of the households who took the benefit of it. So, more stress on NREGP work should be imposed on private land which is very effective for productive asset creation because the assets created in private land can be well maintained. Local panchayat should encourage the poor farm households to take this advantage. This can help the benefitted farm households to get an alternative source of income through fish farming which is comparatively less risky and have high demand of its’ product in our study region. This will also improve the irrigation facility as well as productivity of land. They can cultivate different horticultural product suitable for agro- climatic condition of the land which will also help them to earn some extra net farm income and encourage the marginal farm households to continue agricultural activities.
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precipitation. Strong positive impact of spring and fall temperatures are found in net farm revenue, while as expected, summer temperature has negative impact on farm value. However, the negative impact of winter temperature on farm value is a bit surprising at least in this data set. The intuition behind the negative impact of winter temperature may be due to low productive crops such as wheat planted in the winter season. The productivity of winter crop may be low in the mountain and hilly region due to high cold. This result needs to interpret with caution. The findings of other variables show mixed results. For instance, higher farm output is observed on irrigated farmland compared to non-irrigated farm land, but productivity is high on small farm than the large farm, showing inverse farm size and productivity relationship. Farmers who obtained credit increase farm income, showing the common problems in low-income countries where credit is one of the constraints for small farm holders. The coefficient of head’s education is significant and negative, implying negatively related to net farm income. This result seems to be a bit surprising, probably educated people preferred to work in the off-farm sector due to low wages and returns in the agricultural sector. Moreover, other variables such as sex and age of household head, distance to input markets and family size are not significant at any required level, indicating no impact of these variables on farm value at least in this model and data set.
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International Research Journal of Human Resources and Social Sciences (IRJHRSS) 221 | P a g e Table 2 showed the cost and returns associated with cassava production in the study area. The result showed that the total variable cost was N91,630 Accounting for 89.0% of the total cost of cassava production. The total fixed cost component of cassava production stood at N11,380 accounting for 11.0% of the total cost (TC) of cassava production. The total return/revenue (TR) accruing from the farm business was N325,700. The net farm income (NFI) was N222,690.00 per hectare, this result confirms that cassava production in the study area was profitable. These findings agreed with that of Nzech-Emeka and Ugwu (2014) who reported a net farm income of N347,500.00 per hectare of cassava production in Akoko North- West LGA of Ondo State, Nigeria. The profitability Index (PI) was 0.68, suggesting that for every naira earned as revenue, 68 kobo returned to cassava farmer as net income. The rate of return on investment (IPR) was estimated at 216.2%. Therefore, for every naira invested on cassava producer. Olukosi and Erhabor (1988) suggested that the higher the rate of return on investment the better the farm business. The capital turnover (CTO) per hectare is greater than 1 (3.16), indicating that for every naira invested per hectare of cassava production about N3.16 Kobo returned as revenue to the producers.
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Smallholder farmers face various challenges in production one of them being inaccessibility to credit. This study specifically sought to identify household socio-economic characteristics and institutional requirements influencing access to credit among smallholder farmers in Nyandarua District. The study used a Logit model. Both quantitative and qualitative data were acquired from primary and secondary sources. Primary data was collected using questionnaires through a survey design. A sample of 264 smallholder farmers was selected using stratified, multi-stage random sampling techniques. Data was analyzed using descriptive statistics and regression analysis using Statistical Package for Social Sciences (SPSS). The study established that socio-economic constraints such as age, gender, household size, farm income, collateral and awareness are critical determinants of access to credit. The study concludes that household socio-economic characteristics do influence access to credit. Key recommendations made include the need by government to deal with bureaucracies involved in land registration to benefit majority of smallholder farmers who remain insecure in the land they use without proof of ownership and also to make easier the registration of lease certificates for those who do not own land and use land on leasehold tenure system. Financial institutions should also put in place less stringent credit requirements and reduce credit costs especially interest rates to make credit more affordable.
This study focuses on the impact of micro-credit upon the livelihood of rural households based on empirical study over 549 stakeholders of SHGs (Self Help Groups) in Hooghly district of West Bengal State in India. One of the distinct areas of the study concerns with the comparative analysis of different rural enterprises propagated through micro-credit. Another objective of this study has been to compare and contrast income as well as savings position of the sample households before receiving financial credit. Additionally, this study attempted to discriminate between high-performing and low-performing stake-holders on the basis of selected socio-economic indicators.
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From a visual inspection of Figure 3 we selected the events which clearly influenced the pattern of this relationship. Until the 1980s, the personal saving rate increased while the wealth-to-disposable-income- ratio plunged. These trends inverted after the beginning of the 1980s, when there was a change in the slope of both the saving rate and total net wealth ratio. Since the beginning of the 1990s, the wealth-saving relationship has “relaxed”: the saving rate declined very steeply whereas the wealth ratio showed a less conclusive pattern. A view put forward by some authors (e.g. MacDonald et al., 2006) and practitioners is that the rapid increase in the conversion of homeowners equity to cash through borrowing in the home mortgage market (a phenomenon known as MEW) caused the rapid decline in the saving rate during the 1990s. The last 10 years appear to be the most problematic in terms of this relationship: the saving rate is characterised by its strong volatility and the net wealth ratio features accentuated peaks and troughs. This dynamic is due to numerous events that affected the US economy during this period (e.g. the rise and fall in the stock market, the housing market bubble and the financial crisis).
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The analysis is based on the available statistical data on agriculture of the EU countries. For international comparisons, we used the Eurostat database from 2016 and the data for the years 2004–2013 of the EU’s Farm Accountancy Data Network (FADN), which describe a series of indicators for an average farm in individual countries, including the V4 countries. We focused mainly on comparing the performance and production factors of farms of the V4 countries on one hand and on the results of the EU-27 farms and Germany on the other hand. The benchmark value also includes the EU-27 or the EU-28 average. We also used the information provided in annual reports on agriculture and food published by the Ministry of Agriculture and Regional Development of the Slovak Republic, which also features some in- ternational reviews. We draw additional information from scientific and expert literary sources.
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Farm operators with a college education and whose health insurance is ob- tained from sources “other” than from an “off-farm” employer are found with higher returns to schooling if their off-farm wages are in the two quantiles above the middle of the off-farm wage distribution. The fact that not all of these col- lege-educated farmers across the off-farm wage distribution are shown to benefit from higher returns to their higher education may be related to the type of off-farm occupation that was held by these farmers. Findings by , which may help in explaining the lackluster off-farm wage premium for this group of far- mers, point to a flattening in the growth of the wage gap that was witnessed in the U.S. between 2010 and 2015 between workers with a college or graduate de- gree and those with only a high school. Valletta  attributes this lack of growth in higher educational returns to two factors. The first of these factors is “polarization”, which is interpreted as a shift away from middle-skilled careers driven mainly by technological change. The second cause is “skill downgrading”, which is construed as a general weakening in the demand for advanced cognitive skills. In contrast, farm operators whose health insurance is provided by an “employer” seem to benefit the most from having a college education as the educational impact on off-farm wages seems to have higher wage premia across all of the off-farm wage quantiles with the exception of the lowest and highest quantiles. In that farmers in this category who benefit from the higher educa- tional returns may be explained by both the level of their higher education and the type of occupation that they hold. Valletta  notes that the higher educa- tion wage premium, which may be relevant to farmers in this category of health insurance coverage, results from the rising demand for the most highly educated individuals in careers that require extensive non-routine cognitive skills.
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The analysis of a time series carried out by the Swiss Farm Accountancy Data Network (FADN) showed that, over a long period, family farm income is consider- ably lower than private consumption (Meier 2005). If we include opportunity costs of own land, labour and capital, the gap even increased. Author Meier (2005) explained the income gap by the difference between true opportunity costs and estimated opportunity costs, non-economic factors that allow individuals to become or to continue as farmers (tradition, affection to the pro- fession, independence) and the difference between income and cash flow. The off-farm income is really a crucial issue when estimating farm economic viability. Results of the Eurostat Farm Structure Survey 2013 provides interesting findings. “Around 22 million people worked regularly in agriculture in the EU-28, but only 16.4% of them worked on a farm full time. The pro- portion varied from slightly over 50% in the Czech Republic, France, Luxembourg and Belgium to less than 10% in Malta, Austria and Cyprus. Romania had the lowest proportion, with only 1.5% of people engaged in agricultural work full-time” (Forti 2017).
Equivalisation: Equivalising income takes into account economies of scale and household size. It enables comparisons to be made across different family types, albeit in an imperfect way. The equivalence scale used is the OECD scale in which a single person with no children is taken as the benchmark. Secondary adults contribute 0.5 to the scale, meaning that a couple needs 50% more income than a single in order to be equally well off. Children aged 13 and under contribute 0.3 to the scale and older children contribute 0.5. See Section 3.1 for a more detailed discussion.
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Nigeria is a nation that is endowed with multifarious and multitudinous resources - both human and material. However, there is still much poverty among both rural and urban dwellers in Nigeria, due to high unemployment rate and lack of incomes. To achieve the Sustainable Development Goals (SDGs) of ending all form of poverty and hunger by 2030, it is projected that more than 22 million people must achieve food security every year. This could only be possible if factors influencing their income level are empirically determined. In this regards, the aim of this paper is to determine the socio-economic factors influencing farm income among urban farmers in Niger Delta region of Nigeria. This, it is hoped, will inform pro poor to develop better strategies of reducing poverty and food insecurity to meet the Sustainable Development Goals (SDGs) 1 and 2 which respectively, stresses the need to end poverty in all its forms everywhere; end hunger, achieve food security and improve nutrition and promote sustainable agriculture.
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This theoretical issue was subjected to empirical testing using data from a number of diverse economies and across time. The results have shown that the difference regarding the two types of prices is, in fact, minimal and also many researchers found minimal differences of estimated prices from observed market prices. In the first empirical studies, the closeness of the three types of prices was tested using simple regressions and statistics of deviations all of which were fraught with biases for their dependence on the adopted normalization condition and chosen numéraire (Shaikh, 1984, Ochoa, 1984). Later studies (Tsoulfidis and Maniatis, 2002; Tsoulfidis and Rieu, 2006 and Tsoulfidis, 2008) have shown that the normalization condition does not impact so much on the actual proximity of estimated prices against market prices. In fact, theoretically it has been shown (Mariolis and Tsoulfidis, 2010 and 2016, ch. 3) that if the relative rate of profit (i.e., the ratio of the economy-wide average rate of profit to the maximum rate of profit) is small, smaller than say fifty percent, typically found in a number of empirical studies, all measures of deviation biased or not are bound to give quite similar results. In these empirical studies, the relative rate of profit is approximated by the ratio of net operating surplus to net value added.
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Virginia V. Shue, Taxation - Federal Income Tax - The portion of the net operating loss deduction not absorbed in the "alternative" tax compensation may be carried forward to another year, notwithstanding that it was considered in making the tentative tax computation under "regular" method. Chartier Real Estate Co. v. Commissioner (1st Cir. 1970)., 8 S AN D IEGO L. R EV . 442 (1971).
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Overall in the village, male devotes maximum time in non-farm self-employment category and female in farm self employment category. Maximum wage was also from the respective categories mentioned. Under farm source self-employment category in M.Lhavom both male and female devotes maximum time in paddy cultivation (26.15 and 22.5 man days respectively). Average wage per annum per person was ` 7218.75 and ` 6187.50 respectively. In M. Lhavom male spend 200 man-days as self employed under non-farm earning an average wage of ` 86155.56 per annum per person and female in hotelling activity (300 man-days) earning ` 113333.33. It can be concluded that in the hill village, male and female are mainly self-employed under non-farm sector. Maximum wage was also from the identified categories of employment.
It is how to choose the resources of money to fund the investment in a way enables the investors for earning the barest income by taking the advantage of loans that have fixed coast, this will save the investors from that risks of over borrowing ( Al-Amiri 9:2010). Funding decision is also defined by Al- Amiri as selecting a proper combination for funding that consists of short termed funding, long termed funding, estate funding, property financing and debt financing all these will lead to least coast and huge wealth. (Al-Amri 25: 2013)
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The variable costs are specific to an enterprise and vary with its scale i.e., vari- able cost has direct relationship with the level of output. The variable cost includes the cost incurred on: day old chicks; feed; vaccination; energy charges; litter; lime and medication; wages of casual labor; and, others day to day expenditure of the farm. These costs are known as working capital required for the production cycle (Nix, 1979). Total fixed costs (TFC) are those costs incurred which don’t change when output, changes and therefore no influence on production decisions in the short run. Total variable cost (TVC) is the cost of vari- able inputs used in production. They change directly with the level of production. Gross margin is the difference between total revenue and total variable cost.
The population’s standard of living is often associ- ated with the financial situation of people; however, it is also important to consider the cultural, social, and moral dimensions. Despite some inherent insuf- ficiencies, the living standard is commonly measured by the means of GDP per capita. It is not difficult to find out how specific industries and sectors con- tribute to the GDP, yet it is rather hard to describe what the living conditions are of households living on income in the given industry or sector that is included in the GDP. Agriculture is no exception, having undergone significant changes in the Czech Republic over the past decades. The accession to the EU, on the one hand, has brought significantly more options to receive various subsidies; on the other hand, it has also resulted in the ever inten- sif ying competition in the single market. These facts have undoubtedly influenced the lives of farm households. The main purpose of this article is to establish the living conditions under which Czech farm households have lived after the Czech Republic acceded to the EU. The EU population’s satisfaction with life has been surveyed under the COBEREN international project.
that a number of different support programmes were developed over time. Note that all abbreviations for the direct payment programmes are explained below Figure 2. In the pre-reform period, the roughage animal payments for farmers in the hill and mountain regions (RAUhill) and the arable payments (Arable) were the most important support programmes at farm-level. However, market prices were high and single payments made up only between 6% and 2% of the total household income. With the agricultural policy reform in 1992, the market support was re- duced and the area-based payments (Area) became the most important direct payment programme, making up about 16% of the total household income in 1995. In addition, the payments for the ecological compensation area (Eco), the extensive crop produc- tion (Extenso) and for the integrated production (the latter is included in the category Rest in Figure 2) were introduced for which farmers could voluntarily apply (e.g. Finger 2010). However, these payments made up only 11% of the total household income,
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The cost of cultivation of ginger was directly related with the size of the holding. It was ` 4, 54,991.62 on small farms and ` 4, 94,501.03 on large farms. The cost of human labour, seeds, manures and rental value of owned land accounted for more than 80 per cent of the total cost. The cost of producing a quintal of ginger exhibited inverse relationship with the size of the holding as it was ` 1643.19 on large farms and ` 1692.86 on small farms. A quintal of ginger yielded a net income of ` 1013.10 and ` 1105.98 on small and large farms respectively. The returns per rupee of expenditure were ` 0.60 and ` 0.67 on small and large farms.
Agril. crops 159.35 124.17 82.83 194.67 273.63 225.23 322.78 246.71 131.77 200.28 Main crops 76.23 44.11 35.22 118.52 152.25 127.87 202.10 170.95 89.86 116.89 Other crops 83.13 80.06 47.61 76.16 121.39 97.36 120.69 75.76 41.92 83.39 Fish 115.70 23.47 8.54 31.34 111.73 67.72 49.43 13.14 46.17 55.45 Livestock 27.43 17.81 22.35 51.76 86.61 57.25 76.48 35.67 26.20 48.60 Non-Ag. profit 260.29 293.63 212.95 304.83 254.71 197.39 338.22 171.49 292.70 262.92 Remittance 138.41 381.12 624.89 225.28 107.64 101.84 77.30 87.37 259.51 212.90 Employment 487.70 676.42 464.06 590.46 542.42 436.33 669.77 582.29 642.59 560.94 Other income 64.65 8.41 90.96 38.22 31.36 32.01 190.70 15.53 60.98 57.61 Total 1253.53 1525.04 1506.60 1436.53 1408.12 1117.77 1724.70 1152.23 1459.92 1398.71 Per-capita 308.93 336.75 378.35 362.17 369.84 307.63 423.63 308.76 301.63 347.39
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