Financial Markets and Poverty: An Algorithm for Policy-based Research and Research-based Policy, Lecture notes of Financial Management

Download Financial Markets and Poverty: An Algorithm for Policy-based Research and Research-based Policy and more Lecture notes Financial Management in PDF only on Docsity! -1- Chapter 1 Financial Markets and Poverty: An Algorithm for Policy-based Research and Research-based Policy Robert M. Townsend University of Chicago* This chapter explores the relationship between poverty and financial markets. The document serves as a guide for operations and policy on the one hand and for research on the other. But the chapter does not distinguish between these two uses. Rather, both are merged into a common goal: policy is based on research and research is geared toward generating policy conclusions. The starting point of each section of this chapter is an explicit structural model. A list of data that would be needed to test the model comes next, followed by an outline of exactly how the data would be used: that is, what procedures or tests would be employed in the analysis. The findings of the empirical work from the analysis are then presented. Then and only then are explicit, detailed recommendations for policy offered. The final section offers a few caveats, notes some weakness, and gives some directions for further efforts along this line. References to the literature are listed by corresponding section at the end of the chapter. The chapter is laid out by topic. The first topic concerns occupation choice and transitions into business. The goal is to use data to identify the real obstacles or barriers to trade. Wealth may appear to facilitate business formation and investment, and so the poor seem to lack opportunities—but that does not explain whether the fundamental problem is commitment and absence of collateral or moral hazard. The second section embeds the same micro-underpinnings into a model of growth with changing inequality. An exogenously expanding financial sector is shown to have a huge multiplier effect on growth, though inequality may increase for a time along the growth path. Among those previously lacking access, the talented poor benefit the most from the expanding formal financial sector: that is, their welfare gains are large. The discussion then returns to the micro data to emphasize another feature: the allocation of risk. The third section evaluates specific financial institutions, formal and informal, to see if they are having a positive impact on households and businesses in terms of ability to smooth idiosyncratic shocks. Then, as before, these micro-underpinnings are embedded into a model of growth with increasing inequality and taken to more macro data, in the fourth section. Policy distortions and barriers to entry are shown to slow down growth, but in this transactions cost framework, the largest gains from financial liberalization are reserved for the middle class. The gain is again quite large. The third and fourth sections provide the background for an evaluation of government development banks. * Charles E. Merriam Distinguished Service Professor, Department of Economics, the University of Chicago. -2- The fifth section outlines how to do the accounting, provisioning, and cost-benefit analysis, given an operating system in which credit contracts have insurance contingencies. The welfare gain from improved risk sharing through the development bank is compared to the government subsidy. The sixth section turns to another institution, village banks, as an example of how to evaluate the microcredit movement. All the sections of this chapter draw on data from one particular country: Thailand. Multiple data bases are used. However, the methods are applicable to any country, with the proviso that there are sufficient data to test the model presented for a given topic. The methods presented in this chapter are applied to countries in Latin America in several companion chapters. Chapter 2 identifies obstacles to small business formation in Nicaragua. Chapter 4 explores growth with changing inequality and financial deepening in Peru. Business Start-ups and SME Business Investment The assessment of this first topic is based on “Distinguishing Limited Commitment from Moral Hazard in Models of Growth With Inequality” (2003), and “Entrepreneurship and Financial Constraints in Thailand” (2004), by Anna Paulson and Robert Townsend. Model-Theory/Logic Three distinct models are to be distinguished: No credit – Households must use their own funds (initial wealth, W) to start up business, paying a fixed cost, x, or expand the business, the usual kind of investment, k. Thus the key financial constraint is Wxk ≤+ . The occupation choice is to go into business in this way and earn profits from hired labor at wage w—namely, xwllk −−),(θ —or stay in the subsistence sector earning a subsistence income, s, or equivalently earn (unskilled) wages, w, (plus a potential cost of living differential, if employment is in the city). Uninvested initial savings are carried over at home at a low return; there are no financial savings. Households vary in the fixed costs x they incur. Specifically, costs vary inversely with talent, which is unobserved but distributed in the population under a to-be- estimated distribution parameterized by m: namely, ),( mxH . A version of this model is Lloyd-Ellis and Bernhardt (2000). Collateral – The model is essentially the same, except that now wealth w can be used as collateral and households lose that collateral with some probability if they default. (Of course, the advantage to default is absence of loan repayment.) Hence businesses can borrow, unlike the no credit economy, but only up to a proportion of wealth, say λ, where λ is determined by the probability of capturing the defaulter and the interest rate. Lending is asset-backed only, and other customer characteristics are not taken into account. Thus the higher is wealth, the more businesses can borrow, up to λW. In Evans and Jovanovic (1989), talent is now supposed to enter multiplicatively into production, raising the marginal productivity of labor and capital: that is, ),( lkfεθ , where θ is talent and ε is an ex post idiosyncratic shock. Talent θ is distributed log normally in the population -5- According to the estimates, as of 1992, a doubling of wealth in the cross-sectional sample is associated with an increase in start-up investment of 40 percent. Likewise, under financial constraints, the returns to business investment will be high for low wealth households and will fall as wealth increases. For the whole sample, median returns to business investment—that is, income to capital ratios—fall from a strikingly high 57 percent for households in the lowest wealth quartile to 16 percent for households in the highest wealth quartile. Entrepreneurial talent, as measured by education and whether parents were in business, does seem to facilitate business entry and the ability to exploit relatively high marginal returns, but it also appears there are a nontrivial number of talented but low-wealth households that are constrained on these margins. Moreover, if the data on credit as function of wealth for those businesses that report credit constraints are examined, the level of credit decreases with wealth: that is, net savings increases with wealth. Among the subsample of relatively wealthy households in the central region, a doubling of wealth leads to a 40,000 baht increase in savings. This is not true in the northeast. Likewise, the moral hazard model predicts that virtually all businesses that borrow will report some degree of constraints, whereas the asset-based lending model allows low-talent households to borrow and go into business without hitting constraints. The data reveal that being constrained is strongly associated with borrowing in the central region; nearly three-quarters (73 percent) of constrained business in the central region have outstanding debt, as compared to only about half (54 percent) of unconstrained businesses. Constrained businesses in the central region also have more debt than unconstrained businesses: a median of 50,000 baht versus 30,000 baht. That is, businesses that have managed to secure more credit are businesses more likely to complain about persistent constraints. Neither of these relationships holds in the northeast. The implication of some of the models that investment should increase with education and talent is strongly supported in the data, contrary to the presumption that talented households will need to invest less. Thus physical capital and human capital are complements. More educated households will want to invest more—and holding wealth fixed, increasing education causes more households to complain of credit constraints. Policy Implications: The policy implication is that overall wealth does limit access to credit in the northeast of Thailand in a way that might be remedied by relationship-based lending. The joint liability groups of the agricultural development bank, the Bank for Agriculture and Agricultural Cooperatives (BAAC), are not helping as much as might have been anticipated. The level of credit is still limited by wealth, and indeed entry into a joint liability group may be limited by wealth. This is not to say that the BAAC is not helping. Despite its charter and history, the BAAC does facilitate business entry and business -6- investment. But it is doing so in a way that links its credit access and credit supply to wealth. In contrast, neither village-level institutions nor networks of friends and families in the northeast of Thailand have sufficient resources to overcome the simple observed relationship between wealth and credit in a substantial way—despite alternative selection and lending procedures. (For a rigorous assessment of village funds, see Kaboski and Townsend 2001.) Though helping to alleviate constraints in business and agriculture, business start-ups are apparently not facilitated. Commercial bank lending is so rare in the northeast that it fails to be a consideration in business starts and investment; less than 2 percent of the population have loans. This is not to say that one should give up on commercial bank lending. It appears that there would be a way for commercial banks to make profits in this sector, in the northeast. In the central region, BAAC credit, though still dominant, at 24 percent of all lending, is matched closely by commercial bank lending, at 21 percent, and from friends and relatives, at 17 percent, respectively. In the sample period, the BAAC gained more in interest income from larger, wealthier clients—precisely those households eligible for commercial banks loans. Thus it is a reasonable inference that the BAAC might be less willing to foreclose when such clients run into difficulties, potentially smoothing consumption or lessening investment fluctuations. The BAAC does have in place a risk- contingent lending system that would allow delayed repayment in some events. This should be viewed a good thing, a priori, although it is not clear why this plays less of a role in business start-ups and financing in the northeast. Ways to make the BAAC risk- contingent credit system more explicit and improve the accounting so as to better assess its benefits are discussed in Townsend and Yaron (2001). On the other hand, ample credit from the informal sector in the wealthier central region may be the key ingredient that allows for risk-contingencies in loans. When in trouble, a relative or moneylenders pays for the client. Thus the Thai government should reconsider its efforts to eliminate money lending, especially if the credit instruments it promotes in government institutions or imposes through regulation are limited to simple noncontingent loans. What matters is the nature of the financial instrument. Higher wealth households may be able to piece together a variety of financial instruments in a way that makes the whole greater than the sum of the parts. In contrast, in the northeast, households are more dependent on BAAC—if they can secure credit at all—so much attention should be given to the financial instruments currently offered by the BAAC or those to be offered by newly emerging institutions such as the People’s Bank. Optimally designed credit contracts need to take into account risk, incentives, and the ability to repay. Caveat/Sensitivity/Extensions These models are static and hence do not capture possible interactions between wealth and talent. That is, wealth may appear to alleviate constraints but part of that may be correlated with underlying, unobserved talent. Instruments for exogenous wealth should -7- be used where possible. Extensions underway allow for multiple lenders, making explicit a tradeoff between money lenders with full enforcement and lower transactions costs, smaller loans at high interest, versus commercial banks requiring collateral and larger transactions costs, higher rates (Xavier Giné). A larger array of credit contracts allowing for blends of moral hazard and default are being estimated (Alex Karaivanov). Finally, less structure is imposed on the production function and unobserved distributions of talent in each sector (Buera 2002b). The bibliography for this first topic appears at the end of this chapter. Financial Liberalization and Growth: Poverty Reduction through Improved Occupation Choice The assessment of this second topic is based on “Evaluation of Financial Liberalization: A General Equilibrium Model with Constrained Occupation Choice” by Xavier Giné and Robert Townsend. Model There is a sector of the economy without any intermediation (or in an extension, informal credit only). This sector is like the Lloyd-Ellis and Bernhardt (2000) economy described earlier as the no credit economy (actually its no credit and no financial savings). Wealth is a constraining feature on investment k and occupation choice (particularly transitions into business). In a second, intermediated sector, there is an endogenously determined interest rate, r, at which all households can borrow and lend—so it is as if all initial wealth, W, were put on deposit in a bank, earning Wr)1( + and those who start a business borrow to cover the setup cost, x, and investment, k. Thus investment and occupation choice are not related to wealth in this sector. Production takes place in cities, and there is a cost-of-living urban/rural differential. The wage rate is common to both sectors, so migrants from the nonintermediated sector can earn wages but cannot deposit earnings in a savings account. Again, setup costs vary inversely with talent, and there are some poor talented households and some rich but not-so-talented households, among other categories. This intermediated sector is small initially, but is presumed to grow slowly at the rate observed in the data (to move with measures of financial deepening). More specifically, the rate can be varied exogenously in the model and policy experiments can be conducted. Households choose occupations at the beginning of the period, either nonfarm investment, wage earnings, or subsistence agriculture. Initially, low wealth constrains choice, so wages are low and profits for those in business are high. End-of- period wealth is saved at a fixed rate (myopic savings) or in another interpretation, passed along to heirs (inheritance). As entrepreneurs earn rent, initial inequality grows. Overtime, as wealth accumulates, more households can transit into business. Eventually, however, the wage increases and hence profits decrease. Income differentials decrease and inequality decreases as well. The model has no endogenous growth and so the match should be with GDP growth less total factor productivity. In principle, informal credit can -10- to switch occupations and go into business gained the most; the welfare numbers would be even higher if the simple arithmetic average had been used. By the year 1996, the wage is roughly 60 percent higher than it would have been without the expansion. Such price movements help determine the distribution of welfare gains and losses attributable to expansion of the financial sector. The bottom line is that there were still substantial winners in 1996: that is, wealth accumulation had not overcome financing constraints, so the economy without intermediation suffers relative to the one with intermediation at the observed rate. The modal increase in welfare was 25,000 baht, or approximately 26 percent of 1997 average household annual income, equivalent to $1,000. With the wage increase, unskilled laborers employed by business also gained. However, that wage increase created welfare losses for those running firms: namely 116,000 baht each for such households, on average—roughly $4,600. Surprisingly, capital inflows do not seem to lie behind the dramatic expansion and the welfare gains associated with intermediation. The gains are coming from access for those who previously did not have it, and not from increased credit for those who already have it. Even the addition of informal credit for those without formal access does not alter this picture. Building financial infrastructure would seem to be the key to growth, although again, there would be some who lose. Policy Implications If intermediaries had been allowed to expand at a faster pace, and if these same institutions had efficiently allocated credit to productive sectors, as the model assumes, then growth would have been even higher. However, in 1996, the Thai economy still displayed the same symptoms as in its earlier history, and there is no indication that the situation is any different today. That is, in 1996, the number of households with nonfarm businesses stood at only 20 percent of the population. More telling perhaps, the cross- sectional relationship between wealth and entrepreneurship was quite sharp: 8 percent for the low wealth deciles and 30 percent for the highest. Similarly, the number of those with transactions with a financial intermediary in the prior month stood at only 27 percent of the population in 1996, and the cross-sectional gradient was even steeper: 9 percent at the lowest deciles to 45 percent for the highest. These numbers can be adjusted so that they reflect initial conditions: for example, for the young who have most of their wealth from bequests and little from business operations. One suspects such numbers deteriorated only during the financial crisis. For example, according to the Townsend-Thai data, commercial banks had only a 16 percent share in total lending in semi-urban and rural areas in 1997; this declined to 9 percent by 2000. The number of commercial bank borrowers stood at only 3 percent in 1997, and this dropped to 2 percent by 2000. The bottom line for policy is that an efficient expansion of the Thai financial system now could be an engine for much higher growth. The logic and numbers behind that recommendation are of exactly the same kind as economic/historical mechanics described above. -11- Still, the solution does not lie in simplistic or blunt policy instruments aimed at expanding credit and saving facilities. It is important that any such expansion take place efficiently. Specific policies need to be directed at specific institutions. Savings mobilization programs and the establishment of village funds continue to be promoted by government agencies, but unfortunately without much critical review. Not all institutions and polices are successful. For a more detailed assessment of the impact of particular institutions and particular policies, see Kaboski and Townsend (1998). Likewise, access to credit is limited and often linked to land as collateral, especially among low-wealth households and those in the northeast. Client-based lending procedures would seem to be helpful, rather than client-blind, collateral-based, asset-backed lending. These are already used, but ironically seem more effective in the central region and among higher wealth households. For a more detailed analysis of micro underpinnings of credit markets and the macro economy, see Paulson and Townsend (2001). More flexible risk-contingent lending could be helpful, but the current regulatory system forces Thai policymakers to face a hard choice between seemingly popular but potential ad hoc and inefficient debt moratoria, on the one hand, versus inappropriate classification of nonperforming loans and inefficient provisioning, on the other. More generally, there seems to be poor understanding of a risk-contingency system that has served the BAAC and Thai population well. See Yaron and Townsend (2001) for an analysis of the larger, BAAC system and how the regulatory accounting framework and the operation of the BAAC could be improved, specifically coupling accounting standards with micro economic data. Finally, there has not been, to our knowledge, a rigorous assessment of the efficiency of commercial bank lending: an assessment that would also combine models and data, along the lines of this chapter. In summary, Thailand has within its grasp the ability to increase the growth rate of national income and improve the well-being of talented entrepreneurial households among the poor and middle classes. What is required is a well-functioning financial intermediation system that allows such talented households to go into business or expand existing businesses. Estimation based on a formal economic model suggests that beneficial effects could be large if the financial sector reforms are well-conceived and carefully implemented. Caveat /Sensitivity/Extensions A more realistic household-based, dynamic decision model is needed Similarly, a richer model of the credit market might matter for dynamics. (See Karaivanov, in progress). The current model is sensitive to certain parameter values, which can cause binding corners in hired labor. The simulated paths are sensitive to the timing of the depletion of the subsistence sector, especially labor share and inequality. A bibliography for the second topic appears at the end of this chapter. -12- Risk, Safety Nets, and the Ideal Role of Financial Institutions and Financial Instruments The assessment of this third topic is based “Safety Nets and Financial Institutions in the Asia Crisis: The Allocation of Within Country Risk,” by Mauro Alem and Robert Townsend (2001). Model There are idiosyncratic shocks, ε, hitting households individually and aggregate shocks, θ, hitting everyone simultaneously. The essential idea is that, without moral hazard or reneging problems, idiosyncratic shocks can be shared or pooled, leaving only aggregate shocks to influence consumption. Thus individual income movement, and other shocks, should not determine individual consumption once one controls for aggregate consumption. The basic regression equation captures this succinctly: j tt j tt j tt j tttttt j tt uXhsADc 1,1,1,1,1,1,1, +++++++ +∆+∆+∆+=∆ ξηδβ Household consumption change, ∆c, is regressed on to fixed time effect, D, and household income change, ∆X, as well as changing household demographics, ∆hs, relative to population average demographics. The coefficient ξ should be zero. This is the benchmark. In practice the issue is whether a positive coefficient is reduced by access to a financial institution or is lower for certain demographic or income groups. Likewise, in a full neoclassical model, capital should be allocated across projects so as to equate the value of marginal products. Thus individual income change should not determine investment once one controls for aggregate shocks that determine future valuation. Again, a regression equation of household investment, I, onto time fixed effects D and household income change ∆X is: j tt j tt j tt j tttttt j tt eXhsADI 1,1,1,1,1,1,1, +++++++ +∆+∆+∆+= ξηδβ The coefficient ξ should be zero. This is the benchmark. In practice the issue is whether a positive coefficient is reduced by financial access or is lower for the wealthy, for example. Alternatively, change in investment should be insensitive to cash flow. Data Household consumption for a number of years, possibly estimated by a subset of items every year and then weighted and scaled up. Household income, as measured by gross revenue less expenses for agriculture, business, wage earning, fish/shrimp, and livestock. Recall, retrospective data on whether this past year was better or worse than the year before, and if worse, the shock or cause, and also the response -15- income. Shrimp growers in Chachoengsao seem especially vulnerable to income change on both consumption and investment. Safety net policies attempt to target particular groups. There is not apparent evidence in the panel data, which reveals particular and consistent vulnerability for the elderly, female-headed households, those with low education, or those with low wealth. There is, however, a distinct regional pattern. Apart from low education, all the other potential targeted groups do worse in investment stabilization in the northeast. But overall, those households suffering a direct consumption impact from bad years lie not in the poorer northeast but rather in the industrialized central region. There are also variations within regions, and drought, flood, pests, and illness compete with macro shocks such as unemployment and price movements in an explanation of investment and consumption change. The primary source of formal credit to Thai farm is the Bank for Agriculture and Agricultural Cooperatives (BAAC), the government’s primary development bank. It has in place a risk-contingency system under which loans are extended and interest and/or principal partially forgiven for farmers experiencing adverse events, both household- specific and regional. Thus one would have thought the BAAC would do a reasonably good job in smoothing consumption or maintaining investment. See, for example, Townsend and Yaron (2001) and Chiarawongsee (2000). But the analysis of the consumption and income panel data here shows that the BAAC was not particularly helpful in buffering consumption from adverse shocks. There are exceptions, mostly in the northeast. It is conceivable that outside agencies misunderstood the risk– contingency system of the BAAC, and that, as with commercial banks, they curtailed on- lending accordingly. In contrast, on the investment side, the BAAC has performed quite well in buffering investment from adverse shocks. Evidently credit from the BAAC is used to finance the levels of and fluctuations in investment. Village funds have long been promoted in Thailand as a cooperative solution to an otherwise restricted financial system. Local, microcredit institutions have been established in many villages to expand credit to farmers or small business, as with Poverty Eradication Funds; to promote change of occupation, as with Women’s Groups; to mobilize saving, as with Production Credit Groups; and to provide assistance in emergencies, as with Rice Banks. In the larger 1997 retrospective survey, Women’s Groups and Production Credit Groups show up as having had a beneficial role in risk reduction, although funds in general suffer from failure and much turnover (see Kaboski and Townsend 2001). The panel data paint a interesting if complicated picture, with funds seemingly helping to smooth consumption when the BAAC does not, and helping to smooth investment when the informal sector is inadequate. Help from friends and relatives, and from moneylenders, traders, storeowners, and others in the informal sector, shows up as particularly helpful in smoothing the effect of adverse shocks onto consumption. The informal sector is less successful overall in smoothing investment, but there are many helpful exceptions, particularly business investment. -16- In times of global instability, self-reliance is particularly appealing. Thai farmers free from drought or flood have ample crops of rice, which they store locally, in anticipation of future shortfalls. But the data reveal little beneficial year-by-year impact, at least not in the short run. Indeed, northeastern farmers in Srisaket who escaped the El Niño drought increased their stores of rice in the early “crisis” years even as they reduced consumption. Unfortunately, this seems to have reduced insurance, thus resulting in a perverse effect from rice storage. Policy During the financial crisis in Asian countries such as Thailand, macroeconomic aggregates were used to portray the health or state of the impacted economy. Negative GDP growth was taken to indicate a fall in household welfare, for example. As a result, high interest rate policies were initially used to encourage foreign (re)investment and expansionary monetary and fiscal policies were used later. Moreover, as commercial banks and finance companies were thought to be culprits in instigating the crisis, financial sector reforms were also implemented. The focus was on increasing capital adequacy ratios and reducing nonperforming loans. Finally, safety net policies recognized that particular groups or sectors might be more vulnerable than others to downturns, if not to the adverse effects of tight policy. Thus a government agricultural development bank was used as an engine of growth, and the government saving banks was used to promote village funds and small household business. From this discussion, several related points deserve emphasis. First, macro policy, financial sector reform, and safety nets work in varying degrees through the financial system, sometimes through the very same financial institutions. Yet these policies were implemented without a common conceptual framework. Indeed, there has been little theory-based assessment of the financial institutions or the safety net policies. Nor has there been an integration of any such assessment with the construction of improved macro models. All of this suggests an obvious alternative strategy: explicitly incorporate the diversity of shocks, use the theory of an optimal allocation of risk-bearing as a benchmark to evaluate the role of the financial system, and thus appraise financial sector reforms and safety net policies, both for their own importance and to formulate improved macroeconomic policy, both in crisis periods and in the long run. This study utilizes a unique set of panel data for Thailand, and the advantage of hindsight and analysis, to establish and carry out this agenda. More specifically, it should not be presumed that in times of macro crisis and structural reform that macro shocks per se are the main source of the problem, nor that the poor are suffering relatively more. In Thailand, the larger effects on consumption were in the industrialized developed region. On the other hand, investment effects were worse in the semi-arid and poorer northeast. Finally, within the northeast, but not the central region, a better case could be made for targeting. That is, low wealth households in the northeast suffered income fluctuations in consumption more, and female-headed households and low wealth households suffered income fluctuations on investment more. -17- Targeting by occupation group is treacherous. Average wages and remittances did not fall as much as in other sectors , and concerns about unemployment were misplaced. On the other hand, within the group of wage earners, or those within agriculture, there were relatively uncovered idiosyncratic fluctuations. So within-group insurance might be envisioned . In contrast, while profits from nonfarm business and shrimp farmers fell substantially, perhaps justifying efforts to safeguard and encourage small business, within-firm insurance was surprisingly high. It is important to discover what mechanism is at work. Still, fish farmers suffered both relatively uncovered income-induced consumption and investment fluctuations. Thus idiosyncratic shocks retain their importance even in the macro crisis, and though measured aggregate shocks were not large, the associated macro policies and financial sector reforms may have impeded the ability of the financial system to play its traditional risk reduction role. Under the threat of nonperforming loans and regulatory tightening, commercial banks reduced on-lending dramatically, yet this left household more vulnerable than they might otherwise have been. Ironically, reduced savings accounts did provide ample buffer-stocks, though symptomatic of the disintermediation. This suggests that banks be evaluated and regulated on broader criteria, such as diversification, and that banks be encouraged to make explicit contingencies, or create contingencies in their credit contracts. The BAAC does have such instruments, as documented in Townsend and Yaron (2001), and it did play a more constructive risk-reduction role in the crisis, with exceptions. Still it was under pressure to reduce loans judged as nonperforming, using a mechanism inconsistent with its own operating system. Informal sector credit rose in the period, and was quite helpful. The government should view the informal sector and financial markets more generally as co-partners in risk reduction efforts. An enhanced allocation of risk-bearing through formal financial institutions can have a direct, positive effect on growth, as is made clear in the work of Townsend and Ueda (2001). Thus risk reduction is not a simply safety net issue, but rather has consequences for long-run efforts to alleviate poverty. Caveats/Extensions Ideally, the risk-sharing regressions should control for labor/leisure choices and employment should be considered jointly with consumption smoothing. Much work remains to be done with the investment equations, distinguishing by sector and purpose, but also modified to include adjustment costs. It must be emphasized that the standard being employed here is overly strong. A priori, one would not expect many households or businesses to pass the stringent tests of full insurance for consumption and neoclassical efficiency in production. The observed degree of deviation, while a good standard for evaluation, begs for an explicit alternative model that incorporates impediments to trade, private information, limited legal enforcement, or other transactions costs. With these models, one could better gauge whether alternative macro or regulatory policies could have improved matters. Moreover, -20- business cycle literature. Idiosyncratic and aggregate shocks are entered with nontrivial variances (though these will be estimated in subsequent work). Marginal transactions costs are set at plausible values. For a household not in the financial sector, the decision is how much to save and how much to invest between the safe and risky assets. For a household in the financial system, advance information is very good and the only decision is how much to save. A solution thus yields endogenous portfolio and savings policy functions and critical values of capital necessary for entry. As an initial condition, start the economy at the initial date with a wealth distribution as measured in the data in the initial year, but centered so that at the derived critical value of capital, the observed participation rate is mimicked. Next, characterize the mean, analytic path, and the central tendency path of the economy based on the derived policy function, taking expectations over shocks. Plot those dynamics against the observed data on growth and participation for the (Thai) economy. Finally, simulate the same economy 10,000 times and pick the small set of paths from models that are closest to actual dynamic Thai path, comparing the Gini measure of wealth inequality, the participation rate in the financial sector, and the growth rate of income. Alternatively, pick the paths that are closest to the observed Gini coefficient and growth rate of income, and then construct confidence intervals for the range of financial participation predicted from the model, to be compared to the actual participation rate. Findings The model at given and nearby parameters tends to under-predict the growth of income, especially during second decade, the 1986–96 period, though its prediction for Thailand is of high growth in the long run, with the associated nontrivial inequality. Income differentials between sectors widen over time. Growth and income inequality are created by shifts in the population to the intermediated sector over time. But at these values, the model tends to over-predict substantially the fraction of the population participating in the financial system. Making households substantially more risk-averse and giving them a higher preference for current consumption tends to slow growth, thus lowering participation; but the model's simulation still substantially over-predicts the actual lower rate of participation in the Thai population, as historically observed. The conclusion is that something in Thailand was impeding the construction of a far-reaching financial infrastructure that households and businesses would have been willing to pay for. One suspects that otherwise well-intended Thai policy is responsible. Policy Implications The model tries to capture the impact of the wide array of restrictive policies that existed in Thailand up to the early 1990s, by crudely and exogenously restricting entry to those with even higher wealth: higher than the model without restrictions would predict. It is as if commercial banks and other financial institutions were underinvesting in middle- wealth regions, for example. The welfare losses associated with such restricted policies can then be estimated. These losses are nontrivial—averaging from 4 to 10 percent of wealth—and positive for virtually the entire Thai population, except those high wealth -21- individuals and businesses already in the financial system by 1976, and those so poor that without some other form of redistribution, eventual entry would be extremely distant. The magnitude of this loss from restricted policies is thus quite large. This is the main lesson learned from the model exercise. In addition, the concentration of losses in the population is not uniform; it tends to be skewed to the middle class: those with wealth not too far from the imposed value. That is, those that gain the most are those middle-class households and medium-scale enterprises that would be willing to pay fees and enter the system, or obtain yet more credit and insurance, if only Thai financial policy were to permit it. This concentration of gains among the middle class is the second lesson. The policy recommendation is that Thailand take steps to liberalize its financial system further. In particular, access should be increased in semi-urban and rural areas, with the cost passed on to business and households in the form of higher fees. Widely used macroeconomic and financial ratios, such as M2/GDP, possibly indicative of financial deepening or inflation pressures, can be misleading because they do not capture the underlying disparities in access. Similarly, the financial system needs to play the role envisioned in the model, with better pooling of information on project success and enhanced insurance or credit-guarantee schemes, although again these indemnities should be funded with adequate self-generated premia. Current regulatory efforts concentrating on nonperforming loans have underplayed disclosure and underplayed the risk reduction achievable with portfolio diversification and adequate risk contingencies. Caveats/Extensions There is a need to model better, and make a distinction between occupation choice and portfolio choice. Investment in education also needs to be included. Transactions costs need to distinguish households by region and socioeconomic status. Although sensitivity analysis is conduced, some of the parameters need to be estimated from the cross- sections, rather than imposed. As a transition economy, the construction of confidence intervals is also problematic. The model takes the paths of inequality and growth that best fit the Thai data out of the 10,000 simulations at calibrated parameter values, looks at the final value of participation for that subset of paths, clips off the tails—2.5 percent of each tail—and thus plots for the remaining economies a 95 percent band within which the model economy lies. A bibliography for this fourth topic appears at the end of this chapter. -22- Government Development Banks and other Financial Institutions: An Assessment though Operating Systems and Financial Accounts The assessment of this fifth topic is based on “The Credit Risk-Contingency System of an Asian Development Bank,” by Robert Townsend and Jacob Yaron (2001). The intermediary is lending to finance short-term inputs into production (farming and business), long-term investment in these occupations, and credit to smooth consumption and investment from fluctuations from adverse idiosyncratic shocks. The model can allow for interim communication of unobserved shocks to borrowers, costly interim and ex post verification of those shocks, and some nonreneging, or the imposition of continued participation constraints for borrowers. The contract with the intermediary thus consists of a bundle of attributes: capitalization or investment, recommended or induced action, repayment of loans, and insurance against shocks. With competition, such contracts would be fairly priced in the market, and intermediaries would break even. Competitive equilibria would be Pareto optimal. However, target groups such as rural farmers at risk might be given lump sum transfers or grants: for example, for the purchase of insurance. These grants might come from intermediaries, which therefore take losses. Compensation for losses requires transfers from the government, financed in the end by taxpayers—and possibly falling on nontarget groups. In particular, the idea behind provisioning is that not all loans will be repaid, and the intermediary needs to provision against nonpayment, adding to costs. Estimates of nonpayment, or delayed payment in which interest is lost, can vary with client groups, particular branches, or types of idiosyncratic (local or regional) shocks, and vary over time with aggregate shocks. Historical experience can be used to estimate default rates, and priced with risk premia, according to what the market would require. Costs might be covered by higher on- lending rates, as if a premium were charged, or alternatively, covered by transfers from the government. Data Annual balance sheets. Income statements. Annual reports. Interviews with staff in main office and in district offices. Schemata of operating system and procedures. Methods Review and summarize the actual operating system of the intermediary and try to match internal procedures with observed accounting entries. Then compare to what theory and better practice would require. Thus examine required, regulated provisioning rates against the historical time profiles of arrears. Use supplementary material to identify historical events and orders of magnitudes. Likewise, identify in the income statements government -25- which apparently impeded the insurance and banking function, may have caused a welfare loss as high as 7 percent of household wealth, on average. Such numbers can then be compared to government transfers to financial institutions such as the BAAC, which offers such services—using the Subsidy Dependence Index (SDI) methodology developed by Yaron (1992). Unfortunately, however, the magnitude of the government transfer is not yet clear. If there is a probability that a given farmer or group of farmers will not repay principal and interest, then the BAAC needs to provision accordingly: that is, to enter as a cost the amount it estimates that will not be repaid on a timely basis. In principle, the provisioning and cost accounting could be done using the data the BAAC already has, looking at past histories of actual repayments and magnitude of arrears by age. Moreover, this could be done by type of event, location of the branch, and the state of the national economy (in a recession or not). In practice, both previously and under the new crisis-related change, the BAAC uses some fixed formula for provisioning that is not directly related to the data that it has. However, if provisions were done optimally and costs were entered more accurately, then one would better know the magnitude of the gap between these costs and current revenue. It is that gap that would need to be covered either by increased revenue—with premia paid by the farmers themselves—or by the Government of Thailand, with tax revenue, as a subsidy. Acknowledgement of the risk-contingency system and its associated costs, and hard- nosed accurate accounting of the same, is the way to derive the magnitude of the government subsidy: a number to be compared to the welfare gain estimated from the micro data. In practice, however, the government subsidy to the BAAC covers not only the risk-contingency system but also the costs of various government special projects, many of which are acknowledged to be making losses, and which in any event should be assessed with a similar but separate accounting system. With the costs and benefits of the risk-contingency system made clear, a simple cost- benefit calculation would reveal whether the government-paid portion of the insurance is warranted, given the estimated benefit. The larger point is that the government role in the provision of aid to farmers would be rationally assessed as part of a larger well-defined system and not driven in an ad hoc and ill-measured way by year-to-year political pressures. Caveats/Extensions Provisions need to take into account temporal variation and the possibly of large unanticipated shocks. Malfeasance could limit insurance, and full insurance is not reasonable, based on a moral hazard model. The welfare measurement of gain from micro data needs to be improved. Finally, the political situation in Thailand is changing, with pressures for debt moratoria and hence for larger losses. Finally, the BAAC should not be thought of in isolation from other financial institutions or other mechanisms. A bibliography for this fifth topic appears at the end of the chapter. -26- Microenterprise Institutions: Assessment of Local, Village Funds (and Other Financial Institutions) The assessment of this sixth topic is based on “Policies and Impact: An Analysis of Village-level Microfinance Institutions,” by Joseph Kaboski and Robert Townsend (2005). Model The intermediary can provide credit and/or insurance so as to facilitate smoothing of consumption in a bad idiosyncratic year, smoothing of investment in a bad idiosyncratic year, facilitate going into business and occupation transitions, alleviation of credit constraints in agriculture, alleviation of credit constraints in business, reduction of reliance on money lenders, and facilitate asset accumulation. Data Household – Use household retrospective data from village surveys: Whether or not in business and when Timing of occupation transitions Whether potential or actual client claims to be constrained in operation of business or farm Whether had to decrease consumption in bad year in last five due to adverse shock Demographics (age of head; years of schooling of head; gender of head; number of adult females, males, and children; wealth) Whether or not the household participates in a financial institution or agricultural cooperative, or uses a money lender Village-level average wealth, education. Government village census data on the availability of institution, village by village, for various years. Institutional survey using accounts, local records, and interviews: Founding (date, funding) Training Policies on borrowing Policies on saving -27- • Membership criteria • Emergency services, and retrospective data on growth of members, history of borrowing, history of lending, and past failure. a. Headman, Key Informant Survey on the history of village institutions Methods Direct (naïve, without correction for selection). The impact on a household of its use of a financial institution Run a probit on: whether the household went into business in the last five years (yes, no) onto demographic controls (age of head; age squared; education of the head; gender of head; number of males, females, and children in the household), wealth of household six years ago, wealth squared, and use of the institution in question (village fund), as well use of other institutions (BAAC, commercial banks, and moneylender); and onto village controls (average wealth, average wealth squared, fraction of village population that are rice farmers, average education); and finally onto whether the village in which the household resides has ever had a village institution (using retrospective data, including events after the primary retrospective date). Also run probits on occupation transition, if any, in the past five years, whether the household was constrained in business, whether it was constrained in farming, had to reduce consumption in a bad year in the last five years(yes, no), and whether was a customer of a money lender. Run an ordinary least squares regression on asset accumulation using retrospective data. Correction for individual selection. Predict whether the household was a member six years ago, with the dependent variable from the household survey using household demographics, schooling of the head, wealth, and wealth squared six years ago; whether the village had village institutions six years ago; and use of other institutions six years ago. For access to a village-level institution on the right-hand side, use headman’s retrospective history or a GIS measure of availability of village institutions at the retrospective year, using a smoothed probability surface. Then combine the impact equation and the individual selection equation, using simultaneous equation maximum likelihood methods or two stage least squares. Findings Institutions have had very mixed experiences. Many institutions fail within the first year or first five years, while in others, membership lending and savings services grow. Some of these experiences are related to chosen policies. The model finds support overall for -30- with simple availability; that is, the village happened to have an operating financial fund at the time of the retrospective interview data, and was surrounded by other villages with active funds, as promoted by a distinct CDD office, for example. Only when these controls are included does the analysis begin to estimate positive, beneficial effects. We would also recommend the continuation of this kind of evaluation, especially in tambons and amphoes (districts) in which local officials are inclined to promote village funds. More generally, our analysis would be complemented by the gradual expansion of the villages’ funds simultaneously with the implementation of evaluation procedures. Typically, funds cannot be promoted in all villages in a given area all at once, since without training and careful implementation, eventual failure rates would be high (as our evidence shows). Staggered introduction, even with the eventual goal of universal access, would allow randomize experimental controls: that is, initial random expansion. This would allow a much more accurate overall evaluation, with the information gained available to help those villages that get funds later in the implementation process. It would also be possible to evaluate specific policies further, such as deliberate variation in the type of savings accounts that would be appropriate given the mixed evidence presented above. 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