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Article

Quantity Measurement Cost and Reliability of Cereal Commodity Trade: Evidence from Ethiopia

1
College of Economics and Management, Huazhong Agricultural University, No. 1 Shizishan Street, Hongshan District, Wuhan 430070, China
2
School of Commerce, Addis Ababa University, Addis Ababa P.O. Box 3131, Ethiopia
3
College of Social Sciences and Humanities, Ambo University, Ambo P.O. Box 19, Ethiopia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(6), 1521; https://doi.org/10.3390/su11061521
Submission received: 31 January 2019 / Revised: 27 February 2019 / Accepted: 8 March 2019 / Published: 13 March 2019
(This article belongs to the Section Sustainable Urban and Rural Development)

Abstract

:
Measurement reliability is an undervalued aspect of local agricultural marketplace organizations. There are also gaps in identifying the extent of cereal commodity trade measurement costs. Therefore, the aim of this paper is to estimate the magnitude of cereals trade quantity measurement cost caused by instrument error and unreliability in the context of the local marketplace in Ethiopia. In this regard, a survey was conducted in six different districts’ marketplaces (n = 602) of Oromia regional state. In addition to administered structured questionnaires, site mass measurement calibration was employed. The survey data were analysed using an independent samples t-test, one sample t-test and analysis of variance. According to the findings, the actual value measurement means of the quantity of most local units of the farmers were greater than small traders. The independent t-test result indicated that the average values of the quantity of the majority of units of measurement between farmers and small traders were varied significantly. Estimated average measurement cost of the farmers and small trader which occurred due to measuring instruments’ error and unreliability were higher for a sack, bowl and glass units compared with other units of measurement. This study demonstrates that homogeneity in measurement, market regulatory policy and institutions that aid cereals trade have an indispensable role to reduce measurement costs thereby ensure equitable exchange.

1. Introduction

Reliable quantity measurement system has a paramount contribution for economic transaction certainly for reducing the costly transfer of wealth. In Ethiopia, the district level agricultural marketplace is known for trading numerous products. Most people are currently relying on the local marketplace sales location for their agricultural output trade [1]. However, the majority of economic agents are exchanging the cereal commodity in local marketplace by employing multiple, non-uniform and incoherent units of measurement. The local units of quantity measurement are ranging from volumetric (glass, various can and cup, jug and bowl) to weight measures (sack) and mechanical weight balance. In addition, the diverse measuring instrument of the same kind, method of measuring and ways of using instruments are widely used to undertake cereals trade [2]. Moreover, cereal quantity measurement behaviour of trading parties demonstrates the existence of unreliability. The unreliability in measurement behaviours created the cost of transaction, measurement, social capital and two-hand palm cereal gift in contexts of Ethiopia local marketplace [2]. In general, the heterogeneity of the measurement system creates considerable measurement costs, exchange inequity and market disintegration [3,4,5,6,7,8].
Nevertheless, the extent of measurement costs happening due to diverse units in local marketplaces are not yet investigated. The quantity measurement reliability of cereal transaction between marketplace exchange parties over periodic market days, repeated measurement and amongst districts are not known as well. Furthermore, there has been inadequate perception about who is gaining and who is losing in the process of measurement of cereal commodity trade. Generally, it is not simple to create a clear line of agreement about the impact of complex and conventional measurement system upon the local economy of a given nation.
Scholars suggest standardized measures; the extension of Ronald Coase’s arguments of the nature of the firm to the nature of the market to minimize the transaction costs [9,10,11,12,13,14]. First, since the introduction of metric and imperial measures, the emphasis of many countries was on metrological standardization [15]. But the diffusion of metric and imperial units of measurement to developing countries was not successful for a long period of time [16]. Ethiopia government has been implementing metric units of measurement since 1963 [16]. Nevertheless, the diverse kind of local measures are still widely in use in the rural parts of the country. Furthermore, the positive effect of standardized measures upon local economy and measurement problems management are yet unidentified.
In addition, the extension of Coase’s arguments of the nature of the firm to the nature of the market as an organization has an implication for rural trading parties to economize transaction cost. In this regard, the work of Coase is contributing that the economic transaction coordinated through the institution firm can better save marketing costs than the same transaction organized through the invisible hand of the market institution [13]. Indeed, a particular market institution like a firm institution that lessen transaction costs are essential for the marketplace trading system. However, over 80 years, the contribution of Coase’s essay, costs of using the price mechanism, has still not acquired vital academic consideration to solve the real economic growth obstacles such as local marketplace measurement problems.
Therefore, as a solution to the aforementioned disputes, the study aimed at estimating the extent of cereals quantity measurement costs caused by error and unreliability of measuring units in local marketplace of Ethiopia. The study also investigate the quantity measurement reliability from consistency, conformity and uniformity perspectives. In general, the study has empirical and practical contribution to new institutional economics theory. First, few studies have attempted to estimate the level of transaction costs due to the difficulty of measuring these costs [17,18]. Add more, many studies conducted so far overlooked the measurement behaviour and related cost in their definition of transaction cost [19,20]. This study is, therefore, pinpointing that the measurement cost need to be considered as part of transaction cost estimation. Second, it assists as way out to manage measurement cost thereby stakeholders of local marketplace maximize cereal trade gains.

2. Literature and Conceptual Framework

Literally, sources of measurement problems are viewed from two controversial angles. The causes of the measurement problems at the transactional level are emanating from the measurement error [8]. The premise behind this view is that the measurement cost is occurred due to measuring instrument bias or random errors. Hence, managing of measurement issues has been claimed from the management of error or instrument bias predominantly by targeting a given specific measurement unit. On the other hand, measurement reliability or sameness argue that error approach is a narrower and simplistic method to address the entire sources of measurement problems [21,22]. Thus, the originator of the sameness approach, Velker, suggests reliability dimensions (consistency, conformity and uniformity) in his method for addressing the holistic problems of measurement and to fill the gap of the error method. Indeed, measurement costs are happening both in the case of instrument bias and/or the unreliability of measurement system.

3. Methods and Materials

3.1. The Study Site

The aggregate cereal production of the Oromia region alone comprised about 44.5 percent of Ethiopia in 2015 [23]. East and West Shoa zones were chosen among six popular zones in cereal commodity production in the region [24] (Figure 1). Three study areas were taken from each zone. The selected areas are known in producing cereals. Based on the data obtained from the zonal administration, the total post-harvest cereals production of East and West Shoa Zone in 2015 was 7,965,315 and 15,652,419 quintals (1 quintal is equivalent to 100 kg), respectively [25]. Of which, 57.49 and 21.28 percent were produced in Adea, Gimbichu and Lume (East Shoa) and Dendi, Bako-Tibe and Adea Berga (West Shoa) districts, respectively.

3.2. Sampling Methods

The study areas were selected using purposive and random sampling methods. The inclusion criteria were the relative volume of cereals production [23] and proximity to the capital city of the country. Furthermore, cereal commodity consumption expenditures of the household [26] and the cereal trade centre factor were considered. To this end, six district marketplaces—Bako-tibe, Dendi, Adea-Berga, Adea, Gimbichu and Lume—were selected for the purpose of the study. The sample of the farmer was determined by using a supposition of 5 percent level of precision; 95 percent level of confidence; 50 percent degree of variability; and the total size of the population [27]. Accordingly, the total sample size (n = 400) was distributed into six districts’ marketplace depending on their respective number of the farmer households. As a result, the sample size for Dendi, Bako-tibe, Adea-Berga, Adea, Gimbichu and Lume marketplaces were 91, 67, 65, 67, 56 and 54, respectively. In addition, the small traders buying cereals from the same marketplace or another in order to re-sell them to various parties were included. The small trader population was, therefore, any small trading agent selling cereals in each chosen marketplace from 10 July 10 to 2 September 2018. The small trading agent who was buying from the nominated marketplace and selling to non-selected districts were not considered. Hence, 202 (n = 202) small traders from Dendi (43), Bako-Tibe (33), Adea-Berga (31), Adea (33), Gimbichu (31) and Lume (31) were addressed, respectively.

3.3. Data Type and Methods of Collection

In this study, both primary and secondary data were employed. The primary data was collected using the survey method through administered structured questionnaires from 10 July to 2 September, 2018. The survey was mainly focused on the socio-economic characteristics of farmers and small traders, type and variety of cereal traded, the total supply of cereal marketed and marketplace conversion convention between measuring instruments and kilogram unit. Besides, measuring instruments used for transaction, cereals amount of measuring units (kilogram), exchange price of cereals quantity of each unit were emphasized. To measure and record the actual value of the amount of quantity of each instrument, site calibration for the mass measurement in accordance with international system mass unit standard was performed in collaboration with National Metrology Institute of Ethiopia (Table A4). Moreover, the secondary data was obtained from journals, books, working papers and official reports. The district level agricultural marketplace observation was farther conducted to supplement the survey.

3.4. Methods of Data Analysis

The data were analysed by using Statistical Package for Social Sciences (SPSS version 23), (IBM corporation, New York, NY, USA), particularly through descriptive statistics such as percentage, independent and one samples t-test and analysis of variance. The OriginPro 9.1 (OriginLab Corporation, Guangzhou, China) data analysis and graphing software were also employed to illustrate market conversion convention between measuring instruments and kilogram unit and actual values distribution of the quantity of measuring units.

3.5. Measurement Cost Estimation

The study applied two methods to estimate cereals trade measurement costs magnitude of the farmers and small traders in each marketplace. At the district level, agricultural market organization structure of cereal commodity trade is hierarchical. There are two to three marketplaces (MP) in each district. Each marketplace has two to three measurement unit-based market divisions (MD) (Figure 2).
In this structure, each marketplace instrument bias and unreliability of cereal quantity measurement were evaluated for both farmers and small traders. The difference between the actual (real) and ideal (true) value of the amount of individual instrument was assessed to estimate error related measurement cost size. In this undertaking, the actual measurement value average of local units was computed and served as the ideal value. Because most local units used for commodity trade did not have its own scientific standard. In this regard, two things were identified during preliminary study. First, the market convention between quantity of local units and kilogram were studied. Second, the actual value of quantity of each units (in kilogram) was measured using field calibration system. Thus, based on this pilot study, the market conversion convention between measuring instruments and kilogram unit was not used. Because, the market convention and actual measurement values of quantity of most local units were substantially different. Thereby, the difference between the actual value of the quantity of measuring units and their average was considered as instruments’ error. However, the study applied the range conversion convention between the sack and kilogram as an ideal value; specifically to compute the commodity amount of a sack unit’s actual value mean using a one samples t-test. For instance, most markets agreed that the teff quantity of a sack unit is equivalent to 74–80 kg. Such a range conversion convention is comparatively similar to the actual value compared to a fixed conversion volume between other local instruments and kilogram units. Besides, the market conversion convention of each instrument was considered as a baseline to estimate the measurement cost over trading parties’ total cereals supply. According to the pilot study, there was no common convention of local units to a metric unit. This study, thus, used the conversion agreement of local units agreed by the majority of respondents. In general, the actual measurement average computation method was regarded as an ideal value of local units to estimate error related measurement cost using the Figure 3 framework.
Measuring instruments’ unreliability cost were measured from consistency, conformity and uniformity dimension. Measurement consistency deal whether the measurements remained consistent over time, whether measurement made in a given day is consistent with measurements made a day ago, a week ago, a month ago or a year ago [21]. For local marketplace context, markets are conducted once or twice or more in a week. Every week, the nature of one day market is very attractive (hot), in which farmers relatively supply more products and highly populated market day than that of slack market day. Hence, evaluating measurement unit consistency between a hot and slack market day of the same marketplace is essential.
On the other hand, the measurement is precise, if measurements over repeated observation closely resembled an acceptable or pre-specified value [21]. In the rural marketplace, unlike measurement protocols are existing concerning ways of using instruments and methods of measuring. In few marketplace, people handle the bottom and top edge of the volumetric instrument with their two fingers to measure. People are not necessarily heaping mass over the rear of volumetric instruments in this context. Elsewhere, people handle the centre or upper of measures using two hands after heaping mass on the top rear of instruments. Further, common applied measuring ways are heaping of the cereal over the rear of measures. However, the quantity of heaped cereals over the rear of instruments might not be the same in amount. In this case, the source of variation is not due to instrument error but due to confusion or disagreement, regarding the measurements of quantity. Away from these realities, no one can be certain whether over repeated measurement clustered to some average value. Here, the extent to which measurement conform to some pre-specified value was evaluated in the marketplace by testing over repeated measurement of each farmer and small trader.
Additionally, two to three market divisions are structured within a marketplace based on measuring instruments being used for trade. In a division, for instance, a teff cereal measured by can by one party may not be equivalent to another when it is measured and converted into kilogrammes. In another assertion, when merchant use multiple measuring instruments or local norms are unclear, the question of unreliability arise. At this time, nothing is known in all hierarchy of marketplace whether measurements are uniform in between farmers and small traders (buyers). Based on these facts, it is very essential to look at measurement uniformity among the actors. The detail study framework applied for the measurement reliability of districts’ marketplace is depicted in Figure 4 framework.

4. Results and Discussion

This section comprises the demographic characteristics of trading parties, type and variety of traded items and marketplace conversion convention and quantity of local units’ actual values distribution. In addition, the mean comparison of the actual value of the cereals number of units of measurement was subsequently presented. In the last part of the section, the measuring instruments’ error and unreliability associated costs magnitude of both farmers and small traders was computed and discussed.

4.1. Demographic Characteristics of Respondents

The study found that there was high female farmer percentage in Dendi, Bako Tibe and Adea Berga (Table 1a). In contrast, there was high percentage of male farmers in Gimbichu, Adea and Lume (Table 1b). As indicated in Table 1b, most small traders in all local marketplaces were female. Besides, the age and education of most farmers and small trade participants fall between the range of 20–40 and 0–4 schooling years, respectively (Table 1a,b). With regards to marital status, most farmers and small traders were married (Table 1a,b).

4.2. Type and Variety of Cereal Commodity Traded

The study used sack unit for counting the type and variety of cereal commodities supplied; because sack is commonly used tool to transport agricultural commodities to marketplace. The study finding showed that the majority of farmers and small traders traded one type of cereals on one market day (Table 2). In the other way, the farmer and small trader who were selling more than one type of cereal was less. Most farmers were trading teff and wheat cereals in all study districts (Table 2). On the other hand, the small traders were supplying and trading teff, wheat, maize and sorghum dominantly (Table 2). Moreover, the most variety of cereals provided by both farmers and small traders was a white variety (Table 2).

4.3. Marketplace Conversion Convention and the Distribution of Actual Values of the Measurement

Figure 5A–G illustrated the actual value of the quantity of units of measurement (in kilogram (kg)) based on marketplace conversion agreement between each local measures and kilogram unit. The quantity of local units’ values was measured and recorded by considering estimated uncertainty of measurement (showed in the Table A4). The finding indicated that all actual values of cereal quantity of two bowl unit were scattered far below the conversion convention point for both farmers and small traders (Figure 5A). In Bako Tibe, most of the values of the quantity of three glasses were scattered below the conversion point (Figure 5B). In contrast, the quantity of the three can (Merti) unit was greater than the conversion volume in the Adea Berga district (Figure 5C). In Figure 5D, the cereal amount of a sack unit ranged from 40 to 86 kg in Gimbichu, Adea and Lume districts. Most of the actual values of the cereal amount of three cans unit were dispersed above the point of the convention for both farmers and small traders in Gimbichu and Adea marketplace (Figure 5E,F). In the Lume district, most of the cereals amount of the jug unit values for farmers were scattered above the point of agreement. In contrast, most of the values of the jug unit for small traders were dotted below the point of convention (Figure 5G).
In general, the result revealed that the conversion agreement between measuring instruments and kilogram unit was basically fundamental for trading parties from two essential aspects. Marketplace converting agreement serves the market to adjust the price of the quantity of measuring units with metric units’ price that issued and set at the national level. Because the commodity price information provided by the government and agencies in Ethiopia depend on the kilogram unit measurement system. Additionally, reckoning convention helps traders to adjust the price of cereals amount of a particular unit to other sales location measuring instruments while they are trading from one marketplace to other market locations. In fact, such basic function of trading requires the reliability of local measurement system. However, when the cereals amount of local units were converted to kilogram units using site calibration, as depicted in Figure 5A–G, the actual values of measurement were highly scattered. These implied that the commodity amount was costly to transfer from one economic agent to the other. Hence, using homogenous measurement units would mean the system would have huge potential to alleviate the non-uniformity of values of the measurement.

4.4. Comparison of Actual Values Mean of Cereals Quantity of Local Units

Independent samples t-test was employed to compare the actual values summation mean of each cereal quantity of local units (Table 3). The number of values of measurement was taken based on the marketplace conversion convention of each unit. Cereal quantity of two bowl unit, for instance, has been taken as a benchmark in Dendi marketplace for converting the quantity of bowl to kilogram unit, which is equivalent to three kilograms. Hence, two actual values were regarded for the bowl unit to compute t-test. The comparison was also done by considering similar cereal marketed both by farmers and small traders. The results showed that the farmers and small traders actual average value of teff and wheat quantity of glass, can (White Oats) and jug unit were significantly different in Bako-Tibe, Gimbichu and Lume districts, respectively (Table 3). In addition, the average value of barley quantity of bowl and jug unit in Dendi and Lume; sorghum quantity of glass and can (White Oats) in Bako Tibe and Gimbichu were statistically varied, respectively.
In contrast, the average value of teff and wheat amount by bowl unit in Dendi; teff, wheat and barley quantity by can (Bebelac) unit in Adea; teff, maize and sorghum by can (Merti) unit in Adea Berga; and maize quantity by glass in Bako Tibe were not significantly different (Table 3). On the other hand, the small trader actual value means of cereals quantity of local units were less than the farmers except for maize quantity by the glass in Bako Tibe; and teff and wheat quantity by the can in Adea (Table 3). These varied values of the quantities of units between farmers and small traders implied that there was either a difference in measuring units of the same kind, method of measuring, ways of using instruments or diverse mechanisms exist among parties enabling them to cheat each other [2]. This implied that there was measurement cost occurred due to unreliable measurement system of the marketplace. In another assertion, imprecision of measuring units led trading parties to gain or lose a certain amount of cereals while measurement was performed. To the context, such cereal marketing measurement costs can be saved through supporting trade using effective market institutions and policies.

4.5. Comparison of Actual Values of Cereals Quantity of Sack Unit

In the three districts (Gimbichu, Adea and Lume) farmers were using sack unit to trade cereals. Traders having good potential were buying up commodity using sack and resell them to others neighbouring cities using kilogram unit. Small traders were not using sack unit for transacting cereals within the marketplace. Hence, the study conducted one sample t-test to compare cereals amount of sack value mean of farmers in each district. To run this test, the conversion convention value of the quantity of sack for each cereal was taken as population value.
The conversion convention value of the amount of a sack is varied particularly depending on the method of measuring, ways of using sack instrument and types of the sack. In Gimbichu, farmers were ranging a sack value from 74–80 kg and 69–77 kg for teff and wheat quantity, respectively. Teff and wheat quantity of a sack in Adea district, however, was about 65 kg and 60–64 kg, respectively. On another hand, teff, wheat and barley quantity of a sack in Lume are falling in the range of 58–62 kg, 50–54 kg and 40–44 kg, respectively. Hence, the study considered the average of those ranges as population value (test value) of each cereal to compare the actual values mean with test value (Table 4).
The results revealed that the mean significance difference for teff quantity of a sack unit in Gimbichu and Lume and wheat quantity of a sack in Adea were significant (Table 4). On the other hand, the mean of the quantity of wheat per sack in Gimbichu and Lume; teff per sack in Adea; and barley per sack in Lume were not significantly different (Table 4). Yet, the finding clearly showed that the measurement cost occurred for the sack unit (Table 4). From this finding point of view, the sack based quantity measurement and trade is the economy of estimation. In this context, the study suggests either to adopt the standardization of the international measurement unit or develop national idiosyncratic standard measures to reduce such cereals quantity lose.
Local markets have different market conversion convention for cereal quantity of a sack estimated to kilogram unit. These situations were emanated from different kind of sacks, methods of measuring and way of using sack measuring instrument [2]. To test whether those conditions have an impact on cereals quantity of a sack value difference, the analysis of variance was conducted. The result was indicated that teff and wheat quantity of sack unit value mean comparison of three districts were significantly varied (Table 5). From these findings, it inferred that the diverse type of sack unit, method of measuring and ways of using sack instrument were highly influencing quantity variation. Toward these, the policy and institutional intervention are vital to govern the farmer behaviour related to sack measurement system. Effective policy and interventions will reduce costly transfer of cereal quantity that occurs due to varied usage of sack unit.

4.6. Local Measuring Instruments’ Error Associated Measurement Costs Magnitude

Based on the Figure 3 cost estimation framework, the actual measurement summation average of cereal quantity of each measuring unit was computed separately for farmers and small traders thereby taken as ideal value. In the measurement cost computation, the marketplace conversion convention between local measures and kilogram unit was used as a baseline to estimate the measurement cost size over trading parties’ total marketed cereals. Besides, the computation assumed that the price of cereal quantity of local units over total supply marketing was identical.
The finding showed that the estimated average measurement cost magnitude over total teff, wheat and barley traded was ranging from 1.49–4.96 kg (23.42–56.39 birr) for framers and 0.64–2.67 kg (9.91–36.59 birr) for small traders in Dendi district (Table 6). Comparatively, the small traders’ maize and sorghum quantity lost in birr were lowest (Table 6). The small traders’ measurement costs mean of total crop traded in terms of kg and birr were less than that of the farmer (Table A1). However, the mean difference of cereal amount lost between farmers and small traders was not significant except for teff per birr (Table A2). In Bako Tibe, the amount of lost quantity was extended from 8.21 to 42.13 birr for farmers and 22.02 to 53.42 birr for small traders (Table 6). The small traders’ quantity lost the amount of each cereal was greater than farmers in Bako-Tibe except for teff and sorghum. However, the average value summation of the quantity of three glasses of farmers was higher than that of the small traders (Table 6). The t-test showed that the mean quantity lost in terms of kg and birr were not significantly different (Table A2).
In Adea Berga, the maximum costs incurred on the total supply of farmers was 17.93 birr (Table 6). On the other hand, 3.31 birr for maize and 42.43 birr for teff crop were lost by small traders (Table 6). The measurement cost average of farmers for teff was less than that of the small traders, while the mean amount loss of maize was higher for farmers (Table A1). In contrast, the farmers’ average value of teff quantity of three cans was higher than that of the small traders (Table 6). The t-test showed that the mean difference for teff and maize quantity lose in terms of kg and birr were significant except for maize quantity lose in birr (Table A2).
The average teff and wheat amount loss from the market day total supply by farmers in Gimbichu, Adea and Lume was 6.79 kg (134.89 birr) and 3.54 kg (56.89 birr); 6.67 kg (172.48 birr) and 5.13 kg (75.27 birr); and 2.15 kg (46.59 birr) and 1.85 kg (26.79 birr), respectively (Table 6). The measurement cost was comparatively higher in Adea district (Table 6). In contrast, the cost resulted from using a can by farmers and small traders over aggregate teff and wheat crops in Gimbichu, Adea and Lume were relatively smaller than that of the sack-related biased measurement cost. The reason was that the sack is the primary unit of measurement in those districts. Trading parties were using different can and jug tools for micro cereals marketing. The maximum average quantity loss from can and jug unit in Gimbichu, Adea and Lume was 45.34 birr, which was incurred by small traders (Table 6). On the other hand, the sum of small traders’ average value of cereal quantity for three can in Gimbichu and Adea and one jug unit in Lume was smaller than that of the farmers except for teff and wheat in Adea district (Table 6). However, the t-test results showed that the measurement cost size means over total supply of teff and wheat for can unit were not statistically varied in Gimbichu, Adea and Lume except for teff in birr in Gimbichu (Table A2).
In this context, the result implied that the measuring unit error associated cost was higher mainly for the sack, bowl and glass units of measurement. These units were used primarily for macro cereal supply trading. Whereas, the remaining local units like can and jug were employed as an alternative or for micro-cereals supply marketing purpose. Hence, can and jug unit associated bias costs were relatively smaller. The number of farmers and small traders who lost cereals quantity was 50.54 percent (Table A3). In addition, the frequency of most farmers to conduct market within a month is two times on average [2]. This figure become higher if it is projected for all transaction days made in a year. Therefore, if a half percent of farmers and small traders of all districts faced the same situation throughout the year, a big economy loss will be incurred. In general, either the development of homogenous idiosyncratic measures or the adoption of the international metric and imperial measurement system is suggested to reduce such huge loses of cereal amount [21]. Besides, rules for governing behaviour of markets would have a huge potential for economizing measurement costs and making equitable cereals exchanges [28,29,30,31].

4.7. The Magnitude of the Cost of Local Measurement Tools Unreliability

4.7.1. Measurement Consistency

The quantity measurement consistency dimension was evaluated in the marketplace where both hot and slack market day were conducted in a week. However, the hot and slack market day were performed only for sack unit of measurement in Gimbichu district. In the remaining study sites, the hot market alone was conducted in different sales locations of the district. For this reason, the measurement consistency evaluation was limited to participating farmers of one study area. Thereby, the farmers’ sample size (n = 40) was taken for two market days to evaluate the actual value and price mean comparison. The measurement consistency of teff and wheat amount of the sack unit between a hot and slack market day of Gimbichu district was indicated in Table 7. The finding showed that the actual values and transaction price mean difference was insignificant (Table 7). These results inferred that the values and price of the quantity of a sack of two market days in a week were not consistent, though the p-value was greater than 0.05.

4.7.2. Measurement Conformity

The same approach considered for estimating measures error related cost was also used for assessing non-conformity measurement costs. The average actual measurement value was computed and taken as acceptable or pre-specified value. The assumption was to evaluate the deviation of over repeated actual value of cereal quantity of each local unit from their average value. The non-conformity of over repeated actual values of cereal quantity of measuring units were used the conversion convention as a point of reference to estimate cost for the total supply of market actors. The price of cereal quantity of each local unit was identical throughout the total cereal amount trade transaction. The non-conformity associated costs result was the same with local units’ error-related costs specified in Table 6. On the other hand, in both measurement error (Barzel) and sameness (Velkar) methods, the amount of the computed measurement cost was identical. This was possibly caused by the lack of some standard for each local measurement unit. Besides, there was a constraint for determining measurement uniformity among districts due to the heterogeneous nature of local units (see Section 4.7.3).

4.7.3. Measurement Uniformity

As clearly indicated in this study, the cereal commodity amount of the majority of local units value means of the farmers was greater than that of small traders (Table 3). In addition, the average value of the quantity of local unit between farmers and small traders were significantly different for most cereals. From these findings, one can infer that there was no uniformity among local units of measurement. The policy intervention, therefore, is fundamental towards quantity measurement heterogeneity problems.

5. Conclusions

The study was focused to cereal commodity trade quantity measurement cost and reliability in six districts’ agricultural marketplace of Oromia regional state. As per the survey result, the following important findings and conclusions were drawn:
  • The actual values of the quantities of local measures were dispersed far below or above the conversion point of units of measurement. The farmers’ actual values mean of the quantity of most local units were greater than that of the small traders. In addition, the comparison of average values of the quantity of most local units between farmers and small traders were significantly varied. These result also indicated that there was no measurement uniformity between farmers and small traders. Moreover, the study has given insight that the diverse type of sack unit, methods of measuring and ways of using sack instrument were highly influencing quantity variation. These findings were clearly illustrated that the quantity amount was transferred costly from one trading party to the other parties.
  • The estimated average costs of measuring instruments’ error of total cereal commodity traded on one market day were ranging from 17–172.18 birr for sack unit, 6.17–56.39 birr for bowl unit and 8.21–53.42 birr for glass unit both for farmers and small traders. Similarly, error related costs for can (Merti), can (White Oats), can (Bebelac) and jug unit was 3.31–42.43 birr, 2.35–45.34 birr, 1.72–10.11 birr and 0.19–25.26 birr, respectively. Comparatively, the measurement costs were higher for the sack, bowl and glass units of measurement. The reliability method’s quantity measurement cost extent was identical to error related costs due to the application of the same estimation approach.
In general, the study concluded that the cereal commodity trade quantity measurement of farmers and small traders were not reliable. Furthermore, the measuring units’ error and unreliability related estimated average costs magnitude on one market day transaction were higher for most cereals traded except for secondary units of measurement and micro-cereals supply marketing. This indicate that, the farmers and small traders encounter loss of huge economy especially if the extent of measurement costs is projected for the total transaction days made within a year. In this manner, the spill over effects of the multiple and non-uniform unit of measurement upon local economy are enormous. Based on these facts, the study suggested the complete standardization for the cereal commodity trade measurement system. This standardization of measurement system is possible either by the development of national idiosyncratic measuring unit or adopting an international unit of measurement. Besides, institutions and policy interventions are equivalently essential for governing the measurement behaviour of actors of the marketplace. To these ends, the present study is vital in the context of sub-Saharan countries and/or at a country level to create awareness and address controversial arguments pertaining to the economic benefit of measurement. Overall, the results of this study have valuable contribution to improve agricultural market functioning, rural incomes, macroeconomic policy and national markets integration in developing countries where there are complex, multiple and non-uniform local measures.

Author Contributions

Conceptualization, K.A.A.; Data curation, K.A.A.; Formal analysis, K.A.A., B.G.E., N.H.M., D.A.S.; Investigation, K.A.A.; Methodology, K.A.A.; Project administration, D.Z.; Supervision, D.Z.; Validation, B.G.E., N.H.M., D.Z., D.A.S.; Writing—original draft, K.A.A.; Writing—review & editing, B.G.E., D.A.S., N.H.M., D.E.O.

Funding

This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2662017PY071 and the APC was funded by Deyi Zhou.

Acknowledgments

We wish to thank Deyi Zhou for his valuable input at all phases of the research. He has made a vital contribution in developing and pinpointing areas and gaps in the literature and providing guidance throughout the research endeavours. The authors also thank National Metrology Institute of Ethiopia for their technical support upon the site mass measurement calibration.

Conflicts of Interest

The authors declare that there is no conflict of interest.

Appendix A

Table A1. Local units’ bias related measurement costs mean over total traded crops (in kg and birr).
Table A1. Local units’ bias related measurement costs mean over total traded crops (in kg and birr).
DistrictType of Cereals Marketplace ActorsMeanSDStd. Error Mean
Dendi Teff (Kg)farmers1.4971.0570.211
small traders0.7400.7870.394
Teff (Birr)farmers29.82921.2564.251
small traders1.2201.2770.638
Wheat (kg)farmers1.0781.5240.247
small traders0.7700.3490.156
Wheat (birr)farmers16.58324.7514.015
small traders11.8925.4012.416
Barley (kg)farmers4.9622.7321.115
small traders3.3385.2992.650
Barley (birr)farmers56.38730.64012.509
small traders45.64871.29535.647
Bako-Tibe Teff (kg)farmers2.6001.9690.386
small traders2.2001.7240.652
Teff (birr)farmers40.57631.4846.175
small traders39.03929.06710.986
Wheat (kg)farmers0.8400.0000.000
small traders3.1252.2421.585
Wheat (birr)farmers10.9400.0000.000
small traders44.05031.14122.020
Barley (kg)farmers4.5033.2251.613
small traders4.5033.2251.613
Barley (birr)farmers53.19037.68218.841
small traders53.19037.68218.841
Adea- Berga Teff (kg)farmers0.0450.0140.006
small traders2.2091.5210.538
Teff (birr)farmers13.7676.7402.752
small traders42.43431.06510.983
Maize (kg)farmers1.7831.5950.921
small traders0.4800.2160.108
Maize (birr)farmers13.00012.0196.939
small traders3.3081.5190.760
Gimbichu Teff (kg)farmers0.3440.2150.096
small traders2.4000.4530.320
Teff (birr)farmers6.1683.8671.729
small traders45.3405.0203.550
Wheat (kg)farmers0.3320.3440.141
small traders0.5350.4310.305
Wheat (birr)farmers4.9474.8351.974
small traders7.7806.6194.680
Adea Teff (kg)farmers0.3730.2320.067
small traders0.5930.4210.149
Teff (birr)farmers2.6651.6280.470
small traders4.0742.9911.057
Wheat (kg)farmers1.1481.6260.813
small traders0.7330.6080.248
Wheat (birr)farmers5.8758.4884.244
small traders10.1138.5373.485
Barley (kg)farmers0.5670.4020.232
small traders0.1350.1480.105
Barley (birr)farmers2.4101.7160.991
small traders1.7201.9371.370
Lume Teff (kg)farmers0.3570.2810.162
small traders0.4930.6620.331
Teff (birr)farmers7.3606.0033.466
small traders10.35513.7036.851
Wheat (kg)farmers0.2100.0710.050
small traders0.6880.4170.209
Wheat (birr)farmers3.3301.1600.820
small traders11.0037.0863.543
Table A2. Independent samples t-test for local units’ bias related measurement costs over total supply.
Table A2. Independent samples t-test for local units’ bias related measurement costs over total supply.
DistrictLocal UnitKind of Cereals t-ValuedfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the Difference
LowerUpper
Dendibowlteff (Kg)1.365270.1840.7570.555−0.3811.896
teff (birr)6.65524.9930.000 *28.6094.29919.75537.462
wheat (Kg)0.446410.6580.3080.691−1.0871.703
wheat (birr)0.418410.6784.69111.214−17.95727.338
barley (kg)0.64580.5371.6242.516−4.1797.427
barley (birr)0.33380.74810.73932.229−63.58185.059
Bako-Tibeglassteff (kg)0.488310.6290.4000.819−1.2712.071
teff (birr)0.116310.9081.53713.214−25.41228.486
wheat (kg)−1.93430.149−2.2851.181−6.0451.475
wheat (birr)−2.01730.137−33.11016.413−85.34319.123
barley (kg)0.00061.0000.0002.281−5.5815.581
barely (birr)0.00061.0000.0002.281−5.5815.581
Adea-Berga CanTeff (kg)−4.0237.0020.005 *−2.1640.538−3.436−0.892
Teff (birr)−2.5327.8630.036 *−28.66711.322−54.856−2.478
Maize (kg)−2.5327.8630.036 *−28.66711.322−54.856−2.478
Maize (birr) 1.3892.0480.2979.6936.980−19.67739.062
Gimbichu CanTeff (kg)−6.1541.1860.076−2.0560.334−5.0120.900
Teff (birr)−11.35450.000 *−39.1723.450−48.041−30.303
Wheat (kg)−0.69160.515−0.2030.294−0.9230.516
Wheat (birr)−0.67160.527−2.8334.225−13.1737.506
AdeaCanTeff (kg)−1.504180.150−0.2190.146−0.5250.087
Teff (birr)−1.367180.188−1.4091.031−3.5740.756
Wheat (kg)0.58080.5780.4140.714−1.2322.060
Wheat (birr)−0.77180.463−4.2385.499−16.9198.442
Barley (kg)1.39530.2570.4320.309−0.5531.416
Barley (birr)0.42230.7020.6901.637−4.5195.899
LumeJugTeff (kg)−0.32850.756−0.1360.414−1.2010.929
Teff (birr)−0.34850.742−2.9958.610−25.12719.137
Wheat (kg)−1.51840.204−0.4780.315−1.3510.396
Wheat (birr)−1.43740.224−7.6735.338−22.4937.148
* denoted the mean difference is significant at less than 5 percent.

Appendix B

Table A3. Numbers of farmers and small traders gained and lost cereals quantity due to measurement error.
Table A3. Numbers of farmers and small traders gained and lost cereals quantity due to measurement error.
Study SiteMarketplace ActorsType of Cereals No of Farmers and Small Traders Who Gained Quantity No of Farmers and Small Traders Who Lost QuantityNo of Farmers and Small Traders Who Neither Gained nor Lost Quantity
Dendi farmersteff 1025-
wheat1127-
barely136-
small tradersteff 44-
wheat16-
barely45-
maize 68-
sorghum 66-
Bako-Tibe framersteff 1625-
wheat54-
barely13-
maize 38-
sorghum 12-
small tradersteff 87-
wheat34-
barely35-
maize 2--
sorghum 42-
Adea-Bergafarmersteff 1363
wheat912-
barely96-
maize 13-
sorghum 31-
Small tradersTeff 108-
maize34-
Gimbichu Farmers (sack unit)teff97-
wheat1615-
Farmers(can unit)teff25-
wheat26-
Small traders (can unit)teff92-
wheat52-
barely41-
maize431
AdeaFarmers(sack unit)teff1514-
wheat117-
Farmers(can unit)teff412-
wheat64-
barely23-
small traders (can unit)teff891
wheat66-
barely22-
LumeFarmers(sack unit)teff1313-
wheat98-
barely33-
Farmers(can unit)teff334
wheat33-
barely221
small traders (can unit)teff543
wheat643
barely1--
maize322
Total30232718

Appendix C

Table A4. Certificate of calibration.
Table A4. Certificate of calibration.
DistrictMarketplaceUnit of Measurement Certificate Number ObjectCalibrated Object (Manufacturer) Date of CalibrationType of Calibration Measuring RangeCalibration Range Estimated Uncertainty of Measurement
[gram]
DendiGinchibowlOBL-0408Digital balanceChina2018-08-27Site calibration0–50,000 g200–5000 g±0.39
Bako Tibe BakoglassOBL-0398Digital balance0–50,000 g200–5000 g±0.39
Adea BergaIncinicanOBL-0406Digital balance0–50,000 g200–5000 g±0.39
GimbichuChafe DonsasackOBL-0399Mechanical balanceItaly2018-08-090–3000 kg5–100 kg±1.03
canOBL-0400Digital balanceChina2018-08-090–50,000 g200–5000 g±0.40
AdeaGodino sackOBL-0404Mechanical balanceItaly2018-08-110–3000 kg5–100 kg±1.03
RobisackOBL-0409Italy2018-08-140–3000 kg5–100 kg±1.03
Godino canOBL-0405Digital balanceChina2018-08-110–50,000 g200–5000 g±0.40
RobicanOBL-04102018-08-14 0–50,000 kg200–5000 g±0.40
LumeModjo sackOBL-0407Mechanical balanceItaly2018-08-270–3000 kg5–100 kg±1.03
EjeresackOBL-04010–3000 kg5–100 kg±1.03
ModjojugOBL-0408Digital balanceChina2018-08-270–5000 g200–5000 g±0.39
EjerejugOBL-04020–5000 g200–5000 g±0.39
Source: Site calibration measurement collaboration with National metrology institute of Ethiopia (NMIE), 2018.

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Figure 1. Map of the study area.
Figure 1. Map of the study area.
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Figure 2. Local Agricultural Market (LAM) structure of cereal commodity trade. Source: Developed from the marketplace observation.
Figure 2. Local Agricultural Market (LAM) structure of cereal commodity trade. Source: Developed from the marketplace observation.
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Figure 3. Local units’ bias based measurement costs of farmers and small traders.
Figure 3. Local units’ bias based measurement costs of farmers and small traders.
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Figure 4. The farmer and small trader local units’ unreliability based measurement costs.
Figure 4. The farmer and small trader local units’ unreliability based measurement costs.
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Figure 5. Actual measurement values distribution of local units.
Figure 5. Actual measurement values distribution of local units.
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Table 1. Demographic characteristics of farmers and small traders.
Table 1. Demographic characteristics of farmers and small traders.
ItemsStudy Area
Dendi Count (%)Bako Tibe Count (%)Adea Berga Count (%)Gimbichu Count (%)Adea Count (%)Lume Count (%)
(a)
GenderMale43(47.25%)28(41.79%)28(43.08%)43(76.79%)38(56.72%)29(53.70%)
Female48(52.75%)39(58.21%)37(56.92%)13(23.21%)29(43.28%)25(46.30%)
Age ≤2011(12.09%)9(13.43%)12(18.46%)8(14.29%)0(0%)2(3.70%)
20–4063(69.23)44(65.67%)38(58.46%)30(53.57%)51(76.12%)34(62.96%)
>4017(18.68%)14(20.90%)15(23.08%)18(32.14%)16(23.88%)18(33.34%)
Marital statusSingle28(30.77%)21(31.34%)16(24.62%)12(21.43%)4(5.97%)14(25.93%)
Married63(69.23%)46(68.66%)49(75.38%)44(78.57%)63(94.03%)40(74.07%)
Education 0-470(76.92%)47(70.15%)51(78.46%)44(78.57%)64(95.52%)43(79.63%)
5-812(13.18%)14(20.90%)12(18.46%)7(12.50%)2(2.98%)9(16.67%)
≥99(9.90%)6(8.95%)2(3.08%)5(8.93%)1(1.50%)2(3.70%)
Total91(100%)67(100%)65(100%)56(100%)67(100%)54(100%)
(b)
GenderMale2(4.65%)3(9.09%)5(16.13%)12(38.71%)6(18.18%)4(12.90%)
Female41(95.35%)30(90.91%)26(83.87%)19(61.29%)27(81.82%)27(87.10%)
Age≤200(0%)0(0%)2(6.45%)1(3.23%)1(3.03%)2(6.45%)
20–4027(62.79%)28(84.85%)23(74.19%)22(70.97%)23(69.70%)18(58.06%)
>4016(37.21%)5(15.15%)6(19.35%)8(25.81%)9(27.27%)11(35.48%)
Marital statusSingle0(0%)3(9.09%)5(16.13%)4(12.90%)5(15.15%)6(19.35%)
Married42(97.67%)30(90.91%)26(83.87%)27(87.10%)28(84.85%)25(80.65%)
Divorced1(2.33%)0(%)0(%)0(0%)0(%)0(0%)
Education0–430(69.77%)21(63.64%)28(90.32%)30(96.77%)31(93.94%)28(90.32%)
5–89(20.93%)8(24.24%)2(6.45%)1(3.23%)2(6.06%)3(9.68%)
≥94(9.30%)4(12.12%)1(3.23%)0(%)0(0%)0(0%)
Total43(100%)33(100%)31(100%)31(100%)33(100%)31(100%)
Source: Field survey, 2018.
Table 2. Type and variety of cereal commodity traded within the local marketplace.
Table 2. Type and variety of cereal commodity traded within the local marketplace.
Study AreaMarketplace ActorsSample Size (n)Type of Cereals TradedVariety of CerealsTotal
WhiteRedMixedBlack
DendiFarmers91Teff2465-35
Wheat3620-38
Barley1900-19
Small traders43Teff44106
Wheat61007
Barley60118
Maize1400014
Sorghum840012
Bako TibeFarmers67Teff3642-42
Wheat800-8
Barley300-3
Maize1100-11
Sorghum300-3
Small traders33Teff141-015
Wheat60-06
Barley40-26
Maize20-02
Sorghum51-06
Adea Bergafarmers65Teff1552022
Wheat2100021
Barley1400115
Maize30014
Sorghum31004
Small traders31Teff1252-19
Maize700-7
Sorghum331-7
GimbichuFarmers56Teff1211--23
Wheat327--39
Sorghum01--1
Small traders31Teff56--11
Wheat70--7
Barley50--5
Maize80--8
Sorghum20--2
AdeaFarmers67Teff29133-45
Wheat2620-28
Barley500-5
Small traders33Teff1044-18
Wheat1200-12
Barley400-4
LumeFarmers54Teff2817-36
Wheat2300-23
Barley1100-11
Small traders31Teff111--12
Wheat130--13
Barley30--3
Maize50--5
Sorghum12--3
Source: Field survey, 2018.
Table 3. Actual values mean of cereals quantity of local units comparison between farmer and small trader.
Table 3. Actual values mean of cereals quantity of local units comparison between farmer and small trader.
Study AreaKind of Cereals TradedMarketplace ParticipantsLocal UnitActual Values Summation Mean (kg)SDT-Valuedf Sig. (2-tailed)
DendiTeff Farmersbowl2.760.2160.39042.000.699
Small traders2.730.051
Wheat Farmers2.750.1500.622430.537
Small traders2.720.071
Barley Farmers2.330.2724.47110.520.000 ***
Small traders1.980.113
Bako TibeTeff Farmersglass0.950.6179.10351.590.000 ***
Small traders0.840.028
Wheat Farmers0.890.0156.4966.800.000 ***
Small traders0.810.031
Barley Farmers0.760.0471.21380.260
Small traders0.710.066
Maize Farmers0.900.0593.109110.010 **
Small traders0.770.000
Sorghum Farmers0.960.0212.45770.044 *
Small traders0.870.066
Adea BergaTeff Farmerscan2.370.0402.00525.570.056
Small traders2.330.083
Maize Farmers2.140.178−0.2403.0410.826
Small traders2.160.019
Sorghum Farmers2.140.074−1.29890.226
Small traders2.050.124
GimbichuTeff Farmerscan2.580.1232.795160.013 *
Small traders2.410.132
Wheat Farmers2.440.1192.728130.017 *
Small traders2.250.147
Sorghum Farmers2.070.00733.94120.001 **
Small traders1.830.007
AdeaTeff Farmerscan2.780.178−0.223320.825
Small traders2.790.099
Wheat Farmers2.500.096−1.183200.251
Small traders2.560.113
Barley Farmers1.870.0671.3387.000.223
Small traders1.820.053
LumeTeff Farmersjug1.060.0168.148200.000 ***
Small traders1.010.016
Wheat Farmers1.000.0123.199170.005 **
Small traders0.970.023
Barley Farmers0.740.0102.73960.034 *
Small traders0.720.010
*, ** and *** denoted that the mean difference is significant at 1, 5 and 0 percent, respectively. Source: Field survey, 2018.
Table 4. Comparison of values means of cereals quantity of the sack unit.
Table 4. Comparison of values means of cereals quantity of the sack unit.
District Kind of Cereal Test Value (kg)Mean (kg)Std. DeviationT-Valuedf Sig. (2-tailed)Mean Difference (kg)
Gimbichu Teff 7772.067.11−2.78150.014 **−4.94
Wheat 7474.194.530.24300.8140.19
AdeaTeff 6562.838.06−1.45280.158−2.17
Wheat 6259.445.06−2.15170.047 **−2.56
LumeTeff 6057.622.70−4.51250.000 *−2.38
Wheat 5252.652.181.23160.2380.65
Barley 4243.002.101.1750.2961.00
* and ** denoted that the mean difference is significant at 1 and 5 percent, respectively. Source: Field survey, 2018.
Table 5. The actual value means of cereals quantity of sack instrument comparison.
Table 5. The actual value means of cereals quantity of sack instrument comparison.
Kind of Cereal Sum of SquaresdfMean SquareFSig.
TeffBetween Groups2068.77121034.38525.4920.000 *
Within Groups2759.2296840.577
Total4828.00070
WheatBetween Groups5760.36522880.182160.9800.000 *
Within Groups1127.1666317.892
Total6887.53065
* denoted that the mean difference is significant at less than 1 percent. Source: Field survey, 2018.
Table 6. Local units’ error associated measurement costs (in kg and birr).
Table 6. Local units’ error associated measurement costs (in kg and birr).
DistrictMarketplace ActorType of CerealMarketplace Conversion Convention Actual Value Means
(kg)
Gained or Lost QuantityMeasurement Costs Mean over Market Convention Conversion of Local UnitMeasurement Costs Mean over Total Supply
Kg (birr)
Mean
kg (birr)
SD
Kg (birr)
Mean
Kg (birr)
SD
Kg (birr)
DendiFarmersteff the quantity of 2 bowls =3 kg2.76gained 0.23(2.06)0.29(7.66)5.87(154.39)8.10(224.08)
lost 0.10 (6.02)0.06(1.19)1.49(29.83)1.06(21.26)
wheat2.75gained 0.15(2.74)0.21(3.97)3.24(58.98)4.72(88.98)
lost 0.65(0.95)0.04(0.56)1.49(23.42)1.13(18.51)
barley2.33gained 0.17(2.45)0.75(1.15)3.86(57.00)2.83(41.90)
lost 0.36(4.05)0.16(1.83)4.96(56.39)2.73(30.64)
Small tradersteff 2.73gained 0.04(0.82)0.04(0.87)1.29(26.27)1.55(31.78)
lost 0.04(0.74)0.04(0.79)1.22(25.75)1.28(27.30)
wheat2.72gained 0.16(2.00)-(-)2.13(26.56)-(-)
lost 0.02(0.36)0.09(0.29)0.64(9.91)0.44(6.85)
barley1.98gained 0.08(1.33)0.03(0.61)1.51(25.09)0.76(11.63)
lost 0.06(0.91)0.10(1.39)2.67(36.59)4.83(65.03)
maize 2.68gained 0.03(0.24)0.01(0.11)0.83(6.91)0.53(4.44)
lost 0.02(0.19)0.03(0.23)0.77(6.17)0.99(7.94)
sorghum2.67gained 0.02(0.22)0.01(0.13)0.58(6.19)0.43(4.60)
lost 0.02(0.25)0.01(0.09)0.70(8.28)0.44(5.34)
Bako Tibe Farmers teffquantity of 3 glasses = 1 kg0.95gained 0.07(1.16)0.03(0.63)2.59(45.72)1.89(33.68)
lost 0.44(0.67)0.03(0.45)2.69(42.13)1.94(31.09)
wheat0.89gained 0.01(0.14)0.01(0.12)0.47(6.57)0.43(6.00)
lost 1.58(0.16)3.12(0.11)0.63(8.21)0.42(5.47)
barley0.76gained 0.05(1.22)-3.95(89.25)-
lost 0.53(0.35)0.87(0.36)1.70(34.26)2.16(43.06)
maize0.90gained 0.07(0.34)0.32(0.14)4.33(19.00)1.78(7.91)
lost 0.03(0.15)0.03(0.14)2.90(12.66)1.80(9.67)
sorghum0.96gained 0.02(0.21)-2.09(22.05)-
lost 0.01(0.10)0(0)0.83(8.54)0.17(1.88)
Small tradersteff0.84gained 0.03(0.62)0.01(0.28)2.76(52.44)3.04(57.38)
lost 0.02(0.28)0.01(0.19)2.20(39.04)1.72(29.07)
wheat0.81gained 0.03(0.47)0.01(0.16)1.64(25.66)0.62(9.92)
lost 1.02(0.21)2.00(0.27)1.56(22.02)2.22(31.15)
barley0.71gained 0.06(1.25)0.04(1.01)6.89(124.52)5.70(90.76)
lost 0.89(0.47)1.88(0.34)3.60(42.55)3.44(40.38)
maize0.77gained 0.04(0.49)0.03(0.33)3.60(44.35)2.19(27.71)
lost 0.08(0.85)0.02(0.21)5.07(53.42)1.23(11.84)
sorghum0.87gained 0.00(0.00)0.00(0.00)0.00(0.00)0.00(0.00)
lost ----
Adea BergaFarmers teffquantity of 3 cans = 2 kg2.37gained 0.02(0.49)0.03(0.69)0.67(13.95)1.15(24.30)
lost 0.05(0.84)0.01(0.28)0.74(13.77)0.33(6.74)
wheat2.29gained 0.03(0.45)0.01(0.23)0.77(12.14)0.77(12.37)
lost 0.03(0.52)0.03(0.52)0.57(8.98)0.44(7.14)
barley2.29gained 0.04(0.76)0.04(0.70)0.57(10.21)0.53(9.89)
lost 0.07(1.09)0.08(1.12)0.67(10.83)0.42(6.74)
maize2.14gained 0.26(2.08)-2.77(22.07)-
lost 0.09(0.62)0.05(0.37)1.78(13.00)1.59(12.02)
sorghum2.14gained 0.03(0.36)0.02(0.14)1.21(11.80)1.45(11.55)
lost 0.11(1.61)-1.22(17.93)-
Small tradersteff2.33gained 0.06(1.25)0.04(0.86)1.52(30.54)1.55(30.90)
lost 0.06(1.24)0.04(0.78)2.21(42.43)1.52(31.06)
maize2.16gained 0.02(0.12)0.02(0.11)0.76(5.69)0.69(4.94)
lost 0.02(0.11)0.01(0.05)0.48(3.31)0.22(1.52)
sorghum 2.05gained0.03(0.36)0.02(0.14)1.21(11.80)1.45(11.55)
lost0.11(1.61)-1.22(17.93)-
GimbichuFarmers teffsack unit72.06gained --5.28(116.43)3.03(70.16)
lost --6.79(134.89)4.29(86.29)
wheat74.19gained --3.32(49.52)3.55(49.95)
lost --3.54(56.89)2.12(34.76)
Farmersteffquantity of 3 cans = 2 kg2.58gained 0.18(4.15)0.02(0.54)2.58(61.31)0.85(20.60)
lost 0.07(1.29)0.02(0.44)0.34(6.17)0.21(3.87)
wheat2.44gained 0.19(2.95)0.01(0.12)1.64(26.19)0.07(1.17)
lost 0.06(0.92)0.05(0.84)0.33(4.95)0.34(4.83)
Small traders teff2.41gained 0.06(1.32)0.24(0.58)0.88(19.34)0.64(14.53)
lost 0.26(4.97)0.28(0.92)2.40(45.34)0.45(5.02)
wheat2.25gained 0.07(1.20)0.61(1.09)1.77(29.36)2.02(35.47)
lost 0.18(2.49)0.16(2.06)0.54(7.78)0.43(6.62)
barley1.79gained 0.05(0.75)0.04(0.55)1.72(24.39)1.17(16.20)
lost 0.18(2.16)-1.37(16.43)-
maize2.05gained 0.02(0.05)0.01(0.03)0.63(1.86)0.37(1.08)
lost 0.02(0.05)0.01(0.02)0.80(2.35)0.14(0.42)
No g/l0.00(0.00)-0.00(0.00)-
AdeaFarmersteffsack unit62.83gained --6.23(138.97)3.50(111.56)
lost --6.67(172.48)5.83(161.30)
wheat59.44gained --3.26(46.88)2.82(44.32)
lost --5.13(75.27)2.99(43.22)
Farmers teffquantity of 3 cans = 2 kg2.78gained 0.23(1.55)0.25(1.80)0.99(6.95)1.06(7.75)
lost 0.07(0.51)0.05(0.34)0.37(2.66)0.23(1.63)
wheat2.50gained 0.05(0.27)0.06(0.28)0.58(2.88)0.76(3.63)
lost 0.09(0.46)0.08(0.39)1.15(5.88)1.63(8.49)
barley1.87gained 0.07(0.30)0.00(0.00)0.68(2.91)0.69(2.94)
lost 0.05(0.20)0.03(0.12)0.57(2.41)0.40(1.72)
Small traders teff2.79gained 0.09(0.61)0.06(0.36)1.02(6.85)0.72(4.72)
lost 0.08(0.53)0.05(0.34)0.53(3.62)0.44(3.11)
No g/L0.00(0.00)-0.00(0.00)-
wheat2.56gained 0.09(1.42)0.07(1.03)0.43(6.39)0.42(6.18)
lost 0.09(1.24)0.71(0.61)0.73(10.11)0.61(8.54)
barley1.82gained 0.05(0.69)0.06(0.78)0.35(4.68)1.18(2.47)
lost 0.02(0.26)0.01(0.18)0.14(1.72)0.15(1.94)
Lume Farmers teffsack unit57.62gained --2.16(49.94)1.76(44.67)
lost --2.15(46.59)1.42(30.18)
wheat57.62gained --1.65(25.97)0.86(13.78)
lost --1.85(26.79)1.60(23.06)
barley43gained --1.67(18.50)1.15(13.25)
lost --1.67(17.00)1.15(11.25)
Farmersteffquantity of 1 jug = 1 kg1.06gained 0.02(0.33)0.01(0.12)0.49(9.71)0.31(6.82)
lost 0.02(0.41)0.01(0.19)0.36(7.36)0.28(6.00)
No g/l0.00(0.00)0.00(0.00)0.00(0.00)0.00(0.00)
wheat1.00gained 0.00(0.08)0.01(0.09)0.23(3.59)0.24(4.01)
lost 0.00(0.07)0.02(0.24)0.11(0.19)1.71(2.92)
barley0.74gained 0.01(0.10)0.00(0.00)0.24(2.29)0.14(1.39)
lost 0.01(0.09)0.00(0.00)0.30(2.69)0.04(0.28)
No g/l0.00(0.00)-0.00(0.00)-
Small traders teff1.01gained 0.02(0.44)0.01(0.16)0.99(22.18)0.96(21.47)
lost 0.01(0.28)0.01(0.09)0.49(10.35)0.66(13.70)
No g/l0.00(0.00)0.00(0.00)0.00(0.00)0.00(0.00)
wheat0.97gained 0.02(0.31)0.01(0.13)0.75(12.42)0.60(10.13)
lost 0.03(0.37)0.02(0.31)0.69(11.00)0.42(7.09)
No g/l0.00(0.00)0.00(0.00)0.00(0.00)0.00(0.00)
barley0.72gained 0.01(0.11)-0.21(2.31)-
lost 0.00(0.00) 0.00(0.00)0.00(0.00)
maize0.93gained 0.01(0.10)0.01(0.09)0.94(8.24)0.69(6.11)
lost 0.02(0.15)0.01(0.11)1.10(9.23)0.95(7.93)
sorghum0.92gained 0.02(0.15)0.01(0.07)0.91(9.06)0.72(7.29)
lost 0.03(0.32)-2.4(25.26)-
Note: Birr is Ethiopian currency. By the end of January 2019, 1 USA dollar = 28.18 Birr. No g/L depicted zero measurement cost. Source: Field survey, 2018.
Table 7. Hot and slack market day cereal quantity measurement consistency of sack unit.
Table 7. Hot and slack market day cereal quantity measurement consistency of sack unit.
Type of CerealValue and Price of a Sack UnitNature of The Market DayMeanSDT-ValuedfSig. (2-tailed)
TeffActual value (kg) Hot market day72.06257.11307−0.897510.374
Slack market day74.08117.67890
Transaction price (birr)Hot market day1512.1875130.16616−0.920510.362
Slack market day1554.0541160.30283
WheatActual value (kg) Hot market day74.19354.534460.546420.588
Slack market day73.38464.35007
Transaction price (birr)Hot market day1159.354873.163540.692420.493
Slack market day1139.2308117.15101
Source: Field survey, 2018.

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MDPI and ACS Style

Abebe, K.A.; Zhou, D.; Etea, B.G.; Sedebo, D.A.; Muktar, N.H.; Olana, D.E. Quantity Measurement Cost and Reliability of Cereal Commodity Trade: Evidence from Ethiopia. Sustainability 2019, 11, 1521. https://doi.org/10.3390/su11061521

AMA Style

Abebe KA, Zhou D, Etea BG, Sedebo DA, Muktar NH, Olana DE. Quantity Measurement Cost and Reliability of Cereal Commodity Trade: Evidence from Ethiopia. Sustainability. 2019; 11(6):1521. https://doi.org/10.3390/su11061521

Chicago/Turabian Style

Abebe, Kidane Assefa, Deyi Zhou, Bekele Gebisa Etea, Dessalegn Anshiso Sedebo, Neway Habtemariam Muktar, and Dano Endalu Olana. 2019. "Quantity Measurement Cost and Reliability of Cereal Commodity Trade: Evidence from Ethiopia" Sustainability 11, no. 6: 1521. https://doi.org/10.3390/su11061521

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