively strong incentive to take up nonfarm activities.

The results also indicate that education influences the choice of participation in farm and nonfarm activities. In fact, the share of university-level education is negatively and significantly associated with participation in farm activities in all three villages. If the household has more members  with a university education, the probability of participation in farm activities decreases by 1.5% in Abu Senita, 3.5% in Homa, and 1.9% in Awlad Sheykh. However, these are not statistically significant, with the exception of Homa, which has a weak coefficient of 2%.

Regarding the effect of life cycle as measured by the age of the household head, this is positively and significantly associated with participation in farm activities in Abu Senita, with an additional year of age of the household head increasing the probability of participating in farm activities by 10.4%. This implies that farm activity is largely a life cycle phenomenon, commenced at an older age after performing nonfarm activities. We do not observe this same pattern in the two other villages, as the estimated coefficients for the age of the household head is not statistically significant. One possible interpretation is that there are more nonfarm employment opportunities, specifically in the government sector, in Abu Senita, so that the household heads commence farm activities only when they reach retirement age or when inheriting land from their fathers. The age of the household head is not associated with participation in nonfarm activities, because these can start while the household member is living with a father who is the household head.

The probability of participation in both farm and  nonfarm activities increases with labor, as the estimated coefficients for both labor force rate and household size are positive and statistically significant. The estimated coefficients for the percentage of males in farm labor participation are negative and statistically significant in both Homa and Awlad Sheykh (-2.2 and -0.6%, respectively). This suggests that any additional agricultural labor input is mainly female. This reflects the clear gender division of labor in these two villages, as widely observed in Upper Egypt; men engage in nonfarm activities, and women engage in farm activities.

As expected, migration abroad is highly correlated with participation in farm activities, with the associated probability of participation in farm activities decreasing by 83% in Abu Senita, 82.6% in Homa, and 42.4% in Awlad Sheykh. Nonfarm self-employment also has a significantly negative effect on participation in farm activities in Awlad Sheykh, with the probability decreasing by -48.9% if a household engages in nonfarm self-employment.

Results of farm and nonfarm income equations

Tables 9 and 10 present income regressions for  farm  and nonfarm activities, respectively. The samples used in each of the regressions include only households engaged in the activity, so they allow us to assess which factors are associated with higher or lower income within each activity. In each case, we control sample selection bias by including the Inverse Mills Ratio computed from the corresponding participation equation shown in Table 3 as an additional regressor.

 

 

 

We find that the amount of cultivated land owned has a strongly positive correlation with household income in the three villages. With the exception of migration abroad, land displays the highest coefficient in the income equations[i]. The coefficient for land owned displays the magnitude of the effect of land, such that in Abu Senita and Awlad Sheykh, the addition of 1 feddan of land increases farm income by 237 and 391%, respectively. This result is identical to Adam’s (2002) finding that land is important for income distribution in rural Egypt. However, the coefficient for land area differs between the three villages. In fact, it is much lower in Homa than in the other two villages, such that the addition of 1 feddan land increases farm income by only 140% in Homa. This may relate to Homa’s relatively low level of productivity, which  may  be  because of differences in cropping patterns or water scarcity[ii].

Land area, either owned or rented, is uncorrelated with nonfarm income, except for Awlad Sheykh. In Awlad Sheykh, a 1 feddan increase in land decreases nonfarm income by 92%. This reflects the strong effect of land on whether households take up farm or nonfarm activities, as mentioned in the results of the participation equation.

The estimated effects of the labor force rate and the gender composition of the labor force are as expected, as in the results for the participation equation. The level of education has no significant effect on farm income but significantly positive effects on nonfarm income in Abu Senita and Homa. However, the coefficient for the household share of education at a university level is very low. Being a university graduate increases nonfarm income by only 0.8% in Abu Senita, 1.4% in Homa, and only 0.4% and statistically insignificantly in Awlad Sheykh. This may be because the opportunity for nonfarm employment with high educational return is very limited in village[iii], where the labor force is relatively homogeneous and mostly employed in construction, which has low entry barriers.

Having  a  migrant   in   a   household   has   an  extremely important effect on nonfarm income. If a household has a migrant abroad, nonfarm income increases by 76.7% in Homa and 218% Awlad Sheykh. It has a lower effect in Homa, which may relate to the differences in the places of emigration. In fact, migrants in Homa tend to work in Jordan and Libya, where we assume that they earn a lower wage than in the UAE, where the majority of migrants from Awlad Sheykh work. Nonfarm self- employment greatly influences nonfarm income in Abu Senita. In fact, it is the highest income-generating factor, raising nonfarm income by 34.4% if the household is engaged in nonfarm self-employment.

The Inverse Mills Ratio reflects the correlation between the unobserved terms in the participation and the income equations. We first note that the coefficient on the Inverse Mills Ratio in the nonfarm income equation is not significant. Therefore, selectivity is not present, so we could safely estimate this equation without the Inverse Mils Ratio. However, the Inverse Mills Ratio in the nonfarm income equation is significant and positive in Abu Senita and specifically in Awlad Sheykh. Given that the Inverse Mills Ratio is inversely related to the selection criterion to “participate in nonfarm activities,” the negative effect thus implies that these participating households have lower nonfarm income than a purely farming household.

 

This paper examined household income structure and its determinants in three villages as a case study of household income distribution in rural Egypt. A question is why farm income is so unevenly distributed and why it makes such a large contribution to overall income inequality in Abu Senita and Awlad Sheykh when we excluded migrant households.

The results confirm the findings of Adams (2002) that land assets continue to be the major determinant of household incomes in rural Egypt. In fact, the land distribution is highly unequal in all three of the villages that we examined, and as a result, landholders form the high-income classes, whereas landless households, forced into the nonfarm sector, comprise the low-income classes. The observed income distribution pattern in the three villages confirms that access to land, as Adams (2002) has argued, is the key determinant of income in a land-scarce labor-rich setting.

However, the extent of this effect differs between the three villages according to the level of nonfarm employment opportunities[i]. Opportunities for nonfarm wage employment are best in Abu Senita, followed by Homa, and worst in Awlad Sheykh, excluding migration abroad. However, it is the type of market development that most affects the pattern of income distribution. In Abu Senita, where most of the labor force works in the govern- ment sector, incomes are quite equally distributed. Government employment is the largest source of income, and this contributes greatly to reducing income inequality in Abu Senita.

In  Homa,   private   sector  income  constitutes  the  main source of nonfarm wage income. However, the distribution of this income is highly unequal, likely because there are more households in the high-income category engaged in nonfarm activities. Another reason is the linkage between employment in Homa and Cairo’s unskilled construction labor market. In general, the income distribution of private sector employment is more unequal than government sector income because it involves much more diverse activities, in that the labor and other resource requirements and returns are in no way homogeneous[ii].

Awlad Sheykh has the fewest nonfarm employment opportunities, and hence, the ownership of land largely determines the income structure (in the absence of outward migration). In addition, our analysis confirms the importance of emigration for rural income distribution. In fact, emigration contributed to leveling out household income levels in Homa and especially Awlad Sheykh. In Awlad Sheykh, emigration contributed to lowering income inequality, and this more than offsets the effect of land assets on income distribution. This is because most migrant households are landless, and the income generated from emigration is relatively high.

 

In summary, this   paper   found   that   there is no simple correlation between the development of the nonfarm sector, land distribution, and household income in rural Egypt. Although land plays a crucial role throughout rural Egypt because of dependence on the Nile, the mechanisms behind income generation and income distribution differ according to the specific socioeconomic setting. If there is no opportunity for nonfarm activity in a land-scarce and labor-abundant setting, and if outward migration is absent, income distribution will be determined solely by land ownership and therefore quite unequal. If the development of the nonfarm sector takes place in the government sector, as in Abu Senita, income distribution is much more egalitarian. However, given the current economic orientation of a liberal economy, this is unlikely to develop. If land has little effect on income generation, and nonfarm opportunities are limited to sectors with low entry barriers, such as the construction sector in Homa, income distribution may also be equal but associated with low household income.

With regard to the issue of poverty, which is beyond the scope of this article, the findings here suggest that the reduction of poverty depends upon the volume of nonfarm opportunities. This is because as Adams (2002) pointed out for Egypt as a whole, poor households mainly participate in nonfarm activities in rural Egypt. However, it remains unclear whether poor households benefit from nonfarm opportunities in the same way across rural Egypt, the poorest households typically being agricultural wageworkers as in Homa and Awlad Sheykh. This could be one of the possible reasons accounting for the difference in poverty trends in Lower and Upper Egypt.

In future research, it would be necessary to conduct additional case studies in other villages with unlike socioeconomic settings in order to develop these arguments on the relationship between land, nonfarm opportunities, and income in rural Egypt, and to identify different policies for pro-poor development in rural Egypt.

The author has not declared any conflict of interests.