Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration

Analyzing Multidimensional Poverty and Its... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (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+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-203" }, "headline": "Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor...", "datePublished": "2025-02-13T17:04:33", "dateModified": "2025-02-13T17:04:33", "author": [ { "@type": "Person", "name": "Yismaw Ayelign Mengistu" }, { "@type": "Person", "name": "Bantalem Sinishaw Mekonen" }, { "@type": "Person", "name": "Nega Assefa Aynalem" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Poverty remained pervasive in affecting households particularly in developing countries like Ethiopia. Purpose The main target of this study was to estimate the multidimensional poverty index of households in Debre Tabor city along with the analysis of determinants of multidimensional poverty. Method The researchers selected 394 households from six Kebeles using a stratified random sampling technique. The Alkire and Foster dual cut-off method of measuring poverty was employed to estimate the multidimensional poverty index and ordered logistic regression was used to analyze the effects of determinants of multidimensional poverty. Five dimensions of poverty namely, income, education, health, living standards, and energy use were incorporated in measuring it. Results and conclusion The result revealed that 59% of households were multidimensional poor with a poverty cut-off of 33%. The multidimensional poor households were underprivileged in 54% of weighted indicators. The energy use dimension was a serious contributor to the incidence and severity of multidimensional poverty. In addition, poverty incidence by illiterate-headed household heads, with single marital status, and casual wage worker-headed households was higher relative to their counterparts. Policy recommendation We strongly recommended that education must be extensively provided not only to children but also to adults; females must be promoted to participate in business activities, credit must be expanded with an excellent follow-up, and households should participate in existing cooperatives. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-203/v1", "name": "Analyzing Multidimensional Poverty and Its Determinants in Ethiopia:..." } } ] } Home Browse Analyzing Multidimensional Poverty and Its Determinants in Ethiopia:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Mengistu YA, Mekonen BS and Aynalem NA. Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.12688/f1000research.159139.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] Yismaw Ayelign Mengistu https://orcid.org/0000-0002-8777-0627 1 , Bantalem Sinishaw Mekonen 2 , Nega Assefa Aynalem 3 Yismaw Ayelign Mengistu https://orcid.org/0000-0002-8777-0627 1 , Bantalem Sinishaw Mekonen 2 , Nega Assefa Aynalem 3 PUBLISHED 13 Feb 2025 Author details Author details 1 Assistant Professor, Economics, Debre Tabor University, Debre Tabor, Amhara, 272, Ethiopia 2 Lecturer, Economics, Kabridahar University, Kebri Dahir, Somali, 250, Ethiopia 3 Lecturer, Economics, Debre Tabor University, Debre Tabor, Amhara, 272, Ethiopia Yismaw Ayelign Mengistu Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Bantalem Sinishaw Mekonen Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation Nega Assefa Aynalem Roles: Software, Supervision OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Poverty remained pervasive in affecting households particularly in developing countries like Ethiopia. Purpose The main target of this study was to estimate the multidimensional poverty index of households in Debre Tabor city along with the analysis of determinants of multidimensional poverty. Method The researchers selected 394 households from six Kebeles using a stratified random sampling technique. The Alkire and Foster dual cut-off method of measuring poverty was employed to estimate the multidimensional poverty index and ordered logistic regression was used to analyze the effects of determinants of multidimensional poverty. Five dimensions of poverty namely, income, education, health, living standards, and energy use were incorporated in measuring it. Results and conclusion The result revealed that 59% of households were multidimensional poor with a poverty cut-off of 33%. The multidimensional poor households were underprivileged in 54% of weighted indicators. The energy use dimension was a serious contributor to the incidence and severity of multidimensional poverty. In addition, poverty incidence by illiterate-headed household heads, with single marital status, and casual wage worker-headed households was higher relative to their counterparts. Policy recommendation We strongly recommended that education must be extensively provided not only to children but also to adults; females must be promoted to participate in business activities, credit must be expanded with an excellent follow-up, and households should participate in existing cooperatives. READ ALL READ LESS Keywords Alkire and Foster; MPI; MPI decomposition; multidimensional poverty; ordered logistic regression Corresponding Author(s) Yismaw Ayelign Mengistu ( [email protected] ) Close Corresponding author: Yismaw Ayelign Mengistu Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Mengistu YA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Mengistu YA, Mekonen BS and Aynalem NA. Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.12688/f1000research.159139.1 ) First published: 13 Feb 2025, 14 :203 ( https://doi.org/10.12688/f1000research.159139.1 ) Latest published: 13 Feb 2025, 14 :203 ( https://doi.org/10.12688/f1000research.159139.1 ) 1. Introduction Every human being in the world faces the multifaceted threat and challenge of poverty at the levels of the economy, society, politics, and environment ( Kvaratskhelia, 2019 ). Poverty is defined as a lack of resources necessary to maintain a standard of living at an economic level. It also refers to a state in which an individual lives under unsafe circumstances, lacks agency in society, and has no voice. Beyond its financial component, poverty is defined as a household’s inability to obtain standard housing, child mortality, inadequate access to clean water and sanitation, years of education relevant to children’s attendance at school, malnourishment, lack of fixed assets, and lack of clean energy for cooking ( WB, 2018 ; Alkire et al., 2021 ). The inability to buy a home of their own limits the options of the poor; instead, they are more likely to rent poorly maintained homes with inadequate drainage, ventilation, and sanitary facilities ( Haider & Kumar, 2018 ). Additionally, poverty has several detrimental effects on household members’ social relationships. They are embarrassed and furious and are unable to maintain their regular social circles and friendships ( Mood & Jonsson, 2016 ). According to Ethiopian Demographic and Health Survey (EDHS) (2021) , the UNDP and OPHI (2021) , 68.7% of Ethiopians are multidimensional poor. On average, devoid of ten indicators of multidimensional poverty, including years of schooling, child attendance, nutrition, child mortality, access to electricity, clean drinking water, improved sanitation, quality of housing, and assets. With the exception of the Tigray Region, Addis Ababa, and Dire Dawa, the regional breakdown of Ethiopia’s multidimensional poverty level reveals that the percentage of the population living in multidimensional poverty is higher than that of the country’s overall multidimensional poverty rate. Somalia regional state was the area with the highest multidimensional poverty rate (90%). The second most multidimensional impoverished region was Afar at 84.7%). Ethiopia’s third- and fourth most multidimensional impoverished areas are Oromia (71.5%) and Amhara (69.3%), respectively ( OPHI & UNDP, 2021 ). A number of one-dimensional or monetary methods, including the Foster-Greer-Thorbecke method ( Debeli and Endegena, 2019 ; Dagim et al., 2020 ), the basic needs approach ( Melese et al., 2017 ; Debeli, 2019 ), and food energy intake ( Tamirat and Sivakumar, 2020 ), have been used in empirical studies to measure poverty in Ethiopia. This suggests that most studies on poverty measurement in urban areas rely on only one dimension. However, Mekonnen and Heshmati (2021) pointed out that there is a risk of underestimating poverty when utilizing a one-dimensional measure approach to quantify poverty. Thus, the multidimensional method of calculating poverty level developed by Alkire and Foster (2011a , 2011b ) was used in this study. Furthermore, to quantify the impact of determinant variables on poverty level, earlier studies employed binary response regression models (logit and probit). Therefore, households were divided into two categories based on their level of poverty: poor and non-poor, according to earlier research. There must be more ways to alleviate poverty than just this one because not all households with a poverty status below the poverty line and not all households with a poverty status above the poverty line are equally impoverished. The primary issue with this approach is that it may lead to ineffective focus on poverty alleviation by dividing the population into two groups. This indicates that while the extremely impoverished population needs more attention, the population whose economic position is below the poverty line cannot be equally addressed by agencies working to alleviate poverty. Similarly, those whose economic standing is above the poverty line cannot be disregarded in the same way as those who are just above the line are more susceptible to poverty and therefore require greater care. Therefore, further household classification and a suitable econometric model is needed to determine how socioeconomic variables affect multidimensional poverty levels. Four non-overlapping multidimensional poverty level groups were created for homes in this study. According to the multidimensional index, these fall into four categories: extremely multidimensional poor, somewhat multidimensional poor, susceptible to multidimensional poverty, and not impoverished. The deprivation score determined for every household served as the basis for such a classification. Therefore, to evaluate the effects of various socioeconomic variables on these four groups of multidimensional poverty levels in the Debre Tabor city administration, an ordinal logistic regression model was adopted in this work. The main objective of this study was to estimate the multidimensional poverty index (MPI) of the households in Debre Tabor City administration as decomposed by indicator and determinants factors of multidimensional poverty. The remainder of this paper is organized as follows. Section two is devoted to a review of the literature. Section three deals with the methods and materials used in this study. The last two sections present the results, conclusions, and recommendations of this study. 2. Literature Poverty refers to a lack of income and sufficient resources to sustain living conditions. It manifests as hunger and malnutrition, ill-health, limited access to education, high mortality rate from illness, sub-standard housing, insecure environments, and social marginalization. It is also characterized by limited involvement in the decision-making of the social, cultural, and political affairs of a society ( UN, 1995 ; WB, 2000 ). 2.1 Measuring poverty: Multidimensional approach to measuring poverty The Alkier–Foster approach is a well-known and used method of multidimensional poverty measurement. However, the Alkire-Foster method is widely used because other methods have basic drawbacks, such as they are not compatible with making comparisons across regions or countries ( Alkire et al., 2015 ). Alkire and Foster (2007 revised in 2008) have developed a counting approach to measuring multidimensional poverty which dual cut-off approach to measuring multidimensional poverty and it is being utilized since 2010 to estimate the MPI of different countries by UNDP and OPHI. The dual cut-off has two steps; the first is the identification step and the second is the aggregation step ( Alkire & Foster, 2011a ). On the behalf of UNDP and OPHI Alkire et al. (2010) have prepared the dimensions and their respective indicators of poverty that are being used to estimate the multidimensional poverty index of different countries globally. Dimensions and indicators of global multidimensional poverty index The first dimension of poverty is education, which has two indicators: years of schooling and school attendance. According to Alkire et al. (2010) , deprivation in years of education occurs when at least one household member has completed 6 years of schooling. Alternatively, this occurs if not all household members attend school. The deprivation of school attendance exists if school-aged children do not enter school. The second dimension relates to health and contains nutrition and child mortality as poverty indicators. When there is a death of any less than or equal to five years of age in the household and the nutritional information of a family is malnourished, deprivation in the health dimension of poverty exists. Nutritional information was measured using body mass index. The third dimension is living standards, in which there are six poverty indicators. The indicators are cooking fuel, drinking water, sanitation, electricity, housing, and asset ownership. If the household uses animal dung or firewood instead of electricity for cooking purposes, it is considered poor in cooking fuel. Households are said to be impoverished in drinking water when its source is from unimproved locations such as ponds, unprotected dug wells, unprotected rivers, and spring water. The household is also poor in electricity if it does not have access to electric energy in its house at all. In addition, the household is impoverished in housing quality if its house is made of a traditional floor, wall, and roof material such as sand, soil, animal dung floor, grass roof, and bamboo or wood wall, respectively. Poor sanitation exists when there is no modern or improved sanitation system. That is, it occurs when the household uses open defecation, unventilated latrine toilets, and so on. In addition, the household is considered a poor asset if it does not own a radio, television, mobile phone, earth-line phone, refrigerator, motorbike, motorcycle, car, or computer. Alkire-Foster counting approach to estimating multidimensional poverty index Measuring poverty using Alkire-Foster’s (2011a) method follows a dual-cut-off approach. In this method, there are stages, which are the identification and aggregation stages. To accomplish the first stage of MPI, different types of cut-off are required. The First is the deprivation cut-off, which is the process of setting the minimal value of an indicator of poverty below which a household or any unit of analysis is said to be deprived in that dimension or indicator. After the deprivation cutoffs are set, the deprivation scores of each individual or another unit of analysis will be prepared. The second cutoff was poverty cutoff. It is a line that demarcates poor and non-poor individuals. It is mostly determined by policymakers or researchers based on the realities surrounding it. For example, UNDP and OPHI use 0.33 as a poverty cut-off in global MPI. According to the authors, the aggregation stage follows the identification stage, and provides information on the multidimensional levels of a country or region. The multidimensional headcount ratio (H), depth or intensity of multidimensional poverty (A), and adjusted headcount ratio (MPI) are the most widely used. The first case informs the proportion (percentage) of the population that is multidimensional poor. The second shows how the multidimensional poor are poor. In other words, it shows the percentage of weighed indicators of poverty in which the multidimensional poor were deprived. MPI measures the percentage of indicators potentially depriving the total potential deprivations that could be experienced by society. 2.2 Empirical literature: Determinants of multidimensional poverty In South Africa, the higher income of the household head and male head of household is negatively related to poverty while the household size and marriage are negatively related to poverty ( Mdluli and Dunga, 2021 ). According to Lekobane and Seleka (2016) , poverty is determined negatively by the higher level of education and permanent employment and self-employment status of the head of household in Botswana. But large household size is found to increase the probability of being poor. A study by Teshome and Quaicoe (2014) in Ethiopia revealed that variables such as dependency ratio, family size, household head being female, marital status (being married), and living in rural areas are positively correlated with the probability of being poor. However, the number of working members in the household, agricultural land holdings, family size squared, age of household head, having completed elementary education, having completed secondary education, having college education and above, being self-employed, and being household head employed in the formal sector are negatively correlated with the probability of being poor. Another study was conducted in Southern Nations, Nationalities, and Peoples’ Region, Ethiopia education, saving habits and singleness of household heads, and availability of electricity for lighting and cooking are found to be significant variables to decrease the probability of being poor ( Mohammed, 2017 ). In Ethiopia, many studies hav been conducted on the determinants of poverty. Among them, a study by Feleke et al. (2020) , the education level of household heads and time spent on income-generating activities affected negatively poverty in the Kuyu district whereas distance from the market contributes positively to the problem of poverty. Another study was conducted in Southern Nations, Nationalities, and Peoples’ Region, Ethiopia education, saving habits and singleness of household heads, and availability of electricity for lighting and cooking are found to be significant variables to decrease the probability of being poor ( Mohammed, 2017 ). Study in Debre Markos City by Debeli (2019) using binary logit model explained that age, sex, marital status, education, occupation, income, number of working hours, and household health status were negatively related to the probability of being poor. A household with an old-age leader and a female leader has more probability fall into multidimensional poverty in Boorana ( Galgalo et al., 2021 ). Family size, household education, contact with extension services, and access out of household labor work, are variables negatively associated with probability of being multidimensional poor households of Dega Tembien in Tigray regional state while dependency ratio and credit access worsen it ( Desawi et al., 2021 ). Gedefaw (2021) found that family size, access to credit service, and distance from market centers, had increased the probability of households in North Wollo Zone being multidimesnionally poor. According to a study by Dagim et al. (2020) , higher education by head of the household, owning a house had negative relationship with the probability of households being poor while large family size and divorce had positive relationship with the probability of households being poor In summary, the probability that households are poor is highly positively correlated with large family size, lower education level, higher dependency in the household, unemployment, and divorce, while it is negatively correlated with credit access, household income, higher education level, saving, access to extension services, and house ownership. 3. Methods 3.1 Sampling design and sample size Proposal for the study was evaluated in July, 2023 and actual data collection started in October, 2023. In this study, a stratified random sampling technique has been employed to provide the proportional chance of being selected by households of each Kebeles of Debre Tabor city administration to the size of each household. The total number of households in the city was 28,036 ( DTTAO, 2021 ). Using Yamane’s (1967) formula, illustrated in Israel (1992) , the sample size of this study was determined as follows: (1) n = N 1 + N ( e 2 ) where n is the sample size, N is the total household, and the level of precision is specified as 5% in this study. n = 28,036 1 + 28,036 ( 0.05 2 ) = 28,036 71.09 = 394.3 ≈ 394 Thus, 394 households were used as the sample from which the data for this study were collected. Stratified sampling was applied to allocate 394 households to the Kebeles of the Debre Tabor city in proportion to the number of households in each Kebele. The proportional allocation of this sample size to each stratum n i = n ∗ p i where n i is the sample from the i th Kebele, n is the sample size, and p i is the proportion of households in Kebele i to total households ( N i / N ) ( Kothari, 2004 ). 3.2 Data source, type, and collection methods This study used a questionnaire to obtain the necessary primary data. This study used a structured questionnaire. It was translated to the local language Amharic and administered by the researcher by contacting the respondent physically and explaining questions. Using this method of data collection, cross-sectional data were collected on indicators of multidimensional poverty and the determining variables of multidimensional poverty in Debre Tabor City. 3.3 Methods of data analysis This study used descriptive statistics, the method of estimating the multidimensional poverty index, and econometric data analyses. The dual cut-off approach to measuring multidimensional poverty was employed to estimate the multidimensional poverty index. Finally, an ordinal logistic regression model was applied to analyze the determinants of multidimensional household poverty. 3.3.1 Econometric model specification The ordered logistic regression model was chosen because the total number of households was divided into four categories based on their deprivation score ( Y i ∗ ). Based on the values of the deprivation score ( Y i ∗ ), the household categories are severely multidimensional poor households, moderately multidimensional poor households, vulnerable to multidimensional poverty, and non-multidimensional poor households. The model was then specified as follows ( Gujarati and Porter, 2009 ; Liu, 2016 ). (7) Y i ∗ = ∑ n = 1 k β n X in + u i Where Y i ∗ is unobserved, and is a latent variable. In this study, Y i ∗ is the weighted deprivation score of a household, which is calculated using Equation (2); β s are coefficients, Xs are the set of explanatory variables, and u i is the error term. There were 394 independent households (or observations), and they faced four ordered categories, which are stated as follows: Y i = 0 , if Y i ∗ ≤ α 1 Non‐multidimensional poor Y i = 1 , if α 1 < Y i ∗ ≤ α 2 Vulnerable to be multidimensional poor Y i = 2 , if α 2 α 3 Severely multidimensional poor Where Y i is a household’s multidimensional poverty level whose multidimensional poverty status would be in one of the four categories, and α 1 , α 2 and α 3 are thresholds by which the non-overlapping demarcation was made for these categories. In this study, these thresholds are different levels of weighted deprivation scores experienced by households. That is α 1 = 0.20 , α 2 = 0.33 and α 3 = 0.50 . The estimation of the model requires determining the odds ratio. To find the odds ratio, the cumulative probability distribution of Y i is required because it is the ratio of the probability of being in or below category and the probability of being above category j . The cumulative probability distribution is given by equation 6. (8) P ( Y i ≤ j ) = P ( ∑ n = 1 k β n X in + u i ≤ α j ) = P ( u i ≤ α j − ∑ n = 1 k β n X in ) Having this cumulative probability distribution, the probabilities being at or below a category j is given by equation 7 ; the probability of being above category j is given by equation (9) and the odds ratio which is the ratio of the two complementary probabilities is given equation (10) . (9) P ( Y i ≤ j | X ) = 1 1 + e − z i (10) P ( Y i > j | X ) = 1 − P ( Y i ≤ j | X ) = 1 − 1 1 + e − z i = 1 1 + e z i (11) odds ( Y i ≤ j | X ) = P ( Y i ≤ j | X ) P ( Y i > j | X ) = P ( Y i ≤ j | X ) 1 − P ( Y i ≤ j | X ) = 1 1 + e − z i 1 1 + e z i = e z i Where z i = α j − ∑ n = 1 k β n X in Taking natural logs on both sides of Equation (9) gives the following model, which is linear in parameter and makes it easy to interpret the coefficients. (12) Y i = ln [ P ( Y i ≤ j | X ) 1 − P ( Y i ≤ j | X ) ] = ln e z i = z i = α j − ∑ n = 1 k β n X in Where j = 0, 1, 2, 3 This would provide four orders of logit or log-odds, all of which have the same explanatory variables but different intercepts (cut points). In this study, there were four categories with three thresholds. 3.3.2 Diagnostic tests Cross-sectional data usually face multicollinearity and heteroscedasticity problems ( Gujarati and Porter, 2009 ), multicollinearity and heteroscedasticity should be checked and resolved if they appear. However, unlike ordinary least squares and binary logistic regression models, the post-estimation of ordered logistics does not support the techniques of testing multicollinearity and heteroscedasticity problems. Therefore, the model was estimated using robust standard errors to remove heteroscedasticity problems. The other diagnostic test was the proportional odds ratio/parallel regression assumption of an ordered regression model. The assumption is stated that the coefficients of the explanatory variables are equal across ordinal categories ( Liu, 2016 ). The Brant test was used to check whether this assumption holds ( Brant, 1990 ). The intuition of the test is that the model is dichotomized many times to form different binary logits, and then the coefficient across these binary logit models will be equal. The assumption would be violated if the result of the Brant test shows a significant p-value of the chi-square test. Finally, the overall significance of the model is the required characteristic of the econometric estimations. In this study, the log-likelihood ratio test statistic, which follows a chi-squared distribution, was used as a measure of the robustness of the model. 4. Results and Discussions 4.1 Analysis of multidimensional poverty Depending on the weighted deprivation score in the indicators of poverty, respondents were divided into four categories: SMP, MMP, VMP, and NMP. As is depicted in Table 4.1 , only 27.6% of households were NMP, which means that those households were deprived of less than 20% of the 14 indicators of multidimensional poverty used in this study. In addition, 13.45% were also NMP, but they were VMP. Households in this poverty group were deprived of 20%-33% of indicators of multidimensional poverty. 27.41% of households were MMP, which means that they were underprivileged in 33-50% of indicators of multidimensional poverty. The remaining 31.98% of households had SMP. These households are deprived of more than 50% of indicators of multidimensional poverty. SMP households were deprived of at least seven of the 14 indicators of multidimensional poverty. Table 4.1. Multidimensional poverty category. Multidimensional poverty category Frequency Percent Cum. NMP 107 27.16 27.16 VMP 53 13.45 40.61 MMP 108 27.41 68.02 SMP 126 31.98 100.00 Total 394 100.00 4.2 The multidimensional poverty index of Debre Tabor city administration The results in Table 4.2 shows that the multidimensional poverty incidence (headcount ratio) in Debre Tabor city administration was 59.4%, which means that 59.4% of households are multidimensional poor and could not meet at least 33% (poverty cut-off ) of the 14 indicators of poverty that were incorporated in this study. Accordingly, the households in Debre Tabor city are in acute multidimensional poverty. The intensity (depth) of multidimensional poverty was 54.9%. This means that the multidimensional poor households were deprived of 54.9% of weighted indicators of poverty, or these multidimensional poor households need to have 54.9% of indicators of poverty to pass the poverty cut-off. The adjusted headcount (MPI) in the Debre Tabor city is 0.326, which is stated as the total households (both multidimensional poor and multidimensional poor) were deprived in 32.6% of weighted indicators. Table 4.2. Multidimensional poverty index (MPI). Measures Value Standard errors 95% confidence interval Headcount (H) 59.4% 0.025 0.545 0.642 Intensity (A) 54.9% 0.01 0.529 0.568 Adjusted headcount index (MPI) 0.326 0.015 0.297 0.355 The results revealed that the multidimensional poverty incidence, intensity, and adjusted headcount ratio are more severe in Debre Tabor city than in the respective values at the national urban level ( OPHI, 2021 ). The multidimensional poverty incidence, the intensity of multidimensional poverty, and adjusted headcount (MPI) were 39.2%, 47.6%, and 0.187, respectively. 4.3 Percentage contribution of each indicator and dimensions to MPI Figure 4.1 presents the percentage contribution of each indicator and dimension to the MPI. Education as a dimension contributed 7.3% to MPI. Years of schooling and child school attendance each contributed 2.9% and 4.4% to MPI respectively. Indicators of living standards contribute 25.5% to the MPI jointly. Separately, the quality of living houses contributed 6.7%, drinking water contributed 4.8%, sanitation contributed 6.2%, and asset ownership contributed 7.6% to the MPI. Health dimension also contributes 13.4% to the MPI in general and particularly, nutrition contributes 3.6%, child mortality contributes 2.5%, consultation contributes 5.l%, and chronic disease contributes 2.2% to the MPI. Figure 4.1. Percentage contribution of each dimension/indicator to MPI. The greatest contribution to the MPI came from energy which shares 30.4% as a dimension and cooking meals contributes 14.4 %), and baking injera contributes 16%). Finally, income stands up as a dimension and, at the same time, as an indicator itself, contributes 23.7% to the MPI. Thus, the dimensions of poverty that contribute largely to the MPI of Debre Tabor city in the order of their percentage are energy sources for cooking and living standards. 4.4 Decomposition of MPI by sub-group population Table 4.3 presented the decomposition of MPI by gender of household head, and the result shows that 67.9% of female-headed households are multidimensional poor, while 54.75% of male-headed households are multidimensional poor. This result indicates that female-headed households are multidimensional poorer than male-headed households. The depth of multidimensional poverty is also greater for female-headed households. Table 4.3. MPI by gender of household head. Measures Male headed Female-headed Total Headcount ratio (H) 54.7% 67.9% 59.4% Intensity (A) 55.03% 54.49% 54.9% Adjusted headcount ratio (MPI) 0.301 0.370 0.326 Population share (%) 64.5 35.5 As per Figure 4.2 , in the Debre Tabor city, households that have unemployed heads are the multidimensional poorest, which is 84.2% of these households are multidimensional poor. The Households that have daily wage worker heads are the second multidimensional poorest households a multidimensional poverty incidence of 82.4%. Households with heads that have a private business (self-employed) are the third most multidimensional poor. The least multidimensional poor households are those that have permanently salaried household heads a multidimensional headcount ratio of 44.7%. According to the results, households with heads of employed permanent salaries are in a better position of multidimensional richness. Figure 4.2. MPI by employment types of household head. Figure 4.3 presented the decomposition of MPI in Debre Tabor city based on the marital status of the household head. The multidimensional headcount ratio for the single-headed households was 89.5%; for the married head was 45.5%; for divorced 83.3%, and for widowed 50%. This result indicates that staying unmarried and divorced after marriage are caused by a higher multidimensional poverty level. By contrast, households whose heads are married are less multidimensional poor. Figure 4.3. MPI by the marital status of household head. 4.5 Econometric result: Marginal effects Gender of the household head: A male-headed household is associated with being 8.5% more likely to be not non-multidimensional poor, 1.2% more likely to be not vulnerable to multidimensional poverty, 0.7% less likely to be moderately multidimensional poor, and 8.9% less likely to be extremely multidimensional poor. Marital status of household head: As the results revealed, a household with a married head was 20% less likely to be severely multidimensional poor, 1% less likely moderately multidimensional poor, 4.2% less likely to be vulnerable to multidimensional poverty, and 17.1% more likely to be not multidimensional poor. This implies that when a household has a married head, it is less likely to fall into multidimensional poverty relative to single (unmarried), divorced, or widowed marital status. The reason for this might be that married households would have responsibilities, thereby reducing unnecessary expenditures. Beyond this, when marriage takes place, there will be an integration of resources, experiences, business ideas, and so on to create a strong economic environment, which will be one way of attacking multidimensional poverty. Total income of the household: A one percent increase in total household income is associated with being 19% more likely to be in the non-multidimensional poor category, 3% more likely to be vulnerable to multidimensional poverty, 3.3% less likely to be moderately multidimensional poor, and 18.7% less likely to be extremely multidimensional poor. When total household income increases, the household’s command over both commodities and business investments will also increase. Thus, as income increases, the probability of falling into multidimensional poverty decreases. Family size: As the number of household members increased by one person, the household became 1.8% more likely to be severely multidimensional poor, 0.3% moderate multidimensional poor, and 0.3% less likely to be vulnerable to multidimensional poverty, and 1.8% less likely to be non-multidimensional poor. The results show that a large number of household members is associated with a higher probability of households falling into a multidimensional poverty circle. When the number of individuals in the house increases, it affects the welfare of the household by increasing living costs. Even if a new child is born, she/he will require care and consume at least adult labor hours each day to look after the child. Access to credit : When the household had access to credit for business operations, the probability of not being multidimensional poor increased by 11.6%, the probability of not being vulnerable to multidimensional poverty increased by 1.6%, the probability of being moderately multidimensional poor decreased by 1.6%, and the probability of being severely multidimensional poor decreased by 12.2%. Access to credit by a household can provide an opportunity to start a profitable business instead of being jobless or remaining low in business activities. In this way, the household’s welfare would be improved, and the probability of falling into multidimensional poverty would decrease. Cooperative membership : When a household was a member of cooperative (s) in its resident, the probability of not being multidimensional increased poor increased by 13%, the probability of not being vulnerable to multidimensional poverty increased by 1.8%, the probability of being moderately multidimensional poor decreased by 1.6%, and the probability of being severely multidimensional poor decreased by 14%. This result is consistent with a study in Nigeria by Adepoju (2018) , who found that cooperatives were significant factors in poverty reduction. This variable could reduce MDP by sharing work experience, saving and credit provision, and helping each other members of the cooperative during difficulties. Cooperatives can decrease multidimensional poverty by creating a linkage between poor and rich households that integrates social resources, creating employment and income-increasing opportunities for the poor. Female income : When the household had a female household who was working in income-generating activities, the probability of not being multidimensional increased poor increased by 9.1%, the probability of not being vulnerable to multidimensional poverty increased by 1.2%, the probability of being moderately multidimensional poor decreased by 0.8%, and the probability of being severely multidimensional poor decreased by 9.6%. The results prove that whenever females actively participate in business work, they contribute to the income of the household. Second, they would not get time to give many births, thereby decreasing the number of children in the household. Mohamoud and Bulut (2020) suggest that when female members of a household participate in business activities, the chance of the household being poor can be reduced. House ownership : When the household has its dwelling, the probability of not being multidimensional increased poor increases by 6.4%, the probability of not being vulnerable to multidimensional poverty increases by 0.9%, the probability of being moderately multidimensional poor is decreases by 0.6%, and the probability of being severely multidimensional poor decreases by 6.7%. The result states that households that have their own living house would be less multidimensional poor relative to those that do not their own living house. There are two reasons for this finding. First, owning a house can reduce the cost incurred for rent, and second, the household can construct (build) its own house with standardized qualities. Employment types of the household head: Except for casual wage worker household heads, other types of occupation had a negative marginal effect on multidimensional poverty. However, only permanent income has a significant negative marginal effect on the multidimensional poverty levels of households. When a household has a head who is employed in a permanent income job, it will be 10.1% less likely to be SMP, 0.2% less likely to be moderately multidimensional poor, 1.7% more likely to be non-vulnerable to multidimensional poverty, and 8.6% more likely to be non- multidimensional poor. The reason behind this is because permanently employed household heads have a more stable and reliable income for planning and managing their ways of living than other types of employment. Fields (2019) argued that self-employment in developing countries does not contribute as much to poverty alleviation because self-employment in most developing countries is not backed by the traits of entrepreneurship. Education level of the household head: From education levels of the household head that were considered in this study, only higher education was significant, implying that if the household has a head with a higher diploma, it will be 26.3% less likely to be severely multidimensional poor and 18.9% more likely to be non- multidimensional poor. That is, individuals who have attended higher education levels would have relatively good opportunities to get a good job. Saving ratio: A one percent increase in saving ratio is associated with being 23.5% more likely to be in no multidimensional poverty category, 3.2% more likely to be vulnerable to multidimensional poverty, and 2% less likely to be moderately multidimensional poor, and 24.6% less likely to be extremely multidimensional poor. Saving can reduce the probability of a household being multidimensional poor by providing profit from interest rates and removing waste of resources. More precisely, if a household saves a reasonable percentage of its total income, it will be in a better position for welfare in the future. 5. Conclusion and Policy implications 5.1 Conclusion By estimating the multidimensional poverty index of the Debre Tabor city, this study concluded that the majority of households are multidimensional poor. The findings revealed that more than half of the households in this city were deprived of electric energy for cooking purposes, having fixed assets, living in standardized houses, improving sanitation facilities, and receiving health consultations from health careers. Subgroup decomposition of the multidimensional poverty index indicated that there was a different level of multidimensional poverty in households across their different characteristics. Households with a head with a lower education level were multidimensional poorer than those with highly educated heads. Households with permanently employed heads are also multidimensional richer than households with either self-employed or casual wage workers. Female-headed households were also less multidimensional than their counterparts. This study also concluded that when a household has a single head, divorced and widowed are multidimensional poorer than a household with a married head. From the regression analysis of socioeconomic factors on multidimensional poverty levels of households, higher education level of the household head, income, saving ratio, access to credit house ownership, female participation in business activities, cooperative membership, permanent employment status of the household head, marital status of the household head (being married), and gender of the household head (being male) were found to be negatively and significantly related to multidimensional poverty levels of households. Family size is positively related to households’ multidimensional poverty levels. 5.2 Policy implications This study had arrived at the following recommendation based on conclusions: 1. Financial institutions The financial sector should create a good environment for receiving savings and providing loans and credit to households in their location. They can achieve this by creating opportunities for credit and creating awareness of how it should be invested. Both private and public financial institutions must provide an interest rate for saving and borrowing with a moderate gap. 2. Education sector The education office should not leave anyone behind in school. It should create awareness in households to send school-aged children to schools. In addition, in collaboration with non-governmental organizations, the sector must support children who cannot maintain school materials. Furthermore, they must provide basic writing and reading education for adults. 3. Health offices and family planning sector These government administration sectors must predominantly work on birth control. They can do this by clarifying the scientific importance of contraceptives. They can also provide a model for how children grow in greater quality of mind and physical settings when there is an appropriate distance between their birthday. 4. Social and Labor affair sector Officials in this sector must pay great attention to the contract between daily wage workers and their employers. This can be done in a way that prepares strict rules on wages, updates the rules in line with the change in living costs, and follows up on whether the rules are being respected. Otherwise, a measurement must be taken. 5. Municipality of Debre Tabor city This should make the environment conducive for households to have neat and livable housing. That is, they can make land available for households to construct their own houses or construct condominiums and redistribute them to households. 6. Households Households should allow and appreciate their female members to participate in business activities. The can do this by giving the females equal chances to education, and they should allow them to participate in household decisions. Households should also actively participate in existing cooperatives, and they should form new cooperatives for their residents. Household heads should not be divorced. Author contributions All three authors contributed significantly to the writing of this manuscript. They worked on the conception, design, data collection tool development, data collection, entry, analysis, and manuscript writing in consultation as a team. Ethical consideration The study authors collected data from household heads of Debre Tabor University which were selected on random basis. Before data collection respondents gave verbal consent to give responses for the questions related to multidimensional poverty variables. The investigators assured them that their identity will be kept confidential. As a result, the identity of any of the respondents was not mentioned in any part of the study and the dataset. The participants of the study are adults (household heads) and as there is no experiment on this study, participants are just respondents; not subjects of the study. In addition, we have got approval from college research ethics review committee “Research and community service Review Committee”. We confirm that the date of ethical approval is 13-September-2023 Ref No: DTU/CBE-4/07/2023. Therefore, the study is based on scientific ethics. We have taken “verbal consent” from all participants of the study. The name of the college level review committee is: “Research and community service Review Committee”. The University level Institutional Review board has decentralized some review tasks to college level research and community service review committees to facilitate Ethical review. This is particularly true in case when study units are adults and just respondents if the study doesn’t include experimental design. In such a case, verbal consent is assumed to be sufficient to take information using questionnaire. Thus, this duty is assigned to college level research and community service review committee. The committee approved verbal consent in line with the power vested to it. Verbal consent form was applied to this study as respondents are adults and the study doesn’t related to experimental design in human being. The researchers approached each respondent first by explaining the purpose of the study and asked the same verbally whether it is ok to proceed or he/she is not willing to participate. Then, questionnaire is given to those who accepted willfully to participate. Data availability statement Underlying data The dataset used for this study is available as follows: Harvard Dataverse: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration, Dataset https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OVWVOU . This project contains: • MPI_dataset.xlsx Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Extended data The dataset used for this study is available as follows: Harvard Dataverse: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration, Dataset https://doi.org/10.7910/DVN/OVWVOU . This project contains: • MPI_Questionnaire and Long tables.docx Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References Adepoju A: determinants of multidimensional poverty transitions among rural households in Nigeria. 3OTH International Conference of Agricultural Economists. Vancouver; 2018; pp. 1–31. Alkire S, Foster J: Counting and Multidimensional Poverty Measurement. Journal of Public Economics. 2011a; 95 (7-8): 476–487. Publisher Full Text Alkire S, Foster J: Understandings and misunderstandings of multidimensional poverty measurement. Journal of Economic Inequality. 2011b; 9 (2): 289–314. Publisher Full Text Alkire S, Foster J, Seth S, et al. : Multidimensional Poverty Measurement and Analysis. Oxford, UK: Oxford University Press; 2015. Alkire S, Kanagaratnam U, Suppa N: The global multidimensional poverty index (MPI). 2021, OPHI MPI methodological note 51. University of Oxford; 2021. Alkire S, Santos M, Seth S, et al. : Is the Multidimensional Poverty Index robust to different weights? 2010. Brant R: Assessing Proportionality in the Proportional Odds Model for Ordinal Logistic Regression. International Biometric Society. 1990; 46 (4): 1171–1178. PubMed Abstract | Publisher Full Text Dagim T, Adisu A, Abeba T: Determinants of Urban Household Poverty; the Case of North Shewa Zone, Oromia, Ethiopia. Abyssinia Journal of Business and Social Sciences. 2020; 14–21. Debeli CB: Determinants of Urban Poverty in Muklami Town of West Wellega Zone of Oromia, Ethiopia. Journal of Resources Development and Management. 2019; 1–12. Debeli C, Endegena A: Urban Poverty and Its Determinants in Debre Markos City: A household level analysis. Journal of Poverty, Investment and Development. 2019; 15 : 1–13. Desawi KG, Abate MB, Cameron J: Determinants of Multidimensional Poverty among Rural HouseholdsIn Northern Ethiopia. Journal of Rural and Community Development. 2021; 16 (1): 133–151. DTTAO: Debre Tabor City Administration Annual Report for Fiscal Year 2013 E.C. Debre Tabor: Debre Tabor City Administration Office; 2021. Ethiopian Demographic and Health Survey (EDHS): Ethiopia Mini Demographic and Health Survey 2019: Final Report. Rockville, Maryland, USA: EPHI and ICF; 2021. Feleke YM, Rajan SD, Tewodros TA: Determinants of Rural Multi-dimensional Poverty: TheCase from Kuyu District, Central Ethiopia. IOSR Journal Of Humanities And Social Science (IOSR-JHSS). 2020; 46–54. Fields SG: Self-employment and poverty in developing countries. IZA World of Labor. 2019; 1–10. Publisher Full Text Galgalo D, Degefa T, Shiferaw M: Multidimensional poverty of pastoralists and implications for policy in Boorana rangeland system, Southern Ethiopia. World development Perspectives. 2021; (2): 1–11. Gedefaw T: Analysis of rural household’s multidimensional poverty: northern Amhara regional state, Ethiopia. Innovations. 2021; 66 :1193–1205. Gujarati DN, Porter DC: Basic Econometrics. 5th ed.New York: McGraw-Hill Companies,Inc.; 2009. Haider HM, Kumar S: Poverty in Afganistan: Causes, Consequences and Coping Mechanisms. Switzerland: Springer Nature; 2018. Israel GD: Determining sample size. Fact Sheet PEOD-6. University of Florida; 1992. Kothari C: Research methodology: methods and techniques. 2nd ed.New Delhi: New Age International (P) Ltd Publisher; 2004. Kvaratskhelia M: Poverty - Challenge of the Modern World. Educational Foundation “Transformations”; 2019; 124–128. Lekobane RK, Seleka B: Determinants of Household Welfare and Poverty in Botswana, 2002/2003 and 2009/2010. Journal of Poverty. 2016; 1–20. Liu X: Appled ordinal logistic regression using stata. Singapore: SAG Puplications, Inc; 2016. Mdluli P, Dunga S: Determinants of Poverty in South Africa Using the2018 General Household Survey Data. Journal of Poverty. 2021. Mekonnen B, Heshmati A: A distributional analysis of uni-and multidimensional poverty and inequalities in Ethiopia. Soc. Indic. Res. 2021; 155 (3): 138–215. Melese TC, Solomon KM, Amsalu B: Determinants of Urban poverty: the case of Nekemte town, Eastern Wollega zone of Oromia regional state. Journal of poverty, Investment and development. 2017; 32 :19–35. Mohammed MB: Measurement and determinants of urban poverty in case of Southern Nations, Nationalities, and Peoples’ Region (SNNPR), Ethiopia. International Journal of Scientific and Research Publications. 2017; 7 (3):181–189. Mohamoud MA, Bulut E: Determinants of Poverty in Somalia: A Logit Model Analysis. Fiscaoeconomia. 2020; 4 : 437–451. Publisher Full Text Mood C, Jonsson J: The Social Consequences of Poverty: An Empirical Test on Longitudinal Data. Social Indicator Researches. 2016; 127 : 633–652. PubMed Abstract | Publisher Full Text | Free Full Text OPHI: Ethiopia Country Briefing. Multidimensional Poverty Index Data Bank. Oxford Poverty and Human Development Initiative. University of Oxford; 2021. OPHI & UNDP: Global Multidimensional Poverty Index 2021: Unmasking disparities by ethnicity, caste and gender. OPHI and UNDP; 2021. Tamirat TT, Sivakumar S: Determinates of Urban Poverty at Household Level: The Case of Wolaitta Soddo Town, SNNPRS, Ethiopia. International Journal of African and Asian Studies. 2020; 60 : 1–10. Teshome A, Quaicoe N: Causes of Poverty in Sub-Saharan Africa: A Layered Theoy Approach to Understanding Significant Factors. Journal of Economics and International Finance. 2014; 6 (6): 112–124. Publisher Full Text UN: The Copenhagen Declaration and Programme of Action. New Yourk: Unite Nations; 1995. WB: World Development Report 2000: Attacking Poverty. Washington, DC: World Bank; 2000. WB: Poverty and shared prosperity 2018: piecing together the poverty puzzle. Washington, DC: World Bank; 2018. WB: Poverty and shared prosperity: reverals of fortune. Washington DC: World Bank; 2021. Yamane T: Statistics, An Introductory Analysis. 2nd ed.New York: Harper and Row; 1967. Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Feb 2025 ADD YOUR COMMENT Comment Author details Author details 1 Assistant Professor, Economics, Debre Tabor University, Debre Tabor, Amhara, 272, Ethiopia 2 Lecturer, Economics, Kabridahar University, Kebri Dahir, Somali, 250, Ethiopia 3 Lecturer, Economics, Debre Tabor University, Debre Tabor, Amhara, 272, Ethiopia Yismaw Ayelign Mengistu Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Bantalem Sinishaw Mekonen Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing – Original Draft Preparation Nega Assefa Aynalem Roles: Software, Supervision Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 13 Feb 2025, 14:203 https://doi.org/10.12688/f1000research.159139.1 Copyright © 2025 Mengistu YA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Mengistu YA, Mekonen BS and Aynalem NA. Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.12688/f1000research.159139.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 13 Feb 2025 Views 0 Cite How to cite this report: Amina B. Reviewer Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r396601 ) The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-396601 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 01 Aug 2025 Badreddine Amina , M'hamed Bougara Boumerdes University, Bourmerdes, Algeria Approved VIEWS 0 https://doi.org/10.5256/f1000research.174825.r396601 This article presents a rigorous analysis of multidimensional poverty in Debre Tabor, Ethiopia, using the Alkire-Foster method and ordered logistic regression. It identifies energy use, income, and living standards as key poverty drivers, with female-headed, single, and informally employed households ... Continue reading READ ALL This article presents a rigorous analysis of multidimensional poverty in Debre Tabor, Ethiopia, using the Alkire-Foster method and ordered logistic regression. It identifies energy use, income, and living standards as key poverty drivers, with female-headed, single, and informally employed households being most vulnerable. The methodology is robust, and the findings are well-supported and policy-relevant. The paper offers clear recommendations on education, employment, and credit access. Minor improvements in language clarity and literature contextualization are needed. Overall, it is a valuable and timely contribution deserving indexing after minor revisions. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Larbi Cherif H, Badreddine A, Sabri A: Determinants of Household Poverty: Identification Using Logistic Regression. SocioEconomic Challenges . 2024; 8 (1): 78-89 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: econometrics, poverty, startups, sustainability, tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Amina B. Reviewer Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r396601 ) The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-396601 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Onsay EA. Reviewer Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r366535 ) The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-366535 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 28 Feb 2025 Emmanuel A Onsay , University of the Philippines Los Baños, Laguna, Philippines Approved VIEWS 0 https://doi.org/10.5256/f1000research.174825.r366535 Overall, the paper presents a well-structured and insightful discussion. With some enhancements and modifications, it has the potential to be highly relevant for policy and decision-making. The title could be more precise and impactful if revised to: "Multidimensional ... Continue reading READ ALL Overall, the paper presents a well-structured and insightful discussion. With some enhancements and modifications, it has the potential to be highly relevant for policy and decision-making. The title could be more precise and impactful if revised to: "Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-Level Study of Debre Tabor City Administration." The abstract is concise but would benefit from additional sentences to strengthen the arguments. Expanding on the study's key findings, methodology, and significance will enhance its clarity and impact. Additionally, citing the sampling design and the basis for selecting the sample would provide greater methodological transparency. For the keywords, consider making them more direct and closely related to your study to improve their relevance and searchability. In the introduction, the paper is well-supported with citations, effectively integrating multiple sources to establish a strong research foundation. The definition of poverty is comprehensive, moving beyond economic deprivation to highlight its social, political, and environmental dimensions. The inclusion of recent statistical data from sources such as EDHS, UNDP, and OPHI strengthens the argument and provides a clear contextual background for Ethiopia. However, there are areas for improvement. Some sentences are lengthy and complex, making them harder to follow. Breaking them down into more concise statements would improve clarity and readability. The logical progression of ideas is well-structured, transitioning from a broad global perspective to Ethiopia’s specific case. However, smoother transitions between sections would further enhance the flow. A brief connecting sentence before introducing Ethiopia’s multidimensional poverty data would create a more seamless narrative. Maintaining terminology consistency is also important. For example, the discussion of poverty measurement methods in the final paragraph should clearly connect to the concepts introduced earlier. Ensuring uniformity in terminology will help prevent confusion and maintain coherence. Grammar and syntax adjustments will further refine the clarity of the text. For instance, the sentence: "They are embarrassed and furious and are unable to maintain their regular social circles and friendships." Could be rephrased as: "They often experience embarrassment and frustration, which hinder their ability to maintain social connections." Additionally, while statistical data is well-incorporated, a concluding sentence summarizing the significance of these percentages would reinforce their importance. Regarding the literature review (RRL), paragraphs should contain more than 10 sentences each to ensure depth and coherence in discussions. For methodology, you may find the following references useful in enhancing your approach: Ref: 1, 2 To improve the scaling and implications of your results, consider expanding the analysis to include measures of poverty incidence, gap, and severity, provided the data allows for such computations. Finally, the policy implications section should be presented in paragraph form to ensure a structured and comprehensive discussion. Clearly articulating how the findings inform policy and decision-making will strengthen the study’s real-world relevance. By addressing these refinements, your paper will achieve greater clarity, coherence, and impact. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Onsay EA, Rabajante JF: Measuring the unmeasurable multidimensional poverty for economic development: Datasets, algorithms, and models from the poorest region of Luzon, Philippines. Data Brief . 2024; 53 : 110150 PubMed Abstract | Publisher Full Text 2. Onsay E, Rabajante J: Measuring the Unmeasurable: Decomposing Multidimensional Rural Poverty and Promoting Economic Development in the Poorest Region of Luzon, Philippines. Societies . 2024; 14 (11). Publisher Full Text Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Onsay EA. Reviewer Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r366535 ) The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-366535 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Feb 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 13 Feb 25 read read Emmanuel A Onsay , University of the Philippines Los Baños, Laguna, Philippines Badreddine Amina , M'hamed Bougara Boumerdes University, Bourmerdes, Algeria Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Amina B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 01 Aug 2025 | for Version 1 Badreddine Amina , M'hamed Bougara Boumerdes University, Bourmerdes, Algeria 0 Views copyright © 2025 Amina B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This article presents a rigorous analysis of multidimensional poverty in Debre Tabor, Ethiopia, using the Alkire-Foster method and ordered logistic regression. It identifies energy use, income, and living standards as key poverty drivers, with female-headed, single, and informally employed households being most vulnerable. The methodology is robust, and the findings are well-supported and policy-relevant. The paper offers clear recommendations on education, employment, and credit access. Minor improvements in language clarity and literature contextualization are needed. Overall, it is a valuable and timely contribution deserving indexing after minor revisions. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Larbi Cherif H, Badreddine A, Sabri A: Determinants of Household Poverty: Identification Using Logistic Regression. SocioEconomic Challenges . 2024; 8 (1): 78-89 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise econometrics, poverty, startups, sustainability, tourism I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Amina B. Peer Review Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r396601) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-396601 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Onsay E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 28 Feb 2025 | for Version 1 Emmanuel A Onsay , University of the Philippines Los Baños, Laguna, Philippines 0 Views copyright © 2025 Onsay E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Overall, the paper presents a well-structured and insightful discussion. With some enhancements and modifications, it has the potential to be highly relevant for policy and decision-making. The title could be more precise and impactful if revised to: "Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-Level Study of Debre Tabor City Administration." The abstract is concise but would benefit from additional sentences to strengthen the arguments. Expanding on the study's key findings, methodology, and significance will enhance its clarity and impact. Additionally, citing the sampling design and the basis for selecting the sample would provide greater methodological transparency. For the keywords, consider making them more direct and closely related to your study to improve their relevance and searchability. In the introduction, the paper is well-supported with citations, effectively integrating multiple sources to establish a strong research foundation. The definition of poverty is comprehensive, moving beyond economic deprivation to highlight its social, political, and environmental dimensions. The inclusion of recent statistical data from sources such as EDHS, UNDP, and OPHI strengthens the argument and provides a clear contextual background for Ethiopia. However, there are areas for improvement. Some sentences are lengthy and complex, making them harder to follow. Breaking them down into more concise statements would improve clarity and readability. The logical progression of ideas is well-structured, transitioning from a broad global perspective to Ethiopia’s specific case. However, smoother transitions between sections would further enhance the flow. A brief connecting sentence before introducing Ethiopia’s multidimensional poverty data would create a more seamless narrative. Maintaining terminology consistency is also important. For example, the discussion of poverty measurement methods in the final paragraph should clearly connect to the concepts introduced earlier. Ensuring uniformity in terminology will help prevent confusion and maintain coherence. Grammar and syntax adjustments will further refine the clarity of the text. For instance, the sentence: "They are embarrassed and furious and are unable to maintain their regular social circles and friendships." Could be rephrased as: "They often experience embarrassment and frustration, which hinder their ability to maintain social connections." Additionally, while statistical data is well-incorporated, a concluding sentence summarizing the significance of these percentages would reinforce their importance. Regarding the literature review (RRL), paragraphs should contain more than 10 sentences each to ensure depth and coherence in discussions. For methodology, you may find the following references useful in enhancing your approach: Ref: 1, 2 To improve the scaling and implications of your results, consider expanding the analysis to include measures of poverty incidence, gap, and severity, provided the data allows for such computations. Finally, the policy implications section should be presented in paragraph form to ensure a structured and comprehensive discussion. Clearly articulating how the findings inform policy and decision-making will strengthen the study’s real-world relevance. By addressing these refinements, your paper will achieve greater clarity, coherence, and impact. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly References 1. Onsay EA, Rabajante JF: Measuring the unmeasurable multidimensional poverty for economic development: Datasets, algorithms, and models from the poorest region of Luzon, Philippines. Data Brief . 2024; 53 : 110150 PubMed Abstract | Publisher Full Text 2. Onsay E, Rabajante J: Measuring the Unmeasurable: Decomposing Multidimensional Rural Poverty and Promoting Economic Development in the Poorest Region of Luzon, Philippines. Societies . 2024; 14 (11). Publisher Full Text Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Onsay EA. Peer Review Report For: Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City Administration [version 1; peer review: 2 approved] . F1000Research 2025, 14 :203 ( https://doi.org/10.5256/f1000research.174825.r366535) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-203/v1#referee-response-366535 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Analyzing Multidimensional Poverty and Its...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-203/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-203/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-203/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Mengistu YA et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/14-203/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/14-203", templates : { twitter : "Analyzing Multidimensional Poverty and Its Determinants in Ethiopia:.... Mengistu YA et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-203/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/159139/174825") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "174825"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "366535": 25, "396608": 0, "366541": 0, "366540": 0, "366543": 0, "366542": 0, "366537": 0, "366536": 0, "366539": 0, "366538": 0, "366544": 0, "396599": 0, "367927": 0, "367926": 0, "396606": 0, "367933": 0, "396607": 0, "367932": 0, "396604": 0, "367935": 0, "396605": 0, "367934": 0, "396602": 0, "367929": 0, "396603": 0, "367928": 0, "396600": 0, "367931": 0, "367930": 0, "396601": 10, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "b32ff063-07ec-40a5-bf63-92d0678b6fd4"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });