Dynamic Financial Constraints: Distinguishing Mechanism Design from Exogenously Incomplete Regimes

Abstract

We formulate and solve a range of dynamic models of constrained credit/insurance that allow for moral hazard and limited commitment. We compare them to full insurance and exogenously incomplete financial regimes (autarky, saving only, borrowing and lending in a single asset). We develop computational methods based on mechanism design, linear programming, and maximum likelihood to estimate, compare, and statistically test these alternative dynamic models with financial/information constraints. Our methods can use both cross‐sectional and panel data and allow for measurement error and unobserved heterogeneity. We estimate the models using data on Thai households running small businesses from two separate samples. We find that in the rural sample, the exogenously incomplete saving only and borrowing regimes provide the best fit using data on consumption, business assets, investment, and income. Family and other networks help consumption smoothing there, as in a moral hazard constrained regime. In contrast, in urban areas, we find mechanism design financial/information regimes that are decidedly less constrained, with the moral hazard model fitting best combined business and consumption data. We perform numerous robustness checks in both the Thai data and in Monte Carlo simulations and compare our maximum likelihood criterion with results from other metrics and data not used in the estimation. A prototypical counterfactual policy evaluation exercise using the estimation results is also featured.

Identifying Constraints to Financial Inclusion and Their Impact on GDP and Inequality: A Structural Framework for Policy

“…Martin and Taddei (2013) study the implications of adverse selection on macroeconomic aggregates and contrast them with those under limited commitment. Karaivanov and Townsend (2014) estimate the financial/information regime in place for households (including those running businesses) in Thailand and find that a moral hazard constrained financial regime fits the data best in urban areas, while a more limited savings regime is more applicable for rural areas. Similarly, Paulson et al (2006) argue that moral hazard best fits the data in the more urban Central region of Thailand but not in the more rural Northeast.…”

Distinguishing Constraints on Financial Inclusion and Their Impact on GDP, TFP, and the Distribution of Income

“…Martin and Taddei (2013) study the implications of adverse selection on macroeconomic aggregates and contrast them with those under limited commitment. Karaivanov and Townsend (2014) estimate the financial/information regime in place for households (including those running businesses) in Thailand and find that a moral hazard constrained financial regime fits the data best in urban areas, while a more limited savings regime is more applicable for rural areas. Similarly, Paulson, Townsend and Karaivanov (2006) argue that moral hazard best fits the data in the more urban Central region of Thailand but not in the more rural Northeast.…”

Market failures and misallocation

“…One possibility is that markets in agriculture really are more efficient than in manufacturing. But Karaivanov and Townsend (2013) compare households in rural Thailand to their urban counterparts and find that the rural households appear more constrained.…”