can basic entrepreneurship transform the economic lives of the poor?

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1 can basc entrepreneurshp transform the economc lves of the poor? Orana Bandera, Robn Burgess, Narayan Das, Selm Gulesc, Imran Rasul, Munsh Sulaman Aprl 2013 Abstract The world s poorest people lack captal and sklls and tol for others n occupatons that others shun. Usng a large-scale and long-term randomzed control tral n Bangladesh ths paper demonstrates that szable transfers of assets and sklls enable the poorest women to shft out of agrcultural labor and nto runnng small busnesses. Ths shft, whch perssts and strengthens after assstance s wthdrawn, leads to a 38% ncrease n earnngs. Inculcatng basc entrepreneurshp, where severely dsadvantaged women take on occupatons whch were the preserve of non-poor women, s shown to be a powerful means of transformng the economc lves of the poor. Keywords: asset transfers, captal constrants, vocatonal tranng, occupatonal choce, structural change, poverty. JEL Classfcaton: O12; I30; D50. We thank all BRAC staff and especally Mahabub Hossan, W.M.H. Jam, Imran Matn and Rabeya Yasmn for ther collaboratve efforts n ths project. Thanks are also due to Wahduddn Mahmud and the IGC Bangladesh offce for supportng the project. We thank Arun Advan, Abhjt Banerjee, Vttoro Bass, Tmothy Besley, Gharad Bryan, Francsco Buera, Bronwen Burgess, Anne Case, Arun Chandrasekhar, Angus Deaton, Greg Fscher, Dean Karlan, Guy Mchaels, Ted Mguel, Mushfq Mobarak, Benjamn Olken, Steve Pschke, Mark Rosenzweg, Jeremy Shapro, Chrs Udry, Chrs Woodruff and numerous semnar and conference partcpants for useful suggestons. The large-scale survey and data processng whch underpns ths paper was fnanced by BRAC and ts CFPR-TUP donors, whch nclude DFID, AusAID, CIDA and NOVIB, OXFAM-AMERICA. The consortum supported both the nterventon costs as well as costs of drect research actvtes. Ths document s an output from research fundng by the UK Department for Internatonal Development (DFID) as part of the G, a research program to study how to mprove nsttutons for pro-poor growth n Afrca and South Asa. Support was also provded by the Internatonal Growth Centre. The vews expressed are not necessarly those of DFID. All errors reman our own. Author afflatons and contacts: Bandera (LSE, Burgess (LSE, Das (BRAC, Gulesc (Boccon, Rasul Sulaman (BRAC-Afrca, 1

2 1 Introducton The world s poorest people lack both captal and sklls. They tend to engage n low-sklled wage labor actvtes that are nsecure and seasonal n nature [Banerjee and Duflo 2007]. 1 non-poor, n contrast, tend to be engaged n secure wage employment, or employ others n the busnesses they operate [Banerjee and Duflo 2008]. Any attempt to allevate extreme poverty on a large scale therefore requres us to thnk about catalyzng the process of occupatonal change and to understand how ths process s lnked to a paucty of captal and sklls. Economc theory hghlghts mechansms va whch expanded access to captal enables ndvduals to alter ther occupatonal choces and ext poverty [Banerjee and Newman 1993, Besley 1995, Galor and Zera 1993, Gne and Townsend 2004, Aghon et al. 2005, Jeong and Townsend 2008, Karlan and Morduch 2010, Townsend 2011, Buera, Kabosk and Shn 2012] and how lmted human captal formaton constrans occupatonal choces and the ablty to escape poverty [Becker 1964, Schultz 1961, 1980, Strauss and Thomas 1995, Behrman 2010]. In lne wth ths, many antpoverty programs target ether a lack of captal, for nstance through mcrofnance, development bankng or asset transfer programs, or a lack of sklls, for nstance through vocatonal tranng or cash transfers condtoned on school attendance. Whether these programs can permanently transform the lves of the poor crucally depends on the exstence and strength of the causal lnk between the lack of captal and sklls and occupatonal choce and poverty. Although there s a dstngushed and growng lterature n macroeconomcs that documents how occupatonal change and aggregate development proceed together [Kuznets 1966; Chenery and Syrqun 1975, Murphy, Shlefer and Vshny 1989, Casell and Coleman 2001, Nga and Pssardes 2007, Buera and Kabosk 2012], far less s known about whether polcy nterventons that transfer captal and sklls are capable of brngng about structural transformaton through occupatonal change. 2 Ths paper attempts to partly fll the gap between studes of occupatonal change drvng economc development that concern macroeconomsts, and mcroeconomc work evaluatng programs that relax credt or sklls constrants. Our focus s on n stu occupatonal change where the rural poor upgrade to more secure, less seasonal busness actvtes rather than on the shft of rural laborers nto manufacturng and servce sector jobs n ctes. 3 We ask whether tacklng both 1 Agrcultural laborers, whch often consttute the bottom stratum of socety n developng countres, are confronted not only wth seasonal and weather-dependent demand for ther labor but also wth barrers to other forms of employment owng to ther lmted captal and sklls [Sen 1981, Dreze and Sen 1989]. 2 There are of course reasons to be skeptcal about whether antpoverty programs of any strpe can affect occupatonal choce. The very poor may not demand any captal f they perceve lttle use for t [Townsend 2011]. They may not wsh to nvest n human captal f the returns are perceved to be low [Jensen 2010, 2012]. The scale of the nterventon may be nsuffcent to enable the very poor to set up new busnesses or to engage n secure wage employment [Banerjee 2004], a crtcsm often leveled at mcrofnance where loan szes may be too small to allow borrowers to effect a change n busness actvty [Schoar 2009]. Self-control or other behavoral bases my lead the very poor to consume transfers wthout alterng ther occupatonal choces [Banerjee and Mullanathan 2010]. Leakage may mean that the poor receve a very small fracton of the ntended assstance [Renkka and Svensson 2004]. Fnally, socal norms and rules mght constran occupatonal choces, especally of women [Feld et al. 2010]. 3 In stu occupatonal change nvolvng modest changes n the actvtes of poor rural ctzens, sometmes referred The 2

3 captal and sklls constrants smultaneously by provdng busness asset transfers coupled wth complementary and ntensve tranng, can transform the economc lves of some of the world s poorest people. To answer ths queston, we collaborated wth the NGO BRAC to mplement a large-scale and long-term randomzed control tral to evaluate ther Targeted Ultra-Poor (TUP) program n rural Bangladesh. Elgble women - dentfed to be the very poorest n these rural communtes 4 - are offered a menu of possble busness actvtes, rangng from lvestock rearng to small retal operatons, coupled wth complementary and ntensve tranng n runnng whchever busness actvty they choose. 5 The scale of the program combned wth the sze of the transfers mples that, taken as a whole, the TUP program n Bangladesh represents a sgnfcant attempt to lft large numbers of women, and ther dependents, out of extreme poverty. Indeed, as of 2011, the TUP program was already reachng close to 400,000 women and a further 250,000 wll reached between 2012 and The program gves a bg push to relaxng both captal constrants (at $140 the value of the asset transfer s worth roughly ten tmes baselne lvestock wealth) and sklls constrants (the value of the two-year tranng and assstance whch women receve s of a smlar magntude). Ths s done n a context where elgble benefcares are unable to relax these constrants through the market. For captal, the value of mcrofnance loans avalable to them s too low to fnance such large purchases and repayment requrements too strngent to allow them the tme to generate ncome from a new enterprse. For sklls, tranng programs are not avalable and nformal arrangements mght not be suffcent to delver all the assstance requred to operate the small busnesses that women select. In our pre-program settng, the rural poor are faced wth a choce between wage employment (manly as agrcultural laborers and domestc servants) and self-employment (manly n lvestock rearng). The program nfluences ths choce by ncreasng wealth va the asset transfer and the returns to self-employment va sklls tranng. We develop a smple model to understand the occupatonal choces that targeted poor women make at baselne and how the program affects to as subsstence entrepreneurshp, can play a major role n poverty reducton. Ths s dstnct from busness start-ups n manufacturng and servces whch have the potental to grow to a sgnfcant sze [Schoar 2009]. The latter, whch are the tradtonal focus on the study of entrepreneurshp n developed countres are also mportant n Bangladesh but tend to be located n urban areas and are therefore not the focus of ths study. 4 Women are selected on crtera such as not ownng land, not havng a male adult earner n the household, havng to work outsde the household, havng school-aged chldren that work and havng no productve assets. Elgbles must also not be enrolled wth mcrofnance organzatons or recpents of government ant-poverty programs. 5 The majorty choose hgh value lvestock busnesses whch had been manly operated by non-poor women n the communtes we study. In value, scale and complexty these busnesses were dstnct from the more basc lvestock rearng that some poor women were engaged n before the program (e.g. cow rearng versus free range poultry). 6 In Bangladesh the TUP program s know as the specally targeted ultra poor program. Another varant, known as the other targeted poor program (OTUP), targets slghtly less dsadvantaged women wth the asset transfer beng purchased usng a BRAC loan. Ths varant reached 600,000 benefcares n 2011 and wll reach a further 150,000 by 2016 [BRAC 2011]. Non expermental evaluatons of the program are reported n Ahmed et al. [2009] and Emran et al. [2009], trackng 5000 households from 2002 to Both studes fnd postve mpacts on per capta consumpton and mprovements n food securty. Das and Msha [2010] extend the panel to 2008 and fnd postve mpacts on ncome, food securty and asset holdngs. 3

4 these choces on the extensve and ntensve margns of labor suppled to each actvty. shows that both asset transfers and sklls provson components reduce hours devoted to wage employment, through ncome and substtuton effects. On hours devoted to self-employment, the model shows how the effect of both components s heterogeneous dependng on whether ndvduals face a bndng captal constrant at baselne. In partcular, asset transfers can have the unntended consequence of reducng hours devoted to self-employment through a wealth effect. Ultmately the model shows that the effect of the program on occupatonal choces s theoretcally ambguous. The evaluaton sample covers 1409 communtes n 40 regons n rural Bangladesh, half of whch were treated n 2007 and the rest kept as controls untl Ths BRAC program offcers select potental benefcares n 2007 followng the same selecton crtera n treatment and control communtes. We survey and track all poor households (both elgbles and non-elgbles), as well as a 10% random sample of non-poor households from across other wealth classes n the same treated and control communtes. We dentfy the effect of the program by a dfference n dfference estmate that compares the outcome of the elgble poor n treated versus control communtes before and after program mplementaton. Gven that we sample households from across the wealth dstrbuton, we benchmark these estmated mpacts aganst the baselne gap between elgble and non-poor households. Gven our focus on occupatonal change towards basc entrepreneurshp, where new busness actvtes take tme to develop, we survey households two and four years after the program s mplementaton. Ths helps trace out the economc trajectores of poor women over an extended perod, sheddng lght on whether the labor productvty of poor women mproves over tme as they become more adept at runnng ther new busnesses. Ths tme scale also means that we move well beyond the perod when targeted women are recevng drect assstance from BRAC. The data confrm that the program successfully targets the very poorest women n rural Bangladesh: at baselne more than half (52%) own no productve assets, 93% are llterate and 38% are the sole earner n ther households. 80% of them lve below the global poverty lne (US$1.25). They typcally engage n multple occupatons, whch are not held regularly throughout the year and characterzed by ncome seasonalty. The precarousness of ther economc lves though strkng, s typcal of the stuaton that mllons of rural women across the developng world fnd themselves n. 7 In contrast, rcher women n the same communtes typcally shun wage employment and are engaged n fewer, more regular, actvtes wth most of them specalzng n self-employment ether rearng lvestock or cultvatng land. Our estmates of the program s mpact show evdence of a causal lnk from the lack of captal and sklls to occupatonal choce, and ultmately poverty and nsecurty. We fnd that, on the extensve margn, after four years the TUP program reduces the share of women specalzed n 7 It s well documented that landless agrcultural laborers, such as the elgble women here, are exposed to seasonal hunger and famne - monga - as t s referred to Bangladesh [Bryan et al. 2011; Khandker and Mahmud 2012]. Monga s the result of lmted demand for agrcultural labor n the pre-harvest perod. 4

5 wage employment by 17 percentage ponts (pp), correspondng to 65% of the baselne mean. Over the same perod, the share of women specalzed n self-employment ncreases by 15pp and those engaged n both occupatons by 8pp. These changes on the extensve margn of occupatonal choce correspond to 50% and 31% ncreases from ther baselne values, respectvely. Ths dramatc change n occupatonal choce on the extensve margn s accompaned by a correspondng change n hours devoted to the two occupaton categores. After four years, elgble women work 170 fewer hours per year n wage employment (a 26% reducton relatve to baselne) and 388 more hours n self-employment (a 92% ncrease relatve to baselne). Hence total annual labor supply ncreases by an addtonal 218 hours whch represents an ncrease of 19% relatve to baselne. Gven the occupatonal change nduced, ther labor supply becomes more regular throughout the year, whle ncome seasonalty s reduced. The change n occupatonal structure s assocated wth a 15% ncrease n labor productvty and a 38% ncrease n earnngs. leads to a 8% ncrease n household per capta expendture, and a 15% ncrease n self-reported lfe satsfacton among elgble women. Benchmarked aganst the global poverty lne of $1.25 per day and recallng that the average elgble lves on 93c per day at baselne, the program lfts 11% of the elgble women out of extreme poverty. Measures of estmated effects are typcally more pronounced after four relatve to after two years, ndcatng that the program sets benefcares on a sustanable path out of poverty. To probe further whether all elgble women are equally mpacted, we estmate quantle treatment effects. These reveal that the effect on earnngs and expendtures s postve at all decles, but both effects are substantally larger for the top four decles after four years. Ths ndcates that the program ncreases both the mean and the dsperson of total earnngs among the treated. Second, benchmarkng the magntude of the program mpact relatve to dfferences n the same outcome between the elgble poor and other wealth classes we fnd the elgble poor: () overtake the near poor on a host of economc ndcators; and () they close around 40% of the gap to mddle class households on metrcs related to occupatonal choce and earnngs. What we observe, therefore, s sgnfcant occupatonal change and a rch set of socal dynamcs wthn these rural communtes. Ths Large transfers of captal and sklls catapults some of most dsadvantaged women n the world nto labor actvtes whch had been the preserve of non-poor women n the communtes they share. Occupatonal change, whch reflects tself n hgher and less volatle earnngs streams, sets these women on a sustanable path out of poverty. On many margns the program brngs ther economc lves closer to the mddle classes n ther communtes. The paper thus jons the macro and mcro lteratures by pontng to some concrete evdence on how occupatonal change can be engneered n the rural settngs where the bulk of the world s poorest people lve. The TUP program s now beng ploted n many countres. 8 Ths scale-up s crtcal to as- 8 As of March 2013, ten dfferent plots were actve around the world, BRAC s plotng the program n both Afghanstan and Pakstan. Other plots are beng carred out n Andhra Pradesh, 5

6 certanng whether TUP-style programs can be used to fght poverty on a global scale. Fndngs from a plot n West Bengal are consstent wth ours: Banerjee et al. [2011] report mpacts on consumpton expendtures, earnngs and food securty whch are of smlar magntude to those we report. However, Morduch et al. [2012] fnd that a plot n Andhra Pradesh has weak mpacts on earnngs and consumpton. Ths s due, n part, to the fact that the Government of Andhra Pradesh smultaneously ntroduced a guaranteed-employment scheme that substantally ncreased earnngs and expendtures for wage laborers. Our theoretcal framework makes precse how such outsde optons n wage labor are obvously mportant determnants of whether TUP-style programs nduce occupatonal change towards basc entrepreneurshp, and we dscuss our emprcal fndngs relatve to these plot studes throughout. The paper s organzed as follows. Secton 2 develops a framework that hghlghts the man channels through whch the TUP program mpacts occupatonal choces. Secton 3 descrbes the program, our research desgn and data. Secton 4 presents our core results that closely map to the model developed on occupatonal choce, earnngs and labor productvty. Secton 5 documents the mpacts on other margns, heterogeneous mpacts, and benchmarks the mpacts vs-à-vs baselne dfferences n outcomes between elgbles and other wealth classes. Secton 6 conducts a cost beneft analyss of the program, comparng t to the counterfactual polcy of uncondtonal cash transfers. Secton 7 concludes. All proofs and robustness checks are n the Appendx. 2 Theoretcal Framework We model how the poor allocate ther tme between lesure and the two occupatons most common n our settng: wage employment and self-employment. The model makes precse how the program mpacts equlbrum occupatonal choces through asset transfers, that boost wealth endowments, and sklls tranng, that boost the returns to self-employment. 2.1 Set-Up Indvduals lve one perod and are endowed wth one unt of tme to allocate between wage employment (L ), self-employment (S ) and lesure (R ). Indvdual decdes whch occupatons to enter on the extensve margn, and how much labor to supply to each occupaton on the ntensve margn. We assume the tme devoted to occupatonal actvtes s non-negatve, and utlty s addtvely separable n consumpton (C ) and lesure: U = u(c ) + v(r ), where u(.) and v(.) are concave. Indvduals are prce-takers n the labor market recevng an return w per unt of tme, so earnngs from wage employment are wl. 9 Tme devoted to self-employment (S ) s combned Ethopa, Ghana, Hat, Honduras, Pakstan, Peru and Yemen by other organzatons. 9 We rule out the possblty that labor can be hred n, whch s an accurate emprcal descrpton for the elgble poor ndvduals we focus on. For expostonal ease, we also abstract from skll dfferences n the labor market and assume w s the same for all ndvduals. Ths reflects the fact that the study populaton s mostly unsklled and supples labor n two compettve wage labor markets: for agrcultural casual laborers and for domestc servants. The model predctons regardng the program mpacts on the treated poor are robust to ndvduals earnng 6

7 wth assets K to produce output Y, accordng to a producton functon Y = f(θ, K, S ), where θ measures ndvdual s sklls. In our study context, ths form of self-employment corresponds to engagng n basc entrepreneural actvtes, n whch labor s combned wth assets n the form of lvestock and related nputs such as feed and fodder. Output from such self-employment corresponds to mlk, meat and eggs produced for sale n local markets. The prce of lvestock assets s and the prce of output s p y. Indvduals are assumed to be prce-takers n nput and output markets. Earnngs from self-employment are then gven by revenues mnus costs, that s π = p y f(θ, K, S ) K. Indvduals have a resource endowment (I ) that can be used to purchase consumpton or assets. The budget constrant for consumpton s then wl + π + I = C. Fnally, we assume credt markets are such that ndvduals face the constrant K I, namely ndvduals cannot borrow to fnance assets purchases. Ths captures the fact that, although some credt s avalable n the study communtes, the poor only have access to small scale loans. Such mcroloans are nsuffcent to allow them to purchase lumpy lvestock assets. Assumng less severe forms of credt market mperfectons would yeld smlar results. Ths mnmalstc set-up s desgned to starkly llustrate the two man forces at play: wealth effects due to the asset transfers and substtuton effects due to tranng. To do so we abstract from features that could also affect occupatonal choce but are not drectly affected by the program. Most notably n ths context demand for wage labor exhbts strong seasonalty so that L s constraned by ths and the constrant mght be bndng at zero n some perods of the year. Modelng ths explctly would not affect the predcted effect of the program on occupatonal choce. Seasonalty, however, has mplcatons for the emprcal comparson of w and r as the observed wage s effectvely avalable only for part of the year whle ncome from self-employment (e.g. through the sale of lvestock produce) s more stable through the year. 2.2 Occupatonal Choces at Baselne The ndvdual s optmal occupatonal choces are a functon of two exogenous varables: () sklls, namely the returns to self-employment relatve to wage employment (r w); () resource endowments, I. The former determnes the choce between self-employment and wage employment, whereas the latter determnes labor force partcpaton and whether the assets constrant bnds when the ndvdual chooses to engage n self-employment. Substtutng C from the budget constrant yelds the Lagrangan: max L,S l = u(wl + π + I ) + v(1 L S ) + αl + βs + γ(i K ), (1) where α and β are the multplers assocated wth the non-negatvty constrants on tme devoted dfferent wages. Any predctons regardng the general equlbrum effect of the program on wages and the pecunary externaltes on non-treated ndvduals (that are examned n more detal n Bandera et al. 2013), would however depend on the skll dstrbuton n the two populatons and the degree of substtutablty between sklls. 7

8 to wage and self-employment and γ s the multpler assocated wth the assets constrant. All multplers must be non-negatve. To obtan closed form solutons we further assume that Y = θ mn(k, S ), so that n equlbrum K = S and π = p y θ S S = r S, where r = p y θ then measures the ndvdual specfc returns to self-employment. 10 choces n all parts of the parameter space are summarzed as follows. Proposton 1: Indvduals wll be n one of the four followng states: () out of the labor force f: r > w and I Ĩ(r ); or r < w and I Î(w ) () engaged solely n self-employment f: r > w and I [Ĩ (r, w), Ĩ(r )); () engaged n both occupatons f: r > w and I Ĩ (r, w); (v) engaged solely n wage employment f: r < w and I < Î(w ) Equlbrum baselne occupatonal Fgure 1A llustrates the occupatonal choce equlbrum f r w. The resource endowment (I ) s measured on the horzontal axs. The vertcal axs shows the wage and self-employment labor supply functons (L (.), S (.)). The proof of Proposton 1, provded n the Appendx, derves the resource endowment thresholds (I (r ), Ĩ (r ), Ĩ (r, w)), the wage and self-employment labor supply functons, and ther comparatve statcs wth respect to wages, returns to self-employment and resource endowments. Startng from the extreme rght hand sde of Fgure 1A, we see that ndvduals wth the hghest endowments optmally choose to stay out of the labor force (case (), where L = S = 0 for I Ĩ(r )). In the more central secton of Fgure 1A we have a group of ndvduals that are not asset constraned and so, because we are consderng the scenaro where r > w, engage solely n self-employment (case (), where L = 0, S > 0 for I [Ĩ (r ), Ĩ (r ))). For these ndvduals the number of hours devoted to self-employment s decreasng n I because of the ncome effect. The next group of ndvduals also engage solely n self-employment but are asset constraned and so lmted n scale by ther endowment, K = I (case (), where L = 0, S > 0 for I [Ĩ (r, w), Ĩ (r )))). Fnally, on the left hand sde of Fgure 1A we see that ndvduals wth the smallest resource endowments engage n both occupatons as the feasble scale of selfemployment actvtes s too small to afford the desred level of consumpton (case (), where L > 0, S > 0 for I Ĩ (r, w)). 11 For these ndvduals the number of hours devoted to selfemployment s ncreasng n I because an ncrease n I relaxes the bndng asset constrants thus 10 The assumpton of Leontef technology s made for expostonal convenence to keep track of ether the amount of self-employment S or the amount of captal K. Allowng some degree of substtutablty between these factor nputs would not alter the qualtatve nature of the trade-offs dentfed. 11 Indvduals specalze n one of the two occupatons when the asset constrant does not bnd because the margnal returns to both actvtes are lnear. The same result would be obtaned f the margnal return to one or both occupatons were ncreasng. Of course, there can be many other motves for dversfyng economc actvtes, such as spreadng rsk. We focus on asset constrants as beng an mportant drver of occupatonal choce as ths margn s drectly mpacted by the TUP program. Other factors drvng occupatonal dversfcaton such as rsk averson are not mpacted so are less relevant for understandng the changes over tme that we explot between treatment and control communtes. 8

9 allowng ndvduals to ncrease the scale of ther self-employment busness and hence devote more hours to t. Fgure 1B shows the pattern of equlbrum occupatonal choces and correspondng labor supples when r < w (n the proof we derve the relevant endowment threshold, Î (w )). In ths scenaro, no ndvdual specalzes n self-employment and so the assets constrant plays no role n determnng occupatonal choce. Fgure 1B shows that ndvduals wth suffcently hgh resource endowments optmally choose to stay out of the labor force (case (), where L = S = 0 for I Î(w )), whereas ndvduals wth smaller resource endowments all engage solely n wage employment (case (v), where L > 0, S = 0 for I Î(w )). Even ths hghly stylzed model delvers a rch set of predctons on occupaton choces at baselne. As s emprcally valdated below, at baselne we observe a wde range of occupatonal choce allocatons among the poor, rangng from those engaged solely n wage labor or solely self-employment, those engaged n both, and those out of the labor force. Fgures 1A and 1B also hghlght the comparatve statc propertes of the wage and self employment labor supply functons wth respect to wage rates, returns to ndvdual sklls, and resource endowments: these last two channels are the mechansms through whch the TUP program mpacts occupatonal choces. 2.3 The Impact of the Ultra-Poor Program on Occupatonal Choces The TUP program has two components. Frst, lvestock asset transfers, that boost resource endowments from I 0 at baselne, to I 1 = I 0 + A post-nterventon. A represents, n reduced form, the present value of the asset, factorng n the future opton value from sellng or rentng t out. Second, sklls tranng, that boost the returns to self-employment, θ, and hence r, from some baselne level, r 0, to a post-nterventon level r 1 > r As Fgure 1A makes clear, asset transfer mpacts the extensve and ntensve margns of occupatonal choce by causng ndvduals to cross the varous resource thresholds (I (r ), Ĩ (r ), Ĩ (r, w)). Fgure 2A shows the mpact of the program solely though the asset transfer channel (assumng r > w), where the baselne wage and self-employment labor supples are dashed lnes, and the postnterventon labor supples are sold lnes. The left sde of Fgure 2A shows that ndvduals wth the smallest resource endowments at baselne reman engaged n both wage and self-employment 12 Three ponts are of note. Frst, n a dynamc model, ndvduals mght want to retan the asset n the short run f, for nstance, sellng t quckly would damage ther relatonshp wth BRAC. Ths however would not preclude them from rentng t out or hrng labor to tend to t, whch would have the same effect on I and occupatonal choce. We later provde evdence that almost no ndvduals are observed rentng out these assets. Second, we note also that the asset transfer to women can affect I through other channels operatng wthn households, for nstance by affectng husbands labor supply. The predctons below are derved for the case n whch the net effect on I s postve, namely the asset transfer does not reduce the total non-labor ncome avalable to the woman. In lne wth ths, we emprcally document that the husbands labor supply does not decrease followng the mplementaton of the program. Thrd, the program transfers assets (lvestock) that are dentcal to those avalable locally at baselne. Gven that only a relatvely small number of households per communty are elgble, the program has lttle mpact on the prce of lvestock assets,. Hence skll changes nduced by the program translate nto changes n the self-employment outcome r = p y θ f the prce of lvestock produce, p y, does not fall by suffcently to offset any ncrease n θ. 9

10 actvtes although ther tme allocaton shfts towards self-employment. The mpact on the total hours they devote to work, L (.) + S (.), s ambguous. The mddle of Fgure 2A shows that among ndvduals that were ntally engaged solely n self-employment, labor hours mght rse or fall dependng on the ntal resource endowment of the ndvdual. Among those who were asset constraned at baselne, self-employment hours rse, all else equal. However, the framework makes clear that for those who were unconstraned at baselne, the asset transfer wll actually reduce hours of self-employment (and total hours devoted to labor market actvtes) because of the ncome effect. Fnally, the rght hand sde of Fgure 2A shows that asset transfers alone cause more ndvduals to stop workng. The sklls provson component of the program also shfts the wage and self-employment labor supply functons (L (.), S (.)). Fgure 2B shows the mpact of the program solely though the sklls provson channel (assumng r > w), where the baselne wage and self employment labor supples are dashed lnes, and the post-nterventon labor supples are sold lnes. Fgure 2B shows that among ndvduals ntally engaged n self-employment, self-employment hours do not change unless the ndvdual was unconstraned at baselne. The left hand sde of Fgure 2B shows that among those ndvduals wth the lowest resource endowments, sklls provson does not cause the hours devoted to self-employment to change, although ndvduals fnd t optmal to reduce wage labor hours because of the ncreased returns generated when they engage n self-employment. For these ndvduals total work hours unambguously fall. The combned effect of asset transfers and tranng can be thus summarzed as; Proposton 2: If r > w the TUP program weakly reduces wage employment hours for all ndvduals. The effect on self-employment hours s: () weakly negatve for all ndvduals f the effect of the asset transfer s suffcently large relatve to the effect of the sklls provson; () weakly postve for all ndvduals where the effect of the asset transfer s suffcently small relatve to the effect of sklls provson; () postve for resource-poor ndvduals and ambguous for resource-rch ndvduals n ntermedate cases. The framework thus makes precse that both program components, asset transfers and sklls provson, need to be carefully targeted n order to have ther desred mpact on self-employment actvtes. On the extensve margn, only sklls provson wll lkely nduce ndvduals wth hgher resource endowments to start engagng n self-employment, as shown on the rght hand sde of Fgure 2B. In contrast, asset transfers wll have the opposte mpact as shown on the rght hand sde of Fgure 2A. On the ntensve margn, asset transfers have the desred mpact to ncrease S (.) only among those ndvduals constraned at baselne; sklls provson has ths desred mpact on the ntensve margn but only among those ndvduals unconstraned at baselne. The combned effect of the asset transfer and sklls tranng on occupatonal choces then depends on ntal resource endowments and the relatve strength of the two effects shown n Fgures 2A and 2B. The proof s n the Appendx, where we compute the thresholds for cases ()-() as a functon 10

11 of the two program components. The mportance of accurately targetng the program to acheve ts desred mpacts s put sharply nto focus f we consder the remanng case where when r < w, shown n Fgure 2C. None of these ndvduals specalzes n self-employment at baselne. The provson of sklls does not alter ths as long as the post-nterventon returns to self-employment, r 1, reman less than w. Hence only suffcently effectve sklls provson programs wll have the desred mpact of shftng these wage laborers nto self-employment. Other thngs equal, asset transfers targeted towards these ndvduals wll generate an ncome effect that reduce hours worked and labor force partcpaton wthout affectng occupatonal choce. 13 The remander of the paper emprcally measures these combned mpacts of the TUP program on the extensve and ntensve margns of wage employment and self-employment. 3 The Ultra-Poor Program, Evaluaton Desgn and Data 3.1 The Program The TUP program offers elgble women a menu of possble busness actvtes, rangng from lvestock rearng to small retal operatons, coupled wth complementary and ntensve tranng n runnng ther chosen busness actvty. 14 All elgble women n our sample chose one of the sx avalable lvestock packages, whch contan dfferent combnaton of anmals (e.g. two cows or a cow and fve goats) smlarly valued at TK9500 (US$140). Gven that the medan household had no productve assets at baselne, ths represents an enormous change n the resource endowment of households, whch could fundamentally mpact occupatonal choce as s llustrated n Fgure 2A. BRAC encourages program recpents to commt to retan the asset for two years, after whch they can lqudate t. Gven that such commtments cannot be enforced, whether the lvestock asset s retaned or lqudated (partcularly after four years) s tself an outcome of nterest that ultmately determnes whether the program has the desred effect of permanently transformng the occupatonal choces and economc lves of the poor, or merely ncreases ther short run welfare As mentoned earler, Morduch et al. [2012] fnd weak mpacts of an TUP-style program mplemented by SKS n Andhra Pradesh. The model developed provdes one way n whch to reconcle these fndngs and help understand why the mpacts of otherwse smlarly mplemented programs mght dffer across economc envronments. Specfcally, f the envronment s characterzed by hgh labor wages so that r < w, then as shown n Fgure 2C, an TUP-style program wll have lmted mpact on occupatonal choces. Indeed, n the study settng for Morduch et al. [2012], the Government of Andhra Pradesh rolled out a guaranteed employment scheme that substantally ncreased wage labor earnngs n the study area. 14 The program also provdes a subsstence allowance to benefcary women for the frst 40 weeks after the asset transfer to compensate them for the short-run fall n earnngs due to occupatonal changes away from wage labor and nto self-employment. Ths allowance runs out ffteen months before the begnnng of our frst follow-up survey and s therefore not part of the earnngs measures reported below. 15 Morduch et al. [2012] report that n Andhra Pradesh, almost 90% of households opt for lvestock related asset transfers from the wde rangng menu offered, but that many mmedately lqudated the assets n order to pay off debts. The evdence from the TUP-style program n West Bengal n Banerjee et al. [2011] s nconclusve as to whether the lqudaton of transferred assets played an mportant role n ncome ncreases experenced by elgble households. 11

12 The tranng component comprses ntal classroom tranng at BRAC regonal headquarters, followed up by regular assstance: a lvestock specalst vsts benefcares every one to two months for the frst year of the program, and BRAC program offcers vst benefcares weekly for the frst two years. Tranng s meant to ncrease n the returns to self-employment, the mplcatons of whch are shown n Fgure 2B. In partcular, tranng s desgned to help women mantan lvestock health, maxmze lvestock productvty through best practces relatng to feed and water, learn how to best nsemnate anmals to produce offsprng and mlk, rear calves, and to brng produce to market. Relatve to many sklls provson programs, ths tranng s ntensve and suffcently long-lastng to enable women to learn how to successfully rear lvestock through ther calvng cycle and across seasonal condtons. Elgble women are selected by BRAC offcers from the lst of poor households compled by communty members through a partcpatory wealth rankng. 16 Communtes are self-contaned wthn-vllage clusters of 84 households on average. Our sample contans 1409 communtes, where we survey all elgble and poor households, and a 10% random sample of households from hgher wealth classes, whch we later use to benchmark the sze of the program s mpact. 3.2 Evaluaton and Data To evaluate the effect of the TUP program on the elgble poor women, we estmate the followng dfference n dfference specfcaton, y dt = α + 2 t=1 β tw t T d + γt + 2 t=1 δ tw t + η d + ɛ dt, (2) where y dt s the outcome of nterest for ndvdual n subdstrct d at tme t, where the tme perods refer to the 2007 baselne (t = 0), 2009 mdlne (t = 1) and 2011 endlne (t = 2) survey waves. W t are ndcators for survey waves. All monetary values are deflated to 2007 prces usng the Bangladesh Bank s rural CPI estmates. To evaluate the program s mpact on occupatonal choce, we collect detaled nformaton on all ncome generatng actvtes of each household member durng the prevous year. For each economc actvty, we ask whether the ndvdual was self-employed or hred by a thrd party, the number of hours worked per day, the number of days worked durng the 16 To dentfy the communtes where the program operates, BRAC central offce frst selects the most vulnerable dstrcts n rural Bangladesh based on the food securty maps of the World Food Program; and then BRAC employees from local branch offces wthn those dstrcts select the poorest communtes n ther branch. Communtes are then asked to rank all households nto fve wealth bns. Evdence from a randomzed evaluaton of dfferent targetng methods, Alatas et al. [2011], shows that, compared to proxy means tests, communty apprasal methods resulted n hgher satsfacton and greater legtmacy. Ther dstnctve characterstc was that communty methods put a larger weght on earnngs potental. To dentfy elgbles among those ranked poor by ther communtes BRAC uses three bndng excluson crtera: () already borrowng from an NGO provdng mcrofnance, () recevng assstance from government ant-poverty programs, () havng no adult women present. Furthermore, to be selected a household has to satsfy three of the followng fve ncluson crtera: () total land owned ncludng homestead land does not exceed 10 decmals; () there s no adult male ncome earner n the household; () adult women n the household work outsde the homestead; (v) school-aged chldren have to work; and (v) the household has no productve assets. 12

13 prevous year, wage rates, earnngs, and whether earnngs vared throughout the year. From ths data we buld a complete pcture of each ndvdual s occupatonal choce, labor supply, earnngs, and earnngs volatlty by economc actvty, where all actvtes can be classfed as beng a form of wage labor (L ) or self-employment (S ). We randomly select one or two sub-dstrcts (upazlas) from each dstrct where the program operates. Wthn each of the 20 subdstrcts we then randomly assgn one BRAC branch offce to treatment (to receve the program n 2007) and one to control (to receve the program n 2011). Each branch offce s responsble for the provson of BRAC servces to communtes n ts area, so T d = 1 f ndvdual lves n a treated communty and 0 otherwse. η d are subdstrct fxed effects and are ncluded to mprove effcency because the randomzaton s stratfed by subdstrct [Bruhn and McKenze 2009]. 17 For robustness we also allow for trends to dffer by sub-dstrct and all fndngs are quanttatvely and qualtatvely unchanged. The program mpact, β t, s dentfed by comparng changes n ndvdual outcomes among elgbles before and after the program n treatment communtes, to changes among elgbles n control communtes wthn the same subdstrct. We thus control for all tme-varyng factors common to ndvduals n treatment and control communtes, and for all tme-nvarant heterogenety wthn subdstrct. β t dentfes the ntent to treat parameter, whch s close to the average treatment on the treated effect as 87% of selected elgbles take-up the offer to receve the program. Standard errors are clustered at the communty level throughout to account for the fact that outcomes are lkely to be correlated wthn communty. Results are generally robust to clusterng by BRAC branch offce area but ths s less approprate than communty level clusterng because the geographcal coverage of a sngle offce reflects BRAC s capacty rather than any underlyng feature of the economc envronment common to all communtes n the area. β t dentfes the causal effect of the program under the twn assumptons of parallel trends n the outcomes of nterest wthn subdstrct, and of no contamnaton between treatment and control communtes. In ths regard, three features of the research desgn are of note. Frst, elgble women are dentfed n the same way n both treatment and control communtes usng the combnaton of partcpatory wealth rankng and BRAC elgblty crtera descrbed above. As BRAC already operates n all communtes n the evaluaton sample, the partcpatory wealth rankng exercse s justfed as part of BRAC s regular actvtes. BRAC had no other programs targeted to elgble households n treatment or control locatons, nor s partcpaton to the TUP program condtonal on jonng other BRAC actvtes. Second, to ensure our estmates are not contamnated by antcpaton effects, elgble women are nformed of ther elgblty status only when the program 17 The average subdstrct has an area of approxmately 250 square klometers (97 square mles) and consttutes the lowest level of regonal dvson wthn Bangladesh wth admnstratve power and elected members. For each dstrct located n the poorer Northern regon we randomly select two subdstrcts, and for each dstrct located n the rest of the country we randomly select one subdstrct, restrctng the draw to subdstrcts contanng more than one BRAC branch offce. For the one dstrct (Kshoreganj) that dd not have subdstrcts wth more than one BRAC branch offces, we randomly choose on treatment and one control branch wthout stratfyng by subdstrct. 13

14 starts operatng n ther communty, that s after the baselne survey n treatment communtes and after the endlne survey n control communtes. Thrd, usng BRAC branches rather than communtes as the unt of randomzaton mnmzes the rsk of contamnaton, both because communtes wthn the same branch offce are geographcally closer to each other (n contrast, the average dstance between branches s 12km), and because ths mnmzes the rsk that program offcers, who are based at a specfc branch offce, do not comply wth the randomzaton. At baselne, our evaluaton sample contans 7953 elgble women n 1409 communtes n 40 BRAC branches, and an addtonal 19,012 households from all other wealth classes. Over the four years from baselne to endlne, 13% of elgble households attrt. 18 Table A1 estmates the probablty of not attrtng as a functon of treatment status and baselne occupatonal choce, the man outcome of nterest. Three fndngs are of note. Frst, attrton rates are the same n treatment and control communtes. As shown n Column 1, the coeffcent on the treatment status ndcator s close to zero and precsely estmated. Second, attrton s correlated to occupatonal choce at baselne, n partcular women engaged n self-employment actvtes (ether exclusvely or n conjuncton wth wage labor) are 6pp more lkely to be surveyed n all three waves compared to women who were out of the labor force at baselne. Women engaged solely n wage labor are equally lkely to attrt. Thrd, and most mportant, there s no dfferental attrton by baselne occupatonal choces between treatment an control communtes. The coeffcents of the nteracton terms between treatment status and occupatonal choce are all precsely estmated and close to zero. Ths suggests the program tself does not affect the probablty that respondents drop out of the sample (whch s most lkely due to mgraton). As some of the models below are estmated n frst dfferences, to ease comparablty we restrct the sample to households that appear n all three waves throughout. The workng sample thus contans 6732 elgble benefcares and 16,297 households from other wealth classes. 4 Man Results 4.1 Economc Lves at Baselne Table 1 presents descrptve evdence on the characterstcs of elgble women and ther households and how they compare to other wealth classes at baselne. Ths shows the elgble poor to be severely dsadvantaged relatve even to the near poor, never mnd those ranked by communtes as mddle or upper class. Panel A shows that elgble women are more lkely to be sole earners (38% are) n ther households, less lkely to be lterate (only 7% are) and to own lvestock (only 48% do). The asset holdngs of elgble households, whether n lvestock or land, are neglgble 18 Ths attrton rate s comparable to those n other evaluatons of TUP-style programs: Banerjee et al. [2011] fnd that of 978 households surveyed at baselne n West Bengal, 17% attrt over an 18-month perod (predomnantly due to refusal to st the endlne survey). Morduch et al. [2012] fnd that of 1064 households surveyed at baselne n Andhra Pradesh, 12% attrt over a three year perod. 14

15 and ther per capta expendture les below that of near poor, mddle and upper class households. Based on all these metrcs, the TUP program does appear to successfully target the very poorest women (and ther households) n these rural communtes. 19 Expendture levels are low, usng PPP exchange rates (29TK=1US$), the average benefcary lves on 93c per day, and 80% of the elgble women lve below the global poverty lne of US$1.25 a day. Table 1 also llustrates how poor these communtes are and that the wealth rankng s a relatve measure of poverty. Even among those households classfed as upper class, the majorty of prmary women n the household are llterate and one thrd have expendtures below the global poverty lne. Panel B focuses on the occupatonal choces of the prmary women n each household, by wealth class. To map to the occupatonal choce model, we group all actvtes where the ndvdual s employed by another party as wage employment and actvtes where the ndvdual runs her own busness as self-employment. Wthn wage employment, the two most frequent occupatons are casual agrcultural laborer and domestc servant. 20 Wthn self-employment occupatons, most ndvduals are engaged n lvestock rearng and land cultvaton. 21 To measure the total hours devoted to each occupaton durng the last year we multply hours worked n a typcal day by the number of days worked and sum wthn each employment type. Elgble women engage n 2.3 ncome generatng actvtes over the year pror to the baselne survey. We use annual data as several of these actvtes, especally casual labor n agrculture, exhbt strong seasonalty. Lookng across the Columns of Panel B of Table 1 t s clear that n these communtes wage employment goes hand n hand wth poverty. Mddle and upper class women do not labor for others but rather devote effort to self-employment. 52% of elgble women work for a wage, whle the share falls to 35%, 10%, and 2% for near poor, mddle and upper class women, respectvely. Ths also mples that elgble poor, and to a lesser extent near poor, women are often engaged both n selfemployment and wage employment (26% and 21% report both actvtes) whle mddle and upper class women specalze n self-employment. The data are thus consstent wth the wealth orderng across occupatonal choces mpled by the model. Ths holds both across classes, as descrbed above, and wthn elgble women. Indeed, proxyng resource endowments by household wealth 19 Ths s n contrast to many poverty-allevaton government polces and some mcrofnance programs that have been found to mstarget the poorest households or be unable to retan them [Morduch 1998]. In our context, the fact that at baselne the average targeted poor own no lvestock assets, partcularly of the hgh value varety transferred by the program, suggests they also lack sklls n how to rear lvestock. Our evaluaton sheds lght on whether such sklls can be mparted to these ndvduals. 20 No other occupatons apart from agrcultural day laborer or domestc servant account for more than 5% of respondents. 38% of elgble women work solely as agrcultural wage laborers, 43% work solely as domestc servants, and 10% do both. Of those workng for daly wage spot contracts, 87% do so n agrculture. Among domestc servants, two factors pont to these actvtes as not beng stable forms of employment: () the medan number of days worked per year n domestc servce s 180, that s well below full employment; () 86% of elgble women whose man occupaton s domestc servce (defned as that accountng for most hours worked), report not havng stable earnngs from that occupaton, rather they report ther earnngs varyng by month. 21 Of those elgble women specalzed n self-employment actvtes at baselne, 82% report engagng n some anmal husbandry, wth 8% beng talors and the remanng 10% splt across other actvtes. Among those engaged n anmal husbandry at baselne, 13% have one or more cows, 19% have one or more goats, and 81% one or more chckens so that nearly all lvestock related self-employment actvtes at baselne are small-scale poultry rearng. 15

16 (excludng land and lvestock that are mechancally correlated wth self-employment), we fnd that those solely engaged n wage employment own TK1319 of assets, those engaged n both wage and self employment actvtes own TK2995, and ndvduals solely engaged n self-employment own TK4050 worth of assets. All dfferences are precsely estmated at conventonal levels. Wage employment s less regular and exhbts more earnng seasonalty than self-employment. Among elgble women, the average wage employment actvty s undertaken for 77 days per year and 7.4 hours per day, whle the average self-employment actvty s undertaken for 145 days and 1.96 hours per day. Ths naturally leads elgble women to have seasonal earnngs: ndeed two thrds of ncome generatng actvtes exhbt earnngs seasonalty. It s well documented that landless agrcultural laborers, such as the elgble women here, are exposed to seasonal hunger and famne - monga - as t s referred to Bangladesh [Bryan et al. 2011, Khandker and Mahmud 2012]. Relatve to other women n these communtes, targeted poor women are far more relant on wage employment as opposed to self-employment, and thus are more exposed to seasonalty. Table 2 compares elgble women resdent n treatment and control communtes. For each varable we report both the dfference (Column 3) and the normalzed dfference of means (Column 4), computed as the dfference n means dvded by the square root of the sum of the varances. Ths s a scale-free measure and, contrary to the p-value for the null hypothess of equal means, does not ncrease mechancally wth sample sze. The results show that elgble women n treated and control communtes are smlar on observables, as expected wth communtes beng randomly assgned to treatment and control status. Column 4 shows that all normalzed dfferences are smaller than 1/6th of the combned sample varaton, suggestng that the randomzaton yelds a balanced sample, on average. Imbens and Wooldrdge [2009] suggest normalzed dfferences below.25 mply lnear regresson methods are unlkely to be senstve to specfcaton changes. The one dfference of note s that the share of women who are sole earners and hours devoted to wage employment s hgher n control communtes. Whle these dfferences are precsely estmated, they are small relatve to the sample varaton as shown by the normalzed dfferences. In ths regard, t s mportant to note that the dfference n dfference specfcaton descrbed n Secton 3.4 above fully accounts for dfferences n levels between treatment and control communtes. To ensure that our estmated program effects are not contamnated by the fact that the occupatonal choce of sole earners follows a dfferent trend, the Appendx reports estmates of (2) for all our baselne outcomes, augmented by the nteracton of survey waves wth a dummy varable for the elgble woman beng a sole earner. To probe the robustness of our fndngs aganst the concern that elgble benefcares n control communtes mght be an mperfect counterfactual for the poor n treatment communtes we repeat the analyss usng the entre sample of poor women n control communtes, namely ncludng those who the communty ranked as poor but BRAC offcals deemed nelgble for the TUP program, as a control group. 16

17 4.2 Occupatonal Choce, Earnngs and Labor Productvty The TUP program s desgned to promote occupatonal change at the bottom of the wealth dstrbuton. Ths s what dstngushes t from most programs that focus on mprovng sklls or access to captal for poor ndvduals who reman n a gven occupaton. It s n ths sense that t can be descrbed as an attempt to transform the economc lves of the poor. The core fndngs on whether ths attempt was successful are contaned n Fgure 3 and Table 3. Fgure 3 shows the dramatc change n the occupatonal structure of the elgble poor n treated communtes relatve to ther counterparts n control communtes. At baselne, the dstrbuton across actvtes (wage employment only, both wage and self-employment, self-employment only, out of the labor force) s smlar n treatment and control communtes. Two years later, all the elgble women n treated communtes were n the labor force, and almost all of them were engaged n self-employment. In sharp contrast, women n control communtes experenced no notceable change relatve to baselne. Examnng occupatonal choces four years after the program s ntaton, reveals that the sgnfcant changes n the occupatonal choces of the targeted poor acheved two years after program mplementaton, were mantaned four years after mplementaton. contrast, the dstrbuton across occupatons n control communtes s essentally the same across the four years suggestng that the natural pace of occupatonal change s panfully slow n the rural communtes we study. 22 Table 3 reports the ITT mpact estmates of the TUP program from specfcaton (2), and shows the parameters of nterest, β 1 and β 2, measurng the ITT mpacts two and four years after baselne respectvely. The foot of the table shows the p-value on the null that β 1 = β 2, so we can assess the dynamc responses of ndvduals and households along each outcome margn. As descrbed n Secton 3.1, households are not oblged to retan the asset two years nto the program, and the ntensve tranng provded also termnates by two years. Hence the comparson of the two and year four program mpacts s ndcatve of whether the program s self-sustanng and nduces permanent changes n occupatonal choce, or whether ndvduals begn to revert back to ther economc lves at baselne once the perod of program delvery from BRAC ends. To benchmark the magntude of each mpact, the foot of the table also shows the mean of the outcome varable at baselne n treated communtes. The workng sample contans 6732 elgble women, each surveyed three tmes over four years, for a total of 20,196 women-year observatons. We frst present evdence on the program ITT mpacts on the extensve and ntensve margns of 22 Ths s n sharp contrast to the settng n Morduch et al. [2012] who fnd no mpacts of an TUP-style program n Andhra Pradesh. They hghlght that key to understandng ths dvergence n results, s that n Andhra Pradesh, wage labor opportuntes on government programs were dramatcally mprovng over the study perod, and the rural economy was characterzed by a growng movement of labor away from self-employment opportuntes and nto government guaranteed wage labor schemes. As such, the ntroducton of an TUP-style program was very much fghtng aganst such trends, and any gans caused by the program were offset by lost wage labor market opportuntes. As dscussed earler and n relaton to Fgure 2C, ths s a very dfferent scenaro to what we observe n rural Bangladesh where wage labor opportuntes reman uncertan and nsecure. In 17

18 occupatonal choce as emphaszed n the model (Table 3, Columns 1-5), and then on earnngs and ther seasonalty (Columns 6-9). Appendx Tables A5 and A6 present further robustness checks on these man results on occupatonal choce. On the extensve margn of occupatonal choce, Columns 1-3 confrm the transformaton shown n Fgure 3. After four years, the share of women specalzed n wage employment drops by 17pp, 65% of the baselne mean. Over the same perod, the share of women specalzed n self-employment ncreases by 15pp and those engaged n both occupatons by 8pp. These changes on the extensve margn of occupatonal choce correspond to 50% and 31% ncreases from ther baselne values, respectvely. As n Fgure 3, the effect on the extensve margn s largely stable movng from two to four years after the program s ntaton. Ths dramatc change n occupatonal choce on the extensve margn s accompaned by a correspondng change n hours devoted to the two occupaton categores, as shown n Columns 4 and 5. After four years, elgble women work 170 fewer hours n wage employment (a 26% reducton relatve to baselne) and 388 more hours n self-employment (a 92% ncrease relatve to baselne). 23 The comparson of the two and four year effects reveals an nterestng pattern: the reducton of wage employment hours s twce as large after four years than after two (p-value.001), suggestng the long run elastcty of the labor supply of wage employment wth respect to asset transfers and sklls provson, s hgher than the short run elastcty. One nterpretaton s that elgble women hold onto some of ther wage employment actvtes untl ther lvestock busnesses are well-establshed. In contrast, the ncrease n self-employment hours s larger after two years than after four (p-value.00), possbly because between two and four years targeted women became more effcent n producton as they gan experence n lvestock rearng. 24 Table A2 shows that the program has mnmal spllovers on the occupatonal choces of other household members. We fnd small ncreases n hours devoted to self-employment (presumably helpng out the man benefcary) but no effect on wage employment, whch ndcates, reassurngly that the program does not reduce wage earnngs of other household members A natural concern s that respondents falsely report that they devote tme to self-employment only to please BRAC s enumerators. Two consderatons allay ths concern. Frst, the magntude of the ncrease n self-employment hours (just over an hour a day) s n lne wth BRAC s estmate of the tme t takes to tend to one cow. Snce respondents are not told ths and are unlkely to fnd out unless they do t themselves, the fact that the magntudes match reassures us that tme use responses are truthful. The fndng, reported n the next secton, that they stll own a (lve) cow after four years also ndcates that they must be devotng some tme to t. Second, the TUP program dd not requre them to reduce hours n wage labor and gven that the average benefcares reported workng an average of three hours per day at baselne there s no reason to thnk they would falsely report a drop n wage labor hours. 24 These results on the extensve and ntensve margns of occupatonal choce are robust to beng estmated usng non-lnear models. Usng a probt specfcaton for the outcomes n Columns 1 to 3 yelds very smlar two and four year mpacts, wth all coeffcents of nterest beng sgnfcant at the 1% sgnfcance level. When the hours equatons n Columns 4 and 5 are estmated usng a Tobt model, the qualtatve results are unchanged wth all coeffcents of nterest beng sgnfcant at the 1% sgnfcance level, and quanttatvely all the pont estmates are larger n absolute value than the OLS estmates as expected. The total ncrease n annual labor supply s almost dentcal: 216 hours, so that the fgures used for the later cost-beneft analyss are robust to these alternatve regresson models. 25 Ths s not surprsng, as Foster and Rosenzweg [1996] document for rural Inda, rural labor markets tend to 18

19 In both years the ncrease n self-employment hours s larger than the fall n wage employment hours, so that total labor supply, L (.) + S (.), ncreases throughout. After four years targeted women work an addtonal 218 hours, a 19% ncrease relatve to baselne. As Fgures 2A and 2B make clear, there s no ex ante reason for aggregate labor supply to ncrease. The results n Table 3 mply that the postve mpact on self-employment hours that occur through the two channels of the program: () the asset transfer component for households ntally captal constraned at baselne (Fgure 2A, regon (a)); () the sklls provson component for households that are unconstraned at baselne (Fgure 2B, regon (b)), more than offset any wealth effects of lvestock asset transfers, despte the transferred lvestock beng around ten tmes the value of owned lvestock for elgble households at baselne (or more than double the value of per capta expendtures). A key advantage of engagng n lvestock-based forms of self-employment s that such occupatonal actvtes are not seasonal. Startng such busnesses may therefore help the poor to spread labor effort more evenly across the year and to become less relant on hghly seasonal wage employment n agrcultural markets, or more uncertan ncome streams from workng as a domestc servant. Columns 6 and 7 n Table 3 provde drect evdence on ths by estmatng the ITT mpact of the TUP program on the share of occupatonal actvtes held regularly, defned as those performed at least 300 days per year, and on the share of actvtes wth seasonal earnngs, defned as the fracton of occupatonal actvtes engaged n from whch ncome fluctuates over the year. Column 6 shows that the share of occupatonal actvtes held regularly ncreases by 17pp after four years, a 35% ncrease relatve to baselne. Column 7 shows that after four years the targeted poor have reduced relance on busness actvtes wth seasonal earnngs by 8.2pp whch represents a 12% reducton relatve to baselne. 26 The fnal two Columns of Table 3 provde evdence on the overall mpact on earnngs caused through the occupatonal choce changes nduced by the TUP program. Total annual earnngs are computed as the sum of earnngs from all wage employment and self-employment actvtes, where the former equals all monetary and n-knd wage payments and the latter equals revenues mnus costs. Column 8 shows that total annual earnngs of treated poor women rose by TK1548 after two years, and by TK1754 four years after the program s ntaton. These represent earnngs ncreases of 34% and 38% respectvely relatve to baselne. Column 9 shows how labor productvty - measured by hourly earnngs - ncreases over tme. Two years after the program s ntaton, earnngs per hour are not sgnfcantly dfferent for elgbles from baselne. Hence the ncreased earnngs after two years can largely be explaned through the arrval of new lvestock busness opportuntes allowng elgble poor women to work sgnfcantly more hours, as shown n Column be hghly segmented by gender so that any wage mpacts for female occupatons do not affect wages for occupatons engaged n predomnantly by men. 26 Bryan et al. [2011] report the mpacts of an alternatve nterventon to help households counter seasonal fluctuatons n agrcultural labor demand earnngs n rural Bangladesh: the provson of cash ncentves to outmgrate. Usng an RCT desgn, they fnd ths nduces 22% of households to send a seasonal mgrant, and that treated households contnue to re-mgrate at hgher rates even after the fnancal ncentve s removed. 19

20 5. However, after four years, earnngs per hour are sgnfcantly hgher, rsng by 15% over ther baselne level. Hence ths longer term earnngs ncrease s a combned mpact of changes on the ntensve margn n hours devoted to more productve self-employment actvtes (r > w as consdered n Fgure 1A) and the fact that productvty n self-employment actvtes has also rsen (r 1 > r 0 ). 27 To dsentangle the effect of occupatonal change from the ncrease n productvty wthn selfemployment actvtes, we estmate (2) separately for ndvduals specalzed n wage employment and self-employment at baselne, whch are also balanced between treatment and control communtes (Table A3). The results n Table A4 ndcate that the ncrease n productvty occurs entrely wthn occupaton. Women who shft from wage labor to self-employment mantan the same hourly earnngs after four years (Table A4, Panel A, Column 9). For these women total earnngs rse because they work more hours as they shft from wage employment, that s only avalable for part of the year, to self-employment that yelds the same hourly returns but s avalable throughout the year. In contrast, women who were already specalzed n self-employment experence a 50% ncrease n hourly earnngs (Table A4, Panel B, Column 9). 5 Extended Results 5.1 Asset Accumulaton Women elgble for the TUP program can choose the form the asset transfer takes from a wderangng menu of self-employment actvtes, ncludng dfferent combnatons of lvestock, vegetable cultvaton, small-scale retal and crafts lke basket weavng. Among those that took-up the offer, over 97% of benefcares chose lvestock rearng. Of these 50% chose cows, 38% a cow-poultry or cow-goat combnaton,and 9% chose a combnaton not nvolvng cows. Dfferent packages were smlarly valued at TK9500. Table 4 frst documents the program s mpacts on household s lvestock holdngs. The second half of the table examnes the mpact on land holdngs, that allows household to further dversty away from earnngs from uncertan wage labor markets, and are an ntrnsc proxy for socal status n these communtes. Table 4 ndcates that after two and four years households own more lvestock despte beng free to lqudate these assets. For cows (the most common transferred asset and one where ownershp amongst the targeted poor was neglgble at baselne) households have, on average, one more cow after both two and four years, whch corresponds to the average number of cows transferred by the program. The number of poultry and goats also ncreases n lne wth average program transfers 27 These fndngs on total earnngs, combned wth those on labor productvty all pont n the drecton of lvestock rearng beng a proftable actvty n ths settng for treated households. Ths s somewhat n contrast to recent results n Anagol et al. [2012] documentng how the ownershp of lvestock generate relatvely low returns for households n rural Inda. 20

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