Government programs supporting self help groups (SHGs) generally target women on the assumption that doing so enhances women's decision-making. The empirical evidence, however, is mixed. We advance and test one explanation: the loan amounts offered by most SHGs may be too small to impact women. Our analysis is based on SHGs developed under India's National Rural Livelihoods Mission, a program that supported both small loans from internal savings and larger loans through Community Investment Funds (CIFs). Exploiting variation in their phasing and amount, we document a large effect of CIFs on women's decision-making and on intra-household allocations. •The impact on women's decision-making of their improved access to loans depends on loan size.•Targeting women for financial access provides no additional benefit if loan sizes are small.•Our analysis uses variation in implementation of a national program for Self Help Groups in India.•The results suggest the importance of implementation for the success of the program. A review of several microfinance programs finds insignificant effects on women's decision-making ability in three out of the four studies that evaluated this outcome . SimilarOne explanation for the marginal effects of micro-finance programs comes from theoretical models of poverty traps.3We address this question by examining the impact of large increases in loan amounts to women who are members of Self Help Groups formed and supported under India's National Rural Livelihoods Project (NRLP), a program intended to enhance women's livelihoods through a federation of community institutions with SHGs at the lowest level. To evaluate NRLP we, along with other collaborators, implemented a cross-sectional survey of approximately 15,000 SHG members across 8 of India's poorest states in 2019, as the program approached its end.As in other such programs, membership in a NRLP SHG requires regular savings. Loans, in early stages of a SHG's life cycle, are based on the group's accumulated internal savings. Given small monthly savings amounts, SHGs' savings accumulate very slowly. Amongst survey SHGs, monthly savings varied from INR 10 to INR 100, with a mean of INR 30 and a median of only INR 10. Correspondingly, initial loan amounts are small: The average loan size across all SHG members in the first year of formation for SHGs in our sample (formed between 2012 and 2018) is just INR 1,014, with this number increasing to INR. 4150 if the sample is confined to those who borrow. This latter amount is equivalent to wage earnings from approximately 20 days of work.NRLP, however, promoted an institutional architecture that magnified the economic benefits to members over time. This was primarily through a grant-in-perpetuity to SHGs, the Community Investment Fund (CIF), a grant that doubled average loan amounts to individual members. Repaid funds remained within the SHG federation to support continued internal lending, and hence provided the basis for a significant improvement in incomes. Though the central government set guidelines for the CIF amount per SHG, each state had the discretion to independently determine funding amounts. The variation in state funding per SHG is significant, ranging from INR 30,000 to INR 110,000.We evaluate the impact of CIFs on an index of women's decision making using a difference-in-difference methodology that exploits variation in the incidence of treatment at the SHG level and state-level differences in the amount of the grant. Our approach follows In addition to tests of the identification assumptions underlying a difference-in-difference regression, we also subject our results to a broad set of falsification tests, including tests of whether the results merely reflect differences in socio-economic conditions across states. We find strong evidence that these large improvements in a woman's access to financial resources enhance her decision-making role within the household.We extend our analysis to address the policy relevant question of whether targeting women for financial inclusion generates benefits beyond those that would obtained by improving financial access for households, without regard to who within the household has access to loans. Building on a large body of research that tests the unitary or income-pooling model, we confirm that women's financial access to CIF funds affects household consumption allocations, even in regressions that condition on total household savings. This evidence supports the view that women's access to large loans constitute a “distribution factor,” shifting household preferences. We find, however, that this result applies only to large loans: women's access to the significantly smaller loans available after one year of internal SHG savings, without access to CIFs, affect consumption allocations only through their effect on household savings. That is, providing women with access to small loans yields results that are similar to improvements in the overall financial position of the household, implying no benefit from targeting women.Our research builds on theoretical models of inter-temporal household decision making that illustrate how women's bargaining weights are updated over time as a consequence of changes in their reservation utility . Within As previously noted, our research is also related to the growing evidence on multifaceted graduation programs that provide large grants, training and other complementary inputs to ultra-poor households a. In genEven if the effect of large cash transfer programs on women's empowerment were positive, their cost suggests a limited potential to impact outcomes on scale; though BRAC's program covered 360,000 households by 2014 and was intended to reach 650,000 households by 2016 , these nThis, of course, remains the challenge. Our analysis identifies the impact of CIFs by exploiting variation in their amount and timing across SHGs, that is, variation in how the program was implemented across India's states. Thus, the challenge to policy is ensuring that features of the program's design that enable a sizeable impact on women are implemented in full and not scaled back to a level where their effect is negligible.The rest of this paper is organized as follows. Section 2The empirical analysis of this paper evaluates the impact of large SHG loans on one aspect of women's empowerment, an index of their input in household decisions regarding consumption expenditures. In this section, we discuss the theoretical underpinnings for the focus on women's decision-making role, noting its limitations and advantages. This theoretical literature carries implications for the policies that can affect this role. We close with a brief discussion of these implications.The need to improve the economic, political and social standing of women is well recognized particularly in economies such as those of South Asia where a patriarchal culture has long discriminated against women. These economies have historically been characterized by significant differences in women's access and ownership of assets, including education and health . It is wThe concern with this aspect of women's empowerment, the extent to which women can make choices over the goods and services that matter to them, dovetails with theoretical models of household behavior that extend the traditional unitary or common preference model to allow for differences in preferences amongst members and for individual choice within the family unit. Within the context of a common preference model, questions regarding who makes decisions within the household do not arise. In contrast, models that allow for differences in preferences between husband and wife require a stand on how consumption allocations are decided. The “collective” model , for exaThis model is most commonly tested by examining whether a woman's individual income affects expenditures in regressions that control for total household income . Under aAlternatively, one could directly test the impact of any given policy on women's bargaining power, should a measure of her Pareto weight be available. As reduce her involvement in household decisions if she chooses to delegate more of them to her husband.The validity of this approach depends not just on how well a given index measures a woman's bargaining power but also on the relationship between decision-making and the distribution of preferences.Thus, studies that assess the impact of improvements in women's resources on indices of her role in household decisions may yield results that differ from tests of the income pooling hypothesis, rendering difficult their interpretation. While qualitative evidence on the import of decision-making can help in interpretation , such evmagnitudes in the determination of bargaining weights. The literature on intertemporal intra-household decision making under limited commitment that allows for the evolution of bargaining weights over time suggests their importance  bargaining weights will occur only if changes in individual reservation utilities are large enough to cause participation constraints to bind, that is, to make an individual's threat point viable .8 Small 3The analysis of this paper is based on India's National Rural Livelihoods Project (NRLP), a program intended to enhance livelihoods through the formation of Self Help Groups (SHGs) comprising women from amongst the country's poorest households.NRLP was implemented in a phased manner. The program was initiated in 2012 in a set of identified “early blocks,” the four poorest blocks in the four poorest districts of each state. Implementation in “late blocks” of the same districts generally followed with a lag of approximately 4 years. Within each block, the process of SHG formation was undertaken at the level of a “cluster,” a program-specific administrative unit comprising approximately 25 villages. Cluster-level project teams moved from one village to another following a stipulated village entry plan until a first round of SHG formation had been completed. This process was repeated in successive rounds until the entire target population had been covered.Panchsutras  which formed the basis of the program's evaluation and assessment of the quality of SHGs.Project teams responsible for forming a SHG did so by identifying hamlets or narrow residential groupings of relatively disadvantaged households and then intensively working within the hamlet to form groups of approximately 10 women. After group formation, the project team trained members on SHG principles, including a set of five norms or rules, referred to as In early stages, the program's focus was on accumulating savings of members and “internal” lending based on these savings. Groups could select the amount that each member was required to save each month. These amounts were low: The mean amount across the approximately 4700 SHGs we surveyed was INR 30 per month (40 US cents), with the median amount being just INR 10. Not surprisingly, initial loans were small. SHGs formed in 2018 reported an average loan amount of just INR 2415 , with this amount being INR 7015 for older SHGs formed in 2015 and INR 6998 for those formed in 2012.10Panchsutra principles. The second was a much larger grant in the form of “Community Investment Funds” (CIFs). This was provided “in perpetuity” to SHGs approximately 6 months to a year after their formation.12However, NRLP recognized that moving households out of poverty required additional resources. These came from two sources. The first was a “Revolving Fund” (RF), a relatively small grant of INR 15,000 that each SHG was eligible for once it had demonstrated at least 3 months of success in maintaining the 5 SHG to have a bank account, there was no requirement that individual members should have their own accounts. That is, loans from SHGs to members did not have to be deposited in individual bank accounts, the method generally used to ensure women's control over transfers targeted at them. Thus, the question of whether women could control the loans they received is particularly germane in this context.While the attempt to ensure access to large loans recognizes their importance for sustained improvements in poverty, an acknowledgement of the importance of ownership came mainly in the form of the requirement that SHG membership be restricted to women. Correspondingly, loans were provided to female members of the household as were training sessions on financial literacy and on vocational and life skills. However, while training is likely to “stick” to the individual who receives it, the same does not necessarily apply to control over SHG loans. Guidelines for NRLM suggested that state governments assist SHG members lacking bank accounts to avail of the benefits of a program launched in 2014, the Jan Dhan Yojana, that aimed at providing each household access to a basic savings account . HoweverPanchsutras, requirements for the receipt of CIFs , and then into Cluster Level Federations (CLFs). This federated structure was intended to overcome the problems of delivering inputs, including training and market access, to large numbers of small SHGs, while simultaneously providing a means of monitoring and ensuring the quality of each SHG. Equally important, NRLP supported an innovative program that recruited women, at the time of program entry into each village, to a “community cadre.” Following an extensive two year period of training, this cadre enabled an expansion in the number of teams entrusted with the formation of SHGs, VOs and CLFs and hence the growth of the program at the extensive margin. It also enabled the program's growth at the intensive margin or in the quality of each SHG, with cadre members monitoring the operations of SHGs, certifying their quality and providing them the training to effectively access and use program resources including CIFs. Members of the community cadre were responsible for training SHG members in the preparation of micro-investment plans and for certifying the SHG's adherence to the  of CIFs .Panchsutras was low: the aggregate score of surveyed SHGs in terms of their adherence was just 2.5 out of 5. Annual Action Plans prepared by each state and our discussions with government officials suggest that these implementation failures as well as delayed receipt of CIFs were primarily a consequence of weak administrative capacity in the form of insufficient numbers of community cadre members. Thus, program implementation was characterized by a vicious cycle: Slower-than-anticipated progress in the formation of SHGs in early years delayed the recruitment of the community cadre, further slowing the growth of the program at both the extensive and intensive margins.13Had these guidelines been adhered to, only high quality SHGs should have been provided CIFs. However, our data reveal that adherence to these principles was minimal. While 37% of SHGs reported receiving CIF funds, only 12% report having prepared a micro-investment plan. Similarly, adherence to the 44.1Our survey data comes from a large cross-sectional survey of SHGs and their member households across 8 states designed for an evaluation of NRLP.15The survey sample was drawn from districts with at least two early and two late blocks . Within each of these blocks, two clusters were selected using MIS data that identified project clusters and provided information on the year of village entry and SHG formation for the census of all SHGs in the district. In each cluster, two “early” and two “late” villages were chosen, with early SHGs defined as those entered in the first year and late villages being those entered in the last year of SHG formation in the cluster. Within each village, two SHGs were randomly selected from the set of those formed at the time of initial village entry for administration of the household survey to its members. A SHG module was also completed for four additional SHGs in the village, randomly drawn from SHGs listed in the MIS. The survey sample, therefore, does not constitute a random sample of SHGs, comprising instead of SHGs, in each cluster (and hence block) formed in early and late stages of the program.The fact that the study was implemented towards the end of the program allowed the household instrument to be canvassed on SHG members, eliminating the low take-up rate that normally affect evaluations that identify households prior to the implementation of the program and hence prior to SHG formation . EquallyThe sample for this paper is restricted to households who were members of functioning SHGs and those formed after 2012, covering SHGs formed between 2012 and 2019.4.2Our survey instruments included a household survey, a woman's survey administered to one prime-age woman in the household, a village module and surveys of SHGs, VOs and CLFs. The household and woman's survey were intended to be administered to all members of the two SHGs in each village selected for inclusion in these surveys. However, budget and time constraints limited interviews to households who were available at the time of the survey, yielding an average of 5 members per SHG, with three or more members being surveyed in approximately 80% of SHGs.The basic design of the household module followed that of other surveys, such as India's National Sample Surveys, providing information on household rosters and including a standard expenditure module. A distinguishing feature of our survey, however, is its collection of detailed income data on all family enterprises, agricultural and non-agricultural. This includes data on marketed and non-marketed outputs, family and hired labor used in production, and all purchased and home-provided production inputs. This depth of information enables estimates of income and hence of savings as the difference between income and expenditures, enabling regressions that correctly incorporate life cycle dynamics by conditioning on household savings.high and low intensity CIF states, based on the median CIF amount.Our empirical analysis is enabled by additionally matching the survey data to the Project's MIS and to other data on the program. The division of states into groups based on state norms regarding CIF amounts uses information from each state's Annual Action Plans from 2013 to 2018.As noted in the preceding section, the receipt of CIFs was significantly affected by the size of the available community cadre and hence the scale of the program at the time of SHG formation. This suggests the need to control for program scale in order to identify the effect of CIFs. MIS data allows us to construct measures of scale specific to each SHG. Given that community cadre members were required to have completed a minimum of one year of active membership in a SHG, and that their training required an additional 6 months to a year, we develop measures that reflect the scale of the program two years prior to the formation of each SHG. These measures are: the number of SHGs in blocks of the district other than that in which the SHG was located, the number of villages entered in these blocks, and the number of villages entered in other clusters of the same block. These measures thus exclude data from the cluster and the block in which the SHG was located and, as noted above, relate to the scale of the program two years prior to the formation of the SHG in question.4.3We follow the literature in measuring women's decision making based on their answers to a series of questions related to their role in household decisions .20 The sWomen were given the option of not responding to a question if the item was not relevant to them. Averaged across all decisions, the non-response rate was just 5.95%. However, the non-response rate was high (18%) for two items, decisions regarding the marriage of a child and the sale of agricultural land. It also averaged 13% for loans from moneylenders and from banks. The non-response rate for all other items was less than 10%, averaging 4%. To ensure that our results are not driven by variation in the non-response rate, we also report regressions that calculate the decision-making index excluding decisions regarding these four items.. Finally, reverting to the mean score, we also report results for some individual questions (expenditures on food and decisions regarding what food to cook), and sub-groups of the commodities considered in the overall index. These sub-groups are: decisions regarding investments in children ; clothing ; household durables ; and loans .As noted in Section 4.4We commence this section with a set of figures that demonstrate the variation that we exploit in this paper. While we provide descriptive evidence of the impact of CIFs on the size of SHG loans later in this paper, dmindx), characteristics of SHGs, and of the villages in which households are located. The data are provided for the full sample, and then separately by high and low intensity states, and by SHGs with and without CIFs within each of these state groupings.The mean value of the decision-making is 3.08 (out of a maximum score of 5). It is higher in low intensity states and in SHGs with CIF than in those without. These differences are the basis of our identification of the effect of CIFs on women's decision-making and we discuss their implication in detail below.The data also reveal the relative poverty of sample households. An average of 32% of households are from scheduled castes and tribes. The highest level of schooling reported by a prime-age woman in the household, at 4.8 years, is marginally short of the 5 years required to complete primary school. Approximately 66% of households in our survey own some agricultural land, with mean agricultural holdings being just 1.23 acres.For the average household, savings are negative, indicating a dependence on borrowing to finance the deficit in incomes relative to expenditures. Expenditures on items such as clothing are a relatively small proportion of total household expenditures (7%), primarily because of the dominance of food expenditures. The data on SHGs reported at the bottom of the table comes from the set of SHGs covered in our survey (approximately 6 SHGs per village). These data reveal that the average SHG comprises 11 members, with 63% of members being from scheduled castes and tribes. The mean monthly savings rate is INR 27.4.5smaller than this same difference in low intensity states. If the program is assessed just on the basis of these differences, it would lead to the conclusion that women's decision making falls with access to large CIF funds. However, such a comparison does not control for differences within states in the low and high intensity groups, an important difference given the variation in the implementation of the program across states. It additionally bundles the effect of CIFs with differences in the duration of exposure to SHGs, the primary determinant of the phasing of CIFs, and with other aspects of program implementation including variation in its scale across blocks and districts. Our regression approach, described below, controls for these differences.55.1year – cif_year). We then run a standard “normalize and pool” regression of loan amounts on indicator variables for the number of years from receipt of CIFs, with the coefficients on each year representing the mean borrowing in that year.24The hypothesis we test in this paper is premised on the assumption that CIFs provide a large infusion of funds into SHGs, allowing larger loans than were possible earlier and providing the basis for significant improvements in women's decision-making. We therefore start our empirical analysis by providing evidence of the impact of CIFs on the amount of SHG loans using survey data on all current loans and on those that were fully repaid in the three years preceding the survey. Using data on the year in which each of these loans was originated, we construct a panel data set of loans (by source) for each household commencing from 2012. This enables a graphical representation of mean loan amounts in the years preceding and following the receipt of CIFs. To implement this regression, we normalize each loan year by the year of CIF receipt  to this same difference in low intensity states. This regression explicitly controls for selection in the timing of CIFs, allowing for the possibility that those SHGs that received CIFs (conditioning on age) differ from others that did not. It also controls for selection in the amount of CIFs provided by each state.To eliminate residual differences between SHGs with and without CIFs, we implement a difference-in-difference regression that removes unobservable differences by comparing the difference in outcomes across treatment and control SHGs in states that disbursed large amounts of CIF  to those in an older sample of control SHGs without CIFs.In programs in which treatment is phased over time and the order of the phased roll-out is known, the difference-in-difference common trends assumption can be tested using the proposed phasing plan across yet-to-be-treated samples. In our context, such a test would require data on three groups of SHGs: those that have already been treated, those who will receive CIFs next, and a third group that will receive treatment last. We mimic this approach utilizing the sample of SHGs who recently received CIFs in the year preceding our survey (2018 and the first few months of 2019).cif_new x high will obtain only if both conditions, no pre-trends and no initial effect of CIF, are met.This test is not informative if the estimated coefficient is statistically significant: In this case it is not possible to separate out the effect of pre-trends from a violation of the underlying assumption of this test that the benefits of CIF in the first year were negligible. However, a statistically insignificant coefficient on the interacted term CIF*high. If the identifying assumption is valid, then this term should not be statistically significant.We support the results of this test with additional regressions using alternative measures of women's decision making determined prior to the formation of SHGs. One such measure is the woman's age at marriage, a variable widely believed to determine her bargaining power within the marriage. Additional support comes from regressions on women's labour force participation rates (LFPRs), an outcome that is also widely viewed as an indicator of women's status, along with their ownership of assets and measures of their involvement in household decisions. For the South Asian economies, the association between women's LFPRs and their socio-economic status, and hence between LFPRs and an index of their decision-making role, is frequently negative . HoweverWhile the extent to which each of these measures correlates with women's decision-making or empowerment can rightly be questioned, the sum of the evidence from these regressions does help support the robustness of our results.5.5high as an indicator of program intensity. To test this, we report regressions that interact CIF with indicators derived by grouping states by female literacy rates and the state's proportion of households from scheduled castes and tribes, using data from the 2011 Census. We also assess whether our results are merely picking up the effects of other SHG interventions, focusing on the first infusion of funds into SHGs, the Revolving Funds (RF). Revolving Funds averaged about INR 15,000 per SHG with little variation across states. Because of this lack of variation, an interaction of receipt of RFs with the indicator for high intensity states should have no effect on women's decision making, unless this latter indicator is also picking up the effect of other differences across states that, combined with other SHG interventions, may also affect outcomes.We support our interpretation of the regression results through a series of falsification tests. Following CIF*high is statistically insignificant for outcomes that should not be affected by the availability of CIFs. A first set of results examines the effect of this interaction term on household variables: the highest level of schooling of adult males and females in the household, the amount of agricultural land owned, and the number of adult males and females. We also report similar regressions that consider the effect of CIF*high on a broad set of SHG attributes .A second set of falsification tests focuses on “unaffected outcomes,” testing that the coefficient on 5.6As noted in Section In addition to their value in supporting our primary results, these regressions allow us to extend the empirical literature to assess whether smaller loans also constitute distribution factors. We do so using data on SHG monthly savings. While research has previously considered multiple distribution factors, this has been in the context of testing whether distribution factors affect allocations only through their effect on women's bargaining weights. Such tests require at least two distribution factors . In contGiven our focus on SHG savings we frame our analysis within the context of the empirical literature on lifecycle models that use Frisch or Marginal-utility-of-wealth-held-constant demand functions to incorporate inter-temporal decisions . MaCurdyImplementing these regressions requires an instrument for savings other than CIFs and SHG monthly savings, one that is uncorrelated with women's bargaining weight. The empirical literature has most commonly used wages or determinants of wages  as instruments for income (and hence savings), under the assumption that the consumption goods in question are separable from leisure . We baseRabi (winter) season of 2017–18, the year prior to the reference year for our survey.A measure of income shocks is available through survey questions that asked farm households about their expected and realized crop output in the three main agricultural seasons of the previous agricultural year, including for the Rabi shock, SHG monthly savings, CIF x high and the individual components of this interaction. SHG monthly savings, the product of the SHG's required monthly saving amount and the number of members of the SHG, represents the funds that would be available to an SHG for internal lending in its first year of operation, prior to the receipt of CIF funds. These regressions also include an expanded set of controls that affect preferences: measures of the demographic profile of the household, captured by the number of household members in 8 gender-age groupings, household size, agricultural land ownership and the highest level of schooling of adult male and female household members.We first report results from first-stage and reduced form regressions on household savings and consumption allocations respectively, with the set of control variables including Rabi shock. That is, we re-estimate equation SHG monthly savings included amongst the regressors, using a measure of consumption (described below) as the dependent variable. This regression allows us to test whether CIF x high and SHG monthly savings affect allocations, even in regressions that condition on household savings. That is, they provide a test of the unitary model and of the hypothesis that only large improvements in a woman's resource position constitute distribution factors that improve a woman's decision-making authority.We then turn to IV regressions that condition on savings, instrumenting it with In choosing consumption goods for this analysis, a primary concern is that identification assumes that current prices, wages and wage shocks (for this Rabi season) are uncorrelated with individual farmers’ expectations of output and expectational errors from the previous Rabi season.Our analysis follows the former approach, with attention restricted to purchased goods that are likely to be separable from leisure. This excludes an analysis of food expenditures given the importance of home production in food preparation. Following the literature, we confine our attention to the share of clothing in household expenditures . The priCIF x high in regressions on household demand, while suggestive of a role for bargaining weights, may also be consistent with other interpretations such as binding labor or credit constraints. However, the availability of an index of women's decision making and evidence of an effect of CIF x high on this index suggests that an effect through bargaining weights must exist, even if this does not rule out additional pathways.A second concern, common to other research that implements similar tests of the income pooling model, is that evidence of the statistical significance of 66.1CIF x high in regressions that vary in the set of controls, including those for SHG year. The first regression is the simplest, including just the interaction variable CIF x high, and its individual components (CIF and high), without any additional controls. The second regression replaces the indicator variable high with a set of state fixed effects. The third regression additionally includes SHG years (age) and its interaction with high. The next regression represents a far more flexible approach to the incorporation of controls for SHG age, using indicator variables for the year of formation and their interaction with high. Regression 5 replaces the indicator variables for SHG age with a quadratic, while the last regression includes the set of controls discussed in the previous section.CIF x high, with large significant effects of its individual components. Specifically, states that awarded large CIF amounts to SHGs are those in which women report lower levels of decision-making responsibility. However, these results differ significantly from those in specifications 2 through 6 that replace the indicator high with state fixed effects and include additional controls for SHG age and its interaction with high. Though controls for SHG age and their interaction with high are statistically significant in several specifications, the results suggest that the primary difference between simple difference-in-difference regression without controls (regression 1) and those reported in columns 2 through 6 stem from the inclusion of state fixed effects. These incorporate the significant differences across states, within the low and high program intensity groups, in the implementation of the program and in the resources devoted to it, differences that are likely correlated with socio-economic characteristics that affect women making even in the absence of CIFs.The first regression, with no additional controls, corresponds to summary statistics reported in CIF, which changes from positive and statistically significant in regression 1 (without state fixed effects) to negative and insignificant in regressions with state fixed effects. This change suggests that much of the variation in the timing of CIFs reflects differences in state-level implementation, a difference that varies across the states within each of the two state groupings. While never statistically significant, the negative coefficient on CIF in regressions 2 through 6 suggests that the selection of SHGs to receive CIFs early, conditioning on SHG age, targeted relatively disadvantaged SHGs whose members had lower decision-making ability.The inclusion of state fixed effects also affects the coefficient on CIF x high across these specifications, attesting to their robustness to variation in the set of control variables. We use the last specification for the remaining results of this paper. This estimate suggests that the provision of CIF in high states raises women's decision-making by 0.10, a 2.2% increase over the mean value of 3.08.The results of this table also reveal no statistically significant variation in the coefficient on CIF x high is statistically significant for each of these sub-indices.To ensure the robustness of our results to alternative ways of constructing the decision making index, 6.2CIF>=2018) and those that received CIFs in 2017 or earlier, two years prior to the survey year. Under the assumption that the effect of CIFs on women's bargaining weight takes time to manifest, the coefficient on CIF>=2018 x high provides a test for the common trends assumption. The first column reports the results of this test with the set of controls including SHG years and its interaction with high, while the second column replaces this with a quadratic in SHG age. Both regressions support the common trends assumption: for SHGs that have only just received CIFs, the coefficient on CIF x high is smaller in magnitude and statistically insignificant at conventional levels.The first two columns report results from regressions that divide the sample of SHGs with CIFs into those that received CIFs in the last year (high states by 0.25 (in contrast to the earlier estimate of 0.10), a 8.12% increase.This disaggregation by age significantly increases the estimated impact of CIFs on older SHGs, raising decision making in The remaining three columns test the common trends assumption using alternative correlates of women's decision-making. The first measure is women's labor force participation rate, the one variable for which we have survey data as well as (village level) information from 2011, prior to the onset of the program. Column 3 reveals an effect of CIFs on survey data: the provision of CIFs reduces female labor force participation rates, with the estimated coefficient being statistically significant at the 1% level. In contrast, utilizing village female labor force participation rates from the 2011 census, column (4) suggests no impact of CIFs on baseline values of female labor force participation. Similarly, the last regression, on the age of the woman at the time of marriage, also implies that CIFs had no impact on measures of women's bargaining weights at the time of marriage.6.3RF x high does not significantly affect on women's decision making. This is a strong result, one that suggests that the estimated impact of CIFs is not picking up the effect of other earlier SHG inputs or interventions.While the results of the previous table support the identification of CIF effects, regressions reported in high with alternative indicators based on the state's female literacy rate or the state's proportion of the population from scheduled castes and tribes does not have a similar effect. As with the regression on RFs, this provides strong support that CIF x high does, in fact, capture variation in the intensity and incidence of the program.The next two regressions address a primary concern with our methodology, that state groupings may just be reflecting broad state-level differences in economic conditions and are not reflective of variations in the magnitude of CIF funding and hence of the intensity of the CIF input. These regressions reveal that replacing the indicator CIF x high merely reflects geographic variation in socio-economic conditions, one would expect it to effect at least some of these household attributes. The results of the estimation of our basic regression, with controls, on this broad range of outcomes provides further support for our results: The provision of CIFs does not determine any of these outcomes.The remaining 5 columns test the effect of this interaction on “unaffected outcomes” described in the previous section, those that should not be affected by the provision of CIFs. Conversely, if the interaction variable CIF x high on SHG attributes.Finally, regressions reported in CIF x high as indicative of the effect of CIFs on women's intra-household bargaining weight.Taken together, the results of our regression test for common trends as well as this extensive set of falsification tests suggest strong support for our empirical methodology and for our interpretation of the coefficient on 6.4In this section, we report results from regressions that implement the instrumental variable methodology described in the previous section to test the effect of household savings and the relative importance of small versus large increments in women's financial resources on decision-making and household demands. All regressions include the set of controls used in the previous regressions as well as the preference shifters described in Section CIF x high and Rabi shock, as well as that of SHG monthly contributions (significant at the 5% level). Savings fall with higher SHG monthly savings, consistent with the hypothesis that increases in a SHGs internal savings enable small loans that facilitate increases in household expenditures. Savings also fall with negative income shocks. In contrast, CIF x high has a strong statistically significant effect on household savings, suggestive of a positive effect of large loans on household income through the investments in productive assets that they enable.Column 1 reports results from first stage regressions on household savings, revealing a strong statistically significant effect of Rabi shock, with SHGs' monthly savings and CIF x high included as regressors. This regression reveals that SHGs' monthly savings have an insignificant effect on clothing shares in regressions that condition on savings, suggesting that any impact on household allocations comes only through its impact on total household resources. In contrast, the statistically significant coefficient on CIF x high in this same regression suggests that CIFs affect household allocations independently of their effect on savings confirming that large improvements in women's financial access have effects on households that differ from those that would result from a general improvement of the household to financial resources. These differences suggest that who controls household resources only matters if the amounts in question are relatively large.Results from instrumental variable regressions on clothing shares are in columns three through five.SHG monthly savings on clothing shares in this regression combined with its positive effect on total household savings . We confirm that access to these large funds serve as a distribution factor, affecting household allocations directly. Conversely, though access to small loans affects household savings, the effect on household allocations occurs only through household savings. Put differently, financial inclusion policies that target women produce results that differ from those that target households only if they provide women with access to relatively large loans.Our results have significant implications for policy, highlighting the need to pay attention to quantities, rather than to implement financial inclusion policies without regard to the loan amounts that are available through them.Kochar: Conceptualization; Formal analysis; Methodology; Writing of original draft and revisions. Nagabhushana: Conceptualization; Data curation; Investigation. Sarkar: Conceptualization; Investigation; Project administration. Shah: Conceptualization; Data curation; Investigation; Validation. Singh: Conceptualization; Methodology; Validation; Writing of revisions.


In recent decades, the literature on Social Network Analysis and health has experienced a significant increase. Disease transmission, health behavior, organizational networks, social capital, and social support are among the different health areas where Social Network Analysis has been applied. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their carers. Thus, the main aim is to identify, categorize, summarize, synthesize, and map existing knowledge, literature, and evidence about the use of Social Network Analysis to study social support and care in the context of illness and disability.This scoping review will be conducted following Arksey and O'Malley's framework with adaptations from Levac et al. and Joanna Briggs Institute’s methodological guidance for conducting scoping reviews. We will search the following databases (from January 2000 onwards): PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE. Complementary searches will be conducted in selected relevant journals. Only articles related to social support or care in patients or caregivers in the context of acute illnesses, disabilities or long-term conditions will be considered eligible for inclusion. Two reviewers will screen all the citations, full-text articles, and abstract the data independently. A narrative synthesis will be provided with information presented in the main text and tables.The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on Social Network Analysis, and its main contributions will highlight the importance of health-related research's social and relational dimensions. These results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. It is anticipated that results may guide future research on network-based interventions that might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels.https://osf.io/dqkb5.Open Science Framework The online version contains supplementary material available at 10.1186/s13643-021-01876-2. Social Network Analysis (SNA) is a research approach within the social and behavioral sciences which focuses on ways of interaction and interconnection between individuals and social groups to explain social patterns of feelings, thoughts, and behaviors , 2. In rThe literature on social networks and health begins by referring to the idea that people are embedded in a network of relationships. The first empirical studies were published in the Annual Review of Public Health in the mid-1990s . They shDifferent SNA studies have focused on showing the relationship between the characteristics of the social network and different health-related outcomes such as health behaviors , 10, satAs mentioned above, one of the application areas of SNA is social support. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of both social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their careers. In its conceptual differentiation, caring and social support are dynamic processes that allude to interpersonal relationships , 22–24. A scoping review is selected as an exploratory form of knowledge synthesis due to the extensive and growing literature that uses SNA in social support and care. This type of review is commonly undertaken to examine the extent, range, and nature of research activity in a topic area : (a) to Arksey and O’Malley’s methodology framework , its advhttps://osf.io/dqkb5 ). This scoping review has been reported using PRISMA-P [This protocol is registered within the Open Science Framework platform  Table  will be The limits to be used in online databases searches will be: articles published in Spanish and English and the year of publication (from January 2000 onwards). The inclusion criteria will be (a) empirical studies with SNA methodology (quantitative or mixed methods design) and (b) studies whose participants are patients or caregivers as receivers of care or social support in the context of illness or disability provided by both, health professionals or personal/informal contacts with no age limits. The exclusion criteria will be (a) theoretical papers, (b) grey literature, and (c) qualitative studies.The PRISMA flow chart  Additio will capIn the first step, the authors will work with an initial limited search in the PubMed database. The keywords and index terms will be identified in the titles and abstracts of the retrieved papers. In the second step, these keywords and index terms will be used to search across different databases. A structured search strategy will include Boolean operators , and truncations, either individually or in combination to ensure the search process. We will search the following databases: PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE , independently. Disagreements will be resolved by consensus or with the assistance of a third author (PMS). The selected studies’ full text will be retrieved and checked independently by two authors (RFP and NSF). Sources of information that do not meet the eligibility criteria will be disregarded. A record of those sources and the reasons for their exclusion will be kept in a separate file.Scoping reviews are designed to provide an overview of the existing literature, regardless of quality. Therefore, a formal assessment of the quality of the included studies will not be conducted .The data charting aims to provide a descriptive summary of the results that align with this scoping review’s research questions. Thus, a data extraction tool designed for this study has been adapted from the template data extraction instrument for scoping reviews provided for JBI Manual for evidence synthesis  and willCharting the results will be an iterative process. Table According to the data extraction template, the obtained information will be part of built evidence tables with an overall description of the papers. We will follow the Arksey and O’Malley’s methods  to proviThe results of this scoping review will be added to the existing review articles on the use of the SNA in the health research area as complex health care interventions , the behThis type of review is a convenient tool to determine the coverage of the body of literature on this specific area and will give a precise indication of the number of studies available and an overview of its focus. It might be useful for uncovering emerging evidence when it is still unclear what other more explicit questions can be addressed by a more precise systematic review . Thus, tThe authors anticipate that this review’s results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. This differs from many studies in this topic which use non-network approaches. The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on SNA, and its main contributions will highlight the importance of health-related research’s social and relational dimensions. Furthermore, it will identify areas for future research where social networks might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels. The findings of this study will be disseminated through peer-review publications and national and international conferences.Additional file 1. PRISMA-P 2015 Checklist.Additional file 2. PRISMA-Flow Diagram.Additional file 3. Literature search strategy.


Gallstones affect 20% of the Western population and will grow in clinical significance as obesity and metabolic diseases become more prevalent. Gallbladder removal (cholecystectomy) is a common treatment for diseases caused by gallstones, with 1.2 million surgeries in the US each year, each costing USD 10,000. Gallbladder disease has a significant impact on the logistics and economics of healthcare. We discuss the two most common presentations of gallbladder disease (biliary colic and cholecystitis) and their pathophysiology, risk factors, signs and symptoms. We discuss the factors that affect clinical care, including diagnosis, treatment outcomes, surgical risk factors, quality of life and cost-efficacy. We highlight the importance of standardised guidelines and objective scoring systems in improving quality, consistency and compatibility across healthcare providers and in improving patient outcomes, collaborative opportunities and the cost-effectiveness of treatment. Guidelines and scoring only exist in select areas of the care pathway. Opportunities exist elsewhere in the care pathway.  Cholelithiasis  is considered a major public health problem in developed countries, and its symptoms and complications can generate major economic and social burden. Gallstones are masses in the gallbladder or biliary tract formed due to high levels of cholesterol or bilirubin in bile. In 2008, it was estimated that 14.2 million women and 6.3 million men had gallbladder disease (GD) in the USA and that 1.2 million cholecystectomies  are performed each year . In Germany, 190,000 patients with GD undergo surgery each year . AutopsyThere is great variability worldwide regarding the known prevalence of gallstones, in part because the disease may be asymptomatic. High rates of incidence occur in the United States, Chile, Sweden, Germany and Austria, whereas Asian populations appear to have the lowest incidence of gallstone disease ,11,12,13It is noted that 23.2% of cholecystectomies in Ireland take place in an emergency rather than an elective care setting . Across H. pylori . . 119]. Delays to care are undesirable for patients, providers and funders and represent missed opportunities for early clinical engagement with the patient . WaitingOn efficiency grounds, there are clear arguments to be made for the cost effectiveness of improved processes of care. For example, elective LC for symptomatic cholelithiasis has been shown to be inexpensive relative to its associated health gains, although health gains are not experienced uniformly across age groups . NotablyGallbladder diseases share risk factors with other conditions, including obesity, diabetes and metabolic syndrome, which are likely to be comorbidities. Western trends predict an increasing prevalence of metabolic conditions, and the incidence of GD and BC is likely to increase. Risk factors include lifestyle and diet, and twin studies have shown a role for genetic risk factors. At present, the genetic drivers are not well known, and it is clear that much work is to be done if we are to understand the genetic contribution to GD and BC. The explosion in patient data available from ‘omics technologies and electronic health care records, allied to the growth in data analytics tools means that here are many opportunities in this area. As a long-established condition, gallbladder disease is well-characterised. Likewise, the pathologies of biliary colic and acute cholecystitis are well-understood. However, our review of diagnostic guidance indicates that, in normal practice, distinguishing between a mild acute cholecystitis (defined according to TG18) and a severe presentation of biliary colic with associated chronic cholecystitis requires a combination of signs, symptoms and diagnostic markers to achieve acceptable sensitivity and specificity . Given t(1)The consensus-framed, evidence-based approach taken to diagnosis and treatment of acute cholecystitis should be extended to consider the case definition and aetiology of the related pathologies of biliary colic and chronic cholecystitis.(2)This approach should have regard for operative findings and a patient’s clinical history. Furthermore, they should ideally assess the potential for biochemical or genetic markers to stratify patients according to their operative findings.(3)High-quality evidence should be accumulated on the potential of imaging technologies, leveraging the use of MRI and MRCP that is now commonplace in many health systems.A small but growing number of studies are trying to create metrics to assess AC and BC healthcare delivery. Foremost has been the development of the Tokyo guidelines, which have two organisational strengths: (i) they have been developed as a consensus of a global community of expertise, and (ii) they continue to be updated as the clinical understanding of AC progresses. The value of guidelines lies in their ability to create a more objective and standardised delivery of care. This is the first step towards improving care amongst underperforming providers and in creating the consistency and compatibility between providers needed for interaction and collaboration. Similarly, metrics and scoring systems create a more objective and standardised assessment of patients and practices for internal audit. This is the first step towards objective quality improvement in care delivery and cost-efficiency, and it can underpin audit and training activities. It is clear that many aspects of GD and BC healthcare would benefit from guideline and metric development undertaken on the same basis as the Tokyo guidelines. Specifically: (1)The relevance to practice of any derived classification of GD;(2)Facilitating optimisation of the treatment pathway, undertaken on exclusion of the urgent pathologies of acute cholecystitis and common bile duct obstruction;(3)A better understanding of the relationship between acute and chronic forms of GD and the implications of this for resource allocation.The benefits of such an approach would include:The global high prevalence of gallstone disease makes it a significant health concern that is likely to place a growing demand on healthcare providers. Valuable work has been carried out to create metrics and guidelines to standardise healthcare in this area, but their coverage is limited, and the delivery of care remains highly variable. It is clear that much work remains to create consensus and standardisation in the clinical care pathways and to understand their implications for patient outcomes, for quality and cost-efficiency of care and for the healthcare provider. Here we have summarised much of the work in this area and highlighted where the greatest current need lies for standardisation. This need presents opportunities to further develop our understanding of how we can best deliver healthcare for gallstone diseases.


Germ.: “Sprachatlas der Deutschen Schweiz”—SDS), with the explicit aim of selecting survey sites corresponding to the aims of the current project “Swiss German Dialects Across Time and Space” (SDATS), which revisits SDS 70 years later. We find that depending on the circumstances and requirements of a study, the proposed methodology, introducing cluster analysis into the survey site reduction process, allows for a greater objectivity in comparison to traditional approaches. We suggest, however, that the suitability of any set of candidate survey sites resulting from the proposed methodology be rigorously revised by experts due to potential incongruences, such as the overlap of objectives and variables across the original and intended studies and ongoing dialect change.Many language change studies aim for a partial revisitation, i.e., selecting survey sites from previous dialect studies. The central issue of survey site reduction, however, has often been addressed only qualitatively. Cluster analysis offers an innovative means of identifying the most representative survey sites among a set of original survey sites. In this paper, we present a general methodology for finding representative sites for an intended study, potentially applicable to any collection of data about dialects or linguistic variation. We elaborate the quantitative steps of the proposed methodology in the context of the “Linguistic Atlas of Japan” (LAJ). Next, we demonstrate the full application of the methodology on the “Linguistic Atlas of German-speaking Switzerland” ( The issue of survey site reduction is as old as surveying itself. Linguistic and, specifically, dialect studies often select their survey sites from earlier data collections, such as linguistic atlases, and choose sites where certain linguistic variables of interest have already been documented. However, site reduction is usually done qualitatively, based on linguistic expertise, without quantitative arguments supporting selection procedures. Thus, despite their importance, most methodologies used for the reduction of survey sites in dialect studies are not reproducible in detail.Spatial sampling for a dialect study, i.e., choosing localities to survey, has been one of the central issues in dialectology. Similar to the selection of speakers, the selection of surveyed localities  and proposes an application of cluster analysis. To demonstrate the versatility of the approach, the general methodology is also applied to real dialect data sets, in one case with the aim of finding suitable survey sites for an actual contemporary dialect study.Researchers from different areas of linguistics could potentially benefit from the methodology proposed in this paper, by utilizing survey site networks of previous studies. A potential research aim may be to conduct a dialect interview campaign, revisiting numerous phenomena in a dialect atlas, or recording new phenomena at the same linguistic level as the atlas, with the objective of covering the expected variation in fewer locations. As another example, researchers may want to test how the dialects in a certain area have changed, and so they plan to revisit a previous survey. Assuming that dialect leveling has occurred since the survey, they may only want to visit a sufficient number of sites representing the contemporary dialectal landscape. Additionally, the methodology could be implemented for larger databases based on online crawling or digitized corpora —Nerbonne and Kleiweg, Linguistic studies often start with the task of survey site selection based on the sites of a previous larger scale survey. Our aim is to provide general suggestions about optimal survey site subsampling to the linguistic/dialectology community. As summarized in the “first law of geography” Tobler, , variatiSprachatlas der Deutschen Schweiz”  for representing reality is a key issue in spatial sciences, and representativeness is heavily dependent on the spatial structure of the variable of interest. Effective spatial sampling has to consider the spatial autocorrelation in the population, and the variables investigated  has an equal probability of being selected. At the same time, the spatial distribution patterns of linguistic phenomena do not always follow the spatial distribution of other population traits. Therefore, random sampling might lead to oversampling the variable of interest in densely populated regions where few variants prevail, or to undersampling in areas with low, isolated populations that use diverse variants. In linguistics, randomly selecting people has been, however, successfully utilized for sociolinguistic studies, as a large enough sample may be representative of the entire population  as inputs, based on which clusters are compared  method assesses the dissimilarity between the new cluster and the existing cluster based on the distance between their means. In this process, each element in a cluster gets an equal weight, independent of the number of elements in the clusters  algorithm  and the widely used k-means algorithm is that in each step, PAM appoints actual data points (medoids) as the centers of clusters by minimizing the distance between the points and the medoid. K-means, however, minimizes the sum of squared Euclidean distances, which makes it less robust to noise and outliers than k-medoids . Hierarchical clustering is, additionally, prone to large differences in results caused by small changes in the input matrix  metric Previous site reduction attempts on SDS have been arbitrary and not replicable. Kelle  digitizeDespite being the most comprehensive collection of Swiss German dialect data, SDS has not yet been entirely digitized. Starting in 2007, Yves Scherrer (with the help of his colleagues) undertook the partial digitization of SDS for the sake of several projects; categorization of variants, and select linguistic items appropriate to represent the original data in consideration of the intended study (this step is not explained in detail)Calculate linguistic distance matrices based on the selected linguistic items, thus obtaining association measures among the survey sites, as detailed in section 3.2Carry out the clustering procedures and appoint candidate survey sites in the resulting clusters. Typically, this step involves clustering survey sites based on one or multiple linguistic distance matrices and performing validation tests on clusters. The reduction and the subsequent selection of candidate sites are detailed in sections 3.3 and 3.4Evaluate the candidate survey sites, involving  revision by dialect experts and through sociogeographic filtering to find sites that correspond to the criteria of the intended study, detailed in section 3.5To aid researchers potentially implementing this outline in their flow of research, we add a non-exhaustive list of further considerations. Our methodology assumes that the original study is part of a large-scale dialect survey. The correspondence of overlapping items in the intended study and the original data needs to be scrutinized and potentially recategorized. As it appears to be a typical task to infer contemporary dialectal variation from the original data, it is important to select items that are representative at both points in time. Thus, items that are irrelevant for the intended study should be removed, such as names of rural work-tools in a large-scale study of vernaculars. The effect of each variable or groups of variables can be tested by, e.g., jackknifing or other cross-validation methods. The selection of data will always depend on the research question, thus in some cases one or a combination of linguistic levels will be used. Although it is crucial from the point of view of data quality, we do not detail the steps of digitization in this general methodology and we assume that the original data is already digitally available.The core of the site reduction methodology is a grouping algorithm, which classifies the survey sites within the original database into (a desired amount of) groups, with the aim of finding candidate survey sites in the resulting groups, similar to stratified sampling strategies. As in geospatial analysis, no single resampling strategy is optimal or superior: the method for subsampling also has to be appropriately selected depending on the objectives of the intended study and the original data  Typical quantitative steps in the proposed methodology are demonstrated using data from the LAJ NLRI, , the larThe data set used in this example contains 37 publicly availableTo prepare the survey sites for a representative clustering, well-known outliers are removed, leaving 2,238 survey sites. The Ryukyu Islands, in the southwest, are removed due to their large linguistic distance from other parts of Japan. Hokkaido, in the north, was settled by the Japanese primarily as of the end of the 19th century, and thus is removed due to small dialectal variation and mixture.There should be significant overlap between the original data and the intended study. If that cannot be achieved, a distribution of linguistic data balanced across linguistic levels might be beneficial. Similar to clustering in dialectometry, it is advisable to take as many variables as possible from the original data set, curated for the objectives of the intended study and categorized accordingly.Once the linguistic basis of the site reduction has been determined, researchers must construct an association measure among the survey sites. In a typical case, a linguistic distance matrix is calculated in a site × site manner, based on a set of linguistic variables.Relative Identity Value, calculated on pairwise matches and mismatches. At this point, it would also be possible to test the effect of single variables. Researchers may consider removing variables with spatially similar or correlating distributions as duplicates.Methods of linguistic distance calculation vary depending on the linguistic level, the variants' categorization granularity, and, if involved, the details of transcription. For calculating phonetic similarity across variants, edit distances are used most often   , similar (RIVjk)  as appli (RIVjk) . For eacWe use an MDS approach to discover latent clusters and dialect continua in the data. We plot the first two or three dimensions of the multidimensional scaling results and associate the first three dimensions to RGB colors and map themThe linguistic distance matrix is the input of clustering algorithms used for site reduction. Dialectometry often uses cluster analysis to find the internally most homogeneous and externally most heterogeneous groups in dialect data. Importantly, however, clustering techniques have mostly been used to find the optimal splitk clusters directly for a partitional method, or, for a hierarchical method, they might select a cophenetic distance, beneath which they find their k clusters in the dendrogram. It is always possible to adjust the final number of survey sites in a qualitative revision.Hierarchical clustering results in dendrograms and association values between survey sites. Dendrograms cut at the desired or optimized level can also be spatially represented by a cluster map. Partitional clustering produces a predetermined number of clusters, the optimal number of which can be determined by optimization. Researchers might not know the exact number of survey sites they want to extract from the original set, which might influence the choice of clustering method. In any case, it is worth experimenting with different numbers of clusters, also around a previously decided number, in particular for exploratory analyses. Depending on their aims, researchers might determine fpc package , using the example of LAJ. All three clustering algorithms are implemented using the  Hennig, 7 in R  with resampled data. For each cluster, the Jaccard-similarities of the initial cluster solution (in bootstrap nr. 1) to cluster solutions in all other bootstraps are computed  can be assessed.To validate the results of the different clustering methods, we used a bootstrapping approach  as they may vary substantially across clusters, further justifying mapping. Maps D–F also contain the histograms of the stability values, presenting considerable deviations from a normal distribution.As linguistic variation is assumed to be spatially autocorrelated, members of clusters found in the data are supposed to be clustered in space as well. To confirm this, and to visually explore the spatial patterns, we map the clusters produced, along with their stability values. singletons) are thus possibly outliers in the linguistic space, found as clusters by this method. UPGMA shows 39 singletons for k = 150 while the other methods show none. Ward's method, based on their positions in the map (C) seems to find clusters structurally more similar to UPGMA. Compact clusters are a characteristic of Ward's method, contributing to its popularity in dialectology. The clusters in Map F look somewhat more stable and spatially more compact than those in PAM, and clusters of the same color (Map C) present clearer boundaries in space, appearing to overlap less. Based on the stability histograms in D–F, PAM seems to have the lowest overall values, making it less suitable for clustering than the hierarchical methods (in case of k = 150). It is intuitive to expect lower stability when k is higher, as smaller clusters are expected to be also more similar to neighboring clusters. This is even more significant when  continua are present in the data, as in the case of Japanese. When applying multiple clustering algorithms with different k, it is interesting to see which algorithm produces more stable results with a certain number of clusters.In PAM's map (A), clusters do not appear spatially compact, although members are clustered in the same region. In UPGMA's map (B), several clusters are visible with a high number of members. UPGMA is based on the average difference between clusters and, because of this, chaining effects are not typical for this algorithm . Accepting these candidate sites as a reduced set of survey sites would cause problems in representation of spatially surrounding dialects.k = 150. Due to their spatially compact clusters it yields, Ward's method presents itself as the safest bet, knowing the bias in the data because of the phenomena it contains. In contrast, UPGMA produces unrealistically large dialect areas and unreasonable singletons, and PAM is less stable with more clusters overlapping in space.The continuous nature of the data and the validity of FDP are confirmed by Jeszenszky et al. . Based oThe candidate survey site sets resulting from the site reduction procedures are assumed to be representative of the original data. However, their main aim, as candidates, is to provide a quantitatively supported starting point for determining the sites that actually need to be researched. Several reasons call for a further qualitative evaluation of candidate survey sites. First, the linguistic basis of the site reduction might not be perfect due to various potential factors within the original database, the requirements of the intended study, and the circumstances that might have changed since the original survey. Second, dialect change may have progressed, due to people's changing way of life, mobility patterns, language attitudes, etc. Third, potential survey sites might have changed with regard to their sociodemographic settings, language policies, etc. Therefore, any set of candidate survey sites has to be revised in accordance with the requirements of the intended research, which potentially collects contemporary dialect data. Generally, the potential uncertainty about representativeness of contemporary dialectal variation increases with time elapsed since the original data was recorded, thus increasing the value of expert revision. Depending on the study's aims, the step of evaluation may result in swapping sites, adding sites that were originally not recorded, selecting more than one site from a cluster, rebalancing a clustering solution based on a spatial grid, etc. In section 4.4, we provide a qualitative revision of a candidate site set from SDS.In this real-life example, we present the entire site reduction procedure as applied to digital data from the “Sprachatlas der deutschen Schweiz” (SDS), with the aim of finding survey sites corresponding to the requirements of the contemporary dialect research project SDATS. Thus, the final goal is to find a way to represent the estimated contemporary variation, inferred from the original data and revised based on experts' field knowledge.“Swiss German Dialects Across Time and Space” aims for candidate sites that are linguistically as different from one another as possible, thereby covering the largest swath of dialectal forms used. We carry out the clustering experiment with two different approaches on the same data set. First, Approach I is used for the demonstration of a generalizable methodology, presented in section 4.2. This approach applies the quantitative steps of the methodology similarly to the example in section 3. Second, Approach II is used to arrive at the survey sites actually used in SDATS, as detailed in section 4.3. This approach applies only the PAM clustering algorithm with a different custom-made validation approach. Then, candidate survey sites are revised to represent the contemporary dialectal variation, in section 4.4.Scherrer's digitized SDS database (termed Scherrer's data)Relative Identity Value (RIVjk) , orThe linguistic distance matrix is calculated similarly to section 3.2.1, based on Goebl's ) Goebl, . For eacDQ is the number of diverging variables regarding survey sites i and j. For example, if in survey sites i and j answers for all linguistic variables are in different variant categories, then a linguistic distance of 1 is assigned to this survey site pairwhere mean linguistic distance matrix, “Swiss German Dialects Across Time and Space” aims to select 125 survey sites and has the objective of collecting a balanced set of phenomena across linguistic levels Therefore, we intend to use data from SDS such that is also balanced across the linguistic levels. We group the linguistic variables according to the linguistic levels and calculate the linguistic distance matrices for each of them. To counter the higher numbers of morphosyntactic and phonetic variables, the mean value of these three matrices . Finally, the compact and similar-sized clusters of Ward's method are tempting for dialectology, but they are often unreasonable, e.g., in the Swiss Plateau.Stability values are also presented as histograms in The next step in the methodology is appointing a candidate survey site within each cluster. We can select linguistically central sites, defined by the smallest total linguistic distance toward cluster members. Appointing this site intrinsically makes PAM a practical method for the application. However, as SDATS aims to investigate contemporary dialectal variation, we select candidate survey sited based on estimated potential dialectal change since data collection in SDS. We aim to find sites that have potentially influenced their local surroundings since 1950, assuming, based on Trudgill's linguistic gravity theory , their sKruskal—Wallis test to test the significance of the differences. Affirming this, the pairwise Wilcoxon rank sum test allows us to test which candidate sets' linguistic distance matrices have a significantly different distribution from the original To evaluate these candidate site sets, we test if they are representative of the original survey site set of SDS (573 sites). Technically, we test if the similarity of the distributions in the linguistic distance matrices of the candidate set and the SDS set , and another based on their population (Ptop).This section serves the purpose of detailing the site reduction approach implemented to define the conclusive candidate site set for SDATS, which is used from section 4.4 for the qualitative revision. We present a customized methodology of cluster analysis and site selection to fulfill two aims. First, we address SDATS' requirement of balance across linguistic levels. Second, we address the requirement of inferring a future linguistic situation based on the theory of linguistic gravity. To this end, we use a special-purpose cluster validation technique and build the qualitative requirements of the SDATS project partly into the clustering step . We arrive at the candidate survey site set by ranking the survey sites based on two measures introduced below, one related to their stability in their clusters S20). The number 64 is decided by the number of lexical items in Scherrer's data, the lowest among the linguistic levels. In parallel, sets of 20 items are used to decrease the bias assumed to be caused by the constant presence of all 64 lexical items in the S64 subsets. We create 35 subsets of S20 , with an average of around 70% overlap. The overlaps among S64 subsets are more uniform , as they include by definition all 64 lexical items and the majority of phonetic and morphosyntactic items.The overlap of items across random subsets is visualized in S subset, we calculate the linguistic matrices. Effects of random item selection in the subsets are shown by Pearson correlation coefficient values across their linguistic matrices, which are almost always above 0.9, with the lowest values around 0.75. The high values (R2 ≥ 0.62) confirm the similarity across the random subsets even in cases of smaller item overlaps across S20 subsets.For each k = 125 on the linguistic distance matrix calculated from each of the S subsets, using the cluster package  than the Ward's (_Ward) or UPGMA clustering of _UPGMA). Therefore, if we accept that each of the clustering methods produce linguistically plausible cluster partitions on S subsets can also be accepted with a high probability.To justify using PAM, we test the similarity of J) of clustering runs, in which the site is clustered together with the same other survey sites. If survey sites h, i, and j always fall into the same cluster and there is no other survey site ever falling into this cluster, then each of the sites h, i, and j get the maximal J value. A survey site that always becomes a singleton would also get this value, giving the chance for very local but unique dialects to stand out as stable clusters.We validate clusters using a custom method resembling the noisy clustering and bootstrapping approaches often used for cluster validation in dialectometry. For each survey site pair, we note the number of occurrences when the two sites are clustered together. Then, for each survey site, we calculate the percentage . The y-axis, along with the background color, shows the J value of the site, running from dark purple (low “stability in own cluster”) to yellow (high “stability in own cluster”). An ideal survey site would score high with regard to both requirements, reaching the top right corner of the scatterplots. Based on the point color, size, and background color, J and Ptop can be transferred to the maps on the left. Values of J are shown in Maps A–C as the colors of Voronoi-polygons around their SDS sites, illustrating the areal distribution of J. Because clusters mostly contain more than one survey site, similar J values are expected to cluster in space.Survey site eligibility is then ranked based on Before we select the candidate sites based on the clustering solutions of all subsets, however, we revisit the potential bias caused by the imbalance across linguistic levels. In the data set, lexical variables make up the smallest portion. However, lexical variables are the most diverse, therefore their variation patterns are potentially the most different from one another and, thus, are associated with greater linguistic distances. S20 subsets, there will be a variation in the effect across subsets  in J values not being equivalent to the stability values in a bootstrapping approach), which hints at the similarity of Scenario C and the bootstrap clustering in section 4.2.1. Although our original aim was countering the overweight of lexicon present in Ptop as a candidacy factor overwrites the effect of linguistic levels. Still, the potential effect of linguistic imbalance is a valid limitation for applications of the general methodology.With the qualitative revision already taking a foothold in the cluster validation steps, the initial candidate survey sites are selected based on their ranking of J and Ptop, 114 candidate survey sites are selected, fewer than the number of clusters originally sought. This is a result of a manual intervention, which is due to the fact that for the aims of SDATS, it is not the number of clusters or their identity that is relevant, but the survey sites' ranking on J and Ptop values. Beyond the first 114 survey sites, further sites' ranking with regard to either J or Ptop was too low, thus we decided to leave it to the qualitative revision to fill up the selection, as we ultimately maintain the aim of selecting 125 survey sites.Initially, based on the rankings in For the qualitative revision of candidate survey sites, we use the candidate site set resulting from section 4.3. This means that the candidate set of survey sites is not equal to the number of clusters sought in the earlier steps. Nevertheless, qualitative revision is not necessarily bound by the clusters or candidate sites yielded by the quantitative steps.Several reasons impede us from relying fully on the clustering results. First, the cluster partitions reflect the state of the dialectal landscape around 1950, in contrast to the SDATS requirements of investigating contemporary local colloquial dialects. Second, Switzerland has undergone sociodemographic changes, often affecting the composition of the population in settlements. People have become more mobile, and the communities in certain towns and villages recorded in SDS might have changed massively due to industrialization, urbanization, and suburbanization. Third, the digital linguistic data are not entirely optimal for the site reduction. Even if the 289 items in Scherrer's data were representative of SDS, the categorization of the variants within an item corresponds to the needs of SDATS for only 45 items. Besides, if each item is supposed to have the same weight in the process, neither Approach I nor Approach II can completely preclude the disproportionate effect of linguistic levels.To address these factors, the initial candidate survey site set is evaluated from the following viewpoints, including the indispensable insight of linguists with expertise in past and contemporary dialectal variation. We inspect:whether important sociodemographic changes could have occurred at the candidate sites leading to a change or a mixture of dialects. If there was a remarkable change , the location was eliminated from the list of candidate sites ;whether a candidate site's location has merged into another community ;whether a community is very small or has lost many inhabitants, creating difficulties for recruiting enough respondents from all social backgrounds ;whether it is known that the local dialect is remarkably interesting . In some cases, local studies have documented change and peculiarities, validating the candidacy of some potential sites ;whether the candidate site is perceived as linguistically representative of the region. For example, the city of Basel is traditionally not regarded as representative of its surrounding region;and, less importantly, whether there is a chance for an equidistant choice of survey sites. Following traditional sampling in dialectology, we might select a survey site that makes the survey site set equidistant .J and Ptop: when a candidate has to be removed, we often turn to these rankings for the next candidate or to validate the choice made based on other factors. For example, Lucerne, Horw, and Ebikon (LU), each were included in the initial set of 114 candidates. However, their geographic proximity allowed them to became a city complex in the last half century, functioning essentially as one unit, with a potential to drawing population from all over Switzerland. Therefore, we only chose Lucerne, the center, to represent this complex. Further, SDS survey sites that have merged in the last 70 years are treated as one, such as Schwamendingen and Zürich (ZH), or Masans and Chur (GR).Following the aspects listed, the candidate survey sites are revised. The revision is done partly based on sites' rankings on Next, we amended the candidate list in a qualitative fashion based on dialect expertise of the SDATS project members. For example, we added locations with local research already present and deemed interesting, such as Jaun (canton of Freiburg—FR), a German-speaking isolate, and Obersaxen (GR), a former island of Walser dialect. We also strived to cover some local dialects deemed peculiar due to the dwindling German-speaking population  or isolation . Importantly, major cities and towns of central importance have been taken into the SDATS sample regardless of the clustering results.At this stage, external dialect experts made further suggestions about available SDS survey sites that are overall representative in their region today, thus not necessarily reflected in the digital data. Some external dialect experts objected to the involvement of urban centers due to the assumptions that urban mixed dialects have already been outliers in SDS, a dialectal phenomenon of rural-urban contrast which needs to be addressed. As we assume dialects of less populated places to converge toward regional hubs, it is indeed beneficial to test this assumption in later analyses with contemporary data if smaller communities are selected along with regional hubs. For example, Reigoldswil (BL), Maur (ZH), and Wilchingen (canton of Schaffhausen—SH) were added for this reason. Additionally, this step has led to dropping some touristic locations assumed to have changed their dialects, such as Klosters (GR), places that became suburbs, such as Pratteln (BL), and to adding more rural varieties, such as Mammern (canton of Thurgau—TG) and Linthal (GL). After consolidating external experts' opinions, the overlap of the initial set of candidate sites and the final selection was 91 out of 114.Finally, Dialectometry uses clustering extensively for determining dialect areas based on linguistic similarity. However, such methods have not been utilized so far for the task of site reduction. We explored this direction with a linguist in mind who aims to revisit dialectal phenomena at representative survey sites of a previously recorded database. We propose a general pathway for incorporating a clustering procedure into the site selection methodology. Since we basically detect clusters in linguistic distance matrices and then appoint a representative survey site in  the clusters as candidates subject to a qualitative revision, the methodology is appropriate for several situations. Essentially, the general methodology is based on suggestions and best practices; there is no one-size-fits-all strategy.Rather than selecting sites based on a grid, we argue for the definition of clusters in non-spatial dimensions, where possible. We demonstrated the quantitative steps of the methodology on data from LAJ as a proof of concept and elaborated a complete application with data from SDS. These examples show that expert revision of candidate survey sites is indispensable, due to the potential bias and uncertainties in the underlying data. Due to constant language change, this expertise appreciates in value with the time elapsed since the collection of the original database.We find that the following three intertwined aspects impact the choice of specific aspects in the methodology and play a role in the feasibility of an objective implementation:An optimal site reduction procedure depends on the overlap of the original and intended studies with regard to their objectives and variables.The dialect change that has potentially occurred between the original and intended studies needs to be considered, since the aim of most site reduction tasks is to represent the contemporary dialectal variation.Local representation, sought by the applied method, may crucially depend on the purpose of the intended study. Therefore, a qualitative revision of candidate sites may overwrite previous decisions based on several considerations.By applying the outlined methodology to two databases, we demonstrate that an arbitrary number of representative survey sites can be found within digitized linguistic survey data. The main benefit of the general methodology is to offer candidate survey sites in a quantitative framework, despite the underlying data being potentially fuzzy and uncertain. In a subsequent step, researchers are encouraged to revise the candidate survey sites according to the requirements of their intended study. Specifically, the magnitude of the potential language change that has occurred since the collection of the original data appears to impact the importance of the (partly) qualitative revision over the quantitative steps resulting in candidate survey sites.The overlap of objectives between the SDS survey and SDATS are decreased by the sociolinguistic aims of SDATS, including SDATS' requirement of more participants per survey site, and its interest in a local colloquial dialect rather than the base dialect. Interpreting the application of the methodology to SDS, we conclude that the individual parameters of the site reduction methodology might be less important than the aims and linguistic knowledge of the researchers. Therefore, the selected 125 survey sites of SDATS are subjective to some degree.Although not often used in dialectology, we argue that random samples are not ideal for site reduction in a project which specifically aims to capture representative linguistic variation. Even with a high number of random points, following the spatial distribution of the survey genitive sites network, random samples might not follow the distributions in linguistic variables as much as clustering solutions do by design. By selecting the linguistically central points in clusters, one can assert with a higher confidence that the selected site represents the other cluster members.Our approach essentially also implies that, if digital data are present, it is possible to achieve the representation of the underlying data based on any number of chosen points, e.g., by taking the 20 most distinctive survey sites. This would, of course, lead to a large-scale loss of variation, and it would imply the need for an even more careful qualitative revision after partitioning the data.The methodology proposed has a number of implications for contemporary research in dialectology and beyond, for sociolinguistics, and more general language surveys. First, the automation of the site reduction process, based on the proposed methodology, allows for greater objectivity in comparison to a traditional approach where researchers have to go through the previous records or atlas data linked to the original survey sites to find the most distinct and/or representative survey sites. The availability of digitized data, clearly, opens opportunities toward faster quantitative approaches.Second, the usage of cluster validation methods can mitigate the uncertain and fuzzy nature of the underlying data, as reflected in the clustering results. Bootstrapping and noisy clustering methods aid the estimation of this uncertainty during the clustering procedure itself, allowing researchers to adjust their site reduction methods, e.g., the intended number of survey sites, based on stability measures and the aims of their study. Most previous studies , would be uncovered by appropriate clustering in the linguistic space, even if they are spatially embedded in an otherwise homogeneous area . If the data presents a perfect continuum (e.g., parts of Sweden, as shown by Leinonen, http://dialektkarten.ch/mapviewer/swg/index.de.html and at the Linguistic Atlas of Japan Database: https://www.lajdb.org/lajdb_data/LAJDB_data_download001_v20180328_rev.html.Publicly available datasets were analyzed in this study. These data can be found at: Yves Scherrer's “Sprachatlas der deutschen Schweiz” Digitized dialect maps: The idea, research questions, and conceptualization for this article was developed by PJ, AL, and CS. The data was prepared by PJ and CS. The methodology was worked out and implemented in R and QGIS by PJ. Visualization and data presentation by PJ. The interpretation of results was done by PJ, AL, and CS. The original manuscript was written by PJ, AL, and CS. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.