metadata: authors: 0: author: Gine, Xavier
1: author: Townsend, Robert M.
count: Thailand
docna: 0: docna: Evaluation of financial liberalization : a
            general equilibrium model with constrained occupation choice
docty: Policy Research Working Paper
subtopic: Fiscal & Monetary Policy,Economic Theory & Research,Environmental Economics & Policies,Banks & Banking Reform,Economic Conditions and Volatility,International Terrorism & Counterterrorism
teratopic: Finance and Financial Sector Development
abstracts: cdata!: The objective of this paper is to assess
            both the aggregate growth effects and the distributional
            consequences of financial liberalization as observed in
            Thailand from 1976 to 1996. A general equilibrium
            occupational choice model with two sectors, one without
            intermediation, and the other with borrowing and lending, is
            taken to Thai data. Key parameters of the production
            technology and the distribution of entrepreneurial talent
            are estimated by maximizing the likelihood of transition
            into business given initial wealth as observed in two
            distinct datasets. Other parameters of the model are
            calibrated to try to match the two decades of growth as well
            as observed changes in inequality, labor share, savings, and
            the number of entrepreneurs. Without an expansion in the
            size of the intermediated sector, Thailand would have
            evolved very differently, namely, with a drastically lower
            growth rate, high residual subsistence sector,
            non-increasing wages, but lower inequality. The financial
            liberalization brings welfare gains and losses to different
            subsets of the population. Primary winners are talented
            would-be entrepreneurs who lack credit and cannot otherwise
            go into business (or invest little capital). Mean gains for
            these winners range from 17 to 34 percent of observed
            overall average household income. But liberalization also
            induces greater demand by entrepreneurs for workers
            resulting in increases in the wage and lower profits of
            relatively rich entrepreneurs of the same order of magnitude
            as the observed overall average income of firm owners.
            Foreign capital has no significant impact on growth or the
            distribution of observed income.
geo_reg_key: 517198,517191
repnme: repnme: Evaluation of financial liberalization : a
            general equilibrium model with constrained occupation choice
display_title: Evaluation of financial
            liberalization : a general equilibrium model with
            constrained occupation choice
listing_relative_url: /research/2003/04/2243833/evaluation-financial-liberalization-general-equilibrium-model-constrained-occupation-choice
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Finance and Financial Sector Development
origu: Development Research Group (DECRG)
no_of_pages: 52
model_extractions: POLICY RESEARCH WORKING PAPER 3014

# Abstract

The objective of this paper is to assess both the aggregate growth effects and the distributional consequences of financial liberalization as observed in Thailand from 1976 to 1996. A general equilibrium occupational choice model with two sectors, one without intermediation, and the other with borrowing and lending, is taken to Thai data. Key parameters of the production technology and the distribution of entrepreneurial talent are estimated by maximizing the likelihood of transition into business given initial wealth as observed in two distinct datasets. Other parameters of the model are calibrated to try to match the two decades of growth as well as observed changes in inequality, labor share, savings, and the number of entrepreneurs. Without an expansion in the size of the intermediated sector, Thailand would have evolved very differently, namely, with a drastically lower growth rate, high residual subsistence sector, nonincreasing wages, but lower inequality. The financial liberalization brings welfare gains and losses to different subsets of the population. Primary winners are talented would-be entrepreneurs who lack credit and cannot otherwise go into business (or invest little capital). Mean gains for these winners range from 17 to 34 percent of observed overall average household income. But liberalization also induces greater demand by entrepreneurs for workers resulting in increases **in** the wage and lower profits of relatively rich entrepreneurs of the same order of magnitude as the observed overall average income of firm owners. Foreign capital has no significant impact on growth or the distribution of observed income.

> This paper-a product of Finance, Development Research Group-is part of a larger effort **in** the group to understand

> financial liberalization and its impacton growth. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-300, telephone 202-473-1001, fax 202-522-1155,

> email address klabrie@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. Xavier Ginm may be contacted at xgine@worldbank.org. April 2003. (48 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about developmenzt issues. An objective of the series is to get the findings out quickly, eveni if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. Ihey do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Research Advisory Staff
Evaluation of Financial Liberalization:<sup>Ageneralequilibrium</sup> model with constrained occupation choice Xavier Gine Robert M. Townsend World Bank University of Chicago

**Keywords:** Economic Development, Income Distribution,<sup>CreditConstraints,Financial Liberalization,</sup> Maximum Likelihood Estimation.

**JEL Classifications:** E2, **G1,** 01, 04.

# 1 Introduction

The objective of the paper is to assess the aggregate,<sup>growtheffectsandthe distributionalconsequences</sup> of financial liberalization and globalization.<sup>There hasbeensomedebateintheliteratureabout the</sup> benefits and potential costs of financial sector<sup>reforms.The microcredit movementhaspushed fortiered</sup>

*We would like to thank Guillermo Moloche<sup>and AnkurVorafor excellentresearchassistance andthereferees fortheir</sup> both detailed and expository comments. Gine<sup>gratefullyacknowledges financialsupportfrom theBankof Spain.Townsend</sup> would like to thank NSF and NIH for their<sup>financialsupportWeareespeciallyindebtedtoSombatSakuntasathienfor</sup> collaboration and for making possible the data<sup>collectionin Thailand.Comments fromAbhijit Banerjee, RicardoCaballero,</sup> Bengt Holmstr6m and participants at the<sup>MIT macrolunchgrouparegratefullyacknowledged.</sup>
lending, or linkages from formal financial intermediaries to small joint liability or community groups. But a major concern with general structural reforms is the idea that benefits will not trickle down, that the poor will be neglected, and that inequality will increase. Similarly, globalization and capital inflows are often claimed to be associated with growth although the effect of growth on poverty is still a much debated topic' Needless to say, we do not study here all possible forms of liberalization. Rather, we focus on reforms that increase outreach on the extensive domestic margin, for example, less restricted licensing requirements for financial institutions (both foreign and domestic), the reduction of excess capitalization requirements, and enhanced ability to open new branches. We capture these reforms, albeit crudely in the model, thinking of them as domestic reforms that allow deposit mobilization and access to credit at market clearing interest rates for a segment of the population that otherwise would have neither formal sector savings nor credit.

We take this methodology to Thailand from 1976 to 19962. Thailand is a good country to study for a number of reasons. First, Thailand is often portrayed as an example of an emerging market, with high income growth and increasing inequality. The GDP growth from 1981-1995 was 8 percent per year, and the Gini measure of inequality increased from .42 in 1976 to .50 in 1996. Second, Jeong (1999) documents in his study of the sources of growth in Thailand,1976-1996, that access to intermediation narrowly defined accounts for 20 percent of the growth in per capita income while occupation shifts alone account for 21 percent. While the fraction of non-farm entrepreneurs does not grow much, the income differential of non-farm entrepreneurs to wage earners is large and thus small shifts in the population create relatively large income changes. In fact, the occupational shift may have been financed by credit. Also related, Jeong finds that 32 percent of changes in inequality between 1976-1996 are due to changes in income differentials across occupations. There is evidence that Thailand had a relatively restrictive credit system but also liberalized during this period. Officially, interest rates ceilings and lending restrictions were progressively removed starting in 19893. The data do seem to suggest a rather substantial increase in the number of households with access to formal intermediaries although this expansion (which we call a liberalization) begins two years earlier, in 1987. Finally, Thailand experienced a relatively large increase in capital inflows from the late 1980's to the mid 1990's.

Our starting point is a relatively simple but general equilibrium model with credit constraints. Specifically, we pick from the literature and extend the Lloyd-Ellis and Bernhard (2000) model (LEB for short) that features wealth-constrained entry into business and wealth-constrained investment for entrepreneurs. For our purposes, this model has several advantages. It allows for ex ante variation in ability. It allows 1 See for example Gallup, Radelet and Warner (1998) and Dollar and Kraay (2002) for evidence that growth helps reduce poverty and the concerns of Ravallion (2001, 2002) about their approach. 2 We focus on this 20 year transition period, not on the financial crisis of 1997. Our own view is that we need to understand the growth that preceded the crisis before we can analyze the crisis itself. 30kuda and Mieno (1999) recount from one perspective the history of financial liberalization in Thailand, that is, with an emphasize on interest rates, foreign exchange liberalization, and scope of operations. They argue that in general there was deregulation and an increase in overal competition, especially from the standpoint of commercial banks. It seems that commercial bank time deposit rates were partially deregulated by June 1989 and on-lending rates by 1993, hence with a lag. They also provide evidence that suggests that the spread between commercial bank deposit rates and on-lending prime rates narrowed from 1986-1990, though it increased somewhat thereafter, to June 1995. Likewise there was apparently greater competition from finance companies, and the gap between deposit and share rates narrowed across these two types of institutions, as did on-lending rates. Thai domestic rates in general approached from above international, LIBOR rates. Most of the regulations concerning scope of operations, including new licenses, the holdmg of equity, and the opening of off-shore international bank facihties are dated March 1992 at the earliest. See also Klinhowhan (1999) for further details.

2
for a variety of occupational<sup>structures,i.e.firmsof varioussizes,e.g.,withandwithoutlabor,andat</sup> various levels of capitalization.<sup>Ithas a general(approximated) productiontechnology,one whichallows</sup> labor share to vary. In addition,<sup>thehouseholdoccupationalchoicehasaclosedformsolutionthat can</sup> easily be estimated. Finally,<sup>itfeaturesa dualeconomydevelopmentmodelwhich hasantecedentsgoing</sup> back to Lewis (1954) and<sup>Feiand Ranis(1964),and thusitcapturesseveralwidelyobservedaspects</sup> of the development process:<sup>industrializationwithpersistentincomedifferentials,aslowdeclineinthe</sup> subsistence sector, and an<sup>eventual increase in wages,all contributing to growthwith changinginequality.</sup> Our extension of the LEB<sup>modelhas twosectors, onewithoutintermediationand theotherallowing</sup> borrowing and lending at<sup>a marketclearinginterestrate.Theintermediatedsectoris allowedto expand</sup> exogenously at the observed<sup>rateintheThaidata,giveninitialparticipationandtheinitialobserved</sup> distribution of wealth.<sup>Ofcourseinothercontexts andformanyquestionsonewouldlikefinancial</sup> deepening to be endogenous<sup>4.</sup> But here the exogeneity<sup>offinancialdeepeninghasapeculiar,distinct</sup> advantage because we can<sup>varyit aswe like,either to mimicthe Thaidata withits acceleratedupturns</sup> in the late 80's and early<sup>90's,orkeep itflatprovidinga counterfactualexperiment.Wecanthus gauge</sup> the consequences of these<sup>variousexperimentsandcompareamongthem.In short,we can dogeneral</sup> equilibrium policy analysis<sup>followingtheseminalwork of Lochner, HeckmanandTaber(1998),despite</sup> endogenous prices and an<sup>evolvingendogenousdistributionofwealthina model where preferencesdo</sup> not aggregate We use the explicit structure<sup>of the modelas givenintheoccupationchoiceandinvestment decision</sup> of households to estimate certain<sup>parametersof the model.Key parametersof theproductiontechnology</sup> used by firms and the distribution<sup>of entrepreneurialtalentin the population are chosento maximizethe</sup> likelihood as predicted by<sup>the model of the transition intobusinessgiven initialwealth. This is donewith</sup> two distinct microeconomic<sup>datasets,onea seriesof nationallyrepresentativehouseholdsurveys(SES),</sup> and the other gathered under<sup>aprojectdirectedby oneof theauthors,withmorereliableestimatesof</sup> wealth, the timing of occupation<sup>transitions,and the use of formal andinformalcredit.Not all parameters</sup> of the model can be estimated<sup>via maximumlikelihood.The savingsrate,the differentialinthe costof</sup> living, and the exogenous<sup>technicalprogressinthe subsistence sectorarecalibratedto try tomatch the</sup> two decades of Thai growth<sup>andobserved changesininequality,laborshare,savingsandthe numberof</sup> entrepreneurs.

As mentioned before, this<sup>structural,estimatedversion of the Thaieconomy canthen be comparedto</sup> what would have happened<sup>if there had been no expansionin the size of the intermediatedsector.Without</sup> liberation, at estimated parameter<sup>valuesfrombothdatasets,themodelpredictsadramaticallylower</sup> growth -rate, high residual subsistence<sup>sector, non-increasingwages,and,granted,loweranddecreasing</sup> inequality. Thus financial<sup>liberalizationappearstobetheengineof growthit issometimes claimedto</sup> be, at least in the context<sup>of Thailand.</sup> However, growth and liberalization<sup>do have uneven consequences,as the critics insist.The distribution</sup> of welfare gains and losses<sup>in theseexperimentsis not at alluniform,as there are various effectsdepending</sup> on wealth and talent: with<sup>liberalization,savingsearn interest,althoughthis tends tobenefit the wealthy</sup> most. On the other hand,<sup>creditisavailabletofacilitate occupationshiftsandto financesetupcosts</sup> and investment. Quantitatively, there is a<sup>strikingconclusion.</sup> The primary winners from<sup>financial</sup> liberalization are talented<sup>butlowwealthwould-beentrepreneurswhowithoutcreditcannotgointo</sup> business at all or entrepreneurs<sup>withverylittle capitalMeangains fromthe winnersrangefrom60,000</sup> 4 See Greenwood **and** Jovanovic (1990) or Townsend and Ueda (2001).

3
to 80,000 baht, and the modal gains from 6,000 to 25,000 baht, depending on the dataset used and the calendar year. To normalize and give more meaning to these numbers, the modal gains ranges from 17 to 34 percent of the observed, overall average of Thai household income.

But there are also losers. Liberalization induces an increase in wages in latter years, and while this benefits workers, ceteris paribus, it hurts entrepreneurs as they face a higher wage bill. The estimated welfare loss in both datasets is approximately 115,000 baht. This is a large number, roughly the same order of magnitude as the observed average income of firm owners overall. This fact suggests a plausible political economy rational for (observed) financial sector repressions.

Finally, we use the estimated structure of the model to conduct two robustness checks. First, we open up the economy to the observed foreign capital inflows. These contribute to increasing growth, increasing inequality, and an increasing number of entrepreneurs, but only slightly, since otherwise the macro and distributional consequences are quite similar to those of the closed economy with liberalization. Indeed, if we change the expansion to grow linearly rather than as observed in the data, the model cannot replicate the high Thai growth rates in the late 80's and early 90's, despite apparently large capital inflows at that time. Second, we allow informal credit in the sector without formal intermediation to see if our characterization of the dual economy with its no-credit sector is too extreme. We find that at the estimated parameters it is not. Changes attributed to access to informal credit are negligible.

The rest of the paper is organized as follows. In Section 2 we describe the LEB model in greater detail. In Section 3 we describe the core of the model as given in an occupational choice map. In Section 4 we discuss the possibility of introducing a credit liberalization. In Section 5 we turn to the maximum likelihood estimation of seven of the ten parameters of the model from micro data, whereas Section 6 focuses on the calibration exercise used to pin down the last three parameters, matching, as explained, more macro, aggregate data. Section 7 reports the simulations at the estimated and calibrated values for each dataset. Section 8 performs a sensitivity analysis of the model around the estimated and the calibrated parameters. Section 9 delivers various measures of the welfare gains and losses associated with the liberalization. Section 10 introduces international capital inflows and informal credit to the model. Finally, Section 11 concludes.

# 2 Environment

The Lloyd-Ellis and Bernhard model (LEB for short) begins with a standard production function mapping a capital input k and a labor input I at the beginning of the period into output q at the end of the period. In the original' LEB model, and in the numerical simulations presented here, this function is taken to be quadratic. In particular, it takes the form This quadratic function can be viewed as an approximation to virtually any production function and has been used in applied work<sup>6</sup> . This function also facilitates the derivation of closed form solutions and allows labor share to vary over time.

'We use the functional **forms** contamned in the 1993 workng paper, although the published version contains slight modifications.

6 See Griffin et al. (1987) and references therein.

4
Each firm also has a beginning-of-period<sup>set-upor fixed cost x,and thissetup costis drawn at random</sup> from a known cumulative distribution<sup>_H(x, m)_with0</sup><sup>_< x_<1. This distributionis parameterizedbythe</sup> number _m:_ _H(x,m)_ = mx<sup>2</sup> + (1 **-** _m)x, m_ E _[-1,1]._ (2) If m = 0, the distribution<sup>isuniform;if</sup><sup>_m_>0thedistributionisskewedtowardslowskilledor,</sup> alternatively, high x people,<sup>andthe converseariseswhen</sup><sup>_m_< 0.Wedo supposethis set upcostvaries</sup> inversely with talent, that<sup>is,it takesbothtalentand aninitial investmentto starta businessbutthey</sup> are negatively correlated<sup>Moregenerally,the cumulativedistribution</sup><sup>_H(x, m)_isa crude wayto capture</sup> and allow estimation of the<sup>distributionof talentinthe populationand is not an unusualspecificationin</sup> the industrial organization literature<sup>7 ,e.g,Daset al.(1998),Veracierto(1998).Costx is expressedin</sup> the same units as wealth.<sup>Everyagentisbornwithaninheritanceorinitialwealth</sup><sup>_b._The distribution</sup> of inheritances in the population<sup>atdatet isgivenby</sup><sup>_Gt(b): Bt_-a[0,1]whereBtcR.4+ isthe changing</sup> support of the distribution at<sup>date t.Thetime argumenttmakes explicitthe evolutionof Btand</sup><sup>**Ct**over</sup> time. The beginning-of-period<sup>wealth</sup><sup>_b_andthe costxare the onlysourcesof heterogeneityamongthe</sup> population These are modelled as independent<sup>ofoneanotherin thespecificationusedhere,andthis</sup> gives us the existence of a _unique_<sup>steady state. If correlationbetweenwealthand abilitywere allowed,we</sup> could have poverty traps,<sup>asinBanerjeeandNewman(1993). Wedo recognizethatinpracticewealth</sup> and ability may be correlated.<sup>Inrelatedwork,Paulson andTownsend(2001)estimatewiththe same</sup> data as here a version of the<sup>Evansand Jovanovic(1989)modelallowingthemean of unobservedability</sup> to be a linear function of wealth<sup>andeducationTheyfind the magnitude of bothcoefficientsto be small8 .</sup> All units of labor can be hired<sup>atacommonwagew,tobedetermined in equilibrium(thereis no</sup> variation in skills for wage<sup>work).The onlyothertechnologyisa storagetechnology whichcarries goods</sup> from the beginning to the<sup>endof the periodata returnofunity.Thiswouldputalowerbound on the</sup> gross interest rate in the corresponding<sup>economywithcreditandin anyeventlimitsthe input</sup><sup>_k_firms</sup> wish to utilize in the production<sup>of outputq,even in theeconomywithout credit.Firms operateincities</sup> and the associated entrepreneurs<sup>and workersincura commoncost of livingmeasured by the parameter</sup> v,.

The choice problem of the entrepreneur<sup>is presentedfirst.</sup> where _7r(b,_ x, _w)_ denotes the<sup>profitsof thefirmwithinitial wealth</sup><sup>_b,_withoutsubtractingthe setupcost</sup> x, given wage w. Since<sup>creditmarketshavenot yetbeenintroduced,capitalinputk cannotexceedthe</sup> initial wealth _b_ less the<sup>setupcost xasin(3).Thisisthe keyfinanceconstraintof the model.Itmay</sup> or may not be binding depending<sup>on</sup><sup>_x,b_andw.Moregenerally,somefirms may produce,butif wealth</sup> _b_ is low relative to cost<sup>x,theymaybe constrainedincapitalinputuse</sup><sup>_k,_that is,forconstrainedfirms,</sup> wealth b limits input k. Otherwise<sup>unconstrainedfirmsareall alikeandhaveidenticalincomesbefore</sup> netting out the cost x. The<sup>capitalinput</sup><sup>_k_canbezero butnotnegative.</sup>

**7In** extended models this<sup>wouldbetheanalogto the</sup><sup>**distribution**ofhuman</sup><sup>**capital,**although</sup><sup>**obviouslythe**education</sup> **investment decision is** not<sup>modelled here</sup>

SWe also **estimate** the LEB<sup>model forvarious</sup><sup>**stratifications of**wealth,e g.,above</sup><sup>**and**belowthe median,tosee</sup><sup>**how**</sup> parameter **m varies** with<sup>wealthThisway,wealth and talentare allowedto be correlated.Even though the point</sup><sup>**estimates**</sup> of **m vary significantly,** simulations<sup>with thedifferentestimates</sup><sup>**ofm are**roughly</sup><sup>**similar.**</sup> 5
Even though all agents are born with an inherited nonnegative initial wealth _b,_ not everyone need be a firm. There is also a subsistence agricultural technology with fixed return y. In the original LEB model everyone is in this subsistence sector initially, at a degenerate steady state distribution of wealth. For various subsequent periods, labor can be hired from this subsistence sector, at subsistence plus cost of living, thus w = y _+.v_ When everyone has left this sector, as either a laborer or an entrepreneur, the equilibrium wage will rise. In the simulations we impose an initial distribution of wealth as estimated in the data and allow the parameter y to increase at an exogenous imposed rate of y,,, thus also increasing the wage.

For a household with a given initial wealth-cost pair _(b, x)_ and wage w, the choice of occupation reduces to an essentially static problem of maximizing end-of-period wealth _W(b, x, w)_ given in equation (4): At the end of the period all agents take this wealth as given and decide how much to consume C and how much to bequest _B_ to their heirs, that is, In the original LEB model and in simulations here the utility function is Cobb-Douglas, that is, This functional form yields consumption and bequest decision rules given by constant fractions 1 - and _w_ of the end-of-period wealth, and indirect utility would be linear in wealth. Parameter w denotes the bequest motive. More general monotonic transformations of the utility function _U(C, B)_ are feasible, allowing utility to be monotonically increasing but concave in wealth. In any event, the overall utility maximization problem is converted into a simple end-of-period wealth maximization problem. If we do not wish to take this short-lived generational overlap too seriously, we can interpret the model as having an exogenously imposed myopic savings rate _w_ which below we calibrate against the data. We can then focus our attention on the nontrivial endogenous evolution of the wealth distribution.

The key to both static and dynamic features of the model is a partition of the equilibrium occupation choice in _(b, x)_ space into three regions: unconstrained firms, constrained firms, and workers or subsisters. These regions are determined by the equilibrium wage w. One can represent these regions as _(b, x)_ combinations yielding the occupation choices of agents of the model, using the exogenous distribution of costs H(x, m) at each period along with the endogenous and evolving distribution Gt(b) of wealth _b._ The population of the economy is normalized so that the fractions of constrained firms, unconstrained firms, workers, and subsisters add to unity. This implies that _Gt(b)_ is a cumulative distribution function.

An equilibrium at any date t given the beginning-of-period wealth distribution _Gt(b)_ is a wage wot, such that given _wt,_ every agent with wealth-cost pair _(b, z)_ chooses occupation and savings to maximize (4) and (5), respectively, and the wage wt clears the labor market in the sense that the number of workers, subsisters and firms adds to unity. As will be made clear below, existence and uniqueness are assured. Because of the myopic nature of the bequest motive, we can often drop explicit reference to date t.

_6_
# 3 The Occupation Partition

For an individual with beginning-of-period<sup>wealth bfacinganequilibrium wagew,there aretwocritical</sup> skill levels _xe(b,_ w) and _xu(b,_<sup>w)as shownin Figure1 inpage 39.If this individual'sskill levelx ishigher</sup> than **_xe_** _(b, w),_ she becomes<sup>a worker,whereasif itislower,shebecomesanentrepreneur.Finally,if</sup><sup>_x_is</sup> lower than _xu(b,_ w) she becomes<sup>anunconstrained entrepreneur.</sup> We proceed to obtain the<sup>curves</sup><sup>**_xe_**(b, w)andzu (b, w).Naturally,thesearerelatedto optimalinput</sup> choice and profitability.<sup>Recallthatgrossprofitsfromsettingupafirmareequalto</sup><sup>_7r(b, z, w)._The</sup> optimal choice of labor I<sup>givencapital</sup><sup>_k(b, x, w)_isgivenby9</sup> Suppressing the arguments<sup>_(b,x),_we canexpress profitsandlabor as a functionof capital</sup><sup>_k_given the</sup> wage w, namely, which yields a quadratic<sup>expressionin</sup><sup>_k._</sup> We define _z_ as the maximum fixed cost,<sup>suchthatforanyx>xz,theagentwillneverbe an</sup> entrepreneur. More formally,<sup>and suppressingthedependenceof profitsonthe wagew,</sup><sup>_xz_issuchthat</sup>

**_x*=_** ir'-w, where 7r' =max _7r(k,w) (9)_

that is, if x > z*, the maximum<sup>incomeas anentrepreneurwillalwaysbe lessthanwand thereforethe</sup> agent is always better off<sup>becominga worker.</sup> Denote by _b_ the wealth<sup>levelofanentrepreneurwithcostxz suchthatsheis</sup><sup>_just_unconstrained.</sup> That is By construction _b*_ is the wealth<sup>levelsuch thatfor anywealth</sup><sup>_b > b'_and x< xz,the householdwould</sup> be both a firm and be unconstrained.<sup>Thereforeby thedefinition of</sup><sup>_x'(b, w)_as definingthefirm-worker</sup> occupation choice indifference<sup>point,x'(b, w)=x*forb></sup><sup>_b._Inaddition,sincexU(b, w) isthecurve</sup> separating constrained and unconstrained<sup>entrepreneurs,</sup><sup>_xu(b,_w)=xfor</sup><sup>_b > b*_alsoandthus thetwo</sup> curves coincide. Again, see<sup>Figure1inpage39.Noticethatforb</sup><sup>_>b-_andx<z-,afirmisfully</sup> capitalized at the (implicit)<sup>rateofreturninthebackyardstoragetechnology.Inthissensetheyare</sup> neoclassical unconstrained<sup>firms</sup> 9For certain combinations of a,t<sup>andp,labordemandcouldactuallybe</sup><sup>_negative._Lloyd-EllisandBernhard didnot</sup> consider these possibilities<sup>byassumingthatt>w,anda ></sup><sup>_0,_p > 0.However,one couldenvisionsituationswhere C < w</sup> and a _>_ 0 in which case,<sup>forlowvaluesof capitalk</sup><sup>**it**maynotpayto use labor.Still atthe same parameters,</sup><sup>**if**the capital</sup> employed were large, then the<sup>expressionin (7)maybe positive.The intuitionis that although laboris rather unproductive,</sup> it is complementary to capital.<sup>In thispaper,however,wefollowLloyd-Ellisand Bernhard andassumethatsuchcasesof</sup> negative labor do not arise.<sup>Therefore,capitalandlabordemandswillalwaysbenonnegative.</sup> 7
Now we proceed to define the occupational choice and constrained/unconstrained cutoffs for b < _be._ We begin by noting that for b _<_ be, the agent will always be constrained as a firm at the point of occupational indifference _xe(b,_ w) between the choice of becoming a worker or an entrepreneur'<sup>0</sup> . This fact implies that we can use the constrained capital input _kC = b -_ x to determine _xe(b,_ w) with the additional restriction that _xe(b, w)_ S b, because the entrepreneur must have enough wealth to afford at least the setup cost.

We define the occupation indifference cost point x'(b, w) by setting profits in (8) less the setup cost equal to the wage. In obvious notation,

## w=7r(kc,w)-x, k'=b-x.

(11) This is a quadratic expression in x which, given b _< b*_ yields the level of x that would make an agent indifferent between becoming an entrepreneur and worker, again, denoted _xe(b,_ w) **1.** It is the only nonlinear segment in Figure 1.

The above equation, however, does not restrict x to be lower than b. Define _b,_ such that _xe(b, w)_ = _b._ For _b < b_ in Figure 1, _xle (b,_ w) would exceed b. Households will not have the wealth to finance the setup cost x, and are forced to become workers. They are constrained on the extensive margin'<sup>2</sup> . Henceforth, we restrict Xe(b, w) to equal to b in this region, b < _b._ Note as well that agents with b _= xe(b,_ w) will start businesses employing only labor as they used up all their wealth financing the setup cost. This captures in an extreme way the idea that small family owned firms use little capital.

# 4 lntroducing an intermediated sector

A major feature of the baseline model is the credit constraint associated with the absence of a capital market. For example, a talented person (low x) may not be able to be an entrepreneur because that person cannot raise the necessary funds to buy capital. Thus the most obvious variation to the baseline model is to introduce credit markets'<sup>3</sup> .

We consider an economy with two sectors of a given size, one open to credit. Agents born in this sector can deposit their beginning-of-period wealth in the financial intermediary and earn interest on it. If they decide to become an entrepreneur, they can borrow at the interest rate to finance their fixed cost x and capital investment k. Still, labor (unlike capital) is assumed to be mobile, so that there is a unique wage rate for the entire economy, common to both sectors.

Let us now turn to the occupational choice of an individual facing the wage rate w and the gross interest rate R. Each agent, as before, starts with an inheritance b and earns 'lIntuitively, if the agent were not constrained, it can be shown that he would strictly prefer to be an entrepreneur than a worker, contradicting the claim Assume that _b < b-_ and suppose the agent is not constrained. Then, _x + k' < b_ or _* < b- -k'_ = x-. Given that _lr'-_ - _x_ = w (from equation (9)), it follows that ir' _- x_ > w, hence the agent is not indifferent. "See Appendix B for the explicit solution. 12 According to the model we need to restrict the values of _x'_ and _xe_ to the range of their imposed domain, namely [0,1]. Note for example that if the previously defined x'(b, w) were negative at some wealth _b,_ everyone with that wealth _b_ would become a worker. Alternatively, if _x'(b, w)_ crossed **1** then everyone with that wealth _b_ would be an entrepreneur. We therefore restrict x' and xl to lie within these boundaries, by letting them coincide with the boundaries {0,1} otherwise. 1<sup>3</sup> The model is at best a first step in making the distinction between agents with and without access to credit. Here we assume that intermediation is perfect for a fraction of the population and nonexistent for the other. We do not model selection of customers by banks, informational asymmetries nor varation in the underlying technologies.

8
where ir(w, R) is the gross profit earned by an entrepreneur when the cost of capital<sup>is</sup><sup>_R_and wageis w.</sup> Notice these gross profits iT(w, _R)_ do not depend on wealth b nor setup<sup>costsx.Sinceallentrepreneurs</sup> operate the same technology _f (k, I)_ and face the same factor prices, they<sup>willalloperateatthesame</sup> scale and demand the same (unconstrained) amount of capital _k_ and labor<sup>1, regardlessoftheir setup</sup> cost x or wealth _b._ The problem they solve is as follows: which yields the optimal choices Note that _kU(w, R)_ is different from the earlier program except when the<sup>interestrateis</sup><sup>_R =_1.</sup> Our next step is to determine who becomes entrepreneur and who a worker or<sup>subsisterintheinter-</sup> mediated sector. All we have to do is to find the value of z(w, _R)_ at<sup>whichanagentwouldbeindifferent</sup> between the two options. Anybody who has a setup cost greater than z(w, _R)_<sup>willbe a workerand vice</sup> versa. The occupational indifference condition is given by

# orf

It is clear that z(w, _R)_ does not depend on initial wealth _b,_ and it is decreasing<sup>inthe interestrate</sup><sup>_R._</sup> _Net_ aggregate deposits **in** the financial intermediary can be expressed<sup>astotal wealthdepositedinthe</sup> intermediated sector less credit demanded for capital and fixed costs: where now Bc denotes the support for the wealth distribution Gc in the intermediated (C for credit) sector. For low levels of aggregate wealth, the amount of deposits will constrain<sup>creditandthe netwill</sup> be zero. However, note that net aggregate deposits can be strictly positive<sup>if</sup> there<sup>isenoughcapital</sup> accumulation, in which case the savings and the storage technology are equally productive,<sup>bothyielding</sup> a gross return of _R =_ 1. Implicit in the notation, each sector wealth distribution<sup>integratesto its relative</sup> size in the aggregate economy. More formally, where unity is the normalized population size.

The labor market clearing condition can be written as _Ec(w,R) + ENC(W) + L d(w, R) + LdC(w) + Sc(w, R) + SNc(w) = 1_ 9
where the mass of entrepreneurs in the intermediated<sup>andnon-intermediatedsectors,</sup><sup>_EC_and</sup><sup>_ENC_</sup> respectively, can be expressed as The mass of workers in each sector is given by where the superscript _d_ denotes labor demand by the entrepreneurs.<sup>Labordemand</sup><sup>_l(b, x, w)_isgiven</sup> in equation (7) and _lU(w,_ R) is in equation (14). Finally _Sc_<sup>and</sup><sup>_SNC_denotethepotentiallypositive</sup> fraction of the population in subsistence in each sector. The<sup>factorprices</sup><sup>_R_andw can be foundsolving</sup> the market clearing conditions (17) and (19). Specifically,<sup>anequilibriumatanydatetgivenwealth</sup> distributions Gc,t(b) and _GNC,t(b),_ is a wage wt and an interest<sup>rateRt,suchthateveryagentwith</sup> wealth-cost pair _(b, z)_ choose in the restricted sector an<sup>occupationtomaximize(4)givenwtand inthe</sup> liberalized sector an occupation to maximize (12) given the<sup>wtand Rt,theinterestrateRt satisfies(17)</sup> in the sense that in the liberalized sector aggregate deposits<sup>are equal or largerthan capital demandand</sup> the wage wt satisfies (19) so that the numbers of subsisters,<sup>workersand entrepreneursacross sectorsadd</sup> to unity. Existence and uniqueness of the equilibrium<sup>isagainassured.</sup>

# **5** Estimation from Micro data

Although the original LEB model without intermediation<sup>is designedto explaingrowth andinequalityin</sup> transition to a steady state, there are recurrent or repetitive<sup>features.Specifically,the decisionproblem</sup> of every household at every date depends only on the individual<sup>beginning-of-periodwealth</sup><sup>_b_andcostx</sup> and on the economy-wide wage w. Further, if the initial wealth<sup>b andthe wagew areobservable,whilex</sup> is not, then the likelihood that an individual will be an entrepreneur<sup>canbe determined entirelyas in the</sup> occupation partition diagram, from the curve z'(b, w) and the<sup>exogenousdistributionof talentH(x, m).</sup> That is, the probability that an individual household with initial<sup>wealth</sup><sup>_b_will be an entrepreneuris given</sup> by _H(ze(b, w),_ m), the likelihood that cost _x_ is less than<sup>or equalto</sup><sup>_xz(b, w)._Theresidual probability</sup> 1 _- H(xe(b,_ w), m) dictates the likelihood that the individual household<sup>willbea wageearner.</sup> The fixed cost z takes on values in the unit interval and yet _enters_<sup>_additively_into theentrepreneur's</sup> problem defined at wealth _b._ Thus setup costs can be<sup>largeor smallrelativeto wealthdepending on how</sup> we convert from 1997 Thai baht into LEB units<sup>14</sup> . We therefore<sup>search overdifferentscalingfactorss in</sup> order to map wealth data into the model units. Related,<sup>we pindownthe subsistencelevel -y in the model</sup> by using the estimated scale s to convert to LEB model units<sup>the counterpartof subsistencemeasured</sup> in Thai baht in the data, corresponding to the earnings of those<sup>in subsistenceagriculture.</sup> 1<sup>4</sup> The relative magnitude of the fixed costs will drop as wealth<sup>evolvesover time.</sup> 10
Now let a denote the vector of parameters<sup>of themodel relatedto theproductionfunction and scaling</sup> factor, that is, a = _(f,_ a,p,a, _,s)._ Suppose<sup>wehada sampleof nhouseholds,andlety,bea zero-</sup> one indicator variable for the observed entrepreneurship<sup>choiceof householdi.Thenwiththenotation</sup> _x'(b, 1, w)_ for the point on the _xe(b,_ w)<sup>curve forhouseholdiwithwealthb,,atparametervector0 with</sup> wage w, we can write the explicit log likelihood<sup>of the entrepreneurshipchoice forthe n householdsas</sup> 1n Ln(9,m) = _-EylnH[xe(btIO,_ w),ml +<sup>(1 -y,)ln{I</sup><sup>_-H[xe(b, 1,w),m]}_</sup> (24) t=1 The parameters over which to search are again<sup>the production parameters(,B, a,p,</sup><sup>_a,(),_the scaling factor</sup> _s_ and the skewness m of H(., m).

Intuitively, however, the production parameters<sup>invector 9cannotbeidentifiedfromapurecross-</sup> section of data at a point in time For if<sup>we</sup> return<sup>tothedecisionproblem ofanentrepreneurfacing</sup> wage w, we recall that the labor hire decision<sup>given by equation(7)is a linear functionof capital</sup><sup>_k._Then</sup> substituting _l (k, w)_ back into the production function<sup>as in equation(8),weobtain a relationship between</sup> output and capital with a constant term, a<sup>linearterm in</sup><sup>_k,_and a quadratic termin</sup><sup>_k._Essentially,then,</sup> only three parameters are determined, not<sup>five.</sup> If data on capital and labor demand at the<sup>firmlevelwereavailable,wecouldsolvethe identification</sup> problem by directly estimating the additional<sup>linearrelation</sup><sup>_l(k)_givenin equation(7).Thiswouldgive</sup> us two more parameters thus obtaining full identification.<sup>Unfortunately, thesedata arenotavailable.</sup> However, equation (8) suggests that we can<sup>fullyidentifythe productionparametersbyexploitingthe</sup> variation in the wages over time observed<sup>inthedata.The Appendixshowsindetailthecoefficients</sup> estimated and how the production parameters<sup>arerecovered.</sup> The derivatives of the likelihood in equation<sup>(24)can be determinedanalytically,andthenwith the</sup> given observations of a database, standard maximization<sup>routinescan be usedto searchforthe maximum</sup> numerically'". The standard errors of the estimated<sup>parameterscanbecomputedby bootstrapmethods</sup> using 100 draws of the original sample with<sup>replacement.</sup> It is worthwhile mentioning that for some<sup>initialpredeterminedguesses,theroutineconvergedto</sup> different local maxima. However, all estimates<sup>usinginitialguessesaroundaneighborhood of anysuch</sup> estimate, converged to the same estimate. The<sup>multiplicityof local maximamaybe due tothe computa-</sup> tional methods available rather than the<sup>non-concavityoftheobjective function in certainregions.See</sup> also the experience of Paulson and Townsend"<sup>6(2001)withLEBandotherstructuralmodels.</sup> We run this maximum hkehhood algorithm with<sup>two differentdata bases.The firstandprimarydata</sup> base is the widely used and highly regarded<sup>Socio-EconomicSurvey'7(SES)conductedbythe National</sup> Statistical Office in Thailand. The sample<sup>isnationallyrepresentative,anditincludeseightrepeated</sup> cross-sections collected between 1976 and<sup>1996.Thesamplesizeineachcrosssection:11,362in1976,</sup> 11,882 in 1981, 10,897 in 1986, 11,046 in<sup>1988,13,177in1990,13,459in1992,25,208in1994and 25,110</sup> in 1996. Unfortunately, the data do not constitute<sup>apanel,butwhenstratifiedbyage ofthehousehold</sup> head, one is left with a substantial sample.<sup>As</sup> in<sup>the complementary work of Jeong and Townsend(2000),</sup> we restrict attention to relatively young households,<sup>aged20-29,whosecurrentassetsmightberegarded</sup> somewhat exogenous to their recent choice of occupation.<sup>Wealsorestrict attentiontohouseholdswho</sup> had no recorded transaction with a financial<sup>institutioninthemonthpriorto theinterview,acrude</sup>

> 1'1n particular, we used the MATLAB routine<sup>fminconstartingfroma variety of predeterminedguesses</sup>

"See their technical appendix for more information<sup>aboutthe estimationtechnique</sup><sup>**and**its drawbacks</sup>

**'**<sup>**7**</sup> **See** Jeong(1999) for details or its use **in**<sup>DeatonandPaxson(2000)or Schultz(1997).</sup>

11
estimate of lack financial access, as assumed in the LEB model.<sup>However,theSESdoesnotrecord</sup> directly measures of wealth. From the ownership of various household<sup>assets, the value of the houseand</sup> other rental assets, Jeong (1999) estimates a measure of wealth<sup>based on Principal ComponentsAnalysis</sup> which essentially estimates a latent variable that can best explain the<sup>overallvariationin the ownership</sup> of the house and other household assets<sup>18</sup> .

We use the observations for the first available years, 1976 and<sup>1981,toobtainfullidentificationas</sup> the wage varied over these two periods. The sample consists<sup>of</sup> a total<sup>of 24,433 observationswith9,028</sup> observations from 1976 and 15,405 from 1981.

The second dataset is a specialized but substantial cross sectional<sup>surveyconductedinThailandin</sup> May 1997 of 2880 households<sup>19</sup> The sample is special in that it was restricted to two provinces<sup>in</sup> the relatively poor semi arid Northeast and two provinces in the<sup>more</sup> industrialized<sup>centralcorridoraround</sup> Bangkok. Within each province, 48 villages were selected in a stratified<sup>clusteredrandomsample.Thus</sup> the sample excludes urban households. Within each village 15 households<sup>wereselectedat random.The</sup> advantage of this survey is that the household questionnaire<sup>elicitsan enumerationof all potentialassets</sup> (household, agricultural and business), finds out what is currently owned, and<sup>if so whenit was acquired.</sup> In this way, as in Paulson and Townsend (2001), we create an estimate<sup>of pastwealth, specificallywealth</sup> of the household 6 years prior to the 1997 interview, 1991. The survey<sup>alsoasksaboutcurrentand</sup> previous occupations of the head, and in this way it creates estimates of<sup>occupationtransitions,thatis</sup> which of the households were not operating their own business<sup>before1992,fiveyears priorto the1997</sup> interview, and started a business in the following five years. Approximately<sup>21percentof the households</sup> made this transition in the last five years and 7 percent between<sup>fiveandtenyearsago.Abusiness</sup> owner in the Townsend-Thai data is a store owner, shrimp farmer, trader<sup>ormechanic2 0.Amongother</sup> variables, the survey also records the current education level of household<sup>members;thehistoryofuse</sup> of the various possible financial institutions: formal (commercial banks,<sup>BAACandvillagefunds)and</sup> informal (friends and relatives, landowners, shopkeepers and moneylenders);<sup>and whetherhouseholds</sup> claimed to be currently constrained in the operation of their business.<sup>21</sup> '<sup>22</sup> Since the LEB model is designed to explain the behavior of those<sup>agentswithoutaccesstocredit,</sup> we restrict our sample to those households that reported having<sup>norelationshipwithanyformalor</sup> informal credit institution, another strength of the survey<sup>2 3</sup> .<sup>A</sup> disadvantage<sup>of theseconddatasetis</sup> that as a single cross section, there is no temporal variation in<sup>wages.Thus,weidentifythe production</sup> parameters by dividing the observations into two subsamples containing<sup>the householdsinthe northeast</sup> 18See Jeong (1999) and Jeong and Townsend (2000) for details.

1<sup>9</sup> Robert M. Townsend is the principal investigator for this survey.<sup>SeeTownsendetal.(1997).</sup>

> 2 0Reassuringly, Table 1C in Paulson and Townsend (2001) shows that the initial investment necessary to open a business is the roughly same in both regions among the most common<sup>types of businesses</sup>

> 2 1The percentage of households m non-farm businesses is 13 percent and 28 percent in the central vs northeast regions. The fraction of the population with access to formal credit (from<sup>commercialbanks or BAAC)is 34percentand 55percent</sup> for non-business vs business, respectively, in the northeast<sup>region,and48 percentand73percent,respectively,forthe</sup> central region.

22Paulson and Townsend (2001) provide a much more extensive discussion<sup>of the original data,the derivationof variables</sup> to match those of the LEB model, additional maximum likelihood<sup>estimates of the LEBmodel andthe relationship of LEB</sup> estimates to those of various other models of occupation choice. However,<sup>themaximumlikelihoodprocedureinPaulson</sup> and Townsend (2001) is different to the one discussed here in that<sup>no attemptis madeto recoverthe underlyingproduction</sup> parameters.

> 23 These households, however, could have borrowed from friends and relatives, although the bulk of the borrowmg through this source consists of consumption loans rather than business<sup>investments.</sup>

12
and central regions, exploiting<sup>regionalvariationinthewages24.Thefinalsample consists ofa total</sup> of 1272 households with<sup>707householdsfrom thenortheastregionand565households fromthe central</sup> region.

Figure 2 in page 39 displays the<sup>occupationalmapgeneratedusingtheestimatedparameters.For</sup> the SES dataset, observations<sup>in1981seemtobelessconstrainedthanthosein1976,naturallyasthe</sup> country was growing and wealth<sup>washigher. For theTownsend-Thaidataset, thecentralregionappears</sup> to be less credit constrained than<sup>theNortheast,reflectingperhapsthefactthatthecentralregionis</sup> more prosperous.

Table 1 reports the estimated<sup>parametersas wellasthe standarderrors25.The parametery forboth</sup> datasets was found by multiplying<sup>anestimate of thesubsistencelevelfromthe data by the scaling factor</sup> estimated. For the SES data,<sup>weusedthemeanincomeof farmersin1976whichamountedto19,274</sup> baht. Analogously, we used the<sup>average incomeofworkersintheNortheastregionwithoutaccessto</sup> credit as reported in the Townsend-Thai<sup>data,or10,727 baht.The wageforthetwotimeperiodsinthe</sup> model units at the estimated scaling<sup>factorswere</sup><sup>**_W_**</sup> **_7 6_**<sup>=0.048and</sup><sup>_w8l_=0.053for theSESdatasetand</sup> **WNE** = 0 016 and wc = 0.037<sup>forthetwo regionsinthe Townsend-Thaidataset.Themaximizedvalue</sup> of the likelihood function obtained<sup>using theSES data was -8,233.92whereastheTownsend-Thaidataset</sup> yielded a value of -616.92.

Table 1: MLE Results

||SES|<br>|Townsend<br>|-Thai<br>|
|---|---|---|---|---|
|||SE|Cffii|SE|
||Coefficient|.|oecent||
|_ScalmngFactor_|||||
|**sa**|1.4236|0.00881|1.4338|0.03978|
|_SubsistenceLevel_|||||
|-y|0.02744|0.00119|0.01538|0.00408|
|_F7:redCostDtstrib_|_uton_||||
|m|-05933|005801|0.00559|0.17056|
|_Technology_|||||
|ca|0.54561|0.06711|0.97545|0.00191<br>|
|_1B_|0.39064|0.09028|0.0033|0.00013<br>|
|p|0.03384|0.00364|0.00966|0.00692<br>|
|or|0.1021|0.02484|0.00432<br>|0.00157<br>|
||0.2582|0.03523|0.12905|0.04146|
|NumberofObs.|24,43|3|1,27<br>|2<br>|
|Log-Likelihood|-8,233.|92|-616.9|2|

The parameter value and standard<sup>errorreportedare multipliedbya factor of</sup><sup>**106.**</sup>

> 24 Unfortunately, estimating a model that, features a unique wage by exploiting the geographical variation m the wage observed **in** the data is a contradiction.<sup>Of coursecostlymigrationcould beintroducedbutwedo nottakethatexplicit</sup> approach here. We draw some<sup>confidencefromthe factthattheseare secondarydata andwearecomparingits estimates</sup> to those from the SES dataset,<sup>with its temporalvariationin wagesconsistentwiththeestimatedmodel.</sup>

> 2 5Note that e > **_"u76,_** C > **_W8i_** and ( > _wNE,_ C > _wC_ and p >0 , **_a_** _>_ **0** for both datasets as required **in** Footnote 9

13
From the standard errors one can construct confidence intervals. Indeed, they reflect the curvature of the likelihood function at the point estimates and hence they also reveal the potential for errors<sup>in</sup> the convergence to a global maximum. The magnitude of the standard errors, however, tell us little about how sensitive the dynamics of the model are to the parameters. In Section 8 below we address this issue by performing a sensitivity analysis. It is also interesting that both estimates of m fall within the permitted boundaries. Related, the SES data estimate of the parameter m implies a distribution of talent more skewed towards low cost agents.

# 6 (Calibration

We still need to pin down the cost of living v and the "dynamic" parameters, namely, the savings rate _w_ and the subsistence income growth rate **Ygr.** One way to determine these parameters is calibration: look for the best _v, w_ and 'yr combination according to some metric relating the dynamic data to be matched with the simulated data.

In this section we first discuss the Thai macro dynamic data that will be used to calibrate the model and then discuss some issues concerning the calibration itself.

## 6.1 Data

The Thai economy from 1976-1996 displayed nontrivial growth with increasing (and then decreasing) inequality. LEB and related models are put forward in the literature as candidate qualitative explanations for this growth experience. Here we naturally go one step further and ask whether the LEB model at some parameter values can match quantitatively the actual Thai economy, focusing in particular on the time series of growth, labor shares, savings rates, fraction of entrepreneurs, and the Gini measure of income inequality. The actual Thai data are summarized in Table 2 in the Appendix.

The data show an initially high net growth rate of roughly 8 percent in the first three years. This then fell to a more modest 4 percent up through 1986. The period 1986-1994 displayed a relatively high and sustained average growth of 8.43 percent, and within that, from 1987-1989 the net growth rate was 8.83 percent. During this same period, the Thai economy GDP growth rate was the highest in the world at 10.3 percent. These high growth periods have attracted much attention. Labor share is relatively stable at 0.40 and rising after 1990, to 0.45 by 1995. A trend from the 1990-1995 data was used to extrapolate labor share for 1996. Savings as a percent of national income were roughly 22 percent from the initial period to 1985. Savings then increased after 1986 to 33 percent, in the higher growth period. These numbers, though typical of Asia, are relatively high. The fraction of entrepreneurs is remarkably steady, though slightly increasing, from 14 percent to 18 percent. The Gini coefficient stood at 0.42 in the 1976 SES survey and increased more or less steadily to 0.53 in 1992. Inequality decreased slightly in both the 1994 and 1996 rounds, to 0.50. This downward trend mirrors the rise in the labor share during the same period, and both may be explained by the increase in the wage rate. This level of inequality is relatively high, especially for Asia, and rivals many countries in Latin America (though dominated as usual by Brazil). Other measures of inequality, e.g., Lorenz, display similar orders of magnitude within Thailand over time and relative to other countries<sup>26</sup> .

> 26 The mterested reader will find a more detailed explanation in Jeong (1999).

14
The fraction of population with<sup>access tocreditin1976was estimatedat6 percentand increasedby</sup> 1996 to 26 percent. The data<sup>alsorevealthatasmeasure offinancial deepening,itgrew slowlyinthe</sup> beginning and from 1986 grew<sup>more sharply.We recognizethatat bestthismeasure ofintermediationis</sup> a limited measure of what<sup>wewouldliketohaveideally,anditseemslikelyweareoff inlevels.</sup>

# 6.2 Issues in the calibration method

## **6.2.1** Financial Liberalization

We begin with the standard, benchmark<sup>LEB model,shuttingdowncredit altogether.Wethen consider</sup> an alternative intermediated<sup>economy,withtwo sectors,oneopentocreditandsaving.Onlylaboris</sup> mobile, hence a unique wage rate,<sup>whereascapitalcannotmovetotheothersector.Inotherwords,a</sup> worker residing in the non intermediated<sup>sector mayfinda job inthe creditsector,even thoughshewill</sup> not be able to deposit her wealth<sup>in the financialintermediary.The relativesize27of eachsectoris taken</sup> to be exogenous and changing<sup>overtimegivenby thefractionof peoplewithaccesstocredit reportedin</sup> Table 2 in the Appendix. As<sup>mentioned,this isourkeymeasureof liberalization.</sup>

## **6.2.2** Initial **wealth** distribution

Relevant for dynamic simulations<sup>is theinitial1976economy-widedistributionof wealth28.Asmentioned</sup> before, Jeong (1999) constructs<sup>a measureof wealthfromtheSES data using observations on household</sup> assets and the value of owner<sup>occupiedhousingunits.</sup>

## **6.2.3** The metric

Any calibration exercise requires<sup>a metric toassess howwell the modelmatchesthe data.Asan example,</sup> the business cycles literature has<sup>focusedon modelsthatareabletogenerateplausibleco-movements</sup> of certain aggregate variables with<sup>output.Almost by definition,themetricrequiresthattheeconomy</sup> displayed by these models be<sup>ina steadystate.Eventhoughtheeconomyweconsiderhere eventually</sup> reaches a steady state, we are interested<sup>inthe(deterministic)transitiontoit, thusthe metricputforth</sup> as our objective function suffers<sup>frombeing somewhatadhoc.Inparticular,weconsiderthenormalized</sup> sum of the period by period squared<sup>deviations of the predictions of the modelfrom the actualThai data</sup> for the five time series<sup>29</sup> displayed<sup>inTable2intheAppendix.Wenormalizethe deviations in thefive</sup> variables by dividing them<sup>bytheircorrespondingmeansfromthe Thaidata.More formally,</sup>

> 27We assume that the intermediated sector, with its distribution of wealth, is scaled up period by period according to the exogenous credit expansion.<sup>Alternatively,wecouldhave sampledfromthe no-credit sector distributionof wealthand</sup> selected the correspondung fraction<sup>totheexogenousexpansion,buttheincreaseissmallandhiswouldhavemadelittle</sup> difference in the numerical computations.

r8Smce this estimated measure<sup>of wealthis likelyto differ in scaleand units to the wealthreportedin the Townsend-Thai</sup> data, we allow for a different scaling<sup>factorto convertSESwealthinto themodelunits. In otherwords,weuse</sup><sup>_two_scaling</sup> factors when we calibrate the model<sup>usingthe parameters estimatedwiththeTownsend-Thaidata.Oneisestimatedwith</sup> ML techniques and converts wealth<sup>andincomes reportedinthe data,whereas the otheris calibratedand convertstheSES</sup> wealth measure used to generate<sup>theeconomy-wideinitial distribution.</sup>

> 2 9Note that in computing the growth rate we lose one observation, so the time index in the formula given in (25) runs from 1977 to 1996 for the growth<sup>ratestatistic.</sup>

15
where zJ denotes the variable s, t denotes time, and **_w,t_** is the weight given to the variable s in year t. In order to focus on a particular period, more weight may be given to those years. Analogously, all the weight may be set to one variable to assess how well the model is able to replicate it alone. All weights are re-normalized so that they add up to unity. Finally, sim and ec denote respectively "simulated" and "Thai economy", and M., denotes the variable zJm ean from the Thai data.

We search over the cost of living **v,** subsistence level growth rate **-g,** and the bequest motive parameter _w_ using a grid of 203 points or combinations of parameters<sup>3 0</sup> .

All the statistics but the savings rate have natural counterparts in the model. We consider "savings" the fraction of end-of-period wealth bequested to the next generation. The savings rate then is computed by dividing this measure of savings by net income<sup>3</sup> l.

# 7 Resullts

In this section we present the simulation results using the calibrated and estimated parameters from both data sets.

## 7.1 Simulations using SES Data Parameters

We begin with the original LEB model without liberalization. If all periods and all variables are weighted equally, the parameters which minimize the squared error metric are _v_ = 0.079, _w_ = 0.479 and **7g.** = 0.042. Figure 3 in page 40 displays the model simulations against the actual Thai data displayed earlier.

The figures show how the model fails to explain the levels and changes in roughly all variables. In the simulation, the growth rate of income is flat at roughly 2 percent. Growth is driven mainly by the exogenous growth of the subsistence level which is set at 4.2 percent per year. Overall, the economy shrinks in the early periods, and then by 1983 it grows at the wage level<sup>32</sup> . The simulated labor share is able to roughly match the trend displayed in the data, although it is always higher in levels. The simulated savings rate is decreasing at first and then slightly increasing, always at a higher level than the actual data. Entrepreneurship in the model is somewhat low. The Gini coefficient is always decreasing and terribly low, in contrast to the first increasing and then decreasing pattern that the Thai data exhibits.

If we had tried to match the growth rate alone, we do somewhat better on that dimension. In fact, we are able to replicate the low growth - high growth phases seen in the data. Figure 4 in page 41 displays the model simulations when we restrict attention to the growth rate. The calibrated parameters here are _v_ = 0.074, _w_ = 0.7 and -g, = .1. The improvement in the growth rate comes at the expense of increasing the model's savings rate above one from 1985 onwards, far above the actual one. Labor share increases sharply in the model, but not in the data. The income Gini coefficient and the fraction of entrepreneurs are very poorly matched as both drop to zero. The reason for such drastic macroeconomic aggregates is _3 0 As_ mention earlier, when we use the Townsend-Thai data, we also search over a grid of 20 scaling factors for the mitial distribution of wealth.

> 31More formally we can express the savings rate (in an economy without credit) as

where income _Y(b, x,_ w) is given by _W(b, x, W) = Y(b, x,_ w) _+b_ as expressed in equation (4). Note that for some parameters, the savings rate may be larger than one. 32 Smce the cost of living v is roughly 3 times the subsistence income -y, wage growth rate wvll always be lower than **7yrg** 16
the choice of model parameters which try to match the growth rate of income. The subsistence sector is so profitable relative to setting up a business that by 1988 all entrepreneurial activity disappears and everyone in the subsistence sector earns the same amount. It is clear that focusing on the growth rate alone has perverse effects on the rest of statistics We now modify the benchmark model to mimic part of the Thai reality, allowing an exogenous increase in the intermediated sector from 6 percent to 26 percent from 1976-1996 as described in Table 2 in the Appendix. We weight each year and all the variables equally and search again for the parameters v, _w_ and **7gr,** allowing the best fit of the five variables. The parameters are M = 0.026, _w_ = 0.321 and<sup>**ygr**=</sup> 0. The corresponding graphs are presented in Figure 5, page 42.

The modified intermediated model's explanation of events differs sharply from that of the benchmark without an intermediated sector. Now the model is able to generate simulated time series which track the Thai economy more accurately. In the model, the growth rate of income is again lower than that of the Thai economy. The model still starts with negative growth until 1984. The initial phase of negative growth comes from an initial overly high aggregate wealth in the economy. But growth jumps to 5.4 percent by 1987. This high growth phase comes from the rapid expansion of credit during those years. Finally, the growth rate declines after 1987 monotonically, driven by the imposed diminishing returns in the production function. The model matches remarkably well the labor share levels and changes, especially after 1990 where they both show a steady rise. The savings rate is only closely matched<sup>for</sup> the period 1987-1996. The model also predicts a slightly decreasing fraction of entrepreneurs until 1985 and then a steady increase from 8.7 percent in 1985 to 16 1 percent in 1995, resembling more the actual levels. Finally, the Gini coefficient folows a slightly decreasing, then slightly increasing, and finally sharply decreasing trend, starting at .481 in 1976, then .377 by 1985, increasing to .451<sup>by1991and</sup> declining again to .284 by 1996. Beneath these macro aggregates lie the model's underpinnings. Growth after 1985 is driven by a steady decline out of the subsistence sector, with income from earned wages and from profits steadily increasing to 1990. Profits per entrepreneur are particularly high. Then, with the subsistence sector depleted entirely, the wage increases faster, and profits begin to decrease. Thus labor share picks up and inequality falls.

To isolate the role of credit, we can consider the same economy, at the same parameter values, but without the intermediated sector. In such a no-credit benchmark economy, roughly 80 percent of the labor force are still subsisters by 1996. In fact, this benchmark model is only capable of replicating the savings rate. It under-predicts labor share, the Gini coefficient, and the fraction of entrepreneurs. Labor share starts (low) at 7.7 percent and rises to 29.8 percent by 1988. The Gini coefficient declines<sup>from.462</sup> in 1976 to .238 by 1988. From then onwards, both simulated series remain constant until 1996. Income growth is very badly matched, starting low initially and converging from negative to zero growth rate by 1996. The negative growth comes from a relative abundance of wealth in the economy: households eat and save that wealth as they move toward the subsistence growth rate of zero. We conclude then that the credit liberalization is solely responsible for the growth experience that the intermediated model displays.

# 7.2 Simulations with Parameters from the Townsend-Thai Data

The simulation generated from the economy with no access to intermediation at the Townsend-Thai parameters, displays similar characteristics to the one using the SES data parameters and hence is not reported.

17
We now turn attention to the intermediated economy at these parameter<sup>values.If weweighteach</sup> year and all the variables equally, the calibrated parameters<sup>are</sup><sup>_v_=0.004,</sup><sup>_w_=0.267and</sup><sup>**-ygr**=.006.</sup> The scaling factor chosen for the initial distribution is<sup>15</sup> percent<sup>of theone used toconvert wealthusing</sup> in the ML estimation. The corresponding graphs<sup>are</sup> presented<sup>inFigure6,page43.</sup> The model here also does well at explaining the<sup>levelsand changesin allvariables, evenbetterthan</sup> above with the SES data. Striking in particular is the growth<sup>rate of income,whichalthoughsomewhat</sup> low in levels, tracks the Thai growth experience well. The<sup>modelalsodoes remarkablywelin matching</sup> labor share and the Gini measure of inequality. It under-predicts,<sup>however,the fractionof entrepreneurs,</sup> although it is able to replicate a positive trend. As usual,<sup>themodel featuresaflattersavingsrate</sup> although it matches well the last subperiod, 1988-1996.<sup>Economywidegrowthisdrivenprimarilyby</sup> growth in the intermediated sector. That is<sup>wherethebulk oftheeconomy'sentrepreneurslieanda</sup> relatively high number of workers, from both the intermediated<sup>andnon-intermediatedsector.</sup>

# 8 Sensitivity Analysis **of** MILE parameters

We address the robustness of the model in two ways. First,<sup>wechange one parameterat a timeand check</sup> whether the new simulation differs significantly from the benchmark<sup>one.Alternatively,we couldseehow</sup> sensitive the model is to changes in **all** the estimated parameters<sup>at the sametime.We nowexplaineach</sup> approach in detail.

From the estimated parameters and their standard errors, confidence<sup>intervals canbeconstructed3 3.</sup> One can then set one parameter at a time to its<sup>confidenceintervallowerorupper boundwhilefixing the</sup> rest of the parameters at their original values. Keeping the calibrated<sup>parametersalso fixed,one can then</sup> simulate the economy. When we do this, it becomes clear that<sup>thesimulationsaremore sensitiveto some</sup> parameters than others. The reason is that some parameters<sup>areclose to the valuethat wouldmakethe</sup> constraints described in Footnote 9 bind. When we perturb these<sup>parametersbychanging themto their</sup> confidence interval bounds, we approach the constraints,<sup>sothemodeldeliversverydifferentdynamics.</sup> This is especially true for the parameters p and (. In fact, the<sup>lowerbound of theconfidenceintervalfor</sup> p obtained from the Townsend-Thai dataset violates<sup>someof the restrictionsthatthe modelmust satisfy</sup> to be well-behaved. Indeed, the unconstrained labor demand<sup>iszero,inwhichcasenoagentwiUlever</sup> want to become an entrepreneur regardless of his setup<sup>cost</sup><sup>_x._</sup> When we change the setup distribution parameter m beyond its<sup>confidenceintervaltoits extreme</sup> values of [-1, 1], and still fix the rest of the parameters,<sup>weobtainsomewhatmoredistortedpictures</sup> than if m were contained in the confidence interval. However,<sup>wedo notobtainthecyclesdiscussedby</sup> Lloyd-EUis and Bernhardt.

From the confidence intervals of the estimated parameters, we<sup>drawat random5,000differentsets of</sup> parameter values. It turns out, by chance, that none violated<sup>theconditionsinFootnote9.Noticethat</sup> since we also vary the scale parameter s, we are examining<sup>sensitivityto theinitialwealthdistribution</sup> when we use the SES dataset. Fixing the calibrated parameters<sup>attheiroriginallevel,werun5,000</sup> simulations for each the SES and the Townsend-Thai dataset.<sup>Wethencomputethemeanandstandard</sup> deviation at each date over these 5,000 simulations of<sup>eachofthefivevariables.Figures5and6also</sup> display (in dots) the 95 percent confidence intervals around<sup>themean.</sup> Figure 5 shows that income growth, the savings rate and the fraction<sup>of entrepreneursare quite</sup> 33We construct standard asymptotic 95% confidence intervals<sup>using thenorrnaldistribution.</sup> 18
insensitive to changes in the parameters within the 95 percent confidence intervals. Labor share<sup>and</sup> the Gini coefficient can potentially display different dynamics judging by the wider bands,<sup>especiallyafter</sup> 1989 at the peak of the credit expansion. The reason for this diversity of paths depends on whether or not the subsistence sector was completely depleted by 1996. If such was the case, then demand<sup>for</sup> workers would drive up wages, increasing the labor share and reducing inequality.<sup>If,on</sup> the<sup>contrary,</sup> such depletion did not occur, labor share would remain fairly stable and inequality<sup>couldincrease34 .</sup> Similar to the SES data results, the confidence intervals in Figure 6 show that the<sup>savingsrate and the</sup> fraction of entrepreneurs are robust to changes in the parameters. Income growth is more sensitive than its SES analogue, especially in the earlier years, 1976-1980 and after 1990. However, the bands<sup>shrink</sup> during the period of high growth. This indicates that all parameter combinations<sup>deliveredthishigh</sup> growth phase. Finally labor share and the Gini coefficient were very simular to their SES counterparts.

We thus conclude that with the exceptions enumerated above, the model<sup>isrobust tochanges inthe</sup> estimated parameters within their confidence intervals. We are yet more confident that the upturn<sup>of</sup> the Thai economy in the late 1980's could be attributed to the expansion of the financial<sup>sector.</sup>

# 9 Welfare Comparisons

We seek a measure of the welfare impact of the observed financial sector liberalization.<sup>Astherecanbe</sup> general equilibrium effects in the model from this liberalization, we need to be<sup>clear</sup> about<sup>the appropriate</sup> welfare comparison. We shall compare the economy with the exogenously expanding intermediated sector to the corresponding economy without an intermediated sector at the same parameter values. The criterion will be end-of-period wealth - that is what households in the model seek to maximize. For a given period, then, we shall characterize a household by its wealth b and beginning-of-period cost * and ask how much end-of-period wealth would increase (or decrease) if that household<sup>wereinthe</sup> intermediated sector in the liberalized economy, as compared<sup>3 5</sup> with the same household in the<sup>economy</sup> without intermediation, a restricted economy.

If in fact the wage is the same in the liberalized and restricted economies, then this is also the obvious, traditional partial equilibrium experiment - a simple comparison of matched pairs, each person with the same (b, x) combination but residing in two different sectors of a given economy, one receiving treatment in the intermediated sector and one without it. The wage is the same with and without intermediation in both SES and Townsend-Thai simulations before 1990, when the subsistence sector is not depleted.

If the wage is different across the two economies, this latter comparison does not measure the net welfare impact of the liberalization. Rather it measures end-of-period welfare differences across sectors of a given economy that has experienced price changes. To be more specific, those in the non-intermediated sector of the liberalized economy will experience the impact of the liberalization through wage changes - workers in the non-intermediated sector may benefit from wage increases while entrepreneurs in the non-intermediated sector suffer losses, since they face a higher wage. And of course there<sup>isa similar</sup> **price** impact for those in the intermediated sector, but there is a credit effect there as well. There<sup>are</sup> such wage effects using the parameters estimated from both datasets after 1990.

**'This dichotomous** feature **of** the model could **be improved by imposing diminishing** returns in the<sup>subsistence</sup><sup>**sector.**</sup> **35We** could compare the end-of-period wealth **of a** given household with _(b, x)_ in the non-intermediated sector **of** the intermediated economy with the **same** (b,x) household **in** the non-intermediated economy This would decompose<sup>the</sup> **welfare gains** (and losses).

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Implicit in this discussion is another problem<sup>whichhasnoobviousremedyhere, giventhemodel.</sup> Although households in the model maximize end-of-period<sup>wealth,they pass on a fraction of that wealth</sup> to their heirs. Thus the end-of-period wealth<sup>effectsof theliberalizationarepassedontosubsequent</sup> generations. The problem is that there is no<sup>obvioussummarydevice-</sup> households do not maximize discounted expected utility, as in Greenwood and<sup>Jovanovic(1990)andtheanalysisofTownsendand</sup> Ueda (2001), for example. Here then we do not attempt<sup>to circumventthe problembutratherpresent</sup> the more static welfare analysis for various separate<sup>periods.Arelatedissue isthe difficultyof weighting</sup> welfare changes by the endogenous and evolving distributions<sup>of wealthinthe two economies-seebelow</sup> for more specifics on that.

We take a look first at the liberalized economy<sup>in1979,threeyearsafterthe1976initialstartup,</sup> using the overall best fit Townsend-Thai data<sup>economywithliberalization.Asnotedearlier,the wage</sup> has not yet increased as a result of the liberalization.<sup>Its valueis .0198inthe liberalizedandrestricted</sup> benchmark economies. The interest rate in the intermediated<sup>sector of theliberalizedeconomyisvery</sup> high, at 93 percent. This reflects the high marginal<sup>productof capitalinaneconomywitha relatively</sup> low distribution of wealth.

Figure 7b in page 44 displays the corresponding occupation<sup>partition,butnowdenotingfor given</sup> beginning-of-period _(b,_ x) combinations the corresponding<sup>occupationofahouseholdintheno-credit</sup> economy and in the credit sector of the intermediated<sup>economy.The darkershadesof Figure7bdenote</sup> households with _(b, x)_ combinations who do not<sup>changetheiroccupationasa resultof the liberalization,</sup> that is, they are entrepreneurs (E) in the no-credit<sup>(NC)economyandintheintermediatedsectorof</sup> the liberalized (C) economy, or workers (W) in both<sup>instances.The lightshades denotehouseholds who</sup> switch: low wealth but low cost agents who were<sup>workersbecomeentrepreneurs,andhigh wealth,high</sup> cost agents who were entrepreneurs become workers.<sup>The picture is the overlap of the occupationalmaps</sup> in both sectors. For the credit sector, the key parameter<sup>is z, whereasforthe no-creditsector,it isthe</sup> curve _xe(b, wo)._ Figure 7a in page 44 displays the corresponding end-of-period<sup>wealthpercentagechangesin the same</sup> _(b, x)_ space. Since the wage is the same in both<sup>sectors, agentswillonlybenefitfrombeing inthe</sup> credit sector, not only because they can freely borrow<sup>attheprevailingrateif theydecidetobecome</sup> entrepreneurs, but because they can deposit their wealth<sup>and earn interestonit.The wealthgain due to</sup> interest rate earnings can be best seen by fixing x and<sup>moving alongthe</sup><sup>_b_axis,notingtherise.</sup> If on the other hand we look at the highest wealth,<sup>b = 0.5 edge,we cantrack the wealth changesthat</sup> correspond to changing set up costs x. Going<sup>from therearof the diagram,athighz,we seethat the</sup> wealth increment is constant, but these households<sup>wereworkersin both economies,so set upcosts xare</sup> never incurred. Then the wealth increment drops -these<sup>householdswereentrepreneursin the no-credit</sup> economy and were investing some of their wealth in<sup>the setupcosts x- those withhighxgain themost,</sup> quitting that investment and becoming workers in the<sup>intermediatedsector.Thus the percentagewealth</sup> increment drops as x decreases. One reaches a trough,<sup>however,whenthehouseholddecidesto remain</sup> an entrepreneur. Yet lower set up costs benefit entrepreneurs<sup>inthe intermediatedsectormorethanin</sup> the corresponding no-credit economy, because the<sup>residualfunds canbeinvestedatinterest.Hence,the</sup> back edge rises up as x decreases further.

The most dramatic welfare gains, however,<sup>are</sup> experienced<sup>bythoseagentswhoarecompelledto</sup> be workers in the no-credit economy but become entrepreneurs<sup>in theintermediatedsector.Although</sup> their setup cost was relatively low, their wealth<sup>wasnotenoughtofinanceit.They were constrainedon</sup> 20
the extensive margin. When credit barriers<sup>areremoved,theybenefitthe most.Thesharpverticalrise</sup> corresponds to those on the margin<sup>ofbecomingaentrepreneurintheno-crediteconomy.Intuitively,</sup> this is because with their low x, they<sup>wouldhaveearnedthehighest profitsif theycould havebeen</sup> entrepreneurs. Credit in the intermediated<sup>sectorallowsthat.</sup> A problem with this analysis, however,<sup>is</sup> that<sup>we maybe computingwelfaregains for householdwith</sup> _(b,_ x) combinations that do not actually exist<sup>in either the liberalizedeconomyor the no-crediteconomy,</sup> that is, have zero probability under the<sup>endogenousdistributionof wealth.To remedythis,Figures7c</sup> and 7d also in page 44 display the wealth<sup>distributionsoftheno-crediteconomyand crediteconomy</sup> (over both sectors) in 1979.

Figures 7f and 7h present a similar version,<sup>plottingthe jointdensity levelcurves of the end-of-period</sup> wealth levels in the no credit and liberalized<sup>sector36 .Thoseonthe45degreelinecorrespondto those</sup> with no change in wealth. We use the<sup>endogenousend-of-periodwealthdistributioninboth thecredit</sup> and no-credit sector and the distribution<sup>of xatthe estimatedtiltparameter mtoweighteach</sup><sup>_(b,x)_</sup> pair.

We report last the density of percentage<sup>changesinwealth inFigures7e and7galsoinpage 44by</sup> integrating the 3D graph in Figure 7a over the<sup>estimated rangeof x andweightingeach</sup><sup>_b_point usingboth</sup> the credit and no-credit wealth distribution<sup>3 7 .We recognizethat the distributionof wealth is endogenous,</sup> so taking any weighting is somewhat arbitrary.<sup>Still,whencompared,theydisplay a somewhatdifferent</sup> pattern. We can convert this relative wealth<sup>gaininto1997bahtfor bothweightingdistributions.The</sup> mean gain amounts to 61,582 baht using the<sup>no-creditdistributionor82,376bahtusingthe creditone.</sup> These numbers correspond to roughly<sup>1.5timesandtwicetheaveragehouseholdyearly1979income38</sup> (measured in 1997 baht) respectively. The<sup>modalgainsaresignificantlylower,6,961bahtor7,779 baht</sup> if we use the no-credit or credit distribution,<sup>respectively,whichamounttoroughly17percentor 19</sup> percent of the 1979 average household<sup>yearlyincome.In dollars,these gainsbecome$278 or$311using</sup> the no-credit or credit distribution at the<sup>1997exchangerates.</sup> By 1996, the wage has risen to 0.022<sup>in</sup> the<sup>non-intermediatedeconomyandto0.029intheinter-</sup> mediated one. The interest rate in the<sup>intermediatedsector hasfallento57percent.Theoccupation</sup> partition diagram has no agents who<sup>wereentrepreneurs becomingworkers(thoughthat numberwas</sup> never large). In contrast, the relative number<sup>ofthosewhowereworkersandbecomeentrepreneursis</sup> higher. The three dimensional diagramn<sup>inFigure8ainpage45ofwealthchangesisstill tiltedupward</sup> towards high wealth, due to the interest<sup>rateeffect.Onthebackedge,atthehighest wealthshown,</sup> wealth increments are positive and constant<sup>for thosewho stay as workers,bothdue to higher wagesand</sup> interest rate earnings, but those who<sup>wereworkersandbecomeentrepreneurshavehighwealthgains,</sup> which increases as _x_ falls, since net profits<sup>ofentrepreneursincreases assetupcostsfallsandfundscan</sup> be put into the money market. However,<sup>onereachesa pointwheretheywould havebeenentrepreneurs</sup> in both economies, incurring x in both<sup>economies,andthenthewealthgainsthoughincreasingasx</sup> decreases are relatively small or negative. Note<sup>that onthe onehand, entrepreneursinthe intermediated</sup> sector face higher wages, obtaining<sup>lowerprofits.Onthe other,theyare able tocollectinterestontheir</sup> 38The **joint** density is approximated using kernel<sup>density estimation,from a generated sampleof 10,000 pointsThe kerrnel</sup> used is Epanechnikov. See Silverman (1986)<sup>fordetails.Somelevelcurvesfallbelowthe 45°line,butthisis anartifact</sup> of the kernel density estimation which tends<sup>to oversmooth.Figure 7a assuresus that the liberalization only improvesthe</sup> welfare. Each contour level represents an increased<sup>densityof 0.01,starting at0.0025</sup>

> 37The density is approximated by the techniques explained **in** Footnote 36 3The 1979 average household yearly income is<sup>estimatedfromtheSESdata.</sup><sup>**Since**wedonothaveactualSESdata m</sup> 1979, we interpolate it **using** the average annual<sup>growthratebetween1976and1981</sup>

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wealth. These opposing wealth effects will translate into net gains or losses depending on their relative magnitude. More salient, the former tent-shaped region has pervaded this graph - the biggest gainers throughout the entire distribution of wealth are those on the margin of indifference between staying<sup>as</sup> a worker or becoming an entrepreneur in the non-intermediated economy.

The distributions of wealth of the no-credit and intermediated sector are also plotted in Figures 8c and 8d in page 45. Note that the scale has changed. Evidently both are more compressed from what they had been in 1979. Applying those distributions as weights to the diagrams with level wealth<sup>changes</sup> around the 45 degree line, one sees that again those at the low end of wealth would be measured as having extraordinary gains, from zero to ten-fold or more, and again in the credit economy a richer class gains somewhat from the yet high interest rates. The mean welfare gains of those who benefit from the liberalization amount to 52,573 baht or 65,789 1997 baht if we use the No-Credit or Credit distribution, respectively. The modal gains are lower at 6,863 baht or 15,905 baht using the No-Credit and Credit distribution. They correspond to roughly 9 percent and 21 percent, respectively, of the 1996 yearly household income as reported in the SES data. If we translate these modal gains into dollars, we obtain $275 or $636 per year. However, there is also a nontrivial fraction of entrepreneurs who end up losing due to higher wages. The mean losses for this subset of the population amount to 9,509 baht using the No-Credit distribution or 34,816 baht using the Credit distribution. They correspond to roughly 9 percent and 32 percent of the 1996 average household yearly income for the sample of entrepreneurs. In dollars, these losses become $380 and $1,393 using the No Credit and Credit distribution, respectively.

We now turn to the welfare comparison from the simulation using the best fit estimated MLE parameters using the SES data. The first thing to notice is that reported welfare gains are much lower. Two years after the outset of the liberalization, 1978, both economies have the same wage, and therefore the credit liberalization only improves the welfare of those who can take advantage of it. The 3D Figure 9a in page 46 resembles somewhat that of 1979 using the parameters estimated from the Townsend-Thai<sup>data.</sup> It is titled upward as we move along the b axis due to interest earning gains and again, the big winners are those who could not afford the set up cost (even though it was low) and had to take a job as a worker. If credit were available to them, they would become entrepreneurs, realizing huge profits, amounting to an increase in their wealth by a factor of up to nine-fold. The kernel density estimates in Figure 9e and 9g in page 46, however, show that the average wealth increase is somewhat lower, around 100 percent. These correspond to a mean wealth gain of 102,586 baht or 90,785 baht if we use the no-credit or credit distribution, respectively. The modal gains are again much lower, 5,560 baht for the no-credit<sup>or17,000</sup> baht for the credit distribution. These correspond to $222 or $680 at the 1997 exchange rate or, roughly 14 percent or 41 percent of the average household yearly income in 1978.

In 1996, the 3D Figure lOa in page 47 shows that welfare gains are somewhat smaller. Those agents that remain workers in the credit sector are better off because they earn a higher wage, and those that become entrepreneurs in both sectors end up losing because they face higher labor costs. As<sup>in</sup> the Townsend-Thai dataset, if we look at the distribution of welfare gains in Figures 10e and 10e, page 47, there is a significant fraction of agents who end up losing about half of their annual wealth. The<sup>average</sup> wealth gain is still positive at around 100 percent if we use the no-credit wealth distribution and around 80 percent if we use the credit one. But both distributions have negative support, a result of the<sup>fact that</sup> agents benefit in diverse ways from the credit liberalization. The mean welfare gains for those who benefit from the liberalization are 83,444 1997 baht or 76,840 1997 baht for the no-credit and credit distribution, respectively, and correspond to 1.09 times or one time the 1996 average household yearly income. The 22
modal welfare gains of those who gain are roughly<sup>18,591baht or25,655underthe no-creditandcredit</sup> distributions which correspond to roughly 24<sup>percentand34percentof 1996average householdyearly</sup> income, equivalent to $1,030 at the 1997 exchange rate.<sup>Themean losses,forthoseworse off are115,861</sup> baht or 117,051 using the no-credit and credit distributions<sup>respectively,whichamountsto 1.06times or</sup> 1.08 times the average household yearly income for the<sup>sample of entrepreneurs,orroughly $4,650 using</sup> again the 1997 exchange rate.

# 10 Extensions: International Capital Inflows and<sup>Alternative</sup> Credit Regimes

In this section, we explore two important extensions to<sup>themodel.Thesemaybeviewedasrobustness</sup> checks to the results presented in the previous sections.<sup>The first concernsthe liberalizationof the capital</sup> account that Thailand experienced, especially after<sup>1988.The secondrelaxes theassumption of restricted</sup> credit to allow for some external financing.<sup>Wenowtakeeachoneinturn.</sup> Figure 11 in page 48 displays the capital inflows<sup>asa fractionof GDP.Thedatacomefrom theBank</sup> of Thailand as reported in Alba, Hernandez and<sup>Klingbiel(1999).From1976to1986,privatecapital</sup> inflows to Thailand remained relatively low at an<sup>average of1.05%of GDP.From1986to1988,however,</sup> they increased rapidly to 10% of GDP, remaining at that<sup>averageleveluntil1996.</sup> This enhanced capital availability was funnelled through<sup>thefinancialsectorandthusitismodelled</sup> here as additional capital for those households that<sup>haveaccessto thefinancialmarket(i.e.residingin</sup> the credit sector). We run this extended (open)<sup>versionofthemodelattheestimatedandcalibrated</sup> parameters and compare it to the previous closed credit<sup>economymodelatthesameestimatedand</sup> calibrated parameter values from the two datasets<sup>39 .Althoughnot shown,capitalinflowscontributeto</sup> a larger number of entrepreneurs, and larger firm<sup>size,inparticularinthe late80's andearly90's.Since</sup> the marginal product of labor increases with capital<sup>utilization,morelaborisdemandedandthus the</sup> fraction of subsisters is depleted earlier. Thus labor<sup>sharerisesandinequalitydecreases,bothrelativeto</sup> the actual path and relative to the earlier simulation.<sup>The interestratetends tobelowerwithcapital</sup> inflows. Nevertheless, the welfare changes are<sup>small,indeed, almostnegligible.</sup> Because the surge in capital inflows coincides with the<sup>phaseof highgrowth of per capitaGDP,it has</sup> often been portrayed as an important factor contributing<sup>tothathigh growth.In orderto disentangle</sup> the extent to which the phase of high growth<sup>wasduetoincreasedparticipationinthe creditmarket</sup> versus additional capital availability due to capital account<sup>liberalization,we simulatethe economyat the</sup> estimated and calibrated parameter values allowing<sup>forinternationalcapitalinflowsbut usinga linearized</sup> credit participation from 6% to 26%, that is, a<sup>one</sup> percent<sup>increaseperyearfor eachof the20 years.As</sup> displayed in Figure 12, page 48, this version of the<sup>modelfailsto matchthe upturninGDPgrowthas</sup> compared to the benchmark credit economy. Thus,<sup>itseemsfromthe modelthatcapitalinflowsperse</sup> were not the cause of the high growth that Thailand experienced<sup>inthe late80's.</sup> The assumption of restricted credit may artificially<sup>deliverquantitativelylargewelfaregainsfrom</sup> liberalization if those assumed to have no access<sup>wereinfactabletoreceivesomecredit,perhapsfrom</sup>

> **391n addition, we re-calibrated** the **cost** of **living v,** subsistence level **growth rate 7gr** and **the** bequest **motive parameter** _w_ **for this open** economy **version** and found **even fewer**<sup>**differencescomparedto**the</sup><sup>**closedeconomymodel.**In</sup><sup>**particular,**</sup> the **calibrated** bequest **motive** parameter **_w_** _is_ lower<sup>**for**</sup> both<sup>datasets</sup><sup>**in**the openeconomy</sup><sup>**version,sothat**the depletion</sup><sup>**of**</sup> **subsisters** happens at **a** slower rate.

23
informal sources. Indeed, in the model so far, we have not allowed any form of lending (formal or informal) for those households residing in the no-credit sector. We now relax that assumption and explore whether the welfare gains from liberalization would differ significantly from those reported in the previous section.

We follow Lloyd-Ellis and Bernhardt (2000) and introduce intermediation which is limited by a moral hazard problem<sup>4 0</sup> . In particular, given an interest rate of _r,_ possibly different from that in the formal sector, entrepreneurs borrow _L_ and put up their wealth _b_ as collateral. After production they can abscond losing _rb_ but escaping the repayment obligation _rL._ Absconders are apprehended with probability p and if so they can hide their income but receive a punishment corresponding to an additive disutility of _d._ Borrowers will renege on the loan contract if _rb + pd_ < _rL,_ so lenders only make loans that satisfy _L < b +_ A, where A = _pd/r._ Thinking again about help from friends and relatives, we set _r_ = _1_ so that the objective function for households that now can "borrow" from the informal sector is the same as before. However, the constraint in (3) is now modified to

# _k+z <b+A._

In effect, parameter A is treated as a lump sum addition to wealth but only for those who borrow from the informal sector and choose to start firms This would allow more talented households to alleviate credit constraints<sup>41</sup> .

As shown in the Appendix, parameter A would be identified in cross sectional maximum likelihood estimation. However, to give parameter A a greater opportunity to influence the dynamic paths relative to the earlier simulation, we calibrate the parameter A against the dynamic aggregate data for each dataset using the previously estimated and calibrated parameter values for the other parameters. The calibrated values of parameter A are A = 0.0163 for the Townsend-Thai dataset and A = 0.0082 for the SES dataset. These correspond to 1.1 times and 30% of the subsistence income in 1976 for each dataset respectively.

Similarly to the open economy version, the increased informal finance speeds up the creation of firms resulting in an earlier depletion of the fraction of subsisters. Again, labor share begins to increase along with a decline in the Gini coefficient, both at an earlier date.

Although the parameter A could in principle have a large welfare impact, at the calibrated parameter A that best fits the Thai economy, these welfare gains and losses remain much as before. In particular, the counterparts of Figures 7a - lOa in pages 44-47 still feature the tent-shaped gains for relatively talented but poor people. Recall that in the intermediated sector, only the most talented individuals will start a firm, irrespective of their wealth. However, even when we relax the constraint in the no-credit sector by alowing informal finance, wealth still matters in determining who becomes an entrepreneur, and thus the tent-shaped welfare gains still appear. In addition, the increased wage in the latter periods does benefit workers in the liberalized economy. In the no-credit economy which now has informal credit, some households become richer but the additional capital from informal sources is insufficient to trigger an increase in the equilibrium wage rate. Thus, in the welfare experiment comparing the intermediated to the no-formal-credit economies, wage earners benefit more from the liberalization, but not much. In contrast, firms lose from the relative increase in the wage, but not much.

4<sup>0</sup> We also tried modifying the constraint in (3) to k E _i4O, b -_ xz, where the parameter N. measures the severity of the financing constraints. Thus, if some of the assets that make up wealth are illiquid, **we** would expect rN < 1. Unfortunately, this specrfication **is** not useful because the parameter N cannot be identified.

4t We are **implicitly** assuming a partial equilibrium, unlimited supply of **funds** by setting r = 1. However, the magnitude of the calibrated parameter A as reported below is comparable to the average amount of informal borrowing found **in** the Townsend-Thai dataset by those households who borrow informally.

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A prediction of the model<sup>thatcould seeminglybecheckedinthe dataconcernsrates of returnto</sup> capital. One might suppose<sup>thatwithlimitedcredittherateof returnto capitalwould be high.Thisis</sup> true for some firms in the<sup>simulations.However,for otherfirmswithoutaccessto intermediation,wealth</sup> cannot be lent at interest<sup>andisthusinvestedinternallyatlowrates of return.Thus theimplicationof</sup> the model is unclear about<sup>the mean,averagerate of return to capitaland only asserts that the dispersion</sup> of rates of return is higher<sup>forfirmswithoutaccessrelativeto those who canborrowandlend.</sup>

# 11 Conclusions

From the welfare numbers<sup>presented,there seems to be a lot at stake in credit liberalizations. Even by our</sup> most conservative estimates<sup>thereis agroupof lowwealthtalentedhouseholdswhohave muchtogain,</sup> period by period, in income<sup>and wealth.On the other hand,the estimatesreveal a group of entrepreneurs</sup> who have much to lose, period<sup>byperiod,inincomeandwealth,particularly ifonetakesintoaccount</sup> the growth in wages. We<sup>do notpush hereanyparticular anynumberas the mostcompelling,because</sup> the numbers do vary and<sup>do dependon the datasetused.Indeed,the largerpointisthatwelfaregains</sup> and losses are sensitive to<sup>thepresumed,estimatedmicrounderpinningsof theeconomy.If therewere</sup> more substantial intermediation,<sup>then variationsassociatedwith further liberalizationwould matterless</sup> Indeed, if there were more<sup>substantialintermediation,thentheimpact ondynamicsof(endogenous)</sup> changes in the wealth distribution<sup>would matterless,as in Kruselland Smith (1998).But the microdata</sup> reject such presumed underpinnings,<sup>makingwelfaregains,potentiallosses,andthedynamicaspectof</sup> liberalization more substantial.

Still, the surprisingly large<sup>order of magnitudeof these gainsand lossessuggests theneedforfurther</sup> refinements along a number<sup>ofdimensions,toseeifthemagnitudesurvivessomewhatmorerealistic</sup> specifications.

One refinement has to do<sup>withlaborandthelabormarket.Thelabor of the model hereisuniform</sup> with respect to productivity,<sup>that is,everylaborerearnsthe samewage.This ffiesin the faceof much</sup> empirical work mapping<sup>wagedifferentialsto skillsdifferentialsandacquiredhumancapital.Moregen-</sup> erally, earnings inequality<sup>contributesto overallinequality,andthis mightbesalient,asin theworkof</sup> Ricardo Paes de Barros<sup>inBrazil,forexample. Jeongand Townsend(2000)documentthe successand</sup> failure of the LEB model in<sup>explaininginequalitymovementsinThailand,comparingitto anextended</sup> version of Greenwood and<sup>Jovanovic(1990).Thoughthe LEB modelat the maximum likelihoodparame-</sup> ter estimates does surprisingly<sup>well,it is clearthat wagedifferentiationisneeded in models and inmodel</sup> based empirical work. In<sup>themodelofoccupationchoiceofEvansandJovanovic(1989),forexample,</sup> unobserved heterogeneous<sup>skills influenceboth wage earnings and the profitsfrom entrepreneurship.This</sup> might suggest that entrepreneurs<sup>worrylessaboutincreasedwagesastheypotentiallyexittobecome</sup> part of a skilled work force.

Second, one could endogenize<sup>accessto creditasintheGreenwoodandJovanovic(1990)modelwith</sup> transactions costs. This<sup>wouldslowdownthegrowthof financialinfrastructureandwouldrationalize</sup> some of the limited participation<sup>that we seebutthere wouldbe no Pareto-improvingpolicy intervention.</sup> However, Townsend and Ueda<sup>(2001)in an extended versionof the Greenwoodand Jovanovicmodel draw</sup> the conclusion that restrictive<sup>financialsectorpoliciesmay haveneverthelesssloweddownentryinto the</sup> financial sector below the<sup>endogenousrate.Thusfinancialsector liberalization,allowingintermediation</sup> at its otherwise endogenous<sup>value,isalsoassociatedwithwelfaregains,asinthispaper</sup> Although 25
occupation choice models should allow more endogenous financial sector participation, they illustrate well as they stand the fact that there may be welfare losses for some sectors of the population, not just gains, to liberalization. This offers a political economy rational for the apparently restrictive policies that we observed in Thailand.

> Third, the imagined industrial organization of the Thai economy is relatively simple. In the model here fixed setup costs are allowed to vary across potential entrepreneurs, as if drawn from a quadratic

> cumulative distribution, but the quadratic production function mapping labor and capital into output is

> uniform across potential entrepreneurs. This delivers the occupation partition diagram and variation over time in the size distribution of firms, a function of the wage and the endogenous distribution of wealth.

> Indeed, an industrial organization literature starting with Lucas (1978) begins the same way, at least in spirit. He postulates an underlying distribution of personal managerial talent and then studies the

> division of persons into managers and employees and the allocation of productive inputs across managers. This has implications for secular changes in average firm size. This point is revisited by Gollin (1999).

> Here the distribution of size and profits among firms is driven by self-financing, an endogenous and

> evolving distribution of wealth, and differential access to credit. That is, high set up costs and limited credit can limit the use of real physical capital in the standard part of the production function or can

> impede entry entirely. Likewise, some portion of end-of period profits is passed on to subsequent time periods if not subsequent generations. This could be an explanation for some of the serial correlation in

> size, profits, and employment that is seen in actual data, even when shocks in the form of set up costs are independent and identically distributed over time and households There would be implications for the cross-sectional dispersion in growth rates. Indeed, it seems that in Thailand larger firms, and those with financial access, may have grown faster than smaller ones as the credit market expanded in the late 1980's. Recent theoretical work is beginning to readdress earlier the supposed facts of firm growth and survival in the context of endogenous limited financial contracts. See Albuquerque and Hopenhayn

> (2001) and Cooley, Marimon and Quadrini (2000)

> More generally both the industrial organization and credit market literature need to be brought together. Existing empirical work has documented relationships between investment and the balance sheet, for example, but much of this work is somewhat atheoretic, documenting that the world is not

> neoclassical but leaving us wondering what the impediments to trade really are. The general equilibrium

> models of Banerjee and Newman (1993), Piketty (1997) and Aghion and Bolton (1997) take different stands on those underpinnings but collectively make the point that growth and inequality can be related to imperfect credit markets. That of course was our starting point here.

Indeed, in related work Paulson and Townsend (2001) use the Townsend-Thai data to estimate via maximum likelihood methods not only the LEB model featured here, but also collateral based lending as in model of Evans and Jovanovic (1989) (EJ for short), for example, and also incentive-based lending as in the mechanism design literature of Aghion and Bolton (1997) and Lehnert (1998) (ABL for short). Observed relationships of entrepreneurship, investment, and access to credit as functions of wealth and talent suggest that the ABL model fits the micro Thai data best, but the EJ model fits well for those with relatively low levels of wealth and those in the Northeast, while LEB, the model here, is a close contender. This suggests that a calculation of the welfare gains and losses to financial intermediation based on these other models would be worthwhile, though the average and modal estimates here should not be rejected out of hand. It does seem plausible, however, that the dramatic gains near the wealth equal set-up costs or 45° line would be vulnerable to alternative specifications.

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The growth and inequality literature<sup>relyingoneachof theseunderpinningspresupposes,asin the</sup> LEB model here, either an<sup>overlappinggenerationsmodel with abequestmotiveor a simplistic,myopic</sup> solution to the household<sup>savingsproblem.More needs to be done to make the models dynamic.Coupling</sup> households with firms and<sup>modellingthe firms'inter-temporaldecisionproblemswillrequire more work,</sup> but again, given the prelinminary<sup>resultshere,that workwouldappear to be warranted.</sup> 27