Employment Dynamics in Key Agricultural Sectors of the Fijian Economy Market Development Facility - Fiji Islands
Novmeber 2014 Mohammad Muaz Jalil
Australian Aid – managed by Cardno on behalf of the Australian Government
Table of Contents
Executive summary ............................................................................................................................... 5 1. Background ........................................................................................................................................ 6 2. How MDF creates employment ........................................................................................................ 6 3. Conceptual framework ..................................................................................................................... 7 4. Research design ................................................................................................................................. 9 5. Key findings ..................................................................................................................................... 10 6. Additional findings .......................................................................................................................... 12 7. Concluding remarks ........................................................................................................................ 14 Annex 1: Sampling plan ...................................................................................................................... 16 Annex 2: Summary statistics .............................................................................................................. 18
List of Tables Table 1: Key steps in the study ............................................................................................................. 9 Table 2: Summary findings for sugar ................................................................................................ 11 Table 3: Summary findings for root crops ........................................................................................ 11 Table 4: Summary findings for horticulture ..................................................................................... 12 Table A 1: Geographic distribution of respondents for primary survey........................................ 17 Table A 2: Crop wise summary statistics .......................................................................................... 19
List of Figures Figure 1: MDF employment effect pathways ...................................................................................... 7 Figure 2: Data analysis process .......................................................................................................... 10
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Abbreviations aglime
agricultural lime
FTE
full-time equivalent
MDF
Market Development Facility
SPC
Secretariat of the Pacific Community
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Executive summary Market Development Facility (MDF) operations in Fiji began in 2011 and is currently focussing on three sectors: horticulture and agricultural exports, export processing, and tourism and related support industries. For each sector, MDF performs an in-house assessment and develops a growth strategy that identifies key constraints and growth areas on which to focus interventions for stimulating growth. Growth in the agricultural sector has potential not only to generate income for Fiji’s farmers but also to create new opportunities for poor men and women farm labour. Farm labour in Fiji can be unpaid (familial or communal) or paid. The purpose of this study was to project how two forms of growth in the agricultural sector—increased land use and increased productivity per unit of land—are likely to affect paid farm employment (male and female) for horticulture (fruits and vegetables), sugar, and root crops (dalo and cassava). The study combined qualitative and quantitative research approaches, analysing the MDF baseline report and then validating and expanding its findings in the field by interviewing farmers, labourers, and key informants and holding focus group discussions. Secondary research was conducted to better understand the context and triangulate the findings. Respondents were chosen from major cropgrowing regions in Viti Levu, Vanua Levu and Taveuni. Primary research (including focus group discussions and key informant interviews) was conducted in June and July 2014. The key findings of the study were as follows:
Each additional acre of land brought into cultivation is likely to bring in annually 24 additional paid person-days for sugar, 11 for root crops, and 16 for horticulture.
Each additional ton of production within existing area of cultivation is likely to bring in annually 0.8 additional paid person-days for sugar, 0.2 for root crops, and 6 for horticulture.
Paid female labour makes up 2% of total paid labour for sugar, 12% for root crops, and 45% for horticulture.Farmers in Fiji hire on average 0.42 individual labourers. Many Fijian farm workers are employed less than full time i.e they are underemployed; thus one full-time equivalent (240 persondays of work per year) is likely to be shared by 1.5 to 2 workers.
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1. Background The Market Development Facility (MDF), an initiative funded by the Australian government, aims to create additional employment and income-earning opportunities for poor women and men through broad-based and sustainable pro-poor growth. The facility follows a market system approach, identifying constraints in market systems where poor people operate and then developing and leveraging partnerships with key market players to trigger lasting systemic changes. At present MDF have operations in Fiji, East Timor, and Pakistan. Operations in Fiji began in 2011 and currently focus on three sectors: horticulture and agricultural exports (e.g., root crops), export processing, and tourism and related support industries.1 While the sugar sector, which is one of key economic sectors of Fiji is not one of the direct sectors MDF is involved in, MDF’s work on the agricultural inputs market, particularly introduction of affordable agricultural lime (aglime) to mitigate soil acidity, is likely to significantly increase yields for sugar. The Donor Committee for Enterprise Development standards suggest full-time-equivalent (FTE) jobs as an indicator for the employment effect of private-sector development projects, where one FTE is defined as 240 person days in a year.2 The goal of MDF Fiji is to create around 1,100 FTEs by the end of the programme.3 The purpose of this study was to estimate the number of jobs that MDF’s projects can create. It involved the following steps: 1. Understand how paid labour is used in three types of farming in Fiji: horticulture, root crops, and sugar. 2. Understand the relative contributions of paid and unpaid labour to farming in Fiji. 3. Understand what proportion of farm labour is carried out by male workers and what proportion by female workers. 4. Develop a multiplier to project how the use of paid farm labour in the selected agricultural sectors is likely to be affected by increases in production area and increases in yield. 5. Explore how FTE converts to number of individual labourers, given the widespread underemployment in Fiji. The next section of the paper describes multiple scenarios under which programme activities can create employment. The sections that follow discuss the conceptual framework, research design, and findings of the study.
2. How MDF creates employment As long as farmers are making a profit, they will continue production by employing (surplus) labour and extending the area under farming. MDF Fiji’s activities can affect employment in different ways, such as creating additional full-time or part-time jobs or enabling more efficient use of existing labour, including that of farmers and their family members.
1
Market Development Facility (2014), Terms of Reference, FTE Multiplier Study (Suva, Fiji: MDF).
2
Sen, Nabanita. 2013. Guidelines to the DCED Standard for Results Measurement: Defining Indicators of Change DCED
3
Market Development Facility (n.d.), Fiji Strategy (Suva, Fiji: MDF), http://marketdevelopmentfacility.org/fiji-strategy/.
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depicts ways that programme activities can foster employment creation under different conditions.
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Figure 1: MDF employment effect pathways
In the long run, these scenarios are unlikely to be mutually exclusive, although for analytical purposes it is helpful to differentiate them. In the short run, farmers are likely to choose one of the strategies first before moving on to others. For instance, MDF is currently working with a number of agricultural exporters to increase their export market share, which is likely to result in increased demand for local sourcing (Scenario 2). The programme is also helping these companies to offer private extension services to encourage farmers to apply good agriculture practices to maximize the efficiency and quality of production (Scenario 3). But in the short run, focus group participants and key informants suggested that farmers are likely to continue current practices while expanding their area of production (Scenario 2). Similar situations may arise when companies promoting high-quality inputs like seeds or aglime (Scenario 1) also offer information on proper cultivation techniques (Scenario 3). Thus while theoretically there can be a mixture of different impact pathways or scenarios, in the short run (during the programme period) in most cases, either Scenario 1 or 2 is likely to dominate. By exclusively focusing on the dominant scenario, impact measurement is likely to be simpler and more cost effective and suffer less from measurement error, although it will also be reporting a conservative impact figure.
3. Conceptual framework One approach to estimating employment impact in agricultural value chains is to conduct end-ofintervention household surveys with a large sample size consisting of beneficiary and non-beneficiary farming households in each value chain. Such exercises are expensive, involve long and complicated questionnaires, and suffer from low reliability and measurement error. Measuring a programme’s impact on farmers’ incomes can be challenging; measuring impact on employment at the same time can potentially double the resources required. Even if a single research design measuring both impacts is feasible, it would require combining two separate questionnaires, which would often be impractical because the length of the resulting questionnaire would make it difficult to administer. Farmers are often unable to give farm labour employment information with sufficient precision for both pre- and post-intervention periods, which would enable programmes to estimate impact in labour used. The margin of error or measurement error is often too large to capture the difference in labour/FTE use due to programme effort. Thus, the cost of such end-of-intervention impact assessments is considerable with low, if any, increase in quality of data gathered; hence, such assessmetns are often not cost-effective for the work that MDF is pursuing. The MDF team already had a significant amount of information on yield and income from a recent baseline study that covered over 300 farmers across three value chains (root crops, horticulture, and 8 | Market Development Facility
sugar) encompassing all the major crop growing regions in Fiji.4 Complementing these findings with new data on labour inputs, particularly the proportion of paid to unpaid and female to male labour, offered a more cost-effective way to estimate an employment multiplier. Thus, this study combined the quantitative information from the baseline study with an additional field survey, secondary research and qualitative information to estimate the multiplier.5 This paper uses a simple labour demand equation in which labour demand depends on the area under farming and production and assumes that wages remain stable during the project operation and hence do not affect labour demand.6 đ??żđ?‘‘ = đ??żđ?‘‘ (đ?‘Œ, đ??´)
(1)
where Y, A, and Ld denote gross production, area under farming, and labour demand, respectively. Labour demand is defined in terms of person-days, production in physical quantity (e.g., kg or tons), and area in acres. It is assumed, in line with similar economic models, that the function exhibits constant returns to scale in its two arguments, production and area.7 If we divide Equation 1 throughout with A, we get đ??żđ?‘‘ /đ??´ = đ??żđ?‘‘ (đ?‘Œ/đ??´, 1) or đ?‘™đ?‘‘ = đ?‘™đ?‘‘ (đ?‘Ś)
(2)
where đ?‘™đ?‘‘ is labour demand (measured in person-days) per acre ( đ??żđ?‘‘ /đ??´), and đ?‘Ś is output per acre/đ??´ . Equation 2 implies that unless yield or productivity goes up, labour demand per acre will remain constant. Assuming there is no market rigidities or friction, labour demand will equal labour supply, i.e., đ??żđ?‘‘ = đ??żđ?‘ , where đ??żđ?‘ is labour supply. Thus this model implies that keeping everything else constant, if cultivable area is increased, labour use will increase in proportion, while only yield increase will result in increased labour use per acre. During cultivation, farm labour may be used in different activities, including land preparation, planting, application of fertilizer and pesticide, weeding, and harvesting. In Fiji, land preparation— including ploughing, harrowing, rotovating, and furrowing—is mostly mechanized.8 This study and the MDF baseline study found that land preparation is mostly mechanized for horticultural and sugar farming in both Viti Levu and Vanua Levu while for root crops, land preparation is mechanized in Viti Levu whilst some of it is done manually in Savusavu, Bua, and Taveuni. Thus for this study, we can assume land preparation entails negligible labour and we can classify farm activities into three broad categories: (1) planting, (2) crop management (including application of fertilizer and pesticide and weeding), and (3) harvesting. It can be postulated that increased yield due to use of high-quality inputs such as aglime or good seeds (Scenario 1 in
4
Market Development Facility (2014), MDF Baseline Study (Suva, Fiji: MDF).
5
Secretariat of the Pacific Community (2013), Gross Margins for Selected Fruit, Vegetable and Root Crops for the Sugar Cane Belt in Fiji (Suva, Fiji: SPC).
6
The model is a simpler form of the model used in F. Bagamba, K. Burger, and A. Kuyvenhoven (2009), Determinant of Smallholder Farmer Labor Allocation Decisions in Uganda. IFPRI Discussion Paper 00887. International Food Policy Research Institute, Washington, D.C., USA and in I.U. Odoemenem and L.N. Odom (2009), “Some Factors Affecting the Demand for Hired Labor: A Case Study of Maize Farmers of Benue State, Nigeria,� Current Research Journal of Social Sciences 2(6): 322–326. 7
D. Romer and C. Chow (1996), Advanced Macroeconomic Theory (McGraw-Hill, USA).
8
Secretariat of the Pacific Community (2013), Gross Margins for Selected Fruit, Vegetable and Root Crops for the Sugar Cane Belt in Fiji (Suva, Fiji: SPC).
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) should have no effect on labour use for planting or crop management, assuming person days used for application of new inputs is negligible. The only impact it is likely to have is increased use of harvesting labour, due to increased yields, whereas following of good agricultural practices (Scenario 3) will result in increased use of labour across three components resulting in higher yields. Scenario 3 is akin to movement towards higher degree of productive efficiency. Under Scenario 2, increase in land area without impact on yield will have no impact on labour used per acre; however, it will result in increase in labour use proportionate to the increased acreage.
4. Research design This study combined qualitative and quantitative research approaches and used the MDF baseline report to estimate labour use at different stages of crop cultivation. 9 The baseline study, which recorded only paid employment, was complemented with a smaller-scale quantitative survey of farmers to ascertain the proportions of male vs. female and paid vs. unpaid farm labour. The results from the new survey were triangulated with qualitative findings from focus group discussions and key informant interviews. This was further supported by secondary research, which involved review of the literature on agricultural employment dynamics, as well as background research to better understand the context and triangulate with the current study’s findings. A key document in the secondary research was a report by the Secretariat of the Pacific Community (SPC) that estimated paid and unpaid labour used in different stages of cultivation of major crops in Fiji.10 The report was based on optimal output, which farmers rarely reach; however, it was useful for triangulation purposes. The research team had four members:
Mohammad Muaz Jalil, study lead, independent consultant
Paul Valemei, MDF results measurement specialist
Ritesh Prasad, MDF results measurement specialist
Josefa Tuisova, MDF field researcher
A validation workshop was held near the end of the study when in which primary findings where shared with the management and implementation staff of MDF Fiji. In addition to the literature review described above, the following steps were carried out during this study:
3 focus group discussions in the horticulture and sugar sectors
12 key informant interviews, mostly with extension officers of the Ministry of Primary Industries and Fiji Sugar Corporation
11 interviews with farm labourers
39 structured farmer surveys covering horticulture, root crops, and sugar
Respondents were chosen on a geographic and representative basis, drawing on major crop-growing regions in Viti Levu, Vanua Levu, and Taveuni. Focus group discussions and key informant interviews were conducted in June and July 2014. A detail sample plan with locations is provided in Annex 1 of this report. Error! Reference source not found. summarizes key steps taken during this study; Error! Reference source not found. shows the keys steps undertaken during data analysis.
9
M.M. Jalil (2013), A Practical Guideline for Conducting Research (Donor Committee for Enterprise Development, UK).
10
Secretariat of the Pacific Community (2013), Gross Margins for Selected Fruit, Vegetable and Root Crops for the Sugar Cane Belt in Fiji (Suva, Fiji: SPC).
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Table 1: Key steps in the study Step
Activity
Days
Information review
Review information on farming and farm labour in Fiji in Ministry of Primary Industries reports, the National Agricultural Census, SPC report, and Reserve Bank of Fiji RBF.
2
Development of study design and work plan
Analyse available data and resources to design the study and develop a robust yet practical workplan .
1
Analysis of information
Analyse MDF baseline study data and labour information from the Ministry of Primary Industries, SPC, and other secondary sources.
3
Field work
Conduct interviews with farmers, labourers, and key informants and hold focus group discussions in Viti Levu, Vanua Levu, and Taveuni.
8
Data analysis
Analyse data collected during field work, compare it with existing data, and develop employment multipliers.
3
Report writing
Write a draft report and present the multipliers.
3
existing
Figure 2: Data analysis process
Analysing MDF baseline data Estimate paid person-days of labour per acre for different farming activities. Estimate paid person-days of labour per ton of crop for harvesting.
Analysing SPC data Calibrate SPC data on yield from optimal to realistic production values by comparing major crop yields in SPC and MDF baseline data and adjusting for inefficiency. Adjust per-acre and per-ton employment estimates after calibrating yield values.
Analysing MDF primary employment survey data Estimate total person-days of labour per acre for different farming activities. Estimate total person-days of labour per ton of crop for harvesting. Estimate male and female involvement in production activities.
Triangulation Triangulate data from all three sources and adjust for consistency. Compare the results with findings from focus group discussions and key informant interviews. Validate findings with programme implementation staffs. Establish FTE multiplier estimates for different scenarios.
In order to adjust for outliers, in many cases median values were used instead of averages, which, especially in small samples, tend to skew findings towards the outliers. During calibration of the SPC data for horticulture, only those crops were taken from MDF baseline data for which 10 or more sample points were available. For both root crops and horticulture, a single employment multiplier for the category as a whole was calculated rather than individual crop multipliers. This is because most farmers cultivate between six and nine crops and to develop multipliers for each would have significantly increased the sample size requirement.
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Further information on research design and analysis can be obtained by contacting MDF: info@cardnomdf.org.
5. Key findings Sugar is one of the most important crops for Fiji and involves a significant number of large-scale commercial farmers. Currently it contributes about 2.2% to Fiji’s gross domestic product and nearly 11.7% to Fiji’s total merchandise exports, with nearly 41,000 people involved in its production.11,12 Data from the earlier SPC and MDF studies and the present primary survey conducted for this study consistently indicate that farmers use a high amount of paid labour in all stages of cultivation, amounting to almost 100% during harvesting. Results are summarized in Error! Reference source not found.. Table 2: Summary findings for sugar Total days per acre Paid days Paid days Replanted Paid female days Unpaid female days Days (paid and % paid labour (paid and unpaid) per acre % of total area % of total % of total paid days % of total unpaid days unpaid) per ton days per ton
Planting
8
5
68%
Crop management
7
4
61%
Harvesting
14
14
100%
Total days
29
24
82%
0.80 16%
2%
100%
13%
For sugar, our analysis indicated that one additional acre of cultivation will require annually an additional 24 paid person-days, and one additional ton of production will require annually an additional 0.8 paid person-days. Female involvement as paid or unpaid labour is minimal in sugar production. Root crops covered by this study are dalo and cassava. For this category, the yield data from the SPC report were significantly overstated and required calibration. The farmer survey conducted for this study found that on average farmers produced about 54% of what the SPC study identified as optimal output. The MDF baseline study produced similar results to this study regarding paid labour used for planting and harvesting, but appeared to overstate labour used for crop management. To be conservative, the data from this study were used to estimate the employment multiplier. Results are summarized in Error! Reference source not found.. Table 3: Summary findings for root crops Total days per acre Paid days Paid days Paid female days Unpaid female days Days (paid and % paid labour (paid and unpaid) per acre % of total % of total paid days % of total unpaid days unpaid) per ton days per ton
Planting
9
4
49%
Crop management
8
2
23%
Harvesting
9
5
52%
Total days
26
11
42%
4.00 12%
52%
11%
For root crops, our analysis indicated that one additional acre of cultivation will require annually an additional 11 paid person-days, and one additional ton of production will require annually an additional 2 paid person-days. Female labour is still limited but higher than for sugar. Most female labourers are employed during planting.
11
Merchandise exports show the f.o.b. value of goods provided to the rest of the world valued in current U.S. dollars. Source : World Bank
Fiji National Agriculture Census Report (2009), Department of Agriculture Economic Planning and Statistics Division. Fiji. and M. Reddy (2003), “Farm Productivity, Efficiency and Profitability in Fiji’s Sugar Industry,” Fijian Studies: A Journal of Contemporary Fiji 1(2): 225–241. 12
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Horticulture includes diverse fruits and vegetables, for which farmers use a number of different units of measurement, including bundles and buckets, which are difficult to translate to uniform metric measurements such as kg or tons. Like for root crops, yield data in the SPC report appeared significantly overstated and required calibration. The MDF baseline study found that on average farmers produced about 50% of what the SPC study identified as optimal output. As mentioned earlier, the production data for calibration were taken from the MDF baseline study and only for those crops for which there were more than 10 data points. Another issue in this category was that farmers had difficulty in disaggregating the time they used for various farming activities, especially for short-duration crops, since there was overlap between crops. Hired labour might weed one crop while planting other crops. Thus, in many cases, significant probing and proxy questions had to be asked in order disaggregate the labour times for different tasks. In this sector, there was also a high proportion of unpaid family labour used in cultivation; this is in line with existing literature on horticulture.13 After calibration, findings from the earlier SPC and MDF reports are consistent with those from the survey conducted for this study; to be conservative, the data from this study, which result in the lowest employment multiplier estimate, were chosen. Our survey found that in many cases women were preferred to men for planting and harvesting crops that required bending or plucking, like eggplants, and papaya. Results are summarized in Error! Reference source not found.. Table 4: Summary findings for horticulture Total days per acre Paid days Paid days Paid female days Unpaid female days Days (paid and % paid labour (paid and unpaid) per acre % of total % of total paid days % of total unpaid days unpaid) per ton days per ton
Planting
7
3
39%
Crop management
9
4
41%
Harvesting
16
9
57%
Total days
32
16
49%
9 45%
66%
22%
Our analysis indicated that one additional acre of horticulture cultivation will require annually an additional 16 paid person-days; one additional ton of horticulture production will require annually an additional 6 paid person-days. Horticulture has the highest female labour involvement of the three crop types under study; for many types of vegetables, women are involved in all stages of the production process.
6. Additional findings One scenario used in this study was that farmers start using good agricultural practices comprehensively (Scenario 3 in
A. Fink, S. Neave, A. Hickes, J.F. Wang, N. Nand (2013), “Vegetable Production, Postharvest Handling and Marketing in Fiji,� in Research in Action No 7 (AVRDC – The World Vegetable Center, Taiwan). 13
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) and are able to produce the maximum possible output per acre. It is expected that farmers who benefit from the intervention will in the long run move in this direction. To estimate the paid labour needed for optimal production, MDF can use the following projections—taken from the SPC report, which assumes optimal production and includes both paid and unpaid labour—and modify them by the percentages from the “paid days % of total” column in the tables above.
One additional acre of sugar cultivation will require an additional 27 person-days (82% paid).
One additional acre of root crop cultivation will require an additional 42 person-days (42% paid).
One additional acre of horticulture cultivation will require an additional 48 person-days (49% paid).
Findings on labour productivity are similar across the two earlier studies and the current study, indicating that the major reason for inefficient production is poor cultivation techniques such as use of poor quality input. Thus, for farmers using good agricultural practices, the aforesaid employment multipliers can be used. Another issue is that Fiji has significant underemployment, and because of this, the numbers of FTEs and actual number of people employed may not be the same. 14 Farmers’ need for additional labour could be filled by the currently underemployed workers taking on more work. Conversely, if individual workers continue to work less than a full FTE each, the number of actual peole employed would be higher than the number of FTEs created. Since any FTE increase will at a minimum benefit existing farm workers, there is a need to estimate the number of individual labourers hired by farmers. Unfortunately, this is difficult to do, as there can be significant overlap. Findings from key informant interviews and focus group discussions suggest that most farmers hire labourers in gangs or from nearby villages. Thus, for an individual farmer, there can be significant overlap between labourers hired during harvesting and those hired during planting, even though the work is counted as separate person-days. Furthermore, there can be overlap between farmers, as most often the same group of labourers work for the same group of farmers. Adjusting for such overlaps is difficult, so it is very difficult to estimate the number of individual labourers per farmer based on micro data. This study used data from the MDF baseline study to estimate the number of individual labourers per farmer. In order to do this, a few highly conservative and restrictive assumptions had to be used. First, for each farmer, the number of labourers hired to perform different farming tasks was calculated and then the maximum value was taken, assuming all other labourers overlapped with that function. In order to estimate between-farmer overlaps, surveys from same location were considered to be 100% overlapped. Based on this, the estimated number of labourers per farmer was found to be 0.6. However, a better estimate can be obtained from a recent agricultural census, since it provides a global figure (adjusting for all overlaps) of the number of farm labourers hired against the total number of farmers hiring them. 15 Based on these two figures, we can estimate that a farmer in Fiji hires on average 0.42 unique individual labourers. Although this is a 2009 figure, it seems to be much more representative and accounts for all possible overlaps. Thus, if MDF activities create 100 FTEs among 100 farmers, then at a minimum, the benefit from this increase will be shared among 42 labourers working on those 100 farms. In order to estimate the maximum value of labourers hired, we have to estimate how many FTEs translate to how many individual labourers. This can be done by estimating how many FTEs an average farmer uses and then comparing it to the number of labourers per farmer. So for instance, an average root crop farmer has 6 acres of land (Annex 2) and therefore, based on table 3, uses on average 66 (6 11) paid person-days or 0.275 FTE (66 ÷ 240). But we know each farmer on average hires 0.42 labourers, which implies 0.275 FTE is actually used by 0.42 jobs or 1 FTE = 1.53 jobs.
14
W. Narsey (2010), Improving the Effectiveness of the Fiji Police Force: How Approach the Problem of Crime in Fiji? presentation to senior officers of the Fiji Police Force, 10 November 2010, https://narseyonfiji.files.wordpress.com/2012/04/presentation-to-senior-officers-of-the-fiji-police-october-2010.pdf. 15
Fiji National Agriculture Census Report (2009), Department of Agriculture Economic Planning and Statistics Division. Fiji.
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Another estimate can be based on data16 that show that although communal workers, family workers and self-employed farmers often say they are fully employed (i.e., have 1 FTE), in reality on average they are employed for only 123 days a year (i.e., 1 FTE = 1.95 jobs). Thus the FTE: job ratio may be between 1:1.5 and 1:2, a figure which appears to be consistent with the limited number of interviews with labourers, that were conducted for this study.
16
Narsey, W. (2010). Improving the effectiveness of the Fiji Police Force How approach the problem of crime in Fiji?. Presentation to Senior Officers of the Fiji Police Force, 10 November 2010; Retrieved from : https://narseyonfiji.files.wordpress.com/2012/04/presentation-to-senior-officers-of-the-fiji-police-october2010.pdf
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7. Concluding remarks During field work it was clear that farmers were having significant difficulty in hiring labourers. One complaint that often emerged was that even when paying on average FJD 15 per day, which seemed to be the standard rate, it was still very difficult to hire farm labourers. In many cases, especially in horticulture, women were preferred to men for planting and harvesting crops that required bending or plucking like eggplants and papaya. Another observation was that main-season farmers were often distinct from off-season farmers; most main-season farmers could not cultivate during the off season due to flooding, while the opposite was true for off-season farmers.17 Sugar farmers had difficulty finding harvesting labourers. Traditional practice entailed contributing 1 labourer per 100 tons of production to a labour gang, but since farmers produced on average nearly 200 tons (Annex 2), nearly all farmers themselves acted as the additional harvesting labourer in the gang. Most sugarcane harvesting labourers also possessed their own small horticulture plot, which they would tend to during the off season when there was no harvesting of sugar. One Ministry of Primary Industries extension officer observed that vegetable production actually goes down during sugar harvesting, corroborating this observation. In the present study in most cases, labourers were of iTaukei origin (native Fijians) . Karl Polanyi, an eminent economist, defined economics as how humans make a living interacting within their social and natural environments.18 This he called the substantive meaning, which, as opposed to the formalist definition, presupposes neither rational decision-making nor conditions of scarcity. Polanyi argued that the market is only one possible way of allocating resources in a society; redistribution and reciprocity are other alternatives and historically more important than the market.19 The mataqali village structure, with the chief taking the role of “redistributor� and the concept of communal labour, very much in line with reciprocity, is a classic Polanyian structure. The communal obligation is very strong in iTaukei communities.20 Thus it seems that the labour institution in Fiji, especially the one that depends on the village as its source, is still out of pace with the market economy structure, and this is creating labour market friction where farmers outside the mataqali have to bear higher transaction costs in hiring labour. Econometric studies have found that farms within mataqali are more resource efficient, precisely because they can tap into social capital, including in the form of shared labour.21 This implies that, in the short run, the best way to ensure increased production is to improve the productivity of existing hired labourers, as it seems that labour is yet to become a commodity in Fiji and there is rigidity in the labour market which inhibits demand and supply from easily clearing or equalizing, as it would in a completely free-market economy. This is not to imply that the mataqali structure is inefficient but rather that one should not assume that increased demand for labour will automatically result in increased supply of labour, even if the population size permits it. Thus, in order to ensure that production growth, due to increased use of better-quality inputs, increased demand, or application of good agricultural practices, is not constrained by labour market rigidities, the productivity of the existing labour force may be increased through introduction of mechanized hand tools, improved efficiency in production processes through time and motion study, and other efforts. At the same time in might be possible to experiment with something similar to a dual-track pricing system, whereby mataqali labourers might be first asked to fulfil their social obligation by offering their labour time based on reciprocity and, once this is fulfilled, to offer their remaining labour at a market wage rate either within or outside the mataqali.22 The dual-track price system is an intermediate price system between regulated price control and the free-market price system. In mataqali villages, individuals have to offer their labour based on 17
Vegetables can be grown during both seasons; the main season is from May to October, and the rainy summer season from November to April is the off season. 18
K. Polyani (1944), The Great Transformation (New York: Rinehart).
19
D. Ankarloo (2002, October), Using Karl Polanyi as a Stepping Stone for a Critique of the New Institutionalist Orthodoxy, paper to be presented at the CRIC workshop Polanyian Perspectives on Instituted Economic Processes: Development and Transformation, University of Manchester, United Kingdom. 20
Market Development Facility (2013), Study on Poverty, Gender and Ethnicity in Key Sectors of the Fijian Economy (Suva, Fiji: MDF).
21
H. Haszler, P. Hone, M. Graham, and C. Doucouliagos (2010), Efficiency of Root Crop Production in the Fiji Islands, Economics Series paper no. 2010_05 (Melbourne: Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance). 22
B. Naughton (2007), The Chinese Economy: Transitions and Growth (Cambridge, MA: MIT Press).
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reciprocity, and therefore a change to a completely wage-based labour market might incur significant transition costs or face resistance from local actors. Therefore it might be a better option to go through a phase in which both systems coexist, as described above. As an earlier analysis suggested, it may be more efficient “to switch from the guild system to the putting-out system, and then to the modern firm” rather than directly moving from guild to modern firm.23
K. Basu, E. Jones, and E. Schlicht (1987), “The Growth and Decay of Custom: The Role of the New Institutional Economics in Economic History, Explorations in Economic History 24(1), 1–21. 23
17 | Market Development Facility
Annex 1: Sampling plan
18 | Market Development Facility
The following table shows crop wise, location specific details of the number of respondents and group discussions covered in the present field research.
Table A 1: Geographic distribution of respondents for primary survey Location
Sigatoka
Horticulture
Root Crop
Key Informant Interview
Survey
Focus Group Discussion
1
7
1
Key Informant Interview
Sugar Survey
Focus Group Discussion
Key Informant Interview
Labour Survey
Ra
2
Naitasiri
1 1
Labasa
1
2
Savu Savu
1
2
1
5
13
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1
6
2
1
1
3
2
4
4 1
2
4
Tavuni Total
Survey
1
Lautoka Tauva, Raki Raki
Focus Group Discussion
1
2
7
3
15
4 0
4
11
2
11
Annex 2: Summary statistics
20 | Market Development Facility
The following table shows crop wise summary statistics in terms of farmers, acerage, production volume and yield24 Table A 2: Crop wise summary statistics Total farmers in Fiji
Average acres per farmer
Average tons per farmer
Average yield (tons/acre)
Horticulture
13,000
6
13
2.19
Root crops
15,000
6
12
2.23
Sugar
14,000
10
181
18.10
Crop
24
Fiji National Agriculture Census Report (2009), Department of Agriculture Economic Planning and Statistics Division. Fiji.
21 | Market Development Facility