Migration gains but not fertility change. An impact evaluation of a municipal family policy in Trentino via Synthetic Difference-in-Differences

Migration gains but not fertility change. An impact evaluation of a municipal family policy in Trentino via Synthetic Difference-in-Differences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Migration gains but not fertility change. An impact evaluation of a municipal family policy in Trentino via Synthetic Difference-in-Differences Olga Gorodetskaya, Pietro Marzani, Federico Podesta' This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8116068/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract This study started from the assumption that family policies, although primarily implemented to improve the well-being of family members in a given territory, can exercise indirect effects on certain demographic outcomes, such as fertility rates and population movements. This led us to ask whether family policies adopted at the local level could, because of these effects, be a tool for combating the depopulation affecting small municipalities in rural areas. To answer this question, we focused on Trentino (a mountainous province located in northeastern Italy) and, more specifically, on a family policy, i.e., Family in Trentino, that has been implemented in several municipalities of this province for several years now. Using municipality-level administrative data and the Synthetic Difference-in-Differences method, we found that this policy had a significant impact on the net migration rate of those municipalities that adopted it but was unable to influence their fertility rate. Our analysis of the Trentino case, therefore, shows that only one of the two causal mechanisms that can activate a family policy appears to be effective in counteracting depopulation trends. This does not mean that the municipalities in Trentino that have adopted the policy under examination are more effective at countering depopulation trends than municipalities providing other types of public services. If anything, it means that by controlling the provision of these other services, the municipalities that implemented Family in Trentino would have had a worse net migration rate if they had not implemented this policy. Depopulation Family policy Indirect effect Migration Fertility Rural areas Figures Figure 1 Figure 2 1. Introduction Currently, small and medium-sized municipalities in rural areas of some European countries are experiencing increasing depopulation trends (Newsham and Rowe 2022 ; Valenzuela and Holl 2023 , Reynaud et al. 2020 ; Graus et al. 2024 ; Conti and Sivini 2023 ). As a result, a wealth of studies has emerged concerning the factors underlying these trends and what else can be promoted to remedy them (Loras-Gimeno et al. 2025 ). In this vein, the present study aims to investigate the role that family policies can play in combating the depopulation of rural areas. Like many other policies, family policies are designed to achieve primary objectives but also yield indirect effects and unintended consequences. The primary aim of family policies is to improve the well-being of family members (Robila 2014 ; Cowan & Cowan 2018 ). However, in light of the growing concerns, particularly in Europe, about a progressive decline in birth rates and an increasingly ageing population, a central and explicit goal of family policies has become that of boosting fertility (Bergsvik et al. 2021 ). Thus, by directly or indirectly enhancing the income of household members, particularly through work-life balance measures, family policies aim to influence reproductive decisions. In other words, by supporting the living conditions of family members, family policies would in turn—and therefore indirectly—affect the number of births. This argument has led to an intense debate on the effective impact of family policies on fertility rate (Gauthier 2007 ; Gauthier and Gietel-Basten 2025 ). The level of analysis in this context has predominantly remained national, as many family policies — including maternity protection, parental leave programs, and child benefits — are defined and administered at the national level (Neyer 2003 ; Nieuwenhuis and Van Lancker 2020 ). However, in some countries, responsibility for these family programs, as well as other welfare policies, has been progressively decentralised to regional or municipal authorities. As a result, increasing attention has also been given to the sub-national level (Kazepov et al. 2022 ). Correspondingly, recent studies have examined individual family programs implemented at the local level across different countries (Béland and Lecours 2018 ; Chirkova 2019 ; André and Teulings 2024 ). An additional, and somewhat unexpected, effect of family policies — and, more broadly, the welfare state — is their potential to attract individuals from other regions, particularly immigrants. This phenomenon is linked to the so-called welfare magnet hypothesis, which posits that individuals’ migration decisions are influenced by the generosity of the welfare system in the destination area. Specifically, it suggests that individuals choose their location based on the welfare benefits available and, as a result, may cluster in areas with higher benefits (Borjas 1999 ). Historically, this literature has also focused on the national level, as significant differences in social policy and welfare provisions are most apparent at this scale, making it attractive to foreign migrants. However, as many countries have delegated authority over welfare benefits to regional and local governments, creating intra-country variations in benefit levels, the welfare magnet hypothesis has also been applied to examine migration patterns between local areas within a single country (Ponce 2018 ). For example, a recent paper from Switzerland explores how welfare benefits influence intra-country residential decisions. The results challenged a “welfare magnet”: immigrant welfare recipients did not move to higher-benefit areas, prioritising instead population size, lower housing costs, and coethnic networks (Ferwerda et al. 2024 ). In these contexts, concerns regarding population movements — including not only immigrants but also other groups — have led to two contrasting policy responses and, consequently, two distinct lines of research. On the one hand, decentralisation trends may give rise to welfare competition, potentially resulting in a race to the bottom in setting welfare benefit levels. Specifically, in areas where both taxpayers and welfare recipients are mobile, local governments are likely to prioritise attracting wealthier households while minimising the influx of potential welfare recipients. In response, some subnational governments have reduced welfare benefits to prevent attracting welfare recipients from neighbouring jurisdictions with more generous provisions (e.g., Fiva and Rattsø 2006 ; Dahlberg and Edmark 2008 ). On the other hand, and as mentioned above, in response to the depopulation of small- and medium-sized towns, particularly in rural and mountainous areas, several policies have been developed to address this issue. The depopulation of these areas is likely linked to structural factors such as limited transport accessibility, inadequate public infrastructure, and the concentration of job opportunities in high-population urban centres. In light of this, enhancing the provision of public services, including healthcare, education, and digital connectivity, is seen as a key strategy for mitigating the demographic challenges faced by rural municipalities (OECD 2021 ; Muñoz et al. 2024 ; Alonso et al. 2025 ; Navarro-Galera et al. 2024 ). While a bunch of literature has focused on the provision of various types of public services, specific attention has yet to be given to family policies. This could constitute a shortcoming, as family policies could indirectly counteract the depopulation of rural areas through two different channels. On the one hand, like other public interventions, they could attract new residents and curb the outflow of others. On the other hand, they could revitalise these areas by increasing the number of births. Accordingly, this study seeks to address this gap by examining whether family policies implemented in small municipalities in rural areas can yield both of the aforementioned effects. In other words, we aim to assess whether family policies can serve as a tool to combat depopulation in small rural towns by improving net migration rates and increasing birth rates. To address this question, we focus on Trentino—a mountainous province in northeastern Italy—and examine the local family policies implemented for several years in multiple municipalities under the “Family in Trentino” certification program. Using municipality-level administrative data and the Synthetic Difference-in-Differences method (Arkhangelsky et. al., 2021 ), we assess whether certified municipalities—those that obtained the “Family in Trentino” label—experienced higher net migration and birth rates relative to their counterfactual outcomes had they not obtained the certification. The family well-being that can result from family policies implemented by Trentino municipalities that have acquired the label could, on the one hand, increase the number of births and, on the other, curb the outflow of current residents and attract new residents from other municipalities. The remainder of the paper is organised as follows: Section 2 provides a detailed description of the context and policy under examination, Section 3 outlines the data, variables, and methodology, Section 4 presents the results, and Section 5 offers the conclusion. 2. Context and Policy Description The Autonomous Province of Trento (Trentino) has a population of approximately 546,709 inhabitants (244,117 households), spread across 166 municipalities. Over a third of the population (35.7%) resides in the 89 municipalities with populations ranging from 1,001 to 5,000, while more than a fifth (21.7%) live in the only municipality with over 100,000 residents (Trento). Trentino faces significant structural challenges related to an ageing population: the average age is rising, the natural population balance is negative (with more deaths than births), and the fertility rate is declining. The total fertility rate in Trentino was 1.37 children per woman in 2022, down from 1.65 in 2010, and lower than the European Union average of 1.46 children per woman. Although the fertility rate remains above the national average (which is 1.24 children per woman), there is still a gap between the number of children desired and those actually born (ISTAT 2025 ; ISPAT 2024 ). To respond to these demographic challenges, the province has introduced a range of family-oriented measures under the “Family in Trentino” certification program ( “Il marchio ‘Family in Trentino’”) , which has been active for nearly twenty years. The overarching objectives of the program are to reorient municipal policies towards family friendliness, actively promoting concrete and consistent measures aimed at supporting the well-being of both resident and non-resident families present within the territory. “Family in Trentino” certification promoted by the Agency for Social Cohesion (ASC) of the Autonomous Province of Trento constitutes a structured governance system based on the strategic model of New Public Family Management (Malfer 2018 ). This framework departs from traditional welfare approaches and advances a generative welfare perspective, in which families are regarded not merely as beneficiaries of services but as key resources producing value for the community. Core principles include continuous innovation, individual autonomy and responsibility, and the use of evidence-based policy planning supported by public administration. Thus, the certification represents a distinctive policy instrument developed by ASC to foster a family-friendly territorial model. Certification requires adherence to requirements across several domains, including planning and evaluation, provision of family services, tariff policies, environmental quality and quality of life, and communication practices. More specifically, the regulatory framework for municipalities sets out 47 requirements, divided into mandatory and optional. Certification requires municipalities to reach a minimum score (derived from the sum of points for mandatory requirements), which varies by the municipality's size, and to develop an annual Municipal Family Well-Being Plan. The plan, approved by the municipal council, specifies measurable interventions—including objectives, responsible actors, and timeline—structured across five macro-areas. The plan functions as a dynamic management tool subject to an annual self-assessment by the mayor and the publication of a monitoring report. This reporting, made publicly available online, ensures transparency and accountability toward citizens and the provincial agency. To obtain the label, a municipality must apply to the ASC for certification, while to maintain the label, a municipality must annually submit its Family Plan and the self-assessment of the previous year's plan. In cases of serious non-compliance, the label can be revoked, but thus far, all municipalities have maintained the certification they obtained. Municipalities that obtain the label are thus recognised as being attentive to family needs and committed to enhancing family well-being. In particular, they commit to offer families certain services, such as, for example, information and education on citizenship, family-friendly services, fare reductions, communication, environment and quality of life. Municipalities undertake to inform families about services, organise meetings on parenting, foster family-work reconciliation, promote youth participation, support services for the elderly and those in need, and organise meetings on various topics such as the environment, culture, tourism, reading, gambling, gender violence, intergenerational communication, bullying and cyberbullying. They implement services for children (daycare, after-school, summer services) and support local associations. Efforts also extend to fostering young people's entry into employment, supporting senior citizens and disabilities, and supporting the integration of foreign families. Family policies are crosscutting, integrating with housing, sports and cultural policies. The primary added value of the certification, therefore, lies not merely in the provision of these individual services—some of which may be offered sporadically by non-certified municipalities—but in the systematic framework it mandates. Certification transforms disparate actions into a cohesive strategy. In essence, the Family in Trentino certification moves policy from a series of ad-hoc interventions to a structured, accountable, and dynamic governance process. By analysing the plans of certified municipalities and grouping similar actions by purpose and mode of application, an official Taxonomy of Municipal Plan Actions ( “Tassonomia delle azioni dei piani comunali” ) was developed. Specifically, the activities of the Family Plans have been grouped into five macro-areas of intervention (Governance and network actions, Economic measures, Information and communication, Educating community, Territorial welfare and sustainability), which are further divided into types of actions. The taxonomy is a widely used instrument for municipalities to identify new family policies to introduce within their territory, presenting a broad spectrum of possible actions to implement for each macro-area. It is a “living” instrument, periodically updated with new types of actions that become established in certified municipalities. In this way, it serves as an important vehicle for the dissemination of good practices and network creation. The majority of taxonomy’s entries pertain to specific actions addressing family needs (e.g., newborn bonuses, financial support for large families). While some entries refer to particular user groups of municipal services (e.g., youth or the elderly), their inclusion in the taxonomy is always linked to the benefit the family unit can derive (in terms of reducing care burdens or providing support in the educational process). The “Family in Trentino” program was launched in 2006. The label expanded gradually, with Trento—the provincial capital—certified in December 2014. As of 2024, 83 of Trentino’s 166 municipalities (50%) hold the label (see the diffusion of the “Family in Trentino” certification on Fig. 1 ). Over this period, no municipality has relinquished or lost its certification. In cases of municipal mergers, the newly formed municipality retained the certification previously held by one or more of the merging municipalities. Darker shades indicate municipalities certified earlier (longer tenure); lighter shades indicate more recent certifications. 3. Data, variables and method 3.1. Data We use publicly accessible municipality-level population data provided by the Italian National Institute of Statistics (ISTAT). The observation period spans from 1992 to 2022, covering a total of 31 years, which represents the most recent data available. Given that some municipalities in Trentino experienced administrative changes over the observation period—such as mergers (unions) or, conversely, separations and the creation of new entities- we restricted the sample to municipalities that remained stable over time. This produced a working sample of 138 municipalities, compared to 233 municipalities in 2009 and 166 municipalities in 2025. This decision was taken to avoid inconsistencies in population denominators and to reduce potential distortions in demographic indicators, particularly in the case of net migration rates, which are highly sensitive to administrative boundary changes. We excluded the two largest municipalities—Trento (the provincial capital, population ≈ 117,000) and Rovereto (population ≈ 40,000)—from the analysis, as they represent outliers in terms of demographic size and urban characteristics. This decision is motivated by the fact that the policy under examination is specifically designed to address depopulation challenges affecting small municipalities. Moreover, it is in these smaller municipalities, rather than in larger urban centers, that there is a pressing need to curb resident outflows and attract new inhabitants In view of that, we relied on a balanced panel dataset consisting of 136 municipalities (76 treated vs 60 never-treated) of different sizes (see Table 1 ) observed over 31 years (1992–2022), i.e. 4,216 municipality-year observations. Table 1 Distribution of analysed Trentino municipalities by population size (average population across 1992–2022) and treatment status All municipalities Among those are treated Population size N Percent N Percent less than 500 residents 23 16.91 10 43.48 500–999 residents 38 27.94 24 63.16 1.000–1.999 residents 37 27.21 18 48.65 2.000–2.999 residents 14 10.29 7 64.29 3.000–4.999 residents 14 10.29 9 64.29 5.000–9.999 residents 7 5.15 5 71.43 10.000–19.999 residents 3 2.21 3 100 Total 136 100 76 56.88 3.2. Variables As outcome variables, we consider two demographic indicators. The fertility rate. It is defined as the number of live births per 1,000 residents in a given year. This measure provides a direct indication of the fertility propensity of the resident population. The net migration rate. It is calculated as the difference between in-migration and out-migration—measured through registrations and cancellations of residence to and from other municipalities (including outside Trentino) per 1,000 residents in a given year. This indicator reflects the attractiveness or depopulation tendency of local areas. Since the raw data for these two indicators yield time series characterised by a remarkable volatility, we smoothed the series by calculating a five-year moving average. This procedure reduces short-term fluctuations caused by annual shocks (e.g., single-year variations in births or population movements in small municipalities) and highlights the underlying demographic trends. By attenuating noise, the moving average improves the comparability of treated and control units and strengthens the validity of our analysis. Treatment status is a time-varying binary indicator equal to 1 in all years from (and including) the year a municipality obtained the “Family in Trentino” certification, and 0 otherwise. The certification start year for each municipality is taken from publicly available records of the Agency for Social Cohesion of the Autonomous Province of Trento. None of the certified municipalities ever relinquished the label; once certified, they remain treated in all subsequent years. The comparison group comprises municipalities that were never certified (never treated), which constitute the donor pool. As control variables, we consider two indicators. The average resident population per year is a time-varying covariate that captures potential size effects in fertility and migration dynamics. The number of available childcare places 1 is included as a time-varying proxy for family-supportive infrastructure at the local level. The descriptive statistics for the variables used in the analysis are represented in Table 2 . Table 2 Descriptive statistics of variables included in the analysis Variable Mean SD p25 p50 p75 Min Max N Fertility rate 9.44 2.36 7.96 9.45 10.91 0 21.49 4216 Net Migration rate 5.50 8.01 0.50 4.98 9.81 -48.43 47.98 4216 Average resident population per year 2053.54 2815.97 657.5 1123 2321.25 90.5 21633.5 4216 Number of available childcare places 7.43 20.55 0 0 0 0 129 4216 In line with the literature on depopulation of rural areas, some other control variables should be taken into account. These are: (1) road distance to the provincial capital, proxying access to the capital’s richer portfolio of public and private services, (2) municipal altitude, capturing remoteness; and (3) the availability of other municipal services, such as schools and healthcare facilities. However, there is no need to include these variables in the model specification because, being time invariant, they are captured by the unit fixed effects in the synthetic difference-in-differences framework. As specified below, this method precisely allows for unit and time fixed effects. 3.3. Method To estimate the demographic impact of the Family in Trentino label, we employ the Synthetic Difference-in-Differences (SDiD) method (with staggered adoption) proposed by Arkhangelsky et al. ( 2021 ). SDiD is a counterfactual causal inference method. It builds an explicit counterfactual outcome for the treated units by combining strengths from both the traditional difference-in-differences procedure and the synthetic control method, as developed by Abadie et al. ( 2010 , 2015 ). Like the difference-in-differences procedure, SDiD allows for treated and control units to be trending on entirely different levels before an event or policy of interest. Like the synthetic control method, SDiD seeks to optimally generate a matched control unit, which considerably loosens the need for parallel trend assumptions. Therefore, SDiD avoids common pitfalls in standard difference-in-differences and synthetic control methods – namely, an inability to estimate causal relationships if parallel trends are not met in aggregate data in the case of difference-in-differences, and a requirement that the treated unit be housed within a “convex hull” of control units in the case of synthetic control (Clarke et al. 2023 ). SDiD is highly appropriate for our setting as it is based on a panel (group by time) set-up, in which certain units are treated (76 municipalities), and the remaining units are untreated (60 municipalities). Accordingly, the SDiD procedure calculates a treatment effect as the pre- versus post-difference-in-difference between treated units and synthetic control units, where synthetic control units are chosen as an optimally weighted function of untreated units (unit-specific weights) and pre-treatment times (time-specific weights). Because municipalities adopt certification in different years, we use the staggered SDiD implementation: unit and time weights are learned by treatment cohort, iterating over the never-treated set and each adoption cohort to obtain cohort-specific effects and their standard errors (Arkhangelsky et al., 2021 ; Clarke et al., 2023 ). We implement SDiD in two steps. Step 1 (baseline): We estimate the ATT for net migration and fertility using all treated municipalities. Step 2 (content-aware analysis): We re-estimate the same outcomes within clusters of municipalities that share similar policy bundles (see SI for clustering construction). Inference is based on municipality-block bootstrap standard errors with 1,999 replications (fixed seed). Robustness checks include treatment-timing shifts (± 3 years), a jackknife SDiD specification (satisfying the cohort-size requirement), and a placebo-in-time check. 4. Results We begin by estimating staggered SDiD average treatment effects on the treated (ATT) for fertility and net migration at the municipality-year level. Table 3 reports (Fig. 2 visualises) the baseline results. For fertility, we find no detectable response to certification: the ATT is small in magnitude and imprecise (ATT = 0.1297, SE = 0.2327, p = 0.577), and the 95% confidence interval comfortably spans zero. This pattern suggests that the local family-policy bundle associated with certification does not shift reproductive outcomes over the evaluation horizon. By contrast, net migration shows a positive response to certification. The ATT equals 1.516 migrants per 1,000 residents (SE = 0.817, p = 0.064). Although marginal at conventional thresholds, the estimate is stable in sign and of policy-relevant magnitude, indicating that certified municipalities experience higher net inflows relative to their synthetic counterfactuals. Interpreted together, these findings are consistent with the causal mechanism that assumes a better migration balance due to the increased attractiveness of the territory guaranteed by certain family policies, and not with the causal mechanism that assumes an increase in births due to better living conditions guaranteed by those same family policies. Table 3 Synthetic Difference-in-Differences Estimator results (staggered) for fertility and net migration rates Outcome ATT Std. Err. t P > t 95% Conf. Interval Fertility rate 0.12972 0.23272 0.56 0.577 -0.32641 0.58586 Net Migration rate 1.51605 0.81711 1.86 0.064 -0.08546 3.11755 Notes. Estimator: staggered SDiD (Arkhangelsky et al., 2021 ) on municipality–year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors. Because effects (as well as the absence of the visible effects) may depend on which local measures are implemented, we next re-estimate SDiD within clusters of municipalities sharing similar policy profiles. By comparing units exposed to comparable policy environments, we assess which elements of “Family in Trentino” are most salient for demographic dynamics. We construct clusters by grouping municipalities on similarity in policy characteristics; methodological details are provided in the Supplementary Information. The five clusters that capture a clear coverage gradient across the policy taxonomy and their defining features—summarised by the program’s five macro-areas—are reported below. Table 4 Clusters of municipalities shared similar policies under the “ Family in Trentino” certification Cluster Description N Cluster 1 Intermediate total coverage of the entire taxonomy and medium-high coverage of macro-area “Governance and network actions” 19 Cluster 2 Low coverage of the entire taxonomy 9 Cluster 3 High coverage of the entire taxonomy 19 Cluster 4 Intermediate total coverage of the entire taxonomy, medium-high coverage of macro-area “Economic measures”, and medium-low coverage of macro-area “Governance and network actions” 16 Cluster 5 Medium-low coverage of the entire taxonomy, particularly macro-area “Governance and network actions” 13 Tables 5 – 6 report staggered SDiD estimates by policy-content clusters—for fertility (Table 5 ) and net migration (Table 6 ). For fertility, we do not detect statistically significant effects in any cluster: point estimates are small, and their confidence intervals overlap zero, reinforcing the baseline result that “Family in Trentino” does not measurably shift reproductive behaviour across policy bundles. Table 5 Synthetic Difference-in-Differences Estimator results (staggered) for fertility across clusters Fertility rate ATT Std. Err. t P > t 95% Conf. Interval Cluster 1 (N = 19) 0.51574 0.32298 1.60 0.110 -0.11728 1.14876 Cluster 2 (N = 9) -0.67739 0.53495 -1.27 0.205 -1.72586 0.37109 Cluster 3 (N = 19) -0.22179 0.41037 -0.54 0.589 -1.02611 0.58252 Cluster 4 (N = 16) 0.08185 0.37610 0.22 0.828 -0.65529 0.81899 Cluster 5 (N = 13) -0.25085 0.50744 -0.49 0.621 -1.24541 0.74370 Notes. Estimator: staggered SDiD (Arkhangelsky et al., 2021 ) on municipality–year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors. Regarding net migration, we find a marginally significant effect in Cluster 3—municipalities with high coverage across the policy taxonomy. The average treatment effect on the treated (ATT) for this cluster is 2.78 (SE = 1.61, p = 0.084). Similarly, Cluster 4—municipalities with intermediate coverage of the taxonomy and higher coverage in Economic measures—shows a positive, marginally significant effect (ATT = 2.80, SE = 1.64, p = 0.088). However, no significant effects are observed in Clusters 1, 2, and 5, suggesting that the “Family in Trentino” certification’s impact on migration is most pronounced in municipalities with a comprehensive policy package. Table 6 Synthetic Difference-in-Differences Estimator results (staggered) for net migration rates across clusters Net Migration rate ATT Std. Err. t P > t 95% Conf. Interval Cluster 1 (N = 19) 1.32357 1.54178 0.86 0.391 -1.69826 4.34539 Cluster 2 (N = 9) 3.54555 2.66521 1.33 0.183 -1.67817 8.76926 Cluster 3 (N = 19) 2.77622 1.60808 1.73 0.084 -0.37556 5.92801 Cluster 4 (N = 16) 2.79608 1.63983 1.71 0.088 -0.41792 6.01008 Cluster 5 (N = 13) 1.11474 2.37820 0.47 0.639 -3.54645 5.77593 Notes . Estimator: staggered SDiD (Arkhangelsky et al., 2021 ) on municipality–year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors. Given that the estimated effects on net migration are marginal—both in the full treated sample and within certain clusters—we conduct additional robustness checks of the main results. Specifically, we implement treatment-timing shifts (± 3 years), a jackknife SDiD specification (restricted to satisfy cohort-size requirements), and a placebo-in-time check. Qualitative conclusions are unchanged; the results are provided below (Table 7 ). Table 7 Robustness of SDiD estimates for net migration: bootstrap, jackknife, and timing shifts Specification ATT Std. Err. t P > t 95% Conf. Interval SDiD (bootstrap, 1,999 reps) 1.51605 0.81711 1.86 0.064 -0.08546 3.11755 SDiD (jackknife) 1.57609 0.83566 1.89 0.059 -0.06177 3.21395 SDiD, timing shift − 3 years 1.58556 0.81707 1.94 0.052 -0.01586 3.18698 SDiD, timing shift + 3 years 0.78309 0.74686 1.05 0.294 -0.68073 2.24691 SDiD, placebo-in-time -0.95422 1.00865 -0.95 0.334 -2.93113 1.02269 Notes. Estimator: staggered SDiD at the municipality–year level; donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213) or jackknife as indicated. Timing shifts advance/delay certification by three years. Jackknife estimated on a sample satisfying cohort-size requirements. The placebo-in-time test assigns a fake treatment start before actual adoption and runs the SDiD estimator with the same specifications. We compare bootstrap and jackknife standard errors because they rest on different inference assumptions under heteroskedasticity and within-municipality serial correlation. The unit-block bootstrap resamples entire municipalities, preserving within-unit dependence and providing an all-purpose choice in large panels, whereas the jackknife re-estimates the model, leaving out one influence unit at a time, directly probing sensitivity to single municipalities or adoption cohorts without resampling randomness. Using both also serves as a design diagnostic—jackknife feasibility requires at least 2 treated units per cohort, therefore we had to exclude one cohort with a single municipality (Arco, 2008)—and, when the two procedures agree, it reduces concerns that statistical significance is an artefact of a particular variance estimator rather than a substantive effect. As the robustness check shows, bootstrap and jackknife yield nearly identical ATTs for net migration (around 1.5 per 1,000) and similar inference (marginal significance), indicating that the migration effect is not sensitive to the choice of variance estimator. Timing shifts confirm that results are not driven by the exact start date: advancing treatment by − 3 years returns a similarly positive estimate (1.586; SE = 0.817; p = 0.052), while delaying by + 3 years produces a smaller but still positive effect (0.783; SE = 0.747; p = 0.294), consistent with attenuation when early post-adoption years are withheld. Taking together, the evidence points to a robust, positive migration response that is insensitive to the variance estimator and reasonably stable to timing. Finally, we conducted a placebo-in-time test by assigning a fake treatment start before the actual adoption of the “Family in Trentino” certification. The result shows an insignificant effect on net migration: the ATT for the placebo treatment is − 0.954 (SE = 1.009, p = 0.334), and the 95% confidence interval includes zero. This non-significant result provides further validation of the identification strategy, indicating that the observed effect on net migration is not driven by pre-existing trends or anticipation of the policy. The placebo-in-time test suggests that there are no spurious pre-treatment effects, supporting the robustness of the main findings. Conclusion This study started from the assumption that family policies, although primarily implemented to improve the well-being of family members in a given territory, can exercise indirect effects on certain demographic variables, such as fertility rates and population movements. This led us to ask whether family policies adopted at the local level could, because of these effects, be a tool for combating the depopulation affecting small municipalities in rural and mountain areas. To answer this question, we estimated—using the synthetic difference-in-differences method—the demographic impact of a family policy, i.e., “Family in Trentino” certification, implemented in numerous municipalities in the province of Trento. Our analysis shows that this policy has a significant impact on the net migration rate of Trentino municipalities that have acquired the label but does not seem to have a significant influence on the fertility rate of these municipalities. In other words, on average, Trentino municipalities that have acquired the label seem to be able to counteract trends of depopulation by attracting new residents and slowing down the outflow of their own residents, but they do not appear to be able to change the reproductive choices of families in their territory. Our analysis of the Trentino case, therefore, shows that only one of the two causal mechanisms that can activate a family policy appears to be effective in counteracting depopulation trends. It is quite plausible that we did not find a significant effect on the fertility rate. The measures adopted by the certified municipalities, although numerous and varied, do not provide substantial support for families in terms of cash transfers and/or the provision of services. Due to their administrative constraints and in line with the mission of the policy under examination, the municipalities that have acquired the certification have limited themselves to implementing measures aimed at reducing the tariffs of certain services, incentivising others, increasing citizen information, and promoting initiatives to increase local participation in activities concerning family issues. In contrast, the existing literature on the effects of family policies shows that fertility is significantly influenced only when large reforms are implemented and policymakers intervene markedly in the areas of maternity leave, income support, and child-care services (Bergsvik et al. 2021 ). On the other hand, it can be argued that the measures implemented by certified municipalities in Trentino, despite their limitations, have had an impact on the net migration rate, since the decision to move residence is less costly in monetary terms than having a child. The decision to have a child certainly involves a significant financial effort, while staying in a certain municipality without moving can, in some cases, also involve certain savings. Furthermore, if a family needs to move, it is likely to make its choice based on several criteria of convenience. For example, immigrants, differently from natives, who live in rural areas, have higher probabilities of moving to another municipality than their peers in more urban areas (Skjerpen & Tønnessen 2025 ). Additionally, the measures implemented in line with “Family in Trentino” may have proved that certified municipalities have a long-standing and diversified public commitment to promoting the well-being of families. This argument is partly consistent with the findings of the literature on the depopulation of rural areas. It is now widely accepted that, in order to stem the outflow of residents and attract newcomers, local authorities must implement a variety of public interventions (Loras-Gimeno et al. 2025 ; Alonso et al. 2025 ). As we have repeatedly observed, the municipalities in Trentino that have obtained the certification have implemented numerous measures. Moreover, the most marked effects on the net migration rate have been observed precisely as a result of a large number of measures in favour of families. However, what distinguishes this study from the literature on the depopulation of small rural municipalities is the fact that the contrast to depopulation trends has been observed by considering only the role of a variety of family-friendly measures . This does not mean that certified municipalities in Trentino are more effective at countering depopulation trends than municipalities equipped with other types of services, such as healthcare facilities, schools, child-care services, etc. If anything, it means that by controlling the provision of these other services, certified municipalities would have had a worse net migration rate if they had not implemented this variety of family policies. In counterfactual terms, many family-friendly measures are, per se, sufficient to exercise a significant impact on the net migration rate. Although this result may contribute to the debate on policies to combat depopulation, one limitation of our analysis is that we cannot say anything about those who move their residence from one municipality to another. The data we used to calculate the net migration rate only provides information on the number of registrations and cancellations of residence to and from other municipalities. Consequently, it is not possible to establish (1) whether moves to certified municipalities in Trentino occur mainly from municipalities in Trentino that are not yet certified or from municipalities in other regions, (2) whether these moves mainly involve young people, and (3) whether they primarily involve people with Italian or foreign citizenship. This lack of information makes it impossible to understand whether Trentino municipalities that are not yet certified are increasing their risk of depopulation due to the movement of their residents to municipalities that are already certified. Furthermore, it is not possible to assume that certified municipalities have a greater chance of revitalisation as a result of a greater influx of young people. Declarations Author Contribution All authors contributed equally to this work. Author order is alphabetical. Data Availability We use publicly accessible municipality-level population data provided by the Italian National Institute of Statistics (ISTAT). The data are available at the following URL: (1991 - 2001) https://demo.istat.it/app/?i=R91&l=it (2001 -2018) https://demo.istat.it/app/?i=RBD&l=it(2018 - 2022) https://demo.istat.it/app/?i=D7B&l=it References Abadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. J Am Stat Assoc 105(490):493–505 Abadie A, Diamond A, Hainmueller J (2015) Comparative politics and the synthetic control method. Am J Polit Sci 59(2):495–510 Alonso MP, Gargallo P, Lample L, López-Escolano C, Miguel JA, Salvador M (2025) How Service Exclusion Affects Rural Depopulation. An Approach Based on Structural Equation Modelling. Sociologia Ruralis, 65(3), e70005 André S, Teulings T (2024) Work-Family Policy for Fathers in Dutch Municipalities: A Vignette Experiment on Contexts for Parental Leave Among Male Civil Servants. Public Personnel Manage 53(4):601–622 Arkhangelsky D, Athey S, Hirshberg DA, Imbens GW, Wager S (2021) Synthetic Difference-in-Differences. Am Econ Rev 111(12):4088–4118 Béland D, Lecours A (2018) Federalism, policy change, and social security in Belgium: Explaining the decentralization of family allowances in the Sixth State Reform. J Eur Social Policy 28(1):55–69 Bergsvik J, Fauske A, Hart RK (2021) Can policies stall the fertility fall? A systematic review of the (quasi-) experimental literature. Popul Dev Rev 47(4):913–964 Borjas GJ (1999) Immigration and welfare magnets. J Labor Econ 17:4:607–637 Chirkova S (2019) ‘The impact of parental leave policy on child-rearing and employment behavior: The case of Germany’. IZA J Labor Policy, 9 (1) Clarke D, Pailañir D, Athey S, Imbens GW (2023) Synthetic Difference-in-Differences Estimation. IZA Discussion Papers 15907. Institute of Labor Economics (IZA) Conti M, Sivini S (2023) Small Municipalities Attracting Rural Newcomers and Fostering Local Cohesion: Innovative Approaches for Rural Regeneration in Italy. https://doi.org/10.3390/su15075837 . Sustainability Cowan C, Cowan P (2018) Enhancing Parenting Effectiveness, Fathers' Involvement, Couple Relationship Quality, and Children's Development: Breaking Down Silos in Family Policy Making and Service Delivery. J Family Theory Rev. https://doi.org/10.1111/jftr.12301 Dahlberg M, Edmark K (2008) Is there a race-to-the-bottom in the setting of welfare benefit levels? Evidence from a policy intervention. J Public Econ 92(5–6):1193–1209 Ferwerda J, Marbach M, Hangartner D (2024) Do immigrants move to welfare? Subnational evidence from Switzerland. Am J Polit Sci 68(3):874–890 Fiva JH, Rattsø J (2006) Welfare competition in Norway: Norms and expenditures. Eur J Polit Econ 22(1):202–222 Gauthier AH (2007) The impact of family policies on fertility in industrialized countries: a review of the literature. Popul Res Policy Rev 26(3):323–346 Gauthier AH, Gietel-Basten S (2025) Family policies in low fertility countries: Evidence and reflections. Popul Dev Rev 51(1):125–161 Graus S, Ferreira T, Vasconcelos G, Ortega J (2024) Changing Conditions: Global Warming-Related Hazards and Vulnerable Rural Populations in Mediterranean Europe. Urban Science. https://doi.org/10.3390/urbansci8020042 Hudečková H, Husák J, Voleská R (2019) Family Policy in the Strategic Planning of Rural Municipalities in the Czech Republic. Eur ISPAT (2024) La natalità in Trentino fra desideri e realtà (Ispat Comunicazioni). Provincia autonoma di Trento. Retrieved at http://www.statistica.provincia.tn.it/binary/pat_statistica_new/famiglia_comportamenti_sociali/NatalitaInTrentino.1732111208.pdf ISTAT (2025) Il Censimento permanente della popolazione in Trentino. Anno 2023. https://www.istat.it/wp-content/uploads/2025/04/Censimento-permanentepopolazione_Anno-2023_Trento.pdf Kazepov Y, Barberis E, Cucca R, Mocca E (eds) (2022) Handbook on urban social policies: International perspectives on multilevel governance and local welfare. Edward Elgar Publishing Loras-Gimeno D, Díaz-Lanchas J, Gómez-Bengoechea G (2025) Rural depopulation in the 21st century: A systematic review of policy assessments. Regional Science Policy & Practice, p 100176 Malfer L (2018) New public family management: welfare generativo, family mainstreaming. networking e partnership Muñoz LA, Galera AN, Bolivar MPR (2024) The financial sustainability of public services as an instrument to combat depopulation in small and medium-sized municipalities. Cities 154:105337 Navarro-Galera A, Buendía-Carrillo D, Gómez-Miranda ME, Lara-Rubio J (2024) Fighting depopulation in Europe by analyzing the financial risks of local governments. Int Rev Admin Sci 90(1):48–64 Newsham N, Rowe F (2022) Understanding trajectories of population decline across rural and urban Europe: A sequence analysis. Population, Space and Place. https://doi.org/10.1002/psp.2630 Neyer G (2003) Family policies and low fertility in Western Europe. Institute of Economic Research, Hitotsubashi University, pp 2003–2021 Nieuwenhuis R, Van Lancker W (eds) (2020) (eds) The Palgrave handbook of family policy. Springer Nature, p 721 OECD (2021) OECD sovereign borrowing outlook 2021. Retrieved from https://www.oecd.org/economy/oecd-sovereign-borrowing-outlook-23060476.htm Ponce A (2018) Is Welfare a Magnet for Migration? Examining Universal Welfare Institutions and Migration Flows. Soc Forces 98:245–278. https://doi.org/10.1093/sf/soy111 Reynaud C, Miccoli S, Benassi F, Naccarato A, Salvati L (2020) Unravelling a demographic ‘Mosaic’: Spatial patterns and contextual factors of depopulation in Italian Municipalities, 1981–2011. Ecol Ind 115:106356. https://doi.org/10.1016/j.ecolind.2020.106356 Robila M (2014) Family Policies Across the Globe: Development, Implementation, and Assessment., 3–11. https://doi.org/10.1007/978-1-4614-6771-7_1 Skjerpen T, Tønnessen M (2025) Try another municipality or leave the country? A disaggregated approach to determinants of internal migration and emigration for immigrants and natives in Norway. Ann Reg Sci 74(2):1–29 Valenzuela V, Holl A (2023) Growth and decline in rural Spain: an exploratory analysis. Eur Plan Stud 32:430–453. https://doi.org/10.1080/09654313.2023.2179390 Footnotes Data on childcare facilities was provided by Statistical Institute of the Province of Trento (ISPAT) at the request of the authors. Additional Declarations No competing interests reported. Supplementary Files SIMigrationgainsbutnotfertilitychange.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 18 Nov, 2025 First submitted to journal 14 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8116068","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610931435,"identity":"47252401-0679-4432-aaaf-edbad0b73ddf","order_by":0,"name":"Olga Gorodetskaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYNACNiDmATEqJKAiBoS1SEC0nIFrIaQHpoWxDS6EWws//+JjHz6U2dTJ9xx+JvFznkW+wfGzxx4wFPzBqUVyxrPkmTPOpUkYnG0zk+zdJmG54UxeugE+hxncOGPMzNt2WMKAn8FMgnebhIFkQ46ZBBFa/kvI97N/k/w7B6il/w0BLed7QFoOSDCc7TGT5m0A2iZBwBbJGWzJjDPOJUtuOHOm2FrmGEjLu3SDBANjnFr4+Q8fZvhQZscv35O+8eabmjoDNv7cYw8+/JHDqYVBIgHOZIHGIw8bQwJWtTBrDsCZzB/gWkbBKBgFo2AUIAEAni9L4hR4WZIAAAAASUVORK5CYII=","orcid":"","institution":"Fondazione Bruno Kessler","correspondingAuthor":true,"prefix":"","firstName":"Olga","middleName":"","lastName":"Gorodetskaya","suffix":""},{"id":610931440,"identity":"d77c0934-f5ca-48a9-88d0-bc5a5b676e84","order_by":1,"name":"Pietro Marzani","email":"","orcid":"","institution":"Fondazione Bruno Kessler","correspondingAuthor":false,"prefix":"","firstName":"Pietro","middleName":"","lastName":"Marzani","suffix":""},{"id":610931441,"identity":"e8fa4f9f-89b0-43c3-8c60-5d29d7421921","order_by":2,"name":"Federico Podesta'","email":"","orcid":"","institution":"Fondazione Bruno Kessler","correspondingAuthor":false,"prefix":"","firstName":"Federico","middleName":"","lastName":"Podesta'","suffix":""}],"badges":[],"createdAt":"2025-11-14 14:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8116068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8116068/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105410043,"identity":"b96aa0f8-34a9-4254-8fbf-32bd1ba80d88","added_by":"auto","created_at":"2026-03-25 17:14:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205483,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial diffusion of the “Family in Trentino” certification (2006–2024).\u003c/p\u003e\n\u003cp\u003eDarker shades indicate municipalities certified earlier (longer tenure); lighter shades indicate more recent certifications.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8116068/v1/889ce62dc344af1778f564e7.png"},{"id":105410041,"identity":"8d666084-e379-47c3-b130-ea4a35048796","added_by":"auto","created_at":"2026-03-25 17:14:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97388,"visible":true,"origin":"","legend":"\u003cp\u003eSynthetic Difference-in-Differences Estimator Results (ATT) for Fertility and Net Migration Rates\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8116068/v1/c9819e51bbf50148d5ddbbac.png"},{"id":105751825,"identity":"66b3dc25-f75f-44f6-befe-c2081399542e","added_by":"auto","created_at":"2026-03-30 15:46:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":984529,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8116068/v1/1bbaeb56-4ec3-4ffb-8296-ac71f182d6a7.pdf"},{"id":105410040,"identity":"1c2cba81-e255-4470-ab90-9cea5cae8259","added_by":"auto","created_at":"2026-03-25 17:14:48","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":118540,"visible":true,"origin":"","legend":"","description":"","filename":"SIMigrationgainsbutnotfertilitychange.docx","url":"https://assets-eu.researchsquare.com/files/rs-8116068/v1/42a81b7b523522b87b30b07e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMigration gains but not fertility change. An impact evaluation of a municipal family policy in Trentino via Synthetic Difference-in-Differences\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCurrently, small and medium-sized municipalities in rural areas of some European countries are experiencing increasing depopulation trends (Newsham and Rowe \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Valenzuela and Holl \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Reynaud et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Graus et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Conti and Sivini \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, a wealth of studies has emerged concerning the factors underlying these trends and what else can be promoted to remedy them (Loras-Gimeno et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In this vein, the present study aims to investigate the role that family policies can play in combating the depopulation of rural areas.\u003c/p\u003e\u003cp\u003eLike many other policies, family policies are designed to achieve primary objectives but also yield indirect effects and unintended consequences. The primary aim of family policies is to improve the well-being of family members (Robila \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cowan \u0026amp; Cowan \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, in light of the growing concerns, particularly in Europe, about a progressive decline in birth rates and an increasingly ageing population, a central and explicit goal of family policies has become that of boosting fertility (Bergsvik et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, by directly or indirectly enhancing the income of household members, particularly through work-life balance measures, family policies aim to influence reproductive decisions. In other words, by supporting the living conditions of family members, family policies would in turn\u0026mdash;and therefore indirectly\u0026mdash;affect the number of births. This argument has led to an intense debate on the effective impact of family policies on fertility rate (Gauthier \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gauthier and Gietel-Basten \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe level of analysis in this context has predominantly remained national, as many family policies \u0026mdash; including maternity protection, parental leave programs, and child benefits \u0026mdash; are defined and administered at the national level (Neyer \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Nieuwenhuis and Van Lancker \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in some countries, responsibility for these family programs, as well as other welfare policies, has been progressively decentralised to regional or municipal authorities. As a result, increasing attention has also been given to the sub-national level (Kazepov et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Correspondingly, recent studies have examined individual family programs implemented at the local level across different countries (B\u0026eacute;land and Lecours \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chirkova \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Andr\u0026eacute; and Teulings \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn additional, and somewhat unexpected, effect of family policies \u0026mdash; and, more broadly, the welfare state \u0026mdash; is their potential to attract individuals from other regions, particularly immigrants. This phenomenon is linked to the so-called welfare magnet hypothesis, which posits that individuals\u0026rsquo; migration decisions are influenced by the generosity of the welfare system in the destination area. Specifically, it suggests that individuals choose their location based on the welfare benefits available and, as a result, may cluster in areas with higher benefits (Borjas \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically, this literature has also focused on the national level, as significant differences in social policy and welfare provisions are most apparent at this scale, making it attractive to foreign migrants. However, as many countries have delegated authority over welfare benefits to regional and local governments, creating intra-country variations in benefit levels, the welfare magnet hypothesis has also been applied to examine migration patterns between local areas within a single country (Ponce \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, a recent paper from Switzerland explores how welfare benefits influence intra-country residential decisions. The results challenged a \u0026ldquo;welfare magnet\u0026rdquo;: immigrant welfare recipients did not move to higher-benefit areas, prioritising instead population size, lower housing costs, and coethnic networks (Ferwerda et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn these contexts, concerns regarding population movements \u0026mdash; including not only immigrants but also other groups \u0026mdash; have led to two contrasting policy responses and, consequently, two distinct lines of research. On the one hand, decentralisation trends may give rise to welfare competition, potentially resulting in a race to the bottom in setting welfare benefit levels. Specifically, in areas where both taxpayers and welfare recipients are mobile, local governments are likely to prioritise attracting wealthier households while minimising the influx of potential welfare recipients. In response, some subnational governments have reduced welfare benefits to prevent attracting welfare recipients from neighbouring jurisdictions with more generous provisions (e.g., Fiva and Ratts\u0026oslash; \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dahlberg and Edmark \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, and as mentioned above, in response to the depopulation of small- and medium-sized towns, particularly in rural and mountainous areas, several policies have been developed to address this issue. The depopulation of these areas is likely linked to structural factors such as limited transport accessibility, inadequate public infrastructure, and the concentration of job opportunities in high-population urban centres. In light of this, enhancing the provision of public services, including healthcare, education, and digital connectivity, is seen as a key strategy for mitigating the demographic challenges faced by rural municipalities (OECD \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mu\u0026ntilde;oz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Navarro-Galera et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile a bunch of literature has focused on the provision of various types of public services, specific attention has yet to be given to family policies. This could constitute a shortcoming, as family policies could indirectly counteract the depopulation of rural areas through two different channels. On the one hand, like other public interventions, they could attract new residents and curb the outflow of others. On the other hand, they could revitalise these areas by increasing the number of births.\u003c/p\u003e\u003cp\u003eAccordingly, this study seeks to address this gap by examining whether family policies implemented in small municipalities in rural areas can yield both of the aforementioned effects. In other words, we aim to assess whether family policies can serve as a tool to combat depopulation in small rural towns by improving net migration rates and increasing birth rates.\u003c/p\u003e\u003cp\u003eTo address this question, we focus on Trentino\u0026mdash;a mountainous province in northeastern Italy\u0026mdash;and examine the local family policies implemented for several years in multiple municipalities under the \u0026ldquo;Family in Trentino\u0026rdquo; certification program. Using municipality-level administrative data and the Synthetic Difference-in-Differences method (Arkhangelsky et. al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we assess whether certified municipalities\u0026mdash;those that obtained the \u0026ldquo;Family in Trentino\u0026rdquo; label\u0026mdash;experienced higher net migration and birth rates relative to their counterfactual outcomes had they not obtained the certification. The family well-being that can result from family policies implemented by Trentino municipalities that have acquired the label could, on the one hand, increase the number of births and, on the other, curb the outflow of current residents and attract new residents from other municipalities.\u003c/p\u003e\u003cp\u003eThe remainder of the paper is organised as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed description of the context and policy under examination, Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e outlines the data, variables, and methodology, Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the results, and Section 5 offers the conclusion.\u003c/p\u003e"},{"header":"2. Context and Policy Description","content":"\u003cp\u003eThe Autonomous Province of Trento (Trentino) has a population of approximately 546,709 inhabitants (244,117 households), spread across 166 municipalities. Over a third of the population (35.7%) resides in the 89 municipalities with populations ranging from 1,001 to 5,000, while more than a fifth (21.7%) live in the only municipality with over 100,000 residents (Trento).\u003c/p\u003e\u003cp\u003eTrentino faces significant structural challenges related to an ageing population: the average age is rising, the natural population balance is negative (with more deaths than births), and the fertility rate is declining. The total fertility rate in Trentino was 1.37 children per woman in 2022, down from 1.65 in 2010, and lower than the European Union average of 1.46 children per woman. Although the fertility rate remains above the national average (which is 1.24 children per woman), there is still a gap between the number of children desired and those actually born (ISTAT \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; ISPAT \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo respond to these demographic challenges, the province has introduced a range of family-oriented measures under the \u0026ldquo;Family in Trentino\u0026rdquo; certification program (\u003cem\u003e\u0026ldquo;Il marchio \u0026lsquo;Family in Trentino\u0026rsquo;\u0026rdquo;)\u003c/em\u003e, which has been active for nearly twenty years. The overarching objectives of the program are to reorient municipal policies towards family friendliness, actively promoting concrete and consistent measures aimed at supporting the well-being of both resident and non-resident families present within the territory.\u003c/p\u003e\u003cp\u003e\u0026ldquo;Family in Trentino\u0026rdquo; certification promoted by the Agency for Social Cohesion (ASC) of the Autonomous Province of Trento constitutes a structured governance system based on the strategic model of New Public Family Management (Malfer \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This framework departs from traditional welfare approaches and advances a generative welfare perspective, in which families are regarded not merely as beneficiaries of services but as key resources producing value for the community. Core principles include continuous innovation, individual autonomy and responsibility, and the use of evidence-based policy planning supported by public administration. Thus, the certification represents a distinctive policy instrument developed by ASC to foster a family-friendly territorial model.\u003c/p\u003e\u003cp\u003eCertification requires adherence to requirements across several domains, including planning and evaluation, provision of family services, tariff policies, environmental quality and quality of life, and communication practices. More specifically, the regulatory framework for municipalities sets out 47 requirements, divided into mandatory and optional. Certification requires municipalities to reach a minimum score (derived from the sum of points for mandatory requirements), which varies by the municipality's size, and to develop an annual Municipal Family Well-Being Plan. The plan, approved by the municipal council, specifies measurable interventions\u0026mdash;including objectives, responsible actors, and timeline\u0026mdash;structured across five macro-areas. The plan functions as a dynamic management tool subject to an annual self-assessment by the mayor and the publication of a monitoring report. This reporting, made publicly available online, ensures transparency and accountability toward citizens and the provincial agency.\u003c/p\u003e\u003cp\u003eTo obtain the label, a municipality must apply to the ASC for certification, while to maintain the label, a municipality must annually submit its Family Plan and the self-assessment of the previous year's plan. In cases of serious non-compliance, the label can be revoked, but thus far, all municipalities have maintained the certification they obtained.\u003c/p\u003e\u003cp\u003eMunicipalities that obtain the label are thus recognised as being attentive to family needs and committed to enhancing family well-being. In particular, they commit to offer families certain services, such as, for example, information and education on citizenship, family-friendly services, fare reductions, communication, environment and quality of life. Municipalities undertake to inform families about services, organise meetings on parenting, foster family-work reconciliation, promote youth participation, support services for the elderly and those in need, and organise meetings on various topics such as the environment, culture, tourism, reading, gambling, gender violence, intergenerational communication, bullying and cyberbullying. They implement services for children (daycare, after-school, summer services) and support local associations. Efforts also extend to fostering young people's entry into employment, supporting senior citizens and disabilities, and supporting the integration of foreign families. Family policies are crosscutting, integrating with housing, sports and cultural policies.\u003c/p\u003e\u003cp\u003eThe primary added value of the certification, therefore, lies not merely in the provision of these individual services\u0026mdash;some of which may be offered sporadically by non-certified municipalities\u0026mdash;but in the systematic framework it mandates. Certification transforms disparate actions into a cohesive strategy. In essence, the Family in Trentino certification moves policy from a series of ad-hoc interventions to a structured, accountable, and dynamic governance process.\u003c/p\u003e\u003cp\u003eBy analysing the plans of certified municipalities and grouping similar actions by purpose and mode of application, an official Taxonomy of Municipal Plan Actions (\u003cem\u003e\u0026ldquo;Tassonomia delle azioni dei piani comunali\u0026rdquo;\u003c/em\u003e) was developed. Specifically, the activities of the Family Plans have been grouped into five macro-areas of intervention (Governance and network actions, Economic measures, Information and communication, Educating community, Territorial welfare and sustainability), which are further divided into types of actions.\u003c/p\u003e\u003cp\u003eThe taxonomy is a widely used instrument for municipalities to identify new family policies to introduce within their territory, presenting a broad spectrum of possible actions to implement for each macro-area. It is a \u0026ldquo;living\u0026rdquo; instrument, periodically updated with new types of actions that become established in certified municipalities. In this way, it serves as an important vehicle for the dissemination of good practices and network creation.\u003c/p\u003e\u003cp\u003eThe majority of taxonomy\u0026rsquo;s entries pertain to specific actions addressing family needs (e.g., newborn bonuses, financial support for large families). While some entries refer to particular user groups of municipal services (e.g., youth or the elderly), their inclusion in the taxonomy is always linked to the benefit the family unit can derive (in terms of reducing care burdens or providing support in the educational process).\u003c/p\u003e\u003cp\u003eThe \u0026ldquo;Family in Trentino\u0026rdquo; program was launched in 2006. The label expanded gradually, with Trento\u0026mdash;the provincial capital\u0026mdash;certified in December 2014. As of 2024, 83 of Trentino\u0026rsquo;s 166 municipalities (50%) hold the label (see the diffusion of the \u0026ldquo;Family in Trentino\u0026rdquo; certification on Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Over this period, no municipality has relinquished or lost its certification. In cases of municipal mergers, the newly formed municipality retained the certification previously held by one or more of the merging municipalities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDarker shades indicate municipalities certified earlier (longer tenure); lighter shades indicate more recent certifications.\u003c/p\u003e"},{"header":"3. Data, variables and method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Data\u003c/h2\u003e\u003cp\u003eWe use publicly accessible municipality-level population data provided by the Italian National Institute of Statistics (ISTAT). The observation period spans from 1992 to 2022, covering a total of 31 years, which represents the most recent data available.\u003c/p\u003e\u003cp\u003eGiven that some municipalities in Trentino experienced administrative changes over the observation period\u0026mdash;such as mergers (unions) or, conversely, separations and the creation of new entities- we restricted the sample to municipalities that remained stable over time. This produced a working sample of 138 municipalities, compared to 233 municipalities in 2009 and 166 municipalities in 2025. This decision was taken to avoid inconsistencies in population denominators and to reduce potential distortions in demographic indicators, particularly in the case of net migration rates, which are highly sensitive to administrative boundary changes.\u003c/p\u003e\u003cp\u003eWe excluded the two largest municipalities\u0026mdash;Trento (the provincial capital, population\u0026thinsp;\u0026asymp;\u0026thinsp;117,000) and Rovereto (population\u0026thinsp;\u0026asymp;\u0026thinsp;40,000)\u0026mdash;from the analysis, as they represent outliers in terms of demographic size and urban characteristics. This decision is motivated by the fact that the policy under examination is specifically designed to address depopulation challenges affecting small municipalities. Moreover, it is in these smaller municipalities, rather than in larger urban centers, that there is a pressing need to curb resident outflows and attract new inhabitants\u003c/p\u003e\u003cp\u003eIn view of that, we relied on a balanced panel dataset consisting of 136 municipalities (76 treated vs 60 never-treated) of different sizes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) observed over 31 years (1992\u0026ndash;2022), i.e. 4,216 municipality-year observations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of analysed Trentino municipalities by population size (average population across 1992\u0026ndash;2022) and treatment status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAll municipalities\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAmong those are treated\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePopulation size\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePercent\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ePercent\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eless than 500 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e500\u0026ndash;999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.000\u0026ndash;1.999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.000\u0026ndash;2.999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.000\u0026ndash;4.999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.000\u0026ndash;9.999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10.000\u0026ndash;19.999 residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Variables\u003c/h2\u003e\u003cp\u003eAs outcome variables, we consider two demographic indicators.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe \u003cem\u003efertility rate.\u003c/em\u003e It is defined as the number of live births per 1,000 residents in a given year. This measure provides a direct indication of the fertility propensity of the resident population.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe \u003cem\u003enet migration rate.\u003c/em\u003e It is calculated as the difference between in-migration and out-migration\u0026mdash;measured through registrations and cancellations of residence to and from other municipalities (including outside Trentino) per 1,000 residents in a given year. This indicator reflects the attractiveness or depopulation tendency of local areas.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eSince the raw data for these two indicators yield time series characterised by a remarkable volatility, we smoothed the series by calculating a five-year moving average. This procedure reduces short-term fluctuations caused by annual shocks (e.g., single-year variations in births or population movements in small municipalities) and highlights the underlying demographic trends. By attenuating noise, the moving average improves the comparability of treated and control units and strengthens the validity of our analysis.\u003c/p\u003e\u003cp\u003eTreatment status is a time-varying binary indicator equal to 1 in all years from (and including) the year a municipality obtained the \u0026ldquo;Family in Trentino\u0026rdquo; certification, and 0 otherwise. The certification start year for each municipality is taken from publicly available records of the Agency for Social Cohesion of the Autonomous Province of Trento. None of the certified municipalities ever relinquished the label; once certified, they remain treated in all subsequent years. The comparison group comprises municipalities that were never certified (never treated), which constitute the donor pool.\u003c/p\u003e\u003cp\u003eAs control variables, we consider two indicators.\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe \u003cem\u003eaverage resident population per year\u003c/em\u003e is a time-varying covariate that captures potential size effects in fertility and migration dynamics.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe \u003cem\u003enumber of available childcare places\u003c/em\u003e\u003csup\u003e1\u003c/sup\u003e is included as a time-varying proxy for family-supportive infrastructure at the local level.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe descriptive statistics for the variables used in the analysis are represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of variables included in the analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep25\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep50\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep75\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eMin\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eMax\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertility rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Migration rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-48.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage resident population per year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2053.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2815.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e657.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2321.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21633.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of available childcare places\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn line with the literature on depopulation of rural areas, some other control variables should be taken into account. These are: (1) road distance to the provincial capital, proxying access to the capital\u0026rsquo;s richer portfolio of public and private services, (2) municipal altitude, capturing remoteness; and (3) the availability of other municipal services, such as schools and healthcare facilities. However, there is no need to include these variables in the model specification because, being time invariant, they are captured by the unit fixed effects in the synthetic difference-in-differences framework. As specified below, this method precisely allows for unit and time fixed effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Method\u003c/h2\u003e\u003cp\u003eTo estimate the demographic impact of the Family in Trentino label, we employ the Synthetic Difference-in-Differences (SDiD) method (with staggered adoption) proposed by Arkhangelsky et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSDiD is a counterfactual causal inference method. It builds an explicit counterfactual outcome for the treated units by combining strengths from both the traditional difference-in-differences procedure and the synthetic control method, as developed by Abadie et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Like the difference-in-differences procedure, SDiD allows for treated and control units to be trending on entirely different levels before an event or policy of interest. Like the synthetic control method, SDiD seeks to optimally generate a matched control unit, which considerably loosens the need for parallel trend assumptions. Therefore, SDiD avoids common pitfalls in standard difference-in-differences and synthetic control methods \u0026ndash; namely, an inability to estimate causal relationships if parallel trends are not met in aggregate data in the case of difference-in-differences, and a requirement that the treated unit be housed within a \u0026ldquo;convex hull\u0026rdquo; of control units in the case of synthetic control (Clarke et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSDiD is highly appropriate for our setting as it is based on a panel (group by time) set-up, in which certain units are treated (76 municipalities), and the remaining units are untreated (60 municipalities). Accordingly, the SDiD procedure calculates a treatment effect as the pre- versus post-difference-in-difference between treated units and synthetic control units, where synthetic control units are chosen as an optimally weighted function of untreated units (unit-specific weights) and pre-treatment times (time-specific weights).\u003c/p\u003e\u003cp\u003eBecause municipalities adopt certification in different years, we use the staggered SDiD implementation: unit and time weights are learned by treatment cohort, iterating over the never-treated set and each adoption cohort to obtain cohort-specific effects and their standard errors (Arkhangelsky et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Clarke et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe implement SDiD in two steps. Step 1 (baseline): We estimate the ATT for net migration and fertility using all treated municipalities. Step 2 (content-aware analysis): We re-estimate the same outcomes within clusters of municipalities that share similar policy bundles (see SI for clustering construction).\u003c/p\u003e\u003cp\u003eInference is based on municipality-block bootstrap standard errors with 1,999 replications (fixed seed). Robustness checks include treatment-timing shifts (\u0026plusmn;\u0026thinsp;3 years), a jackknife SDiD specification (satisfying the cohort-size requirement), and a placebo-in-time check.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eWe begin by estimating staggered SDiD average treatment effects on the treated (ATT) for fertility and net migration at the municipality-year level. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualises) the baseline results. For fertility, we find no detectable response to certification: the ATT is small in magnitude and imprecise (ATT\u0026thinsp;=\u0026thinsp;0.1297, SE\u0026thinsp;=\u0026thinsp;0.2327, p\u0026thinsp;=\u0026thinsp;0.577), and the 95% confidence interval comfortably spans zero. This pattern suggests that the local family-policy bundle associated with certification does not shift reproductive outcomes over the evaluation horizon.\u003c/p\u003e\u003cp\u003eBy contrast, net migration shows a positive response to certification. The ATT equals 1.516 migrants per 1,000 residents (SE\u0026thinsp;=\u0026thinsp;0.817, p\u0026thinsp;=\u0026thinsp;0.064). Although marginal at conventional thresholds, the estimate is stable in sign and of policy-relevant magnitude, indicating that certified municipalities experience higher net inflows relative to their synthetic counterfactuals.\u003c/p\u003e\u003cp\u003eInterpreted together, these findings are consistent with the causal mechanism that assumes a better migration balance due to the increased attractiveness of the territory guaranteed by certain family policies, and not with the causal mechanism that assumes an increase in births due to better living conditions guaranteed by those same family policies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSynthetic Difference-in-Differences Estimator results (staggered) for fertility and net migration rates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOutcome\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eATT\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eStd. Err.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95% Conf.\u0026nbsp; Interval\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertility rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.23272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.32641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.58586\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Migration rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.08546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.11755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Estimator: staggered SDiD (Arkhangelsky et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on municipality\u0026ndash;year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBecause effects (as well as the absence of the visible effects) may depend on which local measures are implemented, we next re-estimate SDiD within clusters of municipalities sharing similar policy profiles. By comparing units exposed to comparable policy environments, we assess which elements of \u0026ldquo;Family in Trentino\u0026rdquo; are most salient for demographic dynamics.\u003c/p\u003e\u003cp\u003eWe construct clusters by grouping municipalities on similarity in policy characteristics; methodological details are provided in the Supplementary Information. The five clusters that capture a clear coverage gradient across the policy taxonomy and their defining features\u0026mdash;summarised by the program\u0026rsquo;s five macro-areas\u0026mdash;are reported below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClusters of municipalities shared similar policies under the \u003cem\u003e\u0026ldquo;\u003c/em\u003eFamily in Trentino\u0026rdquo; certification\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCluster\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDescription\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate total coverage of the entire taxonomy and medium-high coverage of macro-area \u0026ldquo;Governance and network actions\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow coverage of the entire taxonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh coverage of the entire taxonomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntermediate total coverage of the entire taxonomy, medium-high coverage of macro-area \u0026ldquo;Economic measures\u0026rdquo;, and medium-low coverage of macro-area \u0026ldquo;Governance and network actions\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium-low coverage of the entire taxonomy, particularly macro-area \u0026ldquo;Governance and network actions\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e report staggered SDiD estimates by policy-content clusters\u0026mdash;for fertility (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and net migration (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For fertility, we do not detect statistically significant effects in any cluster: point estimates are small, and their confidence intervals overlap zero, reinforcing the baseline result that \u0026ldquo;Family in Trentino\u0026rdquo; does not measurably shift reproductive behaviour across policy bundles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSynthetic Difference-in-Differences Estimator results (staggered) for fertility across clusters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFertility rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eATT\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eStd. Err.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95% Conf.\u0026nbsp; Interval\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 1 (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.51574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.32298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.11728\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.14876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 2 (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.67739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.53495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.72586\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.37109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 3 (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.22179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.41037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.02611\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.58252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 4 (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.37610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.65529\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.81899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 5 (N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.25085\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.24541\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Estimator: staggered SDiD (Arkhangelsky et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on municipality\u0026ndash;year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding net migration, we find a marginally significant effect in Cluster 3\u0026mdash;municipalities with high coverage across the policy taxonomy. The average treatment effect on the treated (ATT) for this cluster is 2.78 (SE\u0026thinsp;=\u0026thinsp;1.61, p\u0026thinsp;=\u0026thinsp;0.084). Similarly, Cluster 4\u0026mdash;municipalities with intermediate coverage of the taxonomy and higher coverage in Economic measures\u0026mdash;shows a positive, marginally significant effect (ATT\u0026thinsp;=\u0026thinsp;2.80, SE\u0026thinsp;=\u0026thinsp;1.64, p\u0026thinsp;=\u0026thinsp;0.088). However, no significant effects are observed in Clusters 1, 2, and 5, suggesting that the \u0026ldquo;Family in Trentino\u0026rdquo; certification\u0026rsquo;s impact on migration is most pronounced in municipalities with a comprehensive policy package.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSynthetic Difference-in-Differences Estimator results (staggered) for net migration rates across clusters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet Migration rate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eATT\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eStd. Err.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95% Conf.\u0026nbsp; Interval\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 1 (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.32357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.54178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.69826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.34539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 2 (N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.54555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.66521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.67817\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.76926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 3 (N\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.77622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.60808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.37556\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.92801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 4 (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.79608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.63983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.41792\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.01008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster 5 (N\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.11474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.37820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.54645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.77593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes\u003c/em\u003e. Estimator: staggered SDiD (Arkhangelsky et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on municipality\u0026ndash;year data. Donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213). 95% CIs and p-values are computed using bootstrap standard errors.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGiven that the estimated effects on net migration are marginal\u0026mdash;both in the full treated sample and within certain clusters\u0026mdash;we conduct additional robustness checks of the main results. Specifically, we implement treatment-timing shifts (\u0026plusmn;\u0026thinsp;3 years), a jackknife SDiD specification (restricted to satisfy cohort-size requirements), and a placebo-in-time check. Qualitative conclusions are unchanged; the results are provided below (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness of SDiD estimates for net migration: bootstrap, jackknife, and timing shifts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSpecification\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eATT\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eStd. Err.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003e95% Conf.\u0026nbsp; Interval\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDiD (bootstrap, 1,999 reps)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.51605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.08546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.11755\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDiD (jackknife)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.57609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.83566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.06177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.21395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDiD, timing shift \u0026minus;\u0026thinsp;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.58556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.01586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.18698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDiD, timing shift\u0026thinsp;+\u0026thinsp;3 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.68073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.24691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDiD, placebo-in-time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.95422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.93113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.02269\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNotes.\u003c/em\u003e Estimator: staggered SDiD at the municipality\u0026ndash;year level; donor pool: never-treated municipalities. Covariates: population size and nursery coverage. Inference: municipality-block bootstrap (1,999 replications; seed 1213) or jackknife as indicated. Timing shifts advance/delay certification by three years. Jackknife estimated on a sample satisfying cohort-size requirements. The placebo-in-time test assigns a fake treatment start before actual adoption and runs the SDiD estimator with the same specifications.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe compare bootstrap and jackknife standard errors because they rest on different inference assumptions under heteroskedasticity and within-municipality serial correlation. The unit-block bootstrap resamples entire municipalities, preserving within-unit dependence and providing an all-purpose choice in large panels, whereas the jackknife re-estimates the model, leaving out one influence unit at a time, directly probing sensitivity to single municipalities or adoption cohorts without resampling randomness. Using both also serves as a design diagnostic\u0026mdash;jackknife feasibility requires at least 2 treated units per cohort, therefore we had to exclude one cohort with a single municipality (Arco, 2008)\u0026mdash;and, when the two procedures agree, it reduces concerns that statistical significance is an artefact of a particular variance estimator rather than a substantive effect. As the robustness check shows, bootstrap and jackknife yield nearly identical ATTs for net migration (around 1.5 per 1,000) and similar inference (marginal significance), indicating that the migration effect is not sensitive to the choice of variance estimator.\u003c/p\u003e\u003cp\u003eTiming shifts confirm that results are not driven by the exact start date: advancing treatment by \u0026minus;\u0026thinsp;3 years returns a similarly positive estimate (1.586; SE\u0026thinsp;=\u0026thinsp;0.817; p\u0026thinsp;=\u0026thinsp;0.052), while delaying by +\u0026thinsp;3 years produces a smaller but still positive effect (0.783; SE\u0026thinsp;=\u0026thinsp;0.747; p\u0026thinsp;=\u0026thinsp;0.294), consistent with attenuation when early post-adoption years are withheld. Taking together, the evidence points to a robust, positive migration response that is insensitive to the variance estimator and reasonably stable to timing.\u003c/p\u003e\u003cp\u003eFinally, we conducted a placebo-in-time test by assigning a fake treatment start before the actual adoption of the \u0026ldquo;Family in Trentino\u0026rdquo; certification. The result shows an insignificant effect on net migration: the ATT for the placebo treatment is \u0026minus;\u0026thinsp;0.954 (SE\u0026thinsp;=\u0026thinsp;1.009, p\u0026thinsp;=\u0026thinsp;0.334), and the 95% confidence interval includes zero. This non-significant result provides further validation of the identification strategy, indicating that the observed effect on net migration is not driven by pre-existing trends or anticipation of the policy. The placebo-in-time test suggests that there are no spurious pre-treatment effects, supporting the robustness of the main findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study started from the assumption that family policies, although primarily implemented to improve the well-being of family members in a given territory, can exercise indirect effects on certain demographic variables, such as fertility rates and population movements. This led us to ask whether family policies adopted at the local level could, because of these effects, be a tool for combating the depopulation affecting small municipalities in rural and mountain areas. To answer this question, we estimated\u0026mdash;using the synthetic difference-in-differences method\u0026mdash;the demographic impact of a family policy, i.e., \u0026ldquo;Family in Trentino\u0026rdquo; certification, implemented in numerous municipalities in the province of Trento.\u003c/p\u003e\u003cp\u003eOur analysis shows that this policy has a significant impact on the net migration rate of Trentino municipalities that have acquired the label but does not seem to have a significant influence on the fertility rate of these municipalities. In other words, on average, Trentino municipalities that have acquired the label seem to be able to counteract trends of depopulation by attracting new residents and slowing down the outflow of their own residents, but they do not appear to be able to change the reproductive choices of families in their territory. Our analysis of the Trentino case, therefore, shows that only one of the two causal mechanisms that can activate a family policy appears to be effective in counteracting depopulation trends.\u003c/p\u003e\u003cp\u003eIt is quite plausible that we did not find a significant effect on the fertility rate. The measures adopted by the certified municipalities, although numerous and varied, do not provide substantial support for families in terms of cash transfers and/or the provision of services. Due to their administrative constraints and in line with the mission of the policy under examination, the municipalities that have acquired the certification have limited themselves to implementing measures aimed at reducing the tariffs of certain services, incentivising others, increasing citizen information, and promoting initiatives to increase local participation in activities concerning family issues. In contrast, the existing literature on the effects of family policies shows that fertility is significantly influenced only when large reforms are implemented and policymakers intervene markedly in the areas of maternity leave, income support, and child-care services (Bergsvik et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn the other hand, it can be argued that the measures implemented by certified municipalities in Trentino, despite their limitations, have had an impact on the net migration rate, since the decision to move residence is less costly in monetary terms than having a child. The decision to have a child certainly involves a significant financial effort, while staying in a certain municipality without moving can, in some cases, also involve certain savings. Furthermore, if a family needs to move, it is likely to make its choice based on several criteria of convenience. For example, immigrants, differently from natives, who live in rural areas, have higher probabilities of moving to another municipality than their peers in more urban areas (Skjerpen \u0026amp; T\u0026oslash;nnessen \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, the measures implemented in line with \u0026ldquo;Family in Trentino\u0026rdquo; may have proved that certified municipalities have a long-standing and diversified public commitment to promoting the well-being of families. This argument is partly consistent with the findings of the literature on the depopulation of rural areas. It is now widely accepted that, in order to stem the outflow of residents and attract newcomers, local authorities must implement a variety of public interventions (Loras-Gimeno et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As we have repeatedly observed, the municipalities in Trentino that have obtained the certification have implemented numerous measures. Moreover, the most marked effects on the net migration rate have been observed precisely as a result of a large number of measures in favour of families. However, what distinguishes this study from the literature on the depopulation of small rural municipalities is the fact that the contrast to depopulation trends has been observed by considering only the role of a variety of family-friendly \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003emeasures\u003c/span\u003e. This does not mean that certified municipalities in Trentino are more effective at countering depopulation trends than municipalities equipped with other types of services, such as healthcare facilities, schools, child-care services, etc. If anything, it means that by controlling the provision of these other services, certified municipalities would have had a worse net migration rate if they had not implemented this variety of family policies. In counterfactual terms, many family-friendly measures are, per se, sufficient to exercise a significant impact on the net migration rate.\u003c/p\u003e\u003cp\u003eAlthough this result may contribute to the debate on policies to combat depopulation, one limitation of our analysis is that we cannot say anything about those who move their residence from one municipality to another. The data we used to calculate the net migration rate only provides information on the number of registrations and cancellations of residence to and from other municipalities. Consequently, it is not possible to establish (1) whether moves to certified municipalities in Trentino occur mainly from municipalities in Trentino that are not yet certified or from municipalities in other regions, (2) whether these moves mainly involve young people, and (3) whether they primarily involve people with Italian or foreign citizenship. This lack of information makes it impossible to understand whether Trentino municipalities that are not yet certified are increasing their risk of depopulation due to the movement of their residents to municipalities that are already certified. Furthermore, it is not possible to assume that certified municipalities have a greater chance of revitalisation as a result of a greater influx of young people.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed equally to this work. Author order is alphabetical.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe use publicly accessible municipality-level population data provided by the Italian National Institute of Statistics (ISTAT). The data are available at the following URL: (1991 - 2001) https://demo.istat.it/app/?i=R91\u0026amp;l=it (2001 -2018) https://demo.istat.it/app/?i=RBD\u0026amp;l=it(2018 - 2022) https://demo.istat.it/app/?i=D7B\u0026amp;l=it\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbadie A, Diamond A, Hainmueller J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California\u0026rsquo;s tobacco control program. J Am Stat Assoc 105(490):493\u0026ndash;505\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbadie A, Diamond A, Hainmueller J (2015) Comparative politics and the synthetic control method. Am J Polit Sci 59(2):495\u0026ndash;510\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlonso MP, Gargallo P, Lample L, L\u0026oacute;pez-Escolano C, Miguel JA, Salvador M (2025) How Service Exclusion Affects Rural Depopulation. An Approach Based on Structural Equation Modelling. Sociologia Ruralis, 65(3), e70005\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndr\u0026eacute; S, Teulings T (2024) Work-Family Policy for Fathers in Dutch Municipalities: A Vignette Experiment on Contexts for Parental Leave Among Male Civil Servants. Public Personnel Manage 53(4):601\u0026ndash;622\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArkhangelsky D, Athey S, Hirshberg DA, Imbens GW, Wager S (2021) Synthetic Difference-in-Differences. Am Econ Rev 111(12):4088\u0026ndash;4118\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026eacute;land D, Lecours A (2018) Federalism, policy change, and social security in Belgium: Explaining the decentralization of family allowances in the Sixth State Reform. J Eur Social Policy 28(1):55\u0026ndash;69\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergsvik J, Fauske A, Hart RK (2021) Can policies stall the fertility fall? A systematic review of the (quasi-) experimental literature. Popul Dev Rev 47(4):913\u0026ndash;964\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBorjas GJ (1999) Immigration and welfare magnets. J Labor Econ 17:4:607\u0026ndash;637\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChirkova S (2019) \u0026lsquo;The impact of parental leave policy on child-rearing and employment behavior: The case of Germany\u0026rsquo;. IZA J Labor Policy, 9 (1)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClarke D, Paila\u0026ntilde;ir D, Athey S, Imbens GW (2023) Synthetic Difference-in-Differences Estimation. IZA Discussion Papers 15907. Institute of Labor Economics (IZA)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConti M, Sivini S (2023) Small Municipalities Attracting Rural Newcomers and Fostering Local Cohesion: Innovative Approaches for Rural Regeneration in Italy. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15075837\u003c/span\u003e\u003cspan address=\"10.3390/su15075837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Sustainability\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCowan C, Cowan P (2018) Enhancing Parenting Effectiveness, Fathers' Involvement, Couple Relationship Quality, and Children's Development: Breaking Down Silos in Family Policy Making and Service Delivery. J Family Theory Rev. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jftr.12301\u003c/span\u003e\u003cspan address=\"10.1111/jftr.12301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDahlberg M, Edmark K (2008) Is there a race-to-the-bottom in the setting of welfare benefit levels? Evidence from a policy intervention. J Public Econ 92(5\u0026ndash;6):1193\u0026ndash;1209\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerwerda J, Marbach M, Hangartner D (2024) Do immigrants move to welfare? Subnational evidence from Switzerland. Am J Polit Sci 68(3):874\u0026ndash;890\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFiva JH, Ratts\u0026oslash; J (2006) Welfare competition in Norway: Norms and expenditures. Eur J Polit Econ 22(1):202\u0026ndash;222\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauthier AH (2007) The impact of family policies on fertility in industrialized countries: a review of the literature. Popul Res Policy Rev 26(3):323\u0026ndash;346\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGauthier AH, Gietel-Basten S (2025) Family policies in low fertility countries: Evidence and reflections. Popul Dev Rev 51(1):125\u0026ndash;161\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraus S, Ferreira T, Vasconcelos G, Ortega J (2024) Changing Conditions: Global Warming-Related Hazards and Vulnerable Rural Populations in Mediterranean Europe. Urban Science. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/urbansci8020042\u003c/span\u003e\u003cspan address=\"10.3390/urbansci8020042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHudečkov\u0026aacute; H, Hus\u0026aacute;k J, Volesk\u0026aacute; R (2019) Family Policy in the Strategic Planning of Rural Municipalities in the Czech Republic. Eur\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eISPAT (2024) La natalit\u0026agrave; in Trentino fra desideri e realt\u0026agrave; (Ispat Comunicazioni). Provincia autonoma di Trento. Retrieved at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.statistica.provincia.tn.it/binary/pat_statistica_new/famiglia_comportamenti_sociali/NatalitaInTrentino.1732111208.pdf\u003c/span\u003e\u003cspan address=\"http://www.statistica.provincia.tn.it/binary/pat_statistica_new/famiglia_comportamenti_sociali/NatalitaInTrentino.1732111208.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eISTAT (2025) Il Censimento permanente della popolazione in Trentino. Anno 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.istat.it/wp-content/uploads/2025/04/Censimento-permanentepopolazione_Anno-2023_Trento.pdf\u003c/span\u003e\u003cspan address=\"https://www.istat.it/wp-content/uploads/2025/04/Censimento-permanentepopolazione_Anno-2023_Trento.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKazepov Y, Barberis E, Cucca R, Mocca E (eds) (2022) Handbook on urban social policies: International perspectives on multilevel governance and local welfare. Edward Elgar Publishing\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoras-Gimeno D, D\u0026iacute;az-Lanchas J, G\u0026oacute;mez-Bengoechea G (2025) Rural depopulation in the 21st century: A systematic review of policy assessments. Regional Science Policy \u0026amp; Practice, p 100176\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalfer L (2018) New public family management: welfare generativo, family mainstreaming. networking e partnership\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMu\u0026ntilde;oz LA, Galera AN, Bolivar MPR (2024) The financial sustainability of public services as an instrument to combat depopulation in small and medium-sized municipalities. Cities 154:105337\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNavarro-Galera A, Buend\u0026iacute;a-Carrillo D, G\u0026oacute;mez-Miranda ME, Lara-Rubio J (2024) Fighting depopulation in Europe by analyzing the financial risks of local governments. Int Rev Admin Sci 90(1):48\u0026ndash;64\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewsham N, Rowe F (2022) Understanding trajectories of population decline across rural and urban Europe: A sequence analysis. Population, Space and Place. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/psp.2630\u003c/span\u003e\u003cspan address=\"10.1002/psp.2630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeyer G (2003) Family policies and low fertility in Western Europe. Institute of Economic Research, Hitotsubashi University, pp 2003\u0026ndash;2021\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNieuwenhuis R, Van Lancker W (eds) (2020) (eds) The Palgrave handbook of family policy. Springer Nature, p 721\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOECD (2021) OECD sovereign borrowing outlook 2021. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/economy/oecd-sovereign-borrowing-outlook-23060476.htm\u003c/span\u003e\u003cspan address=\"https://www.oecd.org/economy/oecd-sovereign-borrowing-outlook-23060476.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePonce A (2018) Is Welfare a Magnet for Migration? Examining Universal Welfare Institutions and Migration Flows. Soc Forces 98:245\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/sf/soy111\u003c/span\u003e\u003cspan address=\"10.1093/sf/soy111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReynaud C, Miccoli S, Benassi F, Naccarato A, Salvati L (2020) Unravelling a demographic \u0026lsquo;Mosaic\u0026rsquo;: Spatial patterns and contextual factors of depopulation in Italian Municipalities, 1981\u0026ndash;2011. Ecol Ind 115:106356. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2020.106356\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2020.106356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobila M (2014) Family Policies Across the Globe: Development, Implementation, and Assessment., 3\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4614-6771-7_1\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4614-6771-7_1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkjerpen T, T\u0026oslash;nnessen M (2025) Try another municipality or leave the country? A disaggregated approach to determinants of internal migration and emigration for immigrants and natives in Norway. Ann Reg Sci 74(2):1\u0026ndash;29\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValenzuela V, Holl A (2023) Growth and decline in rural Spain: an exploratory analysis. Eur Plan Stud 32:430\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09654313.2023.2179390\u003c/span\u003e\u003cspan address=\"10.1080/09654313.2023.2179390\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Data on childcare facilities was provided by Statistical Institute of the Province of Trento (ISPAT) at the request of the authors.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Depopulation, Family policy, Indirect effect, Migration, Fertility, Rural areas","lastPublishedDoi":"10.21203/rs.3.rs-8116068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8116068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study started from the assumption that family policies, although primarily implemented to improve the well-being of family members in a given territory, can exercise indirect effects on certain demographic outcomes, such as fertility rates and population movements. This led us to ask whether family policies adopted at the local level could, because of these effects, be a tool for combating the depopulation affecting small municipalities in rural areas.\u003c/p\u003e\u003cp\u003eTo answer this question, we focused on Trentino (a mountainous province located in northeastern Italy) and, more specifically, on a family policy, i.e., Family in Trentino, that has been implemented in several municipalities of this province for several years now.\u003c/p\u003e\u003cp\u003eUsing municipality-level administrative data and the Synthetic Difference-in-Differences method, we found that this policy had a significant impact on the net migration rate of those municipalities that adopted it but was unable to influence their fertility rate.\u003c/p\u003e\u003cp\u003eOur analysis of the Trentino case, therefore, shows that only one of the two causal mechanisms that can activate a family policy appears to be effective in counteracting depopulation trends. This does not mean that the municipalities in Trentino that have adopted the policy under examination are more effective at countering depopulation trends than municipalities providing other types of public services. If anything, it means that by controlling the provision of these other services, the municipalities that implemented Family in Trentino would have had a worse net migration rate if they had not implemented this policy.\u003c/p\u003e","manuscriptTitle":"Migration gains but not fertility change. An impact evaluation of a municipal family policy in Trentino via Synthetic Difference-in-Differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 17:14:43","doi":"10.21203/rs.3.rs-8116068/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T17:41:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T16:21:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T10:04:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265253059858301552326735463672895392037","date":"2026-03-23T19:54:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79821335232526327803313777646131640562","date":"2026-03-23T19:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234134826162683242025980997411943404155","date":"2026-03-23T09:44:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309226396582878328870654653070936026684","date":"2026-03-21T09:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237016173634742368325295336471859991617","date":"2026-03-20T20:03:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166280876364892773441299337993027289923","date":"2026-03-20T18:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124653315833242147108378404813120822762","date":"2026-03-20T18:11:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T17:27:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T11:22:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T13:34:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Annals of Regional Science","date":"2025-11-14T14:31:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"95551f9b-79cf-4567-97ab-a63b40c6e3b5","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-29T17:41:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T16:21:16+00:00","index":27,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T17:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 17:14:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8116068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8116068","identity":"rs-8116068","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}