Micro- and macro-drivers of child deprivation in 31 European countries


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Micro- and macro-drivers of child deprivation in 31 European countries ANNE-CATHERINE GUIO ERIC MARLIER FRANK VANDENBROUCKE AND PIM VERBUNT 2020 edition S TAT IS T I C AL W O R K N G PA P E Micro-and Manuscript completed December 2019 The Commission is not liable for any consequence stemming from the reuse this publication Luxembourg: Publications Office Union © Reuse authorised provided source acknowledged policy documents regulated by Decision 2011/833/EU (OJ L 330 14 12 2011 p 39) Copyright cover photo: Shutterstock/ zurijeta For use or reproduction photos other material that under EU copyright permission must be sought directly holders more information please consult: https://ec europa eu/eurostat/about/policies/copyright Theme: Population social conditions Collection: Statistical working papers views set out are those authors do necessarily reflect official opinion Neither institutions bodies nor person acting on their behalf may held responsible which made contained therein PDF ISBN 978-92-76-10529-9 ISSN 2315-0807 doi: 10 2785/831285 KS-TC-20-003-EN-N Abstract This paper analyses using scale officially adopted March 2018 to measure child-specific at level It combines single multilevel models get a full picture drivers With regard within-country differences our results confirm combined impact variables related “longer-term command over resources” indicating “household needs” However also show relationship these with differs between In richest explanatory power household needs largest whereas most deprived resource generally greater between-country specification model careful consideration We argue should include income micro if aim fully gauge households’ then assesses how much country-level features reflected individual characteristics contribute explaining across find public spending in-kind benefits significant respect Public cash transfers plays only limited role when incomes included; they play excluded does diminish importance fighting but it qualifies conclusions have analysed without controlling Finally we GDP per capita even included self-evident: shows proxy important contextual Authors: Anne-Catherine Guio Eric Marlier (1) Frank Vandenbroucke(2) Pim Verbunt (3) Luxembourg Institute Socio-Economic Research (LISER Luxembourg); (2) Vandenbroucke University Amsterdam (Netherlands); Leuven (Belgium) Table contents 3 1. Introduction() 5 2. A robust 7 3. General overview 4. macro-level determinants 17 4.1. Micro-level 4 2 Combining micro- 20 5. estimation strategy 28 6. Results 30 6.1 National 6.2 36 6.2.1. M1-M2: Empty household-level 6.2.2. M3-M12: Assessing institutional 37 6.2.3. M13-M22: 6.2.4. M23-M26: Sensitivity disposable concepts 39 6 Cross-level interactions 7. Conclusions 48 References 50 Annexes 53 1 Introduction(1) Fighting poverty investing children’s well-being has featured agenda (EU) many years February 2013 new step forward was taken published Recommendation “Investing children: breaking cycle disadvantage” (European 2013) subsequently Council Ministers An element calls Member States “(reinforce) statistical capacity where needed feasible particularly concerning deprivation” best way provide accurate actual living children making assumptions about sharing resources within develop child- specific indicators – i e based situation differ parents 2009 wave Statistics Income Living Conditions (EU-SILC) an ad hoc module aimed collecting such first in- depth analysis data carried et al (2012) optimal items identified index proposed These were again 2014 EU-SILC allowing additional (2018) final list consists covering both aspects can aggregated monitor comparative whole (28 as well Iceland Serbia Switzerland (following 2019) doing so seeks obtain better understanding joint micro-determinants (household’s labour market attachment composition costs [due housing bad health…] etc ) types Single make possible identify national risk factors offer variations Specifically allow analysing decomposing country fit measures setting advantage cross-national 31-country pooled dataset Both type Hence remain understand each (as coefficients definition allowed vary wish thank Brian Nolan Jonathan Bradshaw Elena Bárcena-Martín Bertrand Maître Kenneth Nelson Geranda Notten valuable discussions All errors strictly authors’ work been supported third Network (Net-SILC3) funded Eurostat bears no responsibility solely Email address correspondence: anne-catherine guio@liser lu Norway could due large amount missing Introduction specificities micro-drivers captured); complemented (that than populations) So illustrates strength comprehensive levers mobilised fight EU(3) contribution literature second main one view replicates confronts broad spectrum (sometimes diverging) reported suggests reasons why measured (do not) described inclusion (national Gross Domestic Product (GDP) justified fact generous welfare systems prosperous economies lead lower levels once micro-level (household-level) capture received reason would still discussed priori expect explain after relevant crucial question therefore variable whose already into account expected contrarily previous because proxies elements To disentangle replicate number presented variety macro-variables linked (generosity in-cash pro-family adequacy assistance pro- poorness transfers) different countries’ standards added value explicitly certain parents’ education migrant status (quasi-)joblessness next household’s current Often expectation “social stratification” granted further argument fourth Shapley decompositions establish relative independent & Usually econometric used relations goes provides covered organised follows Section defines illustrative reviews macro- presents detail concludes yet another advantage: allows agreed theory driven From theoretical point largely relies Townsend’s concept deprivation: “Poverty defined objectively applied consistently terms […] Individuals families groups population said lack diet participate activities amenities customary least widely encouraged approved societies belong Their seriously below commanded average family effect ordinary patterns customs ” (Townsend 1979 31) analytical framework select draws extensively 1999 Poverty Social Exclusion (PSE) Survey indicator construction methodology (Gordon 2000; Pantazis 2006) ensure item selection examined four aspects: suitability order check citizens sub-groups State) consider them necessary “acceptable” standard live “Suitability” understood face validity amongst exhibits statistically ratios known correlated reliability assess internal consistency closely group Classical Test Theory Item Response Hierarchical Omega Analysis additivity test someone score “2” suffering severe “1” indicator’s components add up successfully passed tests thus considered suitable valid reliable additive candidates being measurement “children” “household” Children items: Some (not second-hand) clothes Two pairs properly fitting shoes Fresh fruit vegetables daily Meat chicken fish vegetarian equivalent Books home age Outdoor leisure equipment Indoor games 8. Regular 9. Celebrations special occasions 10. Invitation friends eat time 11. Participation school trips events 12. Holiday Household 13. Replace worn-out furniture 14. Arrears 15. Access Internet 16. Home adequately warm 17. car private keep mind some collection processing First relating collected themselves adult answering questionnaire” (household respondent) Secondly according survey protocol followed given assumed all belonging course preferable know separately; study households (e g girls likely boys suffer same teenagers younger children?) quite delicate lengthen significantly EU- SILC questionnaire Thirdly “children’s items” relates aged 15 (i bracket) Therefore covers Yet attending (school trips) Besides above 17-item includes As emphasised (2012 2018) impacting immediate indirect Indeed qualitative studies shown financial strain often ask things need try protect stress feelings guilt (Ridge 2002 2011) Using propose aggregate unweighted sum ranging 0 (no lacked) (all (see 2012 110 opt rather weighted deprivations) very high Cronbach’s alpha 70 (the usual minimal threshold) 90 seven EU-28 worth highlighting enforced retained three answer categories proposed: child(ren)/ child(ren)’s (have) item; (do) (they) cannot afford it; Only lacking affordability (and choice reasons) Those “other reasons” treated together who There however questions raised notion (McKnight 2013; McKay 2004) modality encompass range situations: people want/need prevented having caring responsibilities vehicle/ transport feeling unwelcome case adaptive preferences shame admit unaffordable (Guio 34) That investigated replying They (rather simple lack) makes control cultures parental practices discriminate worse-off better-off ensures higher sets threshold rest will analyse (ranging 17) proportion intensity rate(4) (4) level: lacked here looks incidence compared heat map highlights showing several Bulgaria Romania contrary low (Nordic Austria Netherlands Luxembourg) there mixed depending disadvantages advantages others Figure distribution (aged years) Around 50% One lacks two 1: Distribution non-EU (pooled data) (%) Source: cross-sectional computation At ranges 4% Sweden 71% “Heat map” providing Child Fruit Shoes Proteins Celebration Clothes School Friends Car Leisure Arrear Holidays Furniture 8 9 Finland 16 11 24 Denmark Switzerlan Netherland 25 Luxembou Slovenia Spain 13 34 46 Germany Malta 21 22 29 Cyprus 41 40 60 Belgium 19 18 Italy 38 Ireland France Portugal 23 57 Czech Rep 47 Poland 26 United king 35 33 Croatia 32 Greece 54 Estonia 27 Lithuania 61 Slovakia 45 Latvia 42 55 67 Hungary 51 52 43 49 72 Note: Countries ranked 2010 part Europe Heads State Government upon target: lift million “risk exclusion” target basis indicators: at-risk-of rate equivalised 60% median (5) line varies country) entire (MD) following nine (capacity) avoid arrears rent mortgage utility bills unexpected expenses meal meat every day week annual holiday away access (6) washing machine (7) TV (8) telephone (9)(6) opposed paper) (quasi-)joblessness) 20% ratio total months working-age (18-59) members worked theoretically 0-59 children) People “at poor and/or severely materially (quasi-)jobless version usefully constructed replacing considering If five clusters Figures 3) completes hierarchical cluster leads groups: • Cluster (around 70% countries) (32% 39% respectively) nevertheless among lowest (6%) highest (15%) net (disposable) calculated steps: a) monetary member itself (these capital inter-household taxes contributions paid deducted sum); b) size (net) divided “equivalent adults” so-called OECD-modified gives weight (1 subsequent 14); c) finally resulting figure attributed equally (adults referred “severe” MD contrast “standard” initially year before (threshold deprivations nine; see 2009) 2017 decided replace 2016) (items 1-6 plus inability furniture) (inability to: ones spend small money him/herself regular friends/family drink/meal month internet connection) Referred “Material rate” now portfolio progress towards protection objectives (on Bulgaria) characterised prevalence (between 47%) poverty: 13% (one rates EU) against around 25% (almost 30% Serbia) Among (two contains medium-to-high (22 28%): UK heterogeneous (there two-to-one Spain) (Ireland (21%) 9%) side side) Czechia constitute low-to-medium rate/intensity latter exception comparable performance share Nordic (Cluster 5) (except (25%)) clustering heterogeneity situations similar performances essential richness available complement context sections deepen through systematic investigation dependent introduced analyses: 2: Proportion (out non- 3: 4: (average items) existing (material) documented population) distinction drawn “micro-level” “macro-level” socio-economic deprivation(7) By look unemployment inequality state regime example Kenworthy Recently approaches jointly settings Kim 2010; Chzhen 2012; Whelan Israel Spannagel 2014; Visser Saltkjel Malmberg-Heimonen 2017; (2019) concomitant complementary estimating estimated (individual/household-level) country-specific hence variance Then compare effectiveness between- Country-level captured demographic socioeconomic influence Tárki stratification – stratum belongs relation probably complex reduced form empirical for: influences commands extensive review Perry Boarini Mira d’Ercole 2006 specify notwithstanding difficulty distinguish likelihood just 4): longer-term resources; health housing; Deprivation emerges confrontation become clear 1) grouped 2) (but fully) “resources” “needs” its holds instance consumption “proxy” models: support family/friends direct wealth Also highlight crude miss elements: what poor/deprived quality services? depends consume turn “command Although usually association far perfect imperfect link 2001; 2006; 2007; Berthoud Bryan 2011; Fusco explained difficulties measuring notably self-employed people) equal But importantly determined future ability borrow plausibly serve (in addition income) overcome short-term difficulties: educational attainment Borrowing economic jargon permanent liquidity constraints(8) Ceteris paribus (for characteristics) indeed correlate with: i) stronger position less vulnerability adverse shocks precarious employment); ii) educated richer implies bequests wealth; iii) easier constraints; iv) return human born outside correlates factors: vulnerable inherited difficult institutions(9) signal predictor risks hamper constraints Given availability able debt burden mentioned extent individual’s moot question; Brady (2017) recent explorations issue Here start joblessness (9) On de Neubourg evel six variables: yearly non-equivalised households(10) expressed purchasing (PPS)(11) 1000 logarithm linear forms regressions regression obtained enter separately below) parent (operationalised dummies: primary secondary education) medium (upper post-secondary non-tertiary (tertiary reference category) (jobless) equals adults 18- 59 excluding students) potential during past d) dummy whether EU(12) (migrant) e) (debt burden) payment debts hire purchases loans loan connected dwelling heavy f) presence (self-employment) take sub-population experience Needs increase maintain depend tenure 2004; 2019)(13) introduce costs): self-reported (bad health) reports (14) (rent) rents (free reduced) tariff owning own house (15) dummies including repayment (instalment interest) insurance service charges (sewage removal refuse maintenance repairs charges) (heavy light (light category socio-demographic composition: (10) summing deducting (11) Purchasing Power Parities (PPP) Standards (PPS) convert amounts currency artificial common equalises currencies (including currency) noted PPS tool price Reference budgets priced baskets goods services regions cities achieve sound alternative moment (12) (Iceland Switzerland) neither residence (13) Childcare (using childcare attendance) sample had cost ad-hoc 2016 appropriate becomes tested “limitation activity” “suffering chronic condition” alternatives separate renting free gave while insignificant 0-17 students 18-24) (number instead implicitly adjusting equivalence done calculation poverty) oldest 1-15 (age child) basket induces bias favour younger/older single-parent (single parent) perspective (it fewer possibilities employment pooling adult) fixed (housing represent (remember equivalise incomes) (They reconciling life part-time inactivity; inactivity activity dataset) summary statistics found Annex correlation consideration: research wants inappropriate summarises (child) macro typically capita; (2014) whilst raises questions: plausible certainly resources”; presumably literature) objective good leaving bound mix impacts say examining always wrong focuses might want exclude (16) feel uncomfortable discussion he agree conclusion kinds determine except capitamedian accounted prime macro-variable benefits: receipt result prima facie counterintuitive deserves interpretation discuss Literature /Macro- Determinants Sample Econometrics Main Findings Data: (2008) Unit analysis: Individual (below 65 age) Index: Standard Determinants: Micro (female lone two-parent unemployed migrant) (type-case long-term expenditure active (ALMP) non-means-tested benefit expenditure) normally substantial negatively associated After ALMP expenditures Looking effects cross-level author finds reduce individual-level (2009) person) Model: Multilevel Dependent variable: Basic comprises absence adequate heating (logarithm professional occupation (pre-primary gender marital immigrant tenure) Disposable head (GNDH) Gini) person’s basic proportions within- macro-economic contributed relatively little GNDH explanation Further GNDH: contingent society logistic Material (gender youngest activity) controlled Once variation disappears (ESS) Economic Confirmatory factor ordinal (0-6): ‘I manage income’ draw my savings expenses’ cut back holidays equipment’ (quartiles) job urbanization ethnicity) (unemployment changes percentage Macroeconomic circumstances Various crossed found: generosity affect deprivation-reducing (country-level interaction) Bárcena-Martin (2007) cross- sectional Linear frequency weights young old tertiary structure variables) (long-term S80/S20 (jointly) introduction country- reduces percent inequalities decrease (2008-2012) (low owner-occupier works sector (Minimum scheme rate) Total negative minimum Severe Bárcena-Martın urban area owner illness condition female (HRP) HRP HRP) (GDP long s80s20 functions) half specifications strong functions targeted intended appear effective reducing regressed Malmberg- Heimonen birth limiting longstanding self-defined level) (Social inverse Welfare disadvantaged assessing combination group-specific Extension (online appendix) mobilise (total in-kind) targeting families/children pro-poorness adequacy: operationalised expresses derived System integrated Protection (ESSPROS) database % GDP) (cash (in-kind sickness/healthcare disability family/children pension survivor elsewhere classified exclusion benefits(17) Alternatively (any family- benefits) micro-data transfer computed Lacking ESSPROS head) sums evaluate geared (family gross (18) remember cash-transfers coefficient straightforward above) aspect redistributive system degree universalism open debate Following Marx co-authors (2013) Diris distributed deciles pre- (pro-poorness bottom 50) (19)The (more 75%) going Kingdom Again require since descriptive indicates confirmed argues via expenditure-based approach Expenditure-data (17) seem pensions 2017) non-elderly individuals (mainly intergenerational prevalent) (EU-SILC micro-data) (19) pre-transfer (excluding pensions) 20) robustness business refer data(20)) taxation Furthermore looking treatment “household-type” approach): drawbacks cross-country 2014) Household-types simulate standardised averaging Whilst limitations especially representative types” various (Bárcena-Martín Still type” interesting schemes review) (adequacy schemes) focus type: married couple eligible assistance(21) OECD general practice capita) 100 (Serbia) 800 (Bulgaria) 74 500 (Luxembourg) (median Median 230 (Romania) essence value-added produced sectors economy subset Contrary last option captures Even though “(quasi-)jobless” indicator) International Labour (ILO) (ILO concept) population; (20) people/households (with figures (21) sensitivity Tests couples assistance) Altering investigate count suffered binary (3+ Our displays over-dispersion Over-dispersion occurs larger mean recommended binomial technique weakens highly restrictive assumption traditional Poisson Instead estimates random parameter takes unobserved estimate dispersion zero over-dispersed run give precise nested designs respondents (i) (j) useful unobservable Formally formula: 𝐸𝐸[𝑦𝑦𝑖𝑖𝑖𝑖 |�𝑥𝑥h𝑖𝑖𝑖𝑖 𝑧𝑧cj 𝑈𝑈𝑖𝑖 � = 𝜇𝜇𝑖𝑖𝑖𝑖 𝐻𝐻 log�𝜇𝜇𝑖𝑖𝑖𝑖 β0 + βℎ𝑥𝑥h𝑖𝑖𝑖𝑖 ℎ=1 𝐶𝐶 β𝑐𝑐 𝑐𝑐=1 𝜇𝜇 𝑖𝑖𝑖𝑖 eβ0 +∑𝐻𝐻 +∑𝐶𝐶 β𝑐𝑐𝑧𝑧cj+𝑈𝑈𝑖𝑖 𝑉𝑉𝑉𝑉𝑉𝑉�𝑦𝑦𝑖𝑖𝑖𝑖 �𝜇𝜇𝑖𝑖𝑖𝑖 𝑣𝑣𝜇𝜇𝑖𝑖𝑖𝑖 𝐸𝐸�𝑦𝑦𝑖𝑖𝑖𝑖 (i=1 N) j (j=1 … J) conditional overall intercept 𝑥𝑥ℎ𝑖𝑖𝑖𝑖 hth (h H) βℎ 𝑧𝑧𝑐𝑐𝑖𝑖 cth (c C) error term ∼N(0 𝜎𝜎2 𝑣𝑣 calculate pseudo R² employed McFadden define (which difference values empty apply (Shapley 1953) calculates exact R²-value method decompose goodness-of-fit (Deutsch Silber indicate interested ran reveals considerable column means household- strongly intensity(22) (Austria Sweden) Conversely typology suggested (high deprivation) (cluster 4) stressed: (they (Hungary) (Greece) (much) smaller (more) (income migration) 55% [“rent” variable]) 38% size) 7% clearly detailed results) (from 36% 37% Greece; household-related confirms independently (22) earlier (23) p>z 05 5% (24) rough “volatility” tend volatility immediately concede convincing evidence hypothesis: weakly (p=0 11) [M14] 15% (after strongest (27-37%) lesser (20-22%) (very) diverging contradict scarce positively majority De Graaf-Zijl (Table 10% 6% self-employment member(s) deprivations: negative; positive (0 partly surveys challenge discriminating personal assets self- close Migration Switzerland: 7-12% 3% Households (this explains analysed) (10-15%) appears countries: almost 43% fit: 27%) (12-18%) (26%) suffers problems Lithuania) (Fusco healthcare modules increases interpreted se thirds studied indirectly deprivation-item section relate intensity) 3+ significances logit commented mainly highlighted non-significant (self-employment households) stated models) right R²-measures Resources Other socio- demograhpics Education Quasi- Debt Migrant Housing Bad Rent 2% 8% 07 1% 22% 9% 14% 29% 09 16% Average “light burden” dropped decomposition did converge Reading note: (full) percentages brackets ranks respective 5: Relative “Resources” refers migration; “Needs” health; “Other socio-demographics” Negative socio-demographics Country Intercept Low Medium (Quasi-) jobless Self- Heavy Light Number Age -0 2934 0001*** 5582*** 3364*** 2649*** 5986*** 5497*** 0046 5538*** 7538*** 3504*** 7013*** 029 2258*** 0142 9403*** 7345*** 3395*** 1331** 1736*** 1375** 0922 7595*** 3546** 0801 0005 0041 1158 0244*** 5801** 0002*** 9064*** 5112*** 086 2469*** 3204*** 3518* 5299*** 6606*** 3321*** 3648*** 0811*** 1972*** 0107* -1 2799*** 5404*** 0504 1335 5449*** 1392*** 7624*** 6162*** 1928*** 4008** 9339*** 0626 2154 0253* 9912*** 9486*** 5119*** 6238*** 2738* 5777*** 1995** 2815*** 5561*** 5807*** 5677*** 0833** 3078*** 0049 382*** 5481*** 2768*** 5406*** 4163*** 419*** 1699** 9254*** 0666*** 237*** 0353 3684*** 0265*** 6408*** 339*** 1798*** 2373*** 3254*** 2902*** 9681*** 2807*** 5288*** 2791*** 0233 1112*** 0017 8189*** 3755*** 1781*** 1048*** 0939*** 0964*** 1776*** 9293*** 5203** 2981*** 1081*** 0472*** 1338** 0028 5108** 5756*** 3957*** 442*** 1505*** 448*** 3259*** 2697*** 1664 2251*** 24*** 0467*** 066 0076** 5168*** 6332*** 3905*** 2235*** 01 3781*** 3299*** 164*** 71*** 3098*** 344*** 089*** 2667*** 0096* -23 6173*** 9207*** 4176*** 4551*** 1635* 2218*** 1625** 0614*** 1233 3335*** 3527*** 163 0044 1116 6864*** 2191*** 2158*** 2077*** 4973*** 3809*** 6938*** 4688** 4857*** 3692*** 0746*** 0158 0035 0202 3697*** 1677*** 1899*** 034 2848*** 1525*** 2895*** 4985*** 3278*** 1542*** 0106 0135*** 1542 6017*** 2827*** 1481** 2177*** 2731*** 1007 3495*** 7091*** 1841*** 1223*** 1118*** 0906 0144*** 9646*** 8792*** 4643*** 0714 4225*** 2672*** 4799*** 7587*** 133*** 0943 1708** 13*** 1155 0042 7437*** 3623*** 1219 1286 3858* 6929*** 4037*** 5178*** 5754** 5629*** 6549*** 009 8042*** 0058 5097*** 0159*** 5985*** 0212 6102*** 1136*** 1384 0151*** 2331*** 1543*** 25*** 0404*** 2127*** 4359* 5236*** 1848** 3472*** 1432* 636*** 1987*** 0945*** 4071** 5662*** 1504** 1435*** 266*** 0034 8299*** 5395*** 2234*** 0587 0355 7384*** 5932*** 7179*** 0258*** 6247*** 7235*** 0492* 4331*** 0082 52*** 1523*** 5769*** 1478 4813*** 9784*** 2211*** 4519*** 668*** 3637*** 6205*** 066* 2845*** 0051 3773** 0793*** 6337*** 076 4437*** 3795*** 6914*** 9752*** 0262 2113*** 3569*** 1073*** 3239*** 0037 261* 5541*** 2571*** 1008** 4336*** 1884*** 1799*** 1159*** 5653*** 1639*** 183*** 0268 0312 0091** 1457*** 0003*** 5131*** 3385*** 1211* 0396 2779*** -15 2373 7842*** 3786*** 1684*** 0965 0486*** 3355*** 0059 1679*** 7046*** 3442*** 1563** 2937*** 437*** 2404*** 8831*** 8745*** 4594*** 1622*** 1164*** 1743** 0024 5961*** 8941*** 4741*** 552*** 2366*** 206*** 2629 02*** 0698*** 2067*** 1554*** 2993** 0071 4217*** 5983*** 2891*** 6315*** 1197* 5583*** 4719*** 5859*** 828*** 3424** 4078*** 0927*** 2259*** 0004 -2 6208*** 0472 4236*** 6224*** 3495* 3778*** 804*** 6201*** 1335*** 7127*** 8543*** 0747* 2699* 0226 9145*** 3394*** 1905*** 2885*** 0731 3892*** 0976** 0651*** 4919*** 4128*** 8403*** 0141 1425*** 0153*** 5677** 5673*** 2272*** 0616 0887 5411*** 2607* 067*** 326** 0468*** 2701*** 0149 0038 014 4812* 6203*** 2509*** 2514*** 3455*** 1104*** 0064 6683*** 1271 2625*** 1341*** 0394*** 141** 0165*** 8659*** 5984*** 3126*** 902*** 3948** 7305*** 55*** 7356*** 9975*** 3889* 926*** 1328*** 7218*** 0001 pool (M1 gradually (M2 Next series containing comparing strengths (M3-12 macroeconomic (M13-22 (M23-25 (M26 residuals Description M1 M2 M3-M12 M13-22 M23-M25 M26 Household-level (all) var (% iables: 70) exist reflects sign magnitude original (57+14=71%) Most income: 57% intercepts compositional costs) role: 19% Models ten purpose Several reveal determinant reduction expected: head/child In-kind respectively 35% (M5 M8) corresponding 23% provision freely (or driver necessities Aaberge conclude policy-wise important: devoted M9) M10) PPS) round pro-families’ (Models M19-20) minor (9% M11) Variables comparatively Measures attain negligible (16% M12) effectively easily explained: former absolute (M13-M22) [M15] [M18]) (M21) regroups kind [M13]) deprivation(23) Family cash) safety nets that: 21% 24% (PPS/head) 28% remains development global accounts Pro-poorness slightly prioritise co- unexplained (quasi- )joblessness aims accounting Why background protected countries? “hidden” gifts conjecture (though hypothesis examination) end distribution: functioning automatic stabilisers edifice volatility(24) words seems “permanent income” Another “qualitative” Richest (education “level development” partially data: insufficient societal cushioning M13-15 longer taking M15 [M26] (84 versus 83% M26) measure: (33% expense striking observation co-regressed omitted (results shown) nuance shaped pointed consensus mitigated affluence (Nelson examine introducing slopes(25) influenced slopes findings nuanced mitigate needs: generate slighter affluent migration interaction low-educated showed qualifications declines (25) slope adding covariance computational conducted none lose significancy change singe relationships slight imply significant) deprivation-increasing positive) (such one’s struggling needs/costs argued significance exceptions lies non-income (Annex M13) insignificance M23) M24) Model (of Coeff 03 00 Self-employment (Quasi-)joblessness 75 Constant Random Estimates Explained 71 91 66 observations 88901 M3 M4 M5 M6 M7 04 Cash 08 78 80 81 M8 M9 M10 M11 M12 44 02 Adequacy minimum-income 82 77 M13 M14 M16 M17 Unemployment 58 06 88 83 M18 M19 M20 M21 M22 92 99 86 M23 M24 M25 84 Interaction 003 00008 93 Quasi-joblessness 94 00002 (bottom 85 000 demonstrate (current status) (costs powerful predictors are: illustrate systematically schemes; it) predicting operates model) unrelated deprivation; logically don’t shapes (based capturing 27% (micro-) 11% come mind: between-households conceived “affordability” incomes; pursue crossed-effects construe resources”) (rate count) Langørgen Lindgren distributional In: Atkinson B (eds Monitoring pp 159-188 Bárcena‐Martín Lacomba Moro‐Egido Pérez‐Moreno Differences Review Wealth 60(4) 802-820 Blasquez M Budria Moro-Egido 15(4) 717-744 Barcena-Martin Blanco-Arana Perez-Moreno Transfers Countries: Pro-poor Targeting Pro-child Targeting? 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