194 i(n2 0s0t a7t)e as nt dh aBt aaer ae nmd oGroey ca rl e(d2i0t o0 r9 )f ,r wi e en delxyp. eI nc t ctohna ftosrtmr oi nt yg ewr i ct hr e Qd ii taonr ar ni gdh tSst rcaahuas ne hr iisgkhse ra sr itshke- tya kh ianvge bae hh ai gvhi oe rr. Wa me oausns ut mo fe utnh da et rl al yrignegr ldoeaanl ss ac on rdr ecsopl loant edr awl ,i tmh amkoi nr ge iatnmd oL raeu rdai f, f2i c0u1l 7t ;aFnudr fcionme , p2l 0e 1x 4t o; J icaanpgt uert ea lp. ,o2t 0e n1 t8i ;a Vl ri ni skk es t aanl .d, 2r0e 2t u1r)n. sW(ee t. gh. e, rFeafhoar de edxepa el sc, t wt hhaetni s(stuheer smaarjeo rmi toy r eo fl )i ktehl ye ttor at na kc he et ’hs ecroi lsl ka st earsasl oics i ai st es ud ewd i ti hn laa rsgteart eR wM iBt hS higher creditor protection. This leads us to our final hypothesis: H3. In a state with higher creditor protection, issuers tend to construct larger RMBS transactions. 5.4 Methodology Wl o ge i at pmp ol yd tehl rteoe edsi ftfi emr ea nt et mt hoed ieml sptaoc tt e os tf oc ur er dhi yt opro tphreosteesc. t Fi oi rns ti,nwUe Su sset aat ne so rodne rt eh de cthreedfiotllroawtiinnggamssoidgenle:d by CRAs (Hypothesis 1). To achieve this, we constructed Credit Rating (Moody’s, S&P, Fitch, DBRS, KBRA)ijt = α 0 + α1 Creditor Friendliness Scores(i) + Tranche, Issuer and Market Controlsijt + ϵijt The tranches in our data differ by year (t), deal (i), security (j) and state (s). The dependent variable is the Credit Rating and the Creditor Friendliness Score is the independent variable. The tranche, issuer and market controls include Log Tranche Value, Log Transaction Value, Tranche Count, Subordination Level, Number of Ratings, Top Ten Issuer, Coupon, and Clustered Collateral. We control for time-fixed effects. The variable definitions are described in Section 5.5. Next, in order to investigate whether creditor protection of US states has an impact on
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