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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 This paper was presented at the Net-SILC3 (Third Network for Analysis of EU- SILC) International Conference in Athens April 2018 How to quote this paper: will be published around June 2019 Eurostat Statistical working papers should referred as: A -C E F P (2019) “Micro- macro-drivers child deprivation European countries“ Working Papers Publications Office Union Luxembourg Verbunt1 Abstract: analyses countries using scale officially adopted March measure child-specific EU 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 that relationship these with differs between In richest explanatory power household needs is largest whereas most deprived resource generally greater between-country specification model careful consideration We argue include income micro if aim fully gauge households’ The then assesses how much country-level features are not reflected other individual characteristics contribute explaining across find public spending on in-kind social benefits significant respect Public cash transfers plays only limited role when incomes included; they play excluded does diminish importance fighting but it qualifies conclusions which have analysed without controlling Finally we GDP per capita even included self-evident: shows proxy important contextual 1. SETTING THE SCENE Fighting poverty investing children’s well-being has featured agenda (EU) many years February 2013 new step forward taken Commission Recommendation “Investing children: breaking cycle disadvantage” (European 2013) subsequently by Council Ministers An element calls Member States “(reinforce) statistical capacity where needed feasible particularly concerning deprivation” best way provide accurate information actual living conditions children making assumptions about sharing resources within develop indicators – i e based specific situation may differ from their parents 2009 wave Statistics Income Living Conditions (EU-SILC) an ad hoc module aimed collecting such first in-depth analysis data carried out et al 1 Institute Socio-Economic Research (LISER Luxembourg) University Amsterdam (Netherlands) Leuven (Belgium) authors wish thank Brian Nolan Jonathan Bradshaw Elena Bárcena- Martín Bertrand Maître Kenneth Nelson Geranda Notten valuable discussions All errors remain strictly authors’ work been supported third EU-SILC (Net-SILC3) funded bears no responsibility solely those Email address correspondence: anne-catherine guio@liser lu (2012) optimal set items identified index proposed These were again 2014 allowing additional (2018) final list consists 17 covering both material aspects can aggregated monitor robust comparative whole determinants (28 as well Iceland Serbia Switzerland 2) (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 within-country variations Specifically allow analysing decomposing fit measures setting advantage cross-national 31-country pooled dataset Both household-level type Hence understand each (as coefficients definition allowed vary specificities micro-drivers captured); complemented (that than populations) So illustrates strength comprehensive policy levers mobilised fight EU3 contribution literature 2 Norway could due large amount missing 3 yet another advantage: allows 4 second indicator level: average number lacked different intensity considered here looks all rather 5 equivalised net (disposable) calculated three steps: a) monetary received any source member or itself added up (these capital inter-household transfers) taxes contributions paid deducted sum); b) order reflect household’s size total (net) divided “equivalent adults” so-called OECD-modified gives weight members (1 adult 0 subsequent person aged 14 under 14); c) finally resulting figure disposable attributed equally (adults children) 6 “severe” MD contrast “standard” initially agreed one year before (threshold deprivations nine; see 2009) 2017 decided replace (2012 2016) 13 items: seven (items 1-6 previous plus inability worn-out furniture) (inability to: main view replicates confronts broad spectrum (sometimes diverging) reported suggests reasons why measured micro- macro-level (do not) described inclusion (national Gross Domestic Product (GDP) justified fact more generous welfare systems prosperous economies lead lower levels once micro-level (household-level) capture reason would still discussed priori expect explain after relevant crucial question therefore variable whose already into account expected contrarily because proxies elements To disentangle replicate variety macro-variables linked (generosity in-cash pro-family adequacy assistance pro-poorness countries’ standards value explicitly certain parents’ education migrant status (quasi-)joblessness next current Often expectation “social stratification” granted further argument fourth use Shapley decompositions establish relative independent & Usually econometric used relations goes provides covered organised follows Section defines illustrative clothes some ones two pairs properly fitting shoes spend small money week him/herself regular leisure activities together friends/family drink/meal least month internet connection) Referred “Material rate” now portfolio progress towards protection objectives covers entire population 7 For extensive review (material) Perry 2002 Boarini Mira d’Ercole 2006 reviews macro- presents estimation strategy detail concludes 2. ROBUST MEASURE CHILD-SPECIFIC theory driven From theoretical point largely relies Townsend’s concept deprivation: “Poverty defined objectively applied consistently terms […] Individuals families groups said lack diet participate amenities customary widely encouraged approved societies belong Their seriously below commanded family effect ordinary patterns customs ” (Townsend 1979 p 31) analytical framework select draws extensively 1999 Poverty Social Exclusion (PSE) Survey construction methodology (Gordon 2000; Pantazis 2006) ensure item selection examined four aspects: suitability check citizens sub-groups State) consider them necessary “acceptable” standard live “Suitability” understood face validity amongst exhibits statistically ratios known correlated 3. reliability assess internal consistency closely group Classical Test Theory Item Response Hierarchical Omega 4. additivity test someone score “2” suffering severe “1” indicator’s components add successfully passed tests thus suitable valid reliable additive candidates being measurement 12 “children” “household” cover Children Some (not second-hand) Two Fresh fruit vegetables daily Meat chicken fish vegetarian equivalent 5. Books home age 6. Outdoor equipment 7. Indoor games 8. Regular 9. Celebrations special occasions 10. Invitation friends eat time 11. Participation school trips events 12. Holiday Household 13. Replace furniture 14. Arrears 15. Access Internet 16. Home adequately warm 17. car private keep mind collection processing First relating collected themselves answering questionnaire” (household respondent) Secondly according survey protocol followed given assumed belonging course preferable know separately; study households (e g girls likely boys suffer same teenagers younger children?) quite delicate lengthen significantly questionnaire Thirdly “children’s items” relates 15 (i bracket) Therefore Yet attending (school trips) Besides directly above 17-item includes As emphasised 2018) impacting immediate indirect Indeed qualitative studies shown financial strain often do ask things need try protect stress feelings guilt (Ridge 2011) Using propose aggregate unweighted sum ranging (no lacked) (all (see 2012 110 opt weighted deprivations) very high Cronbach’s alpha 70 (the usual minimal threshold) 90 EU-28 worth highlighting enforced retained 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 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 analyse (ranging 17) proportion rate4 GENERAL OVERVIEW incidence Table compared heat map highlights showing several Bulgaria Romania contrary low (Nordic Austria Netherlands there mixed depending disadvantages advantages others 1: “Heat map” providing Child non-EU % Fruit Shoes Proteins Celebrati Clothes School Friends Car Leisure Arrear Holidays Furniture Sweden 8 9 Finland 16 11 24 20 Denmark 10 25 Slovenia 28 Spain 34 46 Germany Malta 21 22 29 Cyprus 41 40 60 Belgium 19 18 Italy 38 Ireland 53 France Portugal 23 36 57 Czech Republic 47 Poland 26 United kingdom 35 33 Croatia 32 Greece 30 54 Estonia 27 Lithuania 50 48 39 61 Slovakia 45 Latvia 42 55 67 Hungary 51 52 43 49 72 Source: Figure distribution (aged years) Around 50% One lacks Distribution (pooled data) cross-sectional computation At ranges 4% 71% 2010 part Europe 2020 Heads State Government upon target: lift million “risk exclusion” target basis indicators: at-risk-of rate 60% median line varies country) (MD) following nine (capacity) avoid arrears rent mortgage utility bills (1) (2) unexpected expenses (3) meal meat every day (4) annual holiday away (5) access (6) washing machine (7) TV (8) telephone (9) opposed paper) (quasi-)joblessness) 20% ratio months working-age (18-59) worked theoretically 0-59 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%) (on Bulgaria) characterised prevalence (between 47%) poverty: 13% (one rates EU) against 25% (almost 30% Serbia) Among (two contains medium-to-high (22 28%): UK heterogeneous (there two-to-one Spain) (Ireland (21%) 9%) side side) constitute low-to-medium rate/intensity latter exception comparable performance share Nordic (Cluster 5) (except (25%)) clustering heterogeneity situations Countries similar performances institutional essential richness available complement context sections deepen through systematic investigation dependent introduced analyses: 2: Proportion (out NB: abbreviations Annex 3: 4: (average items) MACRO-LEVEL DETERMINANTS existing documented population) distinction drawn “micro-level” “macro-level” socio-economic deprivation7 By look unemployment inequality state regime example Kenworthy Recently approaches jointly settings Kim 2010; Chzhen 2012; Whelan Israel Spannagel Bárcena-Martín 2014; Visser Saltkjel Malmberg-Heimonen 2017; concomitant complementary estimating estimated (individual/household-level) country-specific hence variance socio- economic Then compare effectiveness Country- captured 4.1. Micro-level demographic socioeconomic influence Tárki stratification – stratum belongs relation probably complex reduced form empirical for: influences commands specify notwithstanding difficulty distinguish likelihood just 4): 1) longer-term resources; health housing; Deprivation emerges confrontation become clear grouped (but fully) “resources” “needs” its holds instance consumption in- kind “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 jargon permanent liquidity constraints8 Ceteris paribus (for characteristics) indeed correlate with: i) stronger position less vulnerability adverse shocks precarious employment); ii) educated richer implies bequests wealth; iii) easier institutions constraints; iv) return human born outside correlates factors: vulnerable inherited difficult institutions9 signal predictor risks hamper constraints Given availability able debt burden mentioned six variables: yearly non-equivalised households10 expressed purchasing (PPS)11 1000 extent individual’s moot question; Brady (2017) recent explorations issue Here start joblessness On de Neubourg obtained summing deducting 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 reference moment logarithm linear forms regressions regression non-logarithm enter separately below) parent (operationalised dummies: primary secondary education) medium (upper post- non-tertiary (tertiary category) (jobless) equals adults 18-59 excluding students) potential during past d) dummy whether EU12 (migrant) e) (debt burden) payment debts hire purchases loans loan connected dwelling heavy f) presence self-employed (self-employment) take sub-population experience Needs increase maintain depend tenure 2004; 2011 2019)13 introduce costs): self-reported (bad health) reports (rent) rents (free reduced) tariff owning own house dummies including repayment (instalment interest) insurance service charges (sewage removal refuse maintenance (Iceland Switzerland) neither residence nor 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 repairs charges) (heavy light (light category socio-demographic composition: 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 correlation 4.2 Combining 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 feel uncomfortable kinds determine except capitamedian accounted prime benefits: receipt result prima facie counterintuitive deserves interpretation discuss discussion he agree conclusion Literature Micro-/Macro- Determinants Sample Econometrics Main Findings Data: (2008) Unit analysis: Individual (below 65 age) Model: Multilevel logistic Dependent variable: Material Index: Standard Determinants: Micro (female lone two- 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 interactions author finds reduce individual-level (2009) person) Basic comprises absence adequate heating (logarithm professional occupation (pre-primary gender marital immigrant tenure) National Disposable head (GNDH) Gini) (gender person’s basic proportions macro-economic contributed relatively little GNDH explanation Further GNDH: contingent society 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) Linear frequency weights young old tertiary structure variables) (long-term S80/S20 (jointly) introduction reduces percent Cross-level inequalities decrease (2008-2012) (low owner- Bárcena-Martin Severe occupier works sector (Minimum scheme rate) Total negative minimum country- 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 Note: Extension (online appendix) mobilise (total in-kind) targeting families/children adequacy: operationalised expresses derived System integrated Protection (ESSPROS) database GDP) (cash (in-kind sickness/healthcare disability family/children pension survivor elsewhere classified exclusion benefits17 Alternatively (any family-related benefits) micro-data transfer computed Lacking ESSPROS head) sums evaluate geared (family gross remember cash- coefficient cash-transfers straightforward above) aspect redistributive system degree universalism open debate Following Marx co-authors (2013) Diris distributed deciles pre-transfer (pro-poorness bottom 50) seem pensions 2017) non-elderly individuals (mainly intergenerational prevalent) (EU-SILC micro-data) (excluding pensions) 20) robustness (more 75%) going Again require since descriptive indicates confirmed argues via expenditure-based approach Expenditure- business refer data20) 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 assistance21 OECD general practice capita) 100 (Serbia) 800 (Bulgaria) 74 500 (Luxembourg) people/households (with figures sensitivity Tests made couples assistance) Altering (median Median between- 230 (Romania) concepts essence value-added produced sectors economy subset Contrary last option captures Even though “(quasi-)jobless” indicator) Labour (ILO) (ILO concept) population; investigate MODEL ESTIMATION STRATEGY count suffered binary (3+ official 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 Country-level Formally formula: ????[????????????|?????h???????? ????cj ????????? = ???????????? ???? log?????????????? ß0 + ? ßh????h???????? h=1 ???? ß????????cj ???????? ????=1 ???? ???????? eß0 +????? +????? ß????????cj+???????? ??????????????????????????????????????? ???????????????? ?????????????????? (i=1 N) j (j=1 … J) conditional overall intercept hth (h H) ß h th ???????????? c (c C) ß???? error term ~N(0 ????2 ???? calculate pseudo R² McFadden define (which difference values empty apply (Shapley 1953) calculates exact R²-value method decompose goodness-of-fit (Deutsch Silber indicate interested RESULTS 6.1 ran reveals considerable column means strongly intensity22 (Austria Sweden) Conversely typology suggested (high deprivation) (cluster 4) stressed: (they (Hungary) (Greece) (much) smaller consequence (more) (income migration) 55% [“rent” variable]) 38% size) 7% clearly earlier p>z 05 5% rough “volatility” tend volatility immediately concede convincing evidence hypothesis: weakly (p=0 11) [M14] detailed results) (from 36% 37% Greece; household- confirms independently 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 39) partly surveys challenge discriminating personal assets 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 single- increases interpreted se thirds studied indirectly deprivation-item Sensitivity section relate intensity) 3+ significances logit commented mainly highlighted non-significant (self- households) stated models) right R²-measures Resources Other demograhpics Education Quasi- Debt Migrant Housing Bad Rent 2% 8% 07 37 1% 22% 9% 14% 29% 09 Kingdom 16% Average “light burden” dropped decomposition did converge Reading note: (full) percentages brackets ranks respective 5: Relative “Resources” refers migration; “Needs” health; “Other 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 6.2 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- (all) Empty (% 6.2.1. M1-M2: 70) exist reflects sign magnitude original (57+14=71%) Most income: 57% intercepts compositional costs) role: 19% 6.2.2. M3-M12: Assessing Models ten purpose Several reveal determinant reduction expected: head/child In-kind respectively 35% (M5 M8) corresponding 23% provision freely (or driver necessities Aaberge must conclude policy-wise important: devoted M9) M10) PPS) round pro-families’ (Models M19-20) minor (9% M11) Variables comparatively Measures attain non-negligible (16% M12) effectively easily explained: former absolute (M13-M22) 6.2.3. M13-M22: [M15] [M18]) (M21) regroups [M13]) deprivation23 Family cash) safety nets that: 21% 24% (PPS/head) 28% remains development global accounts Pro-poorness slightly negligible prioritise co-regressed unexplained aims accounting Why background protected countries? “hidden” gifts conjecture (though hypothesis examination) end distribution: functioning automatic stabilisers edifice volatility24 words seems “permanent income” Another “qualitative” Richest (education “level development” partially data: insufficient societal M23-M26: cushioning M13-15 longer taking M15 [M26] (84 versus 83% M26) measure: (33% expense striking observation omitted (results shown) nuance shaped 6.2.4. pointed consensus mitigated affluence (Nelson examine introducing slopes25 slope adding covariance slopes computational conducted none lose significancy change singe influenced findings nuanced mitigate needs: generate slighter affluent migration interaction low-educated showed qualifications declines imply (quasi- )joblessness significant) deprivation-increasing positive) (such one’s struggling needs/costs argued relationships slight significance exceptions lies non-income (Annex M13) insignificance M23) M24) Model (of Household-level Coeff 03 00 Self-employment (Quasi-)joblessness 75 Constant Random Estimates Explained 71 91 66 N observations 88901 (continued) 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 59 83 M18 M19 M20 M21 M22 92 99 86 M23 M24 M25 84 6: Interaction 003 00008 93 Quasi-joblessness 94 00002 (bottom 85 000 CONCLUSION necessarily demonstrate (current status) (costs powerful predictors are: illustrate systematically schemes; it) provided 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) REFERENCES R Langørgen Lindgren distributional In: Atkinson B C (eds Monitoring pp 159-188 Bárcena-Martín Lacomba Moro-Egido I Pérez-Moreno S Differences Review Wealth 60(4) 802-820 Blasquez M Budria Moro-Egido Socio- 15(4) 717-744 Barcena-Martin Blanco-Arana Perez-Moreno Transfers Countries: Pro-poor Targeting Pro-child Targeting? 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