Understanding rates of return and investment rates in poor countries: non-aggregative approaches

In this section, we review various possible reasons why individuals do not always make the best possible use of resources available to them.

4.1. Government failure

One reason why firms may not choose the latest technologies or make the right invest­ments is because they do not have the proper incentives to do so.

One approach to investigating this hypothesis has been to try to document variations in the quality of institutions, and to try to evaluate their impact. La Porta et al. (1998) document important variations in the degree to which the law protects investors (credi­tors and shareholders) across countries, part of which seem to be explained by the origin of these countries’ legal codes (the French civil law has much less legal protection for investors than Anglo-Saxon common law). Djankov et al. (2002) document wide vari­ation in the ability of someone to start a new firm in 85 countries. They argue that the costs of entry are high in most countries (on average, they sum up to 47% of a country’s GDP per capita), and can be very high indeed: While it take 3 procedures and 3 days to obtain the permit to start company in New Zealand, it takes 19 procedures, 149 business days and 111.5 percent of GDP per capita in Mozambique.

These papers also argue that low investor protections, legal barriers to entry, and long legal procedures have implications for welfare and efficiency. There are indeed sugges­tive associations in the data (for example, ownership is more concentrated when investor protection is worst), but there is always the possibility that the correlation between the quality of the institutions and the real outcomes they consider is due to a third factor. Acemoglu, Johnson and Robinson (2001) try to address this issue by finding exogenous variations in the quality of institutions. They argue that there is a persistence of institu­tions, so that countries which accessed independence with extractive institutions (e.g., Congo) have tended to keep these bad institutions. They then argue that colonial powers were more likely to set up extractive institutions, with an unrestrained executive power, in places where they did not intend to settle. Finally, they were less likely to settle in places where the environment was hostile: In particular, the mortality of early settlers predicted the number of people of European descent who settled in these countries, the quality of institutions at the turn of the 20th century, and the quality of institutions in 1995 (measured as the risk of expropriation perceived by investors).

This evidence suggests that governments matter, and that bad governments will lower returns and discourage new investments. There is a literature that tries to investigate the exact mechanisms through which the government affects the allocation of resources. One version of the story blames excessive intervention, while another talks about the lack of appropriate regulations. We now discuss these two explanations in turn, and try to assess how far they can help us fit the evidence.

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4.1.1. Excessiveintervention

There is a line of work, following Parente and Prescott (1994, 2000), which argues that the productivity gap results from the way the heavy hand of the government operates. The government makes rules that discourage entry and innovation and protects the in­ept, and thereby slows the economy’s progress towards the ideal state where only the most productive firms survive. The regulation may lead the economy to have too few firms, leaving inefficient incumbents to run the firms [see Caselli and Gennaioli (2004) and other references in this study], or too many firms, when regulation discourages con­solidation by treating small firms and larger firms differently.

There is clearly something to this vision. Gelos and Werner (2002) show that financial de-regulation in Mexico (which started in 1988 and eliminated the interest rate ceiling, high reserve requirements which channeled 72% of commercial bank lending to the government, and priority lending) increased the ability of small firms to access the credit market, and reduced the excess cash flow sensitivity of investment for small firms only. Until recently in India, a large number of sectors were reserved for firms below a certain size (the small-scale sector) and/or firms in the cooperative sector.

Nonetheless, this is probably only a part of the story. As we noted in the context of the discussion of Banerjee and Duflo (2004), even medium-sized firms that were well above the cut-off for being included in the small-scale sector seem to be operating well below their optimal scale. In other words, notwithstanding the politically protected presence of the small-scale firms that is presumably driving down profits in the sector, these medium-sized firms were clearly still at the point where further investment would be extremely profitable. There has to be something other than a policy-induced lack of profitability that was holding them back.

The same point is made in a different style in the paper by Banerjee and Munshi (2004), mentioned above. This paper studies investment and productivity differences among firms in the knitted-garment industry in Tirupur, India. The firms owned by the Gounders tend to be much larger than the firms owned by all other participants in the industry: The gap among firms that had just started is on the order of three to one. Yet these Gounder firms produce much less per unit of capital, and Gounder firms that have been in business for more than five years actually produce less in absolute terms than the smaller firms of the same vintage owned by non-Gounders. In other words, it is the bigger firms that are less productive, in an environment where the government discriminates, if at all, in favor of the smaller firms.

To sum up, while there are certainly instances of excessive intervention, it seems that there are many inefficiencies that cannot be blamed on the government.

4.1.2. Lack of appropriate regulations: property rights and legal enforcement

Effective rates of return and investment rates can be low because the responsibilities and/or the benefits of the investments are shared, or the investors are worried about be­ing expropriated: The investor is therefore not capturing the full marginal returns of its investment.

In addition to the macro-economic evidence mentioned above, there is some micro­economic evidence that property rights matter for investment, although the findings are more mixed. Goldstein and Udry (2002) show that, in Ghana, individuals are less likely to leave their land fallow (which is an investment in long run land productivity) if they do not hold a position of power within the family of the village hierarchy which ensures that their land is not taken away from them when it is fallow. However, Besley (1995) finds that, also in Ghana, investment (tree planting) is not significantly larger when indi­viduals have more secure rights to their land. Johnson, McMillan and Woodruff (2002) find that, in five post-Soviet countries, firms that are run by entrepreneurs who perceive that their property rights are more secure invest more than those who do not. The effect is as strong for firms who rely mostly on internal finances as for those who have access to external finance. Entrepreneurs who believe that they have strong property rights in­vest 56% of their profits in their firms (against 32% for those who do not). Do and Iyer (2003) find that a land reform which gave farmers the right to sell, transfer or inherit their land usage rights also increased agricultural investment, in particular the planting of multi-year crops (such as coffee).

Even when property rights themselves are legally well defined and protected, there are institutions which reduce the private incentives to invest.

The example of sharecropping suggests that bad governments are not the only cause for the emergence of bad institutions. If sharecropping is inefficient, why does it arise? In particular, why do the landlord and the tenant not agree on a fixed rent, which will ensure that the tenant is the full beneficiary of his effort at the margin? Explanations of the persistence of sharecropping involve risk aversion [Stiglitz (1974)] or limited liability [Banerjee et al. (2002)]. This suggests that while the proximate explanation for inefficient investment may well be based in a specific institution, the more basic cause may be lying elsewhere, in the way various asset markets function. This is what we turn to next.

4.2. The role of credit constraints

• Why would credit markets function poorly in poor countries?

The fact that the capital market does not function well in poor countries is a result of a number of factors. First, information systems, including property records, are often underdeveloped, making it hard to enforce contracts. This, in turn, partly reflects the fact that people may not know how to read or write and partly the fact that there has not been enough institutional investment.^[288] Second, the fact that potential borrowers are poor and under extreme economic pressure, might make them all too willing to try to cheat the lender. Third, there are political pressures to protect borrowers from lenders in most LDCs.

• Consequence of poorly functioning credit market

Given the problems in enforcing the credit contract, what a lender will be prepared to offer a particular borrower will depend on the quality of the borrower’s collateral, his reputation in the market, the ease of keeping an eye on him and a host of other characteristics of the borrower. This has the obvious implication that two firms facing the exact same technological options may end up choosing very different methods of production. In particular, one person may start a large or more technologically advanced firm because he has money and another may start a small and backward one because he does not.^[289] As a result, neither interest rates, nor TFP, nor the marginal product need be equalized across borrowers.

This would also explain why investment responds so unpredictably to returns: Some­times the opportunities become available when there is large group of people who are looking to invest and have the wherewithal to do it. At other times, the returns may be there but most of those who have money may be heavily involved in promoting some­thing else.

A second set of implications of imperfect contracting in the credit market is that the supply curve of capital to the individual borrower slopes up - a borrower who is more leveraged will need more monitoring and the lender will charge him more to do the extra monitoring. And eventually, the extra monitoring may be too costly to be worth it, and the borrower will face an absolute limit on how much he can borrow.

An immediate consequence of an upward-sloping supply curve is that the marginal product of capital will be higher than what the borrower pays the lender. Indeed, the gap between the two may quite substantial, since the fact that borrowers are constrained in borrowing also implies that the lenders are constrained in how much they can lend at rewarding rates. This drives the interests rates down, as lenders compete for the best borrowers. Moreover, since the rates the lenders charge include the cost of the moni­toring that they have to do, the rates the lenders charge could be much higher than the opportunity cost of capital. In the case of a financial intermediary, such as a bank, this implies that the rates they charge their borrowers may be much higher than the rates they pay their depositors.

This implies, for example, that the American investor who gets 9% on his stock mar­ket investment could not just put the money in a bank in India and earn the 22% average marginal product. Indeed, he may not earn much more than 9% if he were to put it in an Indian bank. However, he could set up a business in India and earn those returns, and presumably if enough people did that, the returns would be equalized; below we will try to say something about why this does not happen.

It also implies that the incentive to save may be low in countries where the marginal product is high, except for those who are planning to invest directly. This might help to explain the low equilibrium investment rate, though it is theoretically possible that the negative effect on the savers would be swamped by the positive effect on investors if the fraction of investors is large enough.

• Evidence

We have already mentioned some evidence from South Asia showing that the interest rate varies enormously across borrowers within the same local capital market and that the extent of variation is too large to be explained by the observed differences in default rates. Banerjee (2003) lists a number of studies that make it clear that this is also true in developing countries outside South Asia. This is suggestive, albeit indirect, evidence of credit constraints.

If the marginal product of capital in the firm is greater than the market interest rate, credit constraints naturally mean that a firm would want to borrow more than what is available. It is, however, not clear how one should go about estimating the marginal product of capital. The most obvious approach, which relies on using shocks to the market supply curve of capital to estimate the demand curve, is only valid under the assumption that the supply is always equal to demand, i.e., if the firm is never credit constrained.

The literature has therefore taken a less direct route: The idea is to study the effects of access to what are taken to be close substitutes for credit - current cash flow, parental wealth, community wealth - on investment. If there are no credit constraints, greater access to a substitute for credit would be irrelevant for the investment decision. While this literature has typically found that these credit substitutes do affect investment,^[290] suggesting that firms are indeed credit constrained, the interpretation of this evidence is not uncontroversial. The problem is that access to these other resources is unlikely to be entirely uncorrelated with other characteristics of the firm (such as productivity) that may influence how much it wants to invest. To take an obvious example, a shock to cash-flow potentially contains information about the firm’s future performance.

The estimation of the effects of credit constraints on farmers is significantly more straightforward since variation in the weather provides a powerful source of exogenous short-term variation in cash flow. Rosenzweig and Wolpin (1993) use this strategy to study the effect of credit constraints on investment in bullocks in rural India.

The paper by Banerjee and Duflo (2004) that we discussed above makes use of an exogenous policy change that affected the flow of directed credit to an identifiable subset of firms in India. Since the credit was subsidized, an increase in sales and investment as a response to the increase in funds available needs to mean that firms are credit constrained, since it may have decreased the marginal cost of capital faced by the firm. However, they argue that if a firm is not credit constrained, then an increase in the supply of subsidized directed credit to the firm must lead it to substitute directed credit for credit from the market. Second, while investment, and therefore total production, may go up even if the firm is not credit constrained, it will only go up if the firm has already fully substituted market credit with directed credit. They showed that bank lending and firm revenues went up for the newly targeted firms in the years when the priority sector was expanded to include them, and declined in the years where they were excluded again. They find no evidence that this was accompanied by substitution of bank credit for borrowing from the market and no evidence that revenue growth was confined to firms that had fully substituted bank credit for market borrowing. As already argued, the last two observations are inconsistent with the firms being unconstrained in their market borrowing.

The logic of credit constraints applies as much or more to human capital investments. Hart and Moore (1994), among others, have used human capital as the archetype of in­vestment that cannot be collateralized, and therefore is hard to borrow against. This is made even more difficult by the fact that children would need to borrow for their educa­tion, or parents would need to borrow on their behalf. We return to this evidence below. The high responsiveness to user fees that we reviewed in Section 2, and the evidence that investment in education are sensitive to parental income,^[291] are both consistent with credit constraints. However, because human capital investments may involve direct util­ity or disutility (for example, a parent may like to see his child being educated), it is more difficult to come up with evidence that systematically nails the role of credit con­straints for human capital investment. Edmonds (2004) is an interesting attempt to try to isolate the effect of credit constraints using household’s response to an anticipated income shock. He studies the effect on child labor and education of a large old age pen­sion program, introduced in South Africa at the end of the Apartheid. Many children live with older family members (often their grandparents). Women become eligible at age 60 and men become eligible at age 65. Since at the time he studies the program, the program was well in place and therefore fully anticipated, he argues that if more children start attending school as soon as their grandfather or grandmother crosses the age threshold and becomes eligible (rather than continuously, as they come closer to eligibility), this must be an indication of credit constraint. Indeed, he finds that child labor declines, and school enrollment increases, discretely when a household member becomes eligible.

• Summary

Credit constraints seem to be pervasive in developing countries. Of course, we are interested in whether the fact that access to capital varies across people helps us un­derstand the productivity gap. If people invest different amounts because of differential access to capital, our intuitive presumption would be that capital is being misallocated, because there is no reason why richer people are always better at making use of the cap­ital. This misallocation could be a source of difference in productivity. We will return to this question in Section 5.

4.3. Problems in the insurance markets

Even if credit markets function well, and there is no limited liability, individuals may be reluctant to invest in any risky activity, for fear of losing their investment, if they are not properly insured against fluctuations in their incomes. Risk aversion leads to inef­ficient investment, and efficiency would improve with insurance [this idea is explored theoretically in Stiglitz (1969), Kanbur (1979), Kihlstrom and Laffont (1979), Banerjee and Newman (1991), Newman (1995) and Banerjee (2005)].

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• Insurance in developing countries

A considerable literature has investigated the extent of insurance in rural areas in developing countries [see Bardhan and Udry (1999) for a survey]. Townsend (1994) used the ICRISAT data, a very detailed panel data set covering agricultural house­holds in four villages in rural India to test for perfect insurance. The main idea behind this test is that with perfect insurance at the village level only aggregate (village­level) income fluctuation, and not idiosyncratic income fluctuations, should translate into fluctuation in individual consumption. He was unable to reject the hypothesis that the villagers insure each other to a considerable extent: Individual consumption seems to appear to be much less volatile than individual income, and to be uncorre­lated with variations in income. This exercise had limits, however [see Ravallion and Chaudhuri (1997) for a comment on the original paper], and subsequent analyses, no­tably by Townsend himself, have shown the picture to be considerably more nuanced. Deaton (1997) shows that there is no evidence of insurance in Cote d’Ivoire. Townsend (1995) finds the same results across different areas in Thailand. Fafchamps and Lund (2003) find that, in the Philippines, households are much better insured against some shocks than against others. In particular, they seem to be poorly insured against health risk, a finding corroborated by Gertler and Gruber (2002) in Indonesia. Most interest­ingly, Townsend (1995) describes in detail how insurance arrangements differ across villages. While in one village there is a web of well-functioning risk-sharing institu­tions, the situations in other villages are different: In one village, the institutions exist but are dysfunctional; in another village, they are non-existent; finally, in a third vil­lage, close to the roads, there seems to be no risk-sharing whatsoever, even within family.

This last fact is attributed to the proximity to the city, which makes the village a less close-knit community, where enforcement of informal insurance contracts is more difficult. Coate and Ravallion (1993) was the first paper to build a theoretical model of insurance with limited commitment, and to show that, when the only incentive to contribute to the insurance scheme in good times is the fear of being cut away from the insurance in future periods, insurance will be limited. It will also be optimal to make payment contingent on past history, which will lead to a blur between credit and insurance [Ray (1998)]. Udry (1990) presents evidence from Nigeria that is consistent with this model. The villages he studies are characterized by a dense network of loan exchange: Over the course of one year, 75% of the households had made loans, 65% had borrowed money, and 50% had been both borrowers and lenders. Ninety-seven percent of these loans took place between neighbors and relatives. Most importantly, the loans are “state-contingent”: Both the repayment schedule and the amount repaid are affected by the lender’s state and the borrower’s state. This is evidence that credit is to some extent used as an insurance device. The resulting system is a mix of credit and insurance close to what the model of limited commitment would predict. However, and still consistent with this prediction, there is not enough of this “security” to fully insure households against income fluctuations: A shock to a particular borrower has a negative impact on the sum of the transfers received by his lender, which means that the lender did not fully diversify risk.

Despite this evidence, we do not fully understand the reasons for the lack of insurance among households. It is unlikely that either limited commitment or the more traditional explanations in terms of moral hazard or adverse selection can explain why the level of insurance seems to vary from one village to the next, or why there is no more insurance against rainfall, for example.

• Consequences for investment

Irrespective of the ultimate reason for the lack of insurance, it may lead households to use productive assets as buffer stocks and consumption smoothing devices, which would be a cause for inefficient investment. Rosenzweig and Wolpin (1993) argue that bullocks (which are an essential productive asset in agriculture) serve this purpose in rural India. Using the ICRISAT data, covering three villages in semi-arid areas in India, they show that bullocks, which constitute a large part of the households’ liquid wealth (50% for the poorest farmers), are bought and sold quite frequently (86% of households had either bought or sold a bullock in the previous year, and a third of the household- year observations are characterized by a purchase or sale), and that sales tend to take place when profit realizations are low, while purchases take place when profit realiza­tions are high. Since there is very little transaction in land, this suggests that bullocks are used for consumption smoothing. Because everybody needs bullocks around the same time, and bullocks are hard to rent out, Rosenzweig and Wolpin estimate that, in order to maximize production efficiency, each household should own exactly two bullocks at any given point in time. The data suggest that, for poor or mid-size farmers there is consider­able underinvestment in bullocks, presumably because of the borrowing constraints and the inability to borrow and accumulate financial assets to smooth consumption: Almost half the households in any given year hold no bullock (most of the others own exactly two).^[292] Using the estimates derived from a structural model where household use bul­locks as a consumption smoothing device in an environment where bullocks cannot be rented and there is no financial asset available to smooth consumption, they simulate a policy in which the farmers are given a certain non-farm income of 500 rupees (which represents 20% of the mean household food consumption) every period. This policy would raise the average bullock holding to 1.56, and considerably reduce its variability, due to two effects: The income is less variable, and by increasing the income, it makes “prudent” farmers (farmers with declining absolute risk aversion) more willing to bear the agricultural risk.

Moreover, we observe only insurance against the risks that people have chosen to bear; the inability to smooth consumption against variation in income may lead house­holds to choose technologies that are less efficient, but also less risky. Banerjee and Newman (1991) argue, for example, that the availability of insurance in one location (the village), while its unavailability in another (the city), may lead to inefficient migra­tion decisions, since some individuals with high potential in the city may prefer to stay in the village to remain insured.

There is empirical evidence that households’ investment is affected by the lack of ex post insurance. Rosenzweig and Binswanger (1993) estimate profit functions for the ICRISAT villages, and look at how input choices are affected by variability in rainfall. They show that more variable rainfall affects input choices, and in particular, poor farm­ers make less efficient input choices in a risky environment. Specifically, a one standard deviation increase in the coefficient of variation of rainfall leads to a 35% reduction in the profit of poor farmers, 15% reduction in the profit of median farmers, and no reduction in the profit of rich farmers. Morduch (1993) specifically investigates how the anticipation of credit constraint affects the decision to invest in HYV seeds. Using a methodology inspired by Zeldes (1989), he splits the sample into two groups, one group of landholders who are expected to have the ability to smooth their consumption, and one group that owns little land, whom we expect a priori to be constrained. He finds that the more constrained group uses significantly less HYV seeds.

It is worth noting that the estimated impact of lack of insurance on investment is likely to be a serious underestimate. It is not clear how one could evaluate how much the lack of insurance affects investment. While we might observe certain options considered by the investor, there is no obvious way for knowing what other, even more lucrative, choices he chose not to even think about.

Another strategy for looking at the effects of underinsurance is to calculate the effect based on the assumption of specific utility function. This, in effect, is what Krussel and Smith (1998) do. They argue that, for reasonable parameter values, the effect on aggregate investment tends to quite small: This is because most people can self-insure quite well against idiosyncratic shocks, and those who cannot, mainly the very poor, do very little of the investing in any case. However as pointed out by a more recent paper by Angeletos and Calvet (2003), the Krusell and Smith result relies heavily on the assumption that one cannot limit exposure to risk by investing less. If investing more exposes you to more risk, even the non-poor will worry about risk, because they are the ones who invest a lot and therefore are exposed to a lot risk.

4.4. Local externalities

As we discussed in Section 4, there is a line of work that focuses on coordination fail­ures at the level of the economy. However, Durlauf (1993) shows that externalities do not have to be aggregated for the economy to exhibit multiple equilibria: Local comple­mentarities (where adoption of a particular technology lowers production costs in a few “neighboring” sectors) can build up over time to affect aggregate behavior and generate lower aggregate growth.

An example of strategic complementarity of this kind arises when agents are learn­ing from each other. Banerjee (1992) shows how, when people try to infer the truth from other people’s actions, this leads them to under-utilize their own information, and leads to “herd behavior”. While this behavior is rational from the point of view of the individual, the resulting equilibrium is inefficient, and can lead to underinvestment, overinvestment, or investment in the wrong technology.^[293]

The impact of learning on technology adoption in agriculture has been studied partic­ularly extensively. Besley and Case (1994) show that in India, adoption of HYV seeds by an individual is correlated with adoption among their neighbors. While this could be due to social learning, it could also be the case that common unobservable variables affect adoption of both neighbors.^[294] To partially address this problem, Foster and Rosenzweig (1995) focus on profitability. As we mentioned previously, during the early years of the green revolution, returns to HYV were uncertain and dependent on adequate use of fer­tilizer. In this context, the paper shows that profitability of HYV seeds increased with past experimentation, by either the farmers or others in the village. Farmers do not fully take this externality into account, and there is therefore underinvestment. In this envi­ronment, the diffusion of a new technology will be slow if one neighbors’ outcomes are not informative about an individual’s own conditions.^[295] Indeed, Munshi (2004) shows that in India, HYV rice, which is characterized by much more varied conditions, dis­played much less social learning than HYV wheat.

All of these results could still be biased in the presence of spatially correlated prof­itability shocks. Using detailed information about social interactions Conley and Udry (2003) distinguish geographical neighbors from “information neighbors”, the set of in­dividuals from whom an individual neighbor may learn about agriculture. They show that pineapple farmers in Ghana imitate the choices (of fertilizer quantity) of their in­formation neighbors when these neighbors have a good shock, and move further away from these decisions when they have a bad shock. Conley and Udry try to rule out that this pattern is due to correlated shocks by observing that the choices made on an es­tablished crop (maize-cassava intercropping), for which there should be no learning, do not exhibit the same pattern.

The ideal experiment to identify social learning is to exogenously affect the choice of technology of a group of farmers and to follow subsequent adoption by themselves and their neighbors, or agricultural contacts. Duflo et al. (2003) performed such an experiment in Western Kenya, where less than 15% of the farmers use fertilizer on their maize crop (the main staple) in any given year despite the official recommendation (based on results from trials in experimental farms), as well as the high returns (in excess of 100%) that they estimated. They randomly selected a group of farmers and provided fertilizer and hybrid seeds sufficient for small demonstration plots in these farmers’ fields. Field officers from an NGO working in the area guided the farmers throughout the trial, which was concluded by a debriefing session. In the next season, the adoption of fertilizer by these farmers increased by 17%, compared to adoption by the comparison group. However, there is no evidence of any diffusion: People named by the treatment farmers as people they talk to about agriculture did not adopt fertilizer any more than the contacts of the comparison group. The neighbors of the treatment group actually tended to adopt fertilizer less often, relative to the neighbors of the comparison group. This is not because only experimentation in one’s own field changes someone’s priors: When randomly selected friends were invited to attend the harvest, the debriefing session, and other key periods of the trials, they were as likely to adopt fertilizer as the farmers who participated in the experiment. Rather, it suggests that, spontaneously, information about agriculture is not shared. This points towards another type of externality and source of multiple steady states: When there is very little innovation in a sector, there is no news to exchange, and people do not discuss agriculture. As a result, innovation dies out before spreading, and no innovation survives.

Depending on the priors of the individuals, social learning can either decrease or increase investment. InKenya, Miguel and Kremer (2003) show that random variation in the number of friends of a child who was given the deworming medicine had a negative impact on the propensity of a child to take the medicine. They attribute this to the fact that parents may have initially over-estimated the benefits of the deworming drug.

In addition to social learning, there are many other sources of local interactions. First, people imitate each other even when they are not trying to learn, because of fashion or social pressure. Social norms may prevent the adoption of new technologies, because coordinating on a new equilibrium may require many people to change their practices at the same time.^[296] Second, there are several sources of positive spillovers between indus­tries located close to each other. Silicon Valley-style geographic agglomerations occur in the developing world as well, such as the software industry in Bangalore. Ellison and Glaeser (1997) show that, in the U.S., most industries are indeed more concentrated than they would be if firms decided to place their plants randomly. Only about half of this concentration is explained by the fact that some locations have natural advantages for specific industries [Ellison and Glaeser (1999)].

In addition to the traditional arguments for positive spillovers, such as transport costs (fast telecommunication lines that were installed for the software industry in Bangalore greatly reduced the cost of setting up call centers, for example), intellectual spillovers or labor market pooling, a powerful reason for geographical agglomeration in developing countries is the role of a town’s reputation in the world market. For example, outsiders who want to start working in garment manufacturing come to Tirupur, the small town studied in Banerjee and Munshi (2004), despite their difficulty in finding credit there, because this is the place where large American stores come to place orders. There is a sense in which the town has a good reputation, for quality and timeliness of delivery, and everybody who works there benefits from it. Tirole (1996) models “collective rep­utation”: If many people in a group are known to deliver good quality products, buyers will have high expectations and be willing to trust the sellers to produce more elab­orate products, where quality matters. In turn, this will encourage sellers to produce high quality products to avoid being outcast from the group, which will sustain a “high quality-high trust equilibrium”. But if buyers are expected to only ask for basic prod­ucts in the future, building a reputation for high quality is not useful, and opportunistic sellers will produce low quality in the first period. Knowing this, sellers indeed have the incentive to ask for simple products, and the bad equilibrium persists. In this world, history matters. A collective reputation for low quality is very difficult to reverse, and a collective reputation for high quality is valuable. We should therefore expect groups to try to set up institutions to develop a good collective reputation. There is certainly some indication that this is happening. For example, the association of Indian software firms (NASSCOM) tries to help the firms access quality certifications such as ISO 9001, SEI, or others. Much more work on whether collective reputation matters in practice is, however, clearly needed before we can assess the empirical relevance of these sources of externality.

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To summarize, externalities can explain very large variations in productivity and in­vestment rates across otherwise similar environments.

4.5. The family: incomplete contracts within and across generations

Investment in human capital often pays in the long term, and in many crucial instances must be done by parents on behalf of the child. In this context, the way the decisions are made in the family has a direct impact on investment decisions. In the benchmark neo-classical model [Barro (1974), Becker (1981)], parents value the utility of their chil­dren, perhaps at some discounted rate. This world tends to be observationally equivalent to one where an individual maximizes his long run income, and has the same strong convergence properties. However, if parents are not perfectly altruistic, the ability to constrain the repayment of future generations influences investment decisions. Banerjee (2004) studies the short and long run implications of different ways to model the family decision-making process. He shows that incomplete contracting between generations generates potentially large deviations from the very strong convergence property of the Barro-Becker model. Deviations also occur if parents value human capital investment for its own sake (for example, because people like to see their children happy).^[297]

In particular, even with perfect credit markets, parental wealth will determine how much is invested in human capital. There can be more than one steady state, and there can be inequality in equilibrium. In this world, increases in returns to human capital may not lead to an increase in human capital, if the production of human capital is skill­intensive (the increase in the price of teachers may dominate the added incentives to invest in education).

Many studies have shown that human capital investment is correlated with family income [see Strauss and Thomas (1995) for references for developing countries]. In general, however, it is difficult to separate out the pure income effect from the effect of an increase in the returns to investing in human capital, differences in the opportunity cost or the direct cost of schooling, and different discount rates. For example, in the Barro-Becker model, families with a lower discount rate will tend to be richer and more likely to invest in education. To avoid this problem, a few studies have focused on exogenous changes in government transfers. For example, Carvalho (2000) shows that an increase in pension income in Brazil led to a decrease in child labor and an increase in school enrollment. Duflo (2003) shows that, in South Africa, girls (though not boys) have better nutritional status (they are taller and heavier) in households where a grandmother is the recipient of a generous old age pension program.

This paper also touches on another set of issues. Different members of the family may have different preferences. If education and health were pure investment, and if the members of the household bargained efficiently [as in Lundberg and Pollack (1994, 1996) or the papers reviewed in Bourguignon and Chiappori (1992)], this would not have any impact on education or health decisions. However, if either assumption is violated, it means that not only the size of the income effects, but who gets the income, will affect investment decisions. In the case of the South African pensions, this was clearly the case: Pensions received by men had no impact on the nutritional status of children of either gender. This may come from the fact that women and men value child health differently, or from the fact that the household is not efficient, and a specific individual is more likely to invest in children if the returns are more likely to directly accrue to her.

If the household does not bargain efficiently, the consequences extend beyond in­vestment in human capital to all investment decisions. In a Pareto efficient household, production and consumption decisions are separable: The household should choose in­puts and investment levels to maximize production, and then bargain over the division of the surplus. This property will be violated if individuals make investment decisions with an eye toward maximizing the share of income that directly accrues to them. Udry (1996) shows that, in Burkina Faso, after controlling for various measures of the pro­ductivity of the field (soil quality, exposure, slope, etc.), crop, year, and household fixed effects, yields on plots controlled by women are 20% smaller than yields on men’s plots.^[298] This does not seem to be due to the fact that women and men have different production functions. Instead, this difference is largely attributed to differences in input intensity: In particular, much less male labor and fertilizer is used on plots controlled by women than on plots controlled by males. The fertilizer result is particularly striking, since there is ample evidence that it has sharply decreasing returns to scale. Udry esti­mates that the households could increase production by 6% just by reallocating factors of production within the household.

Udry explains underinvestment on women’s plots by their fear of being expropriated by their husband if he provides too much labor and inputs. Another reason for inef­ficient investment may be the fear of being fully taxed by family members once the investment bears fruit. Again, an efficient household would first maximize production. However, the specific claims that a household member (or a neighbor, or a member of the extended family) can make on someone’s income stream may lead to inefficient in­vestment. Consider, for example, a situation where individuals have the right to make emergency claims on the income or savings of others in their group (for example, if someone is sick and has no money to pay for the doctors, others in his extended family have an obligation to pay the doctor). Consider a savings opportunity that will increase income by a large amount in the future (for example, saving money after harvest to be able to buy fertilizer at the time of planting). If everybody could commit not to exercise their claim during the period where the income needs to be saved, the money should be saved, and the proceeds eventually distributed to those who have a claim on it, and everybody would be better off. However, if no such commitment is possible, the in­dividual who earned the income knows that it is likely that, should he choose to save enough for fertilizer, a claim will be exercised in the period during which the money needs to be held. He is then better off spending the money right away: Even if individu­als are rational and have a low discount rate, as a group they will behave as “hyperbolic discounters”, who discount the immediate future relative to today more than future pe­riods relative to each other [Laibson (1991)]. The level of investment will be low in the absence of savings opportunities offering some commitment to household members.

The fact that investments are often decided within a family, rather than by a single in­dividual, or that the proceeds of the investment will be shared among a set of people who have not necessarily supported the cost of the investment therefore greatly complicates the incentive to invest. This may, once again, explain why some potential investments with high marginal product are not taken advantage of. It is worth noting that the lack of credit and insurance in poor countries makes these problems particularly acute there. For example, the lack of credit markets means that investment decisions are taken within the families - e.g., women cannot borrow to get the optimal amount of fertilizer on their plot - and the lack of insurance plays an important role in justifying the norms on family solidarity that seem to be hindering productive investment.

4.6. Behavioral issues

Individuals in the developing world appear not only to be credit constrained, but also to be savings constrained. Aportela (1998) shows that when the Mexican Savings In-

stitution “Pahnal” (Patronoto del Ahorro Nacional) expanded its number of branches through post offices in poor areas and introduced new savings instruments in the 1990s, household’s savings rates increased by 3% to 5% in areas where the expansion took place. The largest increase occurred for low income households.

When an individual (or his household) has time-inconsistent preferences, formal sav­ings instruments may increase savings rates even when they offer very low returns (even compared to holding onto cash), because they offer a commitment mechanism. Micro­credit programs may also be understood as programs helping individuals to commit to regular reimbursements. This is particularly clear for programs, like the FINCA pro­gram in Latin America, which require that their clients maintain a positive savings balance even when they borrow.^[299]

Duflo et al. (2003) provide direct evidence that there is an unmet demand for com­mitment savings opportunities among Kenyan farmers, and that investment in fertilizer increases when households have access to this opportunity. In several successive sea­sons, they offered farmers the option to purchase a voucher for fertilizer right after harvest (when farmers are relatively well off). The vouchers could be redeemed for fer­tilizer at the time when it is necessary to plant it. The take up of this program was quite high: 15% of the farmers took up the program the first time it was tried with farmers who had never encountered the NGO before. Net adoption of fertilizer increased in this group. The program was then offered to some of the farmers who had participated in the pilot program mentioned above (and thus had the opportunity to test the fertilizer for themselves, and trusted the NGO), and in this group, the take up was 80%. There is also direct evidence of the difficulty for farmers to hold on to cash: In other experiments, when farmers were given a few days before they could purchase the voucher, the take up fell by more than 50%. When they were offered the option of having the fertilizer delivered at home at the time they actually needed it (and to pay for it then), none of the farmers who had initially signed up for the program had the money to pay for the fertilizer when it was delivered.

This area of research is quite recent, and wide open. Many questions need answer­ing, and the area of applicability is wide. For example, what is the best way to increase parents’ willingness to invest in deworming drugs? Why don’t all parents sign the au­thorization form which will grant free access to deworming to their children [Miguel and Kremer (2003)]? Is it a rational decision or is it procrastination? Why does the take up of the deworming drug fall so rapidly when a small cost-sharing fee is introduced [Miguel and Kremer (2003)]? Understanding the psychological factors that constrain investment decisions, and the role that social norms play in disciplining individuals, but also potentially in limiting their options, is an important area for future research. Several randomized evaluations are trying to make progress in this area. They are addressing questions as diverse as: What is the role of marketing factors in the access of poor peo­ple to loans in South Africa [Bertrand et al. (2004)]? Do poor people take advantage of savings products with commitment options in the Philippines [Ashraf, Karlan and Yin (2004)]? What prevents people from doing a small action that would lead them to a high return [Duflo et al. (2003)]? What factors (deadline, framing, etc.) make it more likely they will take an action [Bertrand et al. (2004)]?

A defining characteristic of these projects is that they do not involve laboratory ex­periments: Like the research on fertilizer in Kenya, they set up real programmes which are likely to increase poor people’s investment and improve welfare if they indeed devi­ate from perfect rationality in the way the psychological literature suggests. In order to be fruitful, this agenda will need to avoid simply transplanting to developing countries some of the insights developed by observing behaviors in rich countries. Being poor almost certainly affects the way people think and decide. Decisions, after all, are based not on actual returns but on what people perceive the returns to be, and these percep­tions may very well be colored by their life experience. Also, when choices involve the subsistence of one’s family, trade-offs are distorted in different ways than when the question is how much money one will enjoy at retirement. Pressure by extended fam­ily members or neighbors is also stronger when they are at risk of starvation. It is also plausible that decision-making is influenced by stress. What is needed is a theory of how poverty influences decision making, not only by affecting the constraints, but by changing the decision making process itself.^[300] That theory can then guide a new round of empirical research, both observational and experimental.

5.