To answer this study’s research question, the method of fuzzy-set qualitative comparative
analysis (fsQCA) was combined with in-depth country case-studies using process tracing
methods.
FsQCA is a subset of qualitative comparative analysis (QCA) which is a configurational
approach focused on whether specific combinations of causal conditions (i.e., configurations)
are associated with an outcome 21]. Configurational methods recognize that 1) outcomes are produced via a combination
of conditions, 2) the same outcome may be produced by different combinations of conditions,
and 3) the context within which conditions combine can influence the impact made on
an outcome 22]. As demonstrated by recent studies (e.g., 23], 24]), configurational analyses can be particularly attractive for public health researchers
when health outcomes are seen to be the result of a complex interplay of causal and
contextual conditions. Such an approach is especially appropriate for this study since
it is expected that employment changes after the MFA phase-out will combine with countries’
labour market and welfare state policies in different ways to influence health.
In contrast to regression models, which use correlational analyses to identify average
effects, QCA uses set-theory to make logical statements about causal conditions—both
alone or in combination–that are necessary and/or sufficient for an outcome 21]. A necessary relation exists if an outcome is a subset of a causal condition. Thus
a condition is considered necessary if all (or virtually all) instances of the outcome
show the condition. A sufficient relation exists if a causal condition, or combination
of conditions, is a subset of an outcome. A condition (or combination thereof) is
thus considered sufficient if an outcome always (or virtually always) occurs when
a causal condition is present (although other conditions may also produce the outcome).
QCA techniques can be used for different purposes such as the testing of specific
hypotheses, data exploration or for theoretical development 25]. This study uses QCA primarily for theoretical development since existing theory
surrounding trade liberalization, labour markets and health remains broad and imprecise.
Towards this end, a main advantage of QCA is that it can offer valuable insights into
the causal processes shaping a relationship between causal conditions and an outcome.
This relates in part to the dual nature of the approach which can be described as
having both quantitative and qualitative features. Like mainstream statistical analyses,
for example, cases differ quantitatively across causal conditions. In contrast to
these methods however, QCA specifies thresholds at which these quantitative differences
denote a causally important, qualitative difference. For example, whereas regression
techniques assume that a causal condition will have an incremental impact on an outcome
across all levels of variation in that condition, QCA specifies a point at which the
condition begins to have a causal influence. QCA then sets to examine how qualitative
differences across cases are associated with an outcome. This qualitative focus on
the causal conditions can shed light on key elements of the mechanisms and processes
behind necessary and/or sufficient relations.
The qualitative features of QCA also mean it can be used to identify specific types
of cases for detailed within-case analyses which in turn can offer insight on the
results of the QCA and its surrounding theory 26]. This can further aid in theory development and help overcome one of the main weaknesses
of QCA, i.e., the fact that like regression techniques, it identifies associations
not causation 26]. It is for these reasons that this study combined fsQCA with in-depth country case-studies
using theory-building process tracing methods. Theory-building process tracing is
an approach which can be used both to further explore the details of cases and to
construct potential causal mechanisms in scenarios where we see associations but theory
is unable to offer precise ideas about the causal processes behind them 27].
FSQCA analysis
FsQCA is carried out in three steps. First, outcome indicators, cases and causal conditions
are identified. Included cases are then assigned membership scores for each of the
outcomes and causal conditions. In a conventional QCA, cases are either members of
the set created by the indicator (with a membership score of 1) or not (with a membership
score of 0). In fsQCA by contrast, cases can have partial membership anywhere in the
range of 0-1 25]. It was decided to use fsQCA instead of conventional (i.e., ‘crisp-set’) QCA since
cases in this study are better characterized through their degree of membership in
the causal conditions under consideration.
In the second stage of fsQCA, examinations of necessity and sufficiency are undertaken.
Here a truth table is constructed which outlines the empirical instances of configurations,
as well as their relationship to the outcome indicators. With fsQCA there are 2
k
possible configurations, where k represents the number of causal conditions.
Because it is rare for cases to conform precisely to either a necessary or sufficient
relation, the concepts of consistency and coverage are used to measure how well this
is achieved 28], 29]. Consistency measures the degree to which a necessary/sufficient relation is met.
In terms of necessity, consistency measures the degree to which an outcome is a subset
of a causal condition. If all instances of the outcome display the causal condition,
consistency will be high. In terms of sufficiency, consistency measures the degree
to which a causal condition (or combination of) is a subset of an outcome. If all
instances of the condition display the outcome, consistency will be high. Scores are
calculated by the fsQCA software and range from 0 (no consistency) to 1 (perfect consistency).
The minimum basis on which a necessary (sufficient) relation can be claimed is 0.90
(0.75) 29], 30].
Coverage by contrast, measures empirical relevance 28], 29]. For necessity, coverage measures the frequency with which an outcome occurs relative
to a causal condition. Here very low coverage scores indicate that a causal condition
is present in almost all cases, regardless of whether they display the outcome. In
this scenario, a necessary condition would be deemed trivial. For sufficiency, coverage
indicates the degree to which a condition (or combination of conditions) explains
all occurrences of an outcome. If coverage scores are very low this would indicate
that the causal condition explains only a limited set of the cases with an outcome.
Coverage scores are also calculated by the fsQCA software and range from 0 (no coverage)
to 1 (full coverage). It is suggested that when testing for necessity, coverage scores
should not be lower than 0.5 and that no cause should be deemed necessary, independent
of a theory that recognizes it as a relevant cause 29]. Minimum coverage scores are not suggested for sufficient relations since configurational
methods recognize that an outcome may be produced via different combinations of conditions.
The final fsQCA stage involves a process of ‘logical reduction’ where a simplified
statement is made about which conditions are necessary/sufficient for an outcome (termed
a solution path). In a conventional QCA, this is achieved through Boolean Algebra.
For example, if two combinations of conditions are found to be sufficient, one with
causal conditions A, B, and C and the other with causal conditions A and B (but not
C), we could reduce this to one configuration: AB, since the outcome occurs whether
condition C is present or absent. In fsQCA, an equivalent process is undertaken by
the software using the Quine-McCluskey algorithm. This algorithm takes into account
the more complex features of fsQCA, including consistency scores 21].
Overall consistency and coverage scores are used to describe the logically reduced
solution paths. These measures are a calculation of how well an outcome is explained
when all of the reduced solution paths are considered. Generally speaking, overall
consistency is an average of the consistency scores of each of the individual solution
paths found for an outcome. Overall coverage is a measure of how well the cases displaying
an outcome are covered by the logically reduced solution paths.
Health outcomes
This study examines two outcomes: adult female mortality rates (AFM) and infant mortality
rates (IMR). The former was chosen since most TC workers are female. The latter was
chosen for its rapid response and sensitivity to macro-level policy changes 31]–33]. IMR was conceptualized to have been potentially impacted both directly, through
TC workers having children, and indirectly, if the phase-out influenced health important
conditions at the national-level. Two national-level conditions highlighted by the
EMCONET framework are material deprivation and economic inequality 14]. Both of these conditions may have been impacted through shifts in TC employment
(e.g., through additional provision or loss of wages) and both have been previously
associated with IMR (e.g., 34], 35]).
There is a relatively robust body of literature which finds evidence for changes in
national-level health outcomes following changing macro-economic conditions 36]–39]. Of particular relevance here is the evidence for the health impacts of job loss
(e.g., 39]). While fewer studies have measured the health effect of employment growth 40], there are many pathways through which we can expect it to impact health at the national-level
9]. Furthermore, although much of the public health literature surrounding changing
macroeconomic conditions is focused on the developed world, evidence suggests that
such changes also have important implications for national-levels of health in poorer
countries 41], 42], where much of the TC sector is concentrated.
AFM and IMR were obtained from Rajaratnam and colleagues 43], 44]. Historically, the usefulness of adult mortality data has been hindered by a range
of well-known weaknesses 45], 46]. Models have often extrapolated adult mortality from child mortality. Ambiguity in
both the sources of data and the methods used has also hindered replication of results
46]. Documenting short-term fluctuations and linking them to changing socio-economic
contexts requires far greater detail than past methods have provided 47].
The authors of the data this study utilizes, by contrast, estimate AFM (IMR) through
a variety of sources including vital registration systems, sample registration systems,
and nationally representative survey/census data 43], 44]. These methods demonstrate a higher predictive validity and are transparent and replicable
43], 44], 46]. Moreover, the authors specifically acknowledge that a main advantage of their data
is that it can be linked to changes in socio-economic contexts. AFM is summarized
by the probability that an individual who is 15Â years old will die before age 60.
IMR is summarized by the probability of death before age 1, conditional on surviving
to 1Â month.
Case selection
Countries were included in this analysis if, between 2000 and 2004, employment in
the TC sector (as a proportion of total manufacturing employment) was greater than
10Â %, given that more than 10Â % of the working population was employed in manufacturing.
Total manufacturing and TC employment figures were obtained from the United Nations
Industrial Development Organization (UNIDO) 20]. Data on the proportion of the working population employed in industry were obtained
from the World Bank 48]. While 53 countries were initially identified as reliant on the sector (Table 1), only 32 countries were ultimately used for the analysis (Table 2). Inclusion of countries was limited by the quality of mortality data and the availability
of data used to operationalize the causal conditions (Table 3). Countries were excluded if mortality data was characterized by relatively high
and/or erratic levels of uncertainty. While excluded countries were comprised of both
highly developed and less developed countries, it is unclear how their inclusion might
have impacted the results of the analyses. This work thus reiterates calls for better
quality cross-national health and social policy data. Despite this limitation, the
number of cases included in this study well exceeds the minimum number of cases below
which there is a high chance that a fsQCA will find an association due to random variation
49].
Table 1. Countries identified for inclusion
Table 2. Final set of included countries
Table 3. Excluded countries and reasons for exclusion
Causal conditions
Five causal conditions were selected for inclusion in the fsQCA: countries’ level
of development; (2) labour market protection; (3) welfare state protection; and (4)
TC employment loss or (5) growth after the phase-out. There are a variety of approaches
that can be used to select causal conditions for a fsQCA 50], 51]. Here conditions were selected in direct response to the research question. A development
indicator was included to contextualize how employment changes impacted health in
countries of different levels of development and to group countries with similar health
profiles together. Since the expectation is that the chosen causal conditions will
combine in different ways to impact the health, they can also be seen as selected
via the conjunctural approach 50], 51]. This approach is described in QCA literature as best aligned with the characteristics
of a fsQCA analysis 50]. Specific hypotheses concerning these conditions were not made since the nature of
this study tends towards theory development rather than theory testing.
Fuzzy-set membership scores
Fuzzy-set membership scores are assigned through a process called calibration 21]. Calibration refers to the transformation of outcome indicators and causal conditions
into membership sets. This procedure requires the use of theoretical and substantive
knowledge to denote meaningful differences in the data in order to define cases’ degree
of membership in the set created by an indicator. Calibration methods can be either
direct or indirect. In the direct method, three thresholds are specified which correspond
to the qualitative breakpoints of full membership (1), the cross-over point (.5),
and full non-membership (0). At the crossover point there is maximum ambiguity of
whether a case is more “in†or “out†of a set. Once these breakpoints are specified,
fuzzy membership scores are assigned by the fsQCA software. Generally speaking, the
software calculates scores by translating variable scores into the metric of log odds
21]. A strength of this method is that it is able to calculate precise fuzzy-set scores
when there is similarly precise variation in the data.
The indirect method, by contrast, relies on a broad grouping of cases into a number
of categories which represent different degrees of membership. This method is generally
used when it is difficult to translate data using the three qualitative breakpoints
or when the data is better aligned with a smaller number of membership categories
(e.g., when there is less precise variation in the data).
In this study, the direct method was used to transform the health outcome indicators,
countries’ level of development and employment growth and loss after the MFA phase-out.
This is because data associated with these conditions could be anchored to the three
qualitative breakpoints and because using the direct calibration resulted in more
precise fuzzy-set scores. An indirect calibration method was used to transform the
causal conditions of countries’ labour market and welfare state protection. This is
because the data used to operationalize these conditions was not aligned with the
direct calibration method and better transformed through the indirect method, as will
be made clearer below.
External standards with which to calibrate the conditions included in this study do
not yet exist. As a consequence, calibration thresholds were established based on
the structure of the data and careful considerations of what meaningful thresholds
would require in terms of best representing the condition. Sensitivity analyses were
carried out which evaluated the impact of lower and higher thresholds and demonstrated
little difference in fuzzy-set scores and final results. Further details of the calibration
process for each of the conditions are noted below. The raw data and corresponding
fuzzy-set scores for each of the outcomes and causal conditions can be found in an
additional file (Additional file 1).
For each outcome indicator, AFM and IMR, a ‘Health Improving’ and ‘Health Worsening’
membership set was constructed. Relative changes in mortality rates were calculated
based on the five-year period preceding (2000–2004) and following the phase-out (2005–2009).
Data for these calculations are displayed in Tables 4 and 5. Although this is a relatively short time to examine changes in population health,
it is consistent with studies which show an association between unemployment and adult
mortality after a similar time lag 41], 52]–56]. In relation to FMR (IMR), the qualitative breakpoints for the health improving set
were conceptualized respectively as a 3Â % (4Â %) increase in mortality rate reduction,
a 0Â % change in mortality rate reduction and a 3Â % (4Â %) decrease in mortality rate
reductions. Scores in the sets of ‘health worsening’ were taken to be the negation
of health improving scores and calculated by subtracting a country’s score in the
health improving set from 1. In terms of AFM, 10 of the 27 analyzed countries experienced
health improvement after the MFA phase-out. In terms of IMR, 17 of the 29 analyzed
countries experienced health improvement.
Table 4. Relative changes in adult female mortality rates
Table 5. Relative changes in infant mortality rates
The United Nation’s Human Development Index (HDI) was used to assign scores in the
set of ‘Highly Developed Countries’ 57]. This data reflects conditions in countries in 2004. Data was directly calibrated
in a way which aligned with the Index’s rating of countries into the categories of
High, Medium and Low Human Development. The qualitative breakpoints were conceptualized
as 0.9, 0.8 and 0.5, respectively. The cross-over point was chosen at 0.8 since below
this point, countries are deemed as having medium human development. Countries receiving
a HDI score of lower than 0.5 are deemed by the Index as having Low Human Development.
Countries’ labour market protection was indirectly calibrated based on the number
of Fundamental ILO Conventions ratified by a country 58]. Here a six-value fuzzy-set 29] was used to assign scores in the set of ‘Protective Labour Market Policies, taking
into account the number of Conventions ratified before the MFA phase-out, as well
as additional ratifications made prior to 2009. Table 6 further demonstrates this calibration process. Since these Conventions represent
minimum standards, relatively strict thresholds were set for countries to be characterized
as having protective policies.
Table 6. Scoring procedure for protective labour market policies
Welfare state protection was measured and calibrated using the ILO Income Security
Index 59]. This Index uses a range of input, process and outcome indicators and categorizes
countries into one of four clusters. ‘Pacesetting’ countries are characterized as
scoring highly across all indicators. ‘Conventional’ countries score highly only on
input and process indicators. ‘Pragmatists’ score high on outcome indicators and ‘Much-to-be-done’
countries score low across all indicators. These categorizations were used to assign
scores in the set of ‘Protective Welfare State Policies’ since they delineate important
qualitative features of countries. Another option would have been to use the individual
index scores to directly calibrate fuzzy-set memberships; however, index scores do
not directly align with the qualitative clusters. For instance, a Conventional country
might score lower on the Index than a Much-To-Be-Done country. Using the direct calibration
method thus would have clouded important qualitative differences between countries.
For this reason, scores were indirectly assigned as follows: Pacesetters (1), Conventionals
(.67), Pragmatists (.33), and Much-To-Be-Dones (0).
A direct calibration method was used to assign fuzzy-set scores in the employment
growth and loss membership sets. Qualitative thresholds were chosen with a consideration
of the variation of change across countries and with a consideration that changes
would need to be somewhat significant to influence health at the population level.
Employment growth and loss were treated separately, rather than as a single employment
change condition, since the qualitative breakpoints of a single membership set were
tasked with meeting two conditions found to be at odds with each other. Specifically,
a single membership set would need both to differentiate between countries experiencing
employment growth and loss (conditions which have different implications for health)
and denote meaningful changes in employment (i.e., changes that would have feasibly
made an impact on population health at the national level). For a single membership
set to differentiate between countries experiencing employment growth and loss, the
cross-over point (of 0.5) would need to be set at a 0Â % change in employment. However,
this would mean that countries experiencing a small change in employment, for example
a 5 % increase, would be characterized as largely ‘in the set’ of employment change.
This was seen as problematic since small changes are unlikely to result in discernable
changes in national levels of health. Using two membership sets however, allowed for
meaningful changes in employment to be more accurately accounted for. This is because
the cross-over point for each of these sets could be set at a 5Â % change in employment
loss/growth. In this way, countries with a small change in employment are characterized
as only somewhat in the membership set. Fuzzy-set scores were therefore calibrated
across two membership sets based on percent changes in TC employment between 2004
and 2008 (or the closest year for which data was available). For the Employment Growth
(Loss) membership set, the qualitative breakpoints were conceptualized at a 15Â % increase
(decrease), a 5Â % increase (decrease), and 0Â % increase (decrease). Employment figures
were obtained from UNIDO 20].
Tables 7 and 8 respectively summarize the fuzzy-set scores for the AFM and IMR membership sets,
as well as for the five causal conditions. These tables demonstrate ample variation
between countries both in terms of the outcomes and causal conditions.
Table 7. Fuzzy-set data matrix for adult female mortality
Table 8. Fuzzy-set data matrix for infant mortality
Process tracing
Ideally, all cases included in an fsQCA would be studied in-depth; however, for this
study this would require a prohibitively large number of studies. Therefore, twelve
countries were selected for in-depth analysis so that each of the fsQCA solutions
could be explored by at least one typical case (i.e., one that is characterized both
by the configuration and outcome of the necessary/sufficient relation). When a fsQCA
solution was characterized by multiple typical cases, a comparative approach was undertaken
since our confidence in a causal mechanism is increased if it is found to be in place
across multiple typical cases 26]. A comparative study design was also undertaken to take advantage of deviant cases.
These cases are members of a configuration characterized by a logically reduced solution,
but are not members of the associated outcome. As such, these cases provide evidence
against a necessary/sufficient relation but represent a potentially useful opportunity
to understand the fsQCA results. For example, the most likely reason for a deviant
case in a sufficient relation is the omission of a causal condition of which the deviant
case is not a member but the typical cases are 26].
In line with process tracing literature 27], evidence was collected to build a narrative about the overall structure of each
country’s TC sector (e.g., its workers, how employment changed after the phase-out,
alternative employment opportunities) and its labour market and welfare state policies.
Next, the aim was to inductively work backwards in search of a plausible causal mechanism
that might help explain the fsQCA results.
Because selection bias is particularly acute in process tracing research 27], an attempt was made to minimize this bias by using a systematic process to search
for evidence. A preliminary search strategy found that traditional databases, such
as the Applied Social Science Index and Abstracts database, returned a dearth of relevant
material; therefore, Google and Google Scholar were used to locate sources of evidence.
Search keywords included the country name, ‘Multi-Fibre Arrangement, ‘health’, ‘employment’,
‘textile and clothing sector’, ‘apparel’, and ‘garments’. Once a narrative of a country’s
TC sector was constructed, material regarding labour market and social policies was
searched for, particularly across international organizations including the International
Labour Organization, World Bank and Asian Development Bank.