McCormick+Schmitz Handbook for value chain research on - PACA

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Table 16.2: Results of Step 1 of Gender Analysis (Producers Only) Gender composition of work<strong>for</strong>ce Age Remuneration Education Training Experience Promotion Hours worked Top management: 90% male Middle management: 80% male Support staff (secretaries, drivers, etc.): 50% male Supervisory staff: 70% male Factory production workers: 80% female Outworkers: 100% female The mean age <strong>for</strong> male workers is 35.4 years; the mean age <strong>for</strong> female workers is 38.9 years. Overall ratio of male to female earnings: 3:1 (based on wages and benefits, excluding outworkers) 4.2:1 (including outworkers’ estimated monthly earnings) Ratio of male to female workers within labour categories ranges from 1.6:1 among top management to 2.5:1 among factory workers Male workers average 12.2 years of schooling, compared with female workers who average 8.3 years. Outworkers, who are 100% female, average 5.2 years of schooling. The intermediaries who deal with these outworkers are, on average, secondary educated. Within the broad categories of management, supervisory workers, and factory production workers, there is no significant difference between male and female education levels. Men are twice as likely as women to have had specific managerial or technical training. Men had a wide variety of training, including management, accountancy, textile design, machine maintenance, computer aided design, and sewing/tailoring. Women, on the other hand, were mainly trained in either secretarial work or sewing. Only a few women reported managerial training, and none had been trained in machine maintenance or computer aided design. Total work experience: Male: 14.1 years Female: 18.3 years Garment industry experience: Male: 12.5 years Female: 17.2 years. Promotions generally occur within the broad employment categories, e.g., factory workers progress through specific job groups and some eventually become supervisors. Men who reached supervisor did so in an average of 7.2 years; women took an average of 8.5 years. We found only 10 instances of factory workers or members of the support staff becoming managers. Nine of these were men. Women and men in most jobs work the same hours. Only in factory work was there a significant difference: Male: 52 hours per week, Female: 45 hours per week. 93
In step one, the <strong>research</strong>er gathered the in<strong>for</strong>mation given in Table 16.2. The data are broken down by sex, and include the composition of the work<strong>for</strong>ce, its age breakdown, remuneration, education, training, experience, promotion patterns, and hours worked. The gender data show that top and middle management are mostly male; support staff are half male, half female; supervisory staff are mostly male; while production workers, both in the factory and the outworkers are overwhelmingly female. The data also show differences in mean age, remuneration, education levels, training, previous experience, patterns of promotion, and average hours worked per week. Once the basic in<strong>for</strong>mation has been gathered, the gender analysis moves to step 2 (see Table 16.1). In this step, the <strong>research</strong>er attempts to put the in<strong>for</strong>mation about the <strong>chain</strong> into a broader context. The nature of the context will depend in large part on what the <strong>research</strong>er is trying to achieve. If, <strong>for</strong> example, it appears that female workers are more disadvantaged in the garment industry than in other industries in the same country, the <strong>research</strong>er may want to make ‘industry as a whole’ or ‘manufacturing industry’ the context. In this case s/he will probably use sources such as government statistical data to gather the same kinds of demographic and labour <strong>for</strong>ce in<strong>for</strong>mation <strong>for</strong> the comparison group. Alternatively, the <strong>research</strong>er may believe that the bigger problem is that female garment workers are worse off in this country than they are in other countries competing <strong>for</strong> the same markets. In this case, s/he may choose ‘the garment industry in competing countries’ as the comparison group. When the reference group has been decided, the <strong>research</strong>er sets about gathering the same in<strong>for</strong>mation <strong>for</strong> that group as was collected <strong>for</strong> the <strong>chain</strong>. As indicated in Table 16.2, this would include gender disaggregated data on work<strong>for</strong>ce composition, age, remuneration, etc. With the comparative in<strong>for</strong>mation in place, the analysis moves to step 3, the institutional assessment. The literature on institutions and their organisational <strong>for</strong>ms suggests that they vary considerably from each other and across cultures. Despite their differences, they can analysed in terms of their common components. We identify five elements <strong>for</strong> this analysis: rules, activities, resources, people, and power (see Table 16.1): 94
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MANUAL FOR VALUE CHAIN RESEARCH ON
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10.1 Mapping homeworkers 57 10.2 Ma
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List of Boxes Box 1.1: Maria: A hom
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Introduction In developed and devel
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to understand the situation of home
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Part I. Homeworkers in Value Chains
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Box 1.2: Agnes: A homeworker moving
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no contact with the main contractor
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A value ch
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dominate certain value</str
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• Finding leverage points <strong
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Figure 4.1: Triangle manufacturing
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Table 4.1: Types of buyers, quality
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5. The new retailing and its implic
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ut more frequent orders. Manufactur
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which demand limited skills and are
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In this manual, we are dealing main
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Part II. Methodology This third par
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8.2 Initial and final map Mapping a
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kinds of connections between the va
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• directed network: firms <strong
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Page 45 and 46: the designs in the various stages?
Page 47 and 48: Figure 9.1: Chain map - workers' ho
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Page 55 and 56: Figure 9.3: Chain map - share of fe
Page 57 and 58: Indicators of directed network: Ma
Page 59 and 60: Figure 10.1 : Network of homeworker
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Page 63 and 64: Figure 10.3 : Producer services <st
Page 65 and 66: Figure 10.4 : Wages and skill short
Page 67 and 68: producers all over the world. This
Page 69 and 70: Long-term, quality growth has alway
Page 71 and 72: Such differences emerge clearly if
Page 73 and 74: it may well be worth recording them
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Page 83 and 84: A major concern in the protection o
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Page 89 and 90: useful source of infor</str
Page 91 and 92: In order to assess the impact of su
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Page 97 and 98: is to examine how such differences
Page 99 and 100: Let us start with the latter, askin
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Page 103 and 104: Given that home-workers are often c
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Page 107 and 108: A second reason for</strong
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Page 111 and 112: In answering the first question, th
Page 113 and 114: Obtaining or creating the sampling
Page 115 and 116: selection of a few illustrative cas
Page 117 and 118: The coding of open-ended questions
Page 119 and 120: You will also need to analyse the q
Page 121 and 122: The third step is data collection.
Page 123 and 124: the society. Neilson (1990: 20) cla
Page 125 and 126: Casson, Mark, and Robert Price. 198
Page 127 and 128: Kaplinsky, Raphael. 1998. “Global
Page 129 and 130: Wilson, Fiona. 1991. Sweaters: Gend
Page 131: Business forums de
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