Poor households regularly borrow and lend to smooth consumption, yet we see much less borrowing for investment. This cannot be explained by a lack of investment opportunities, nor by a lack of resources available for investment. This paper provides a novel explanation for this puzzle: informal risk sharing can crowd out investment. I extend the canonical model of limited commitment in risk-sharing networks to allow for lumpy investment. The key insight is that the cost of losing insurance is lower for a household that has invested, since it has an additional stream of income. This limits its ability to credibly promise future transfers, and so limits its ability to borrow from other households. The key prediction of the model is a non-linear relationship between total income and investment at the network level – namely there is a network level poverty trap. I test this prediction using a randomised control trial in Bangladesh, that provided capital transfers to the poorest households. The data covers 27,000 households from 1,400 villages, and contain information on risk-sharing networks, income and investment. I exploit variation in the number of program recipients in a network to identify the threshold level of capital provision needed at the network level for the program to move the network out of a poverty trap and generate further investment. I also verify additional predictions of the model and rule out alternative explanations. My results highlight how capital transfer programs can be made more cost-effective by targeting communities at the threshold of the aggregate poverty trap.
Understanding tax non-compliance and the effectiveness of strategies to tackle it is crucial for a modern tax authority. In this paper we study how and why audits impact reported tax in the years after audit – the dynamic effect – for individual income taxpayers. We exploit data from a random audit program covering almost 35,000 income tax self assessment returns in the UK. We show that audits raise reported tax liabilities for at least five years after audit, with the magnitude of the impact declining over time. In total this raises an additional £1,230 per audited individual in the five years after audit, 1.5 times the direct revenue raised from the audit. Looking by income source, we see that the magnitude of the initial impact is lower for income components which are third party reported, and the impact declines more quickly for components that are more volatile. We develop a model to allow us to distinguish different mechanisms that might explain the presence of dynamic effects, and show our findings can only be explained by audits providing improved information to the tax authority.
A. Advani, T. Kitagawa and T. Słoczyński (2018), cemmap Working Paper CWP56/18
resubmitted to Journal of Applied Econometrics
Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such ‘empirical Monte Carlo studies’ (EMCS) have been proposed. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. We find that in our setting both EMCS approaches are worse than random at selecting estimators which minimise absolute bias. They are better when selecting estimators that minimise mean squared error. However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.
A. Advani and B. Reich (2015), IFS Working Paper W15/30
Relatively little is known about what determines whether a heterogenous population ends up in a cooperative or divisive situation. This paper proposes a theoretical model to understand what social structures arise in heterogeneous populations. Individuals face a trade-off between cultural and economic incentives: an individual prefers to maintain his cultural practices, but doing so can inhibit interaction and economic exchange with those who adopt different practices. We find that a small minority group will adopt majority cultural practices and integrate. In contrast, minority groups above a certain critical mass, may retain diverse practices and may also segregate from the majority. The size of this critical mass depends on the cultural distance between groups, the importance of culture in day to day life, and the costs of forming a social tie. We test these predictions using data on migrants to the United States in the era of mass migration, and find support for the existence of a critical mass of migrants above which social structure in heterogeneous populations changes discretely towards cultural distinction and segregation.
A. Advani and B. Malde (2018), Journal of Economic Surveys
Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.
A. Advani and B. Malde (2018), Swiss Journal of Economics and Statistics (solicited)
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the `linear-in-means' local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analyis of social networks. We begin by providing a common framework for models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We discuss identification of these models using both observational and experimental/quasi-experimental data. We then discuss models of network formation, drawing on a range of literatures to cover purely predictive models, reduced form models, and structural models, including those with a strategic element. Finally we discuss how one might collect data on networks, and the measurement error issues caused by sampling of networks, as well as measurement error more broadly.
This IFS Briefing note uses data from HMRC’s random audit programme to show which types of people are more likely to be under-reporting taxes and how their behaviour changes after a tax audit. The results are based on data from audits covering tax returns for the years 1999–2009.
Current UK energy use policies, which primarily aim to reduce carbon emissions, provide abatement incentives which vary by user and fuel, creating inefficiency. Distributional concerns are often given as a justification for the lower carbon price faced by households, but there is little rationale for carbon prices associated with the use of gas to be lower than those for electricity. We consider reforms that raise carbon prices faced by households, and reduce the variation in carbon prices across gas and electricity use, improving the efficiency of emissions reduction. We show that the revenue raised from this can be recycled in a way that ameliorates some of the distributional concerns. Whilst such recycling is not able to protect all poorer households, existing policy also makes distributional trade-offs, but does this in an opaque and inefficient way.
The report analyses and assesses: the rationale and objectives of energy policy;
the current policy landscape faced by UK energy users; how current and future
policy has led to inconsistencies in the implicit carbon prices faced by
different users; and potential ways in which to improve policy affecting domestic
and business energy users.
Government wants both to reduce carbon emissions and to reduce ‘fuel poverty’. Energy prices have risen in part because of a multitude of policies aimed at reducing emissions. There are also multiple policies aimed at ameliorating these effects. Altogether, this leads to a complex policy landscape, inefficient pricing and opaque distributional effects.
In this report, we show the effects of energy price rises over the recent past, look at what current policies mean for effective carbon prices and their impact on bills, and consider the distributional consequences of a more consistent approach to carbon pricing, alongside possible changes to the tax and benefit system that could mitigate these effects.