Person:

Khwaja, Asim

We construct the topology of business networks across the population of firms in an emerging economy, Pakistan, and estimate the value that membership in large yet diffuse networks brings in terms of access to bank credit and improving financial viability. We link two firms if they have a common director. The resulting topology includes a "giant network" that is order of magnitudes larger than the second largest network. While it displays "small world" properties and comprises 5 percent of all firms, it accesses two-thirds of all bank credit. We estimate the value of joining this giant network by exploiting "incidental" entry and exit of firms over time. Membership increases total external financing by 16.6 percent, reduces the propensity to enter financial distress by 9.5 percent, and better insures firms against industry and location shocks. Firms that join improve financial access by borrowing more from new lenders, particularly those already lending to their (new) giant-network neighbors. Network benefits also depend critically on where a firm connects to in the network and on the firm's pre-existing strength.

We estimate the equilibrium effects of a public school grant program administered through school councils in Pakistani villages with multiple public and private schools and clearly defined catchment boundaries. The program was randomized at the village level, allowing us to estimate its causal impact on the market. Four years after the start of the program, test scores were 0.2 sd higher in public schools. We find evidence of an education multiplier : test scores in private schools were also 0.2 sd higher in treated markets. Consistent with standard models of product differentiation, the education multiplier is greater for those private schools that faced a greater threat to their market power. Accounting for private sector responses increases the program’s cost effectiveness by 85% and affects how a policymaker would target spending. Given that markets with several public and private schools are now pervasive in low- and middle income countries, prudent policy requires us to account for private sector responses to public policy, both in their design and in their evaluation.

Learning persistence plays a central role in models of skill formation, estimates of education production functions, and evaluations of educational programs. In non-experimental settings, estimated impacts of educational inputs can be highly sensitive to correctly specifying persistence when inputs are correlated with baseline achievement. While less of a concern in experimental settings, persistence still links short-run treatment effects to long-run impacts. We study learning persistence using dynamic panel methods that account for two key empirical challenges: unobserved student-level heterogeneity in learning and measurement error in test scores. Our estimates, based on detailed primary panel data from Pakistan, suggest that only a fifth to a half of achievement persists between grades. Using private schools as an example, we show that incorrectly assuming high persistence significantly understates and occasionally yields the wrong sign for private schools’ impact on achievement. Towards an economic interpretation of low persistence, we use question-level exam responses as well as household expenditure and time-use data to explore whether psychometric testing issues, behavioral responses, or forgetting contribute to low persistence—causes that have different welfare implications.

The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. This paper evaluates the screening ability of lenders in such peer-to-peer markets. Our methodology takes advantage of the fact that lenders do not observe a borrower’s true credit score but only see an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrower’s credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking “hard” information, they also use non-standard (subjective) information. Our methodology shows, without needing to code subjective information that lenders learn even from such “softer” information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.

Evaluations of educational programs commonly assume that what children learn persists over time. The authors compare learning in Pakistani public and private schools using dynamic panel methods that account for three key empirical challenges to widely used value-added models: imperfect persistence, unobserved student heterogeneity, and measurement error. Their estimates suggest that only a fifth to a half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, estimates from commonly used value-added models significantly understate the impact of private schools' on student achievement and/or overstate persistence. These results have implications for program evaluation and value-added accountability system design.

Lack of trust in state institutions, often due to poor service provision, is a pervasive problem in many developing countries. It may also be one of the reasons citizens turn to non-state actors for services. This paper investigates whether information about improved public services can help build trust in state institutions and move people away from non-state actors. We focus on dispute resolution in rural Pakistan. We find that (truthful) information about reduced delays in state courts leads to citizens reporting higher likelihood of using them and to greater allocations to the state in two high-stakes lab-in-the-field games designed to measure belief in the effectiveness of state courts and willingness to contribute resources for others to access them. More interestingly, we find indirect negative effects on non-state actors in the same high-stakes settings. We show that the positive direct and negative indirect effects are both mediated by changes in beliefs about the effectiveness of these actors. Our preferred interpretation explains these behaviors as a response to improved beliefs about state actors which then motivate individuals to interact less with non-state actors and as a result downgrade their beliefs about them. We provide additional checks bolstering this interpretation and alleviating concerns about potential social experimenter effects or mechanical contrasts between the two actors. These results indicate that, despite distrust of the state in Pakistan, credible new information can change beliefs and behavior.

The recent banking crisis highlights the challenges faced in credit intermediation. New online peer-to-peer lending markets offer opportunities to examine lending models that primarily cater to small borrowers and that generate more types of information on which to screen. This paper evaluates screening in a peer-to-peer market where lenders observe both standard financial information and soft, or nonstandard, information about borrower quality. Our methodology takes advantage of the fact that while lenders do not observe a borrower’s exact credit score, we do. We find that lenders are able to predict default with 45% greater accuracy than what is achievable based on just the borrower’s credit score, the traditional measure of creditworthiness used by banks. We further find that lenders effectively use nonstandard or soft information and that such information is relatively more important when screening borrowers of lower credit quality. In addition to estimating the overall inference of creditworthiness, we also find that lenders infer a third of the variation in the dimension of creditworthiness that is captured by the credit score. This credit-score inference relies primarily upon standard hard information, but still draws relatively more from softer or less standard information when screening lower-quality borrowers. Our results highlight the importance of screening mechanisms that rely on soft information, especially in settings targeted at smaller borrowers.

Using rich panel data from Pakistan, we compute test score based measures of quality (School Value-Addeds or SVAs) for more than 800 schools across 112 villages and verify that they are valid and unbiased. With the SVA measures, we then document three striking features of the schooling environment. First, there is substantial within-village variation in quality. The annualized difference in learning between the best and worst performing school in the same village is 0.4 sd; compounded over 5 years of primary schooling, this difference is similar in size to the test score gap between low- and high-income countries. Second, students learn more in private schools (0.15 sd per year on average), but substantial within-sector variation in quality means that the effects of reallocating students from public to private schools can range from -0.35sd to +0.65sd. Thus, there is a range of possible causal estimates of the private premium, a feature of the environment we illustrate using three different identification approaches. Finally, parents appear to recognize and reward SVA in the private sector, but the link between parental demand and SVA is weaker in the public sector. These results have implications for both the measurement of the private premium and how we design and evaluate policies that reallocate children across schools, such as school closures and vouchers.

We present a model with pre-marital schooling investment, endogenous marital matching and spousal specialization in homework and market production. Investment in schooling raises wages and generates two kinds of returns in our framework: a labor-market return and a marriage-market return because education can affect the intra-marital share of the surplus one can extract from marriage. We consider a household production function in which the schooling of spouses are complements, yielding positive assortative matching. If men and women have different market returns or household roles, they may have different incentives to acquire schooling, yielding a mixed equilibrium where some educated individuals marry uneducated spouses and those who educate less extract a relatively larger share of the marital surplus. The existence of large and frictionless marriage markets creates competition among potential spouses, precludes bargaining and generates premarital investments that are efficient. By combining the observations that the gender earning gap declines with schooling and that women’s household time obligations have declined over time, our model provides an explanation for why women now attain higher schooling levels than men.

We study the impact of providing school and child test scores on subsequent test scores, prices, and enrollment in markets with multiple public and private providers. A randomly selected half of our sample villages (markets) received report cards. This increased test scores by 0.11 standard deviations, decreased private school fees by 17 percent and increased primary enrollment by 4.5 percent. Heterogeneity in the treatment impact by initial school quality is consistent with canonical models of asymmetric information. Information provision facilitates better comparisons across providers, improves market efficiency and raises child welfare through higher test scores, higher enrollment and lower fees.