## **Box 1: The Intuition behind Synthetic Panels**

Analysis of economic mobility over time requires panel data. However, such data are not typically available in many settings. In Nepal, even though the NLSS surveys contained panel components, they suffer from heavy attrition and the resulting population is not representative for the population in any of the rounds. The synthetic panel methodology was introduced to address this key data limitation. Several efforts to validate the results of this methodology against actual panel data have been conducted and the general consensus that has emerged is that synthetic panels provide a promising way forward.

We describe the key elements of how the method works using the example of two LSMS rounds in Nepal, let’s say the 2003/04 and 2010/11 rounds. The essential idea is to use an imputation methodology to predict the consumption of households interviewed in 2003/04 in 2010/11. This prediction is done by first estimating a consumption model for 2010/11. This entails a regression of household per capita consumption on a set of observable household characteristics such as household size, demographic composition, education or asset holdings, etc. The predicted 2010/11 consumption (of households interviewed in 2003/04) is based on two components. The first component is obtained by applying the estimated coefficients on observable characteristics of households interviewed in 2003/04. Implicitly, the question that is being asked is, what would the consumption of the 2003/04 households be if the “returns to their characteristics” were as observed in 2010/11.

But of course, in addition to the deterministic component, there is also an idiosyncratic element to household consumption, e.g., shocks experienced by the households or other unobservable characteristics such as entrepreneurship, industry etc. In order to account for that, a second component has to be included in the predicted consumption. This component is estimated based on the correlation coefficient of the estimated error term obtained from the consumption model for the two years. If a point estimate of this error term is constructed, it can be used to derive point estimates for the mobility measure. If not, an upper or lower bound of the mobility estimates is obtained by making an upper or lower bound assumption on this correlation. The predicted consumption in 2010/11 of each household appearing in 2003/04 is the sum of these components.

For this application in Nepal, we use the so‐called “bounds” method and the reported mobility measures are lower bound estimates. A more formal description of this methodology is provided in Annex 2.

_Source: Based on Dang and Lanjouw (2013); Dang et al. (2014)._

# **_3.1.Poverty Transitions_**

Using this methodology, we are able to carry out a dynamic analysis of poverty in Nepal in a way that has not been done before. The current understanding of poverty is static and it is not always fully appreciated that the poverty rates that are reported are net rates. They obscure two crucial and contrasting phenomena of people falling into poverty and people escaping poverty. People who fall into poverty and who are never able to escape poverty are distinct and the policy response necessary to address the needs of these two disparate groups is also often likely to be different. For those who remain trapped in poverty, the chronic poor, enhancing mobility may require focusing on expanding access to economic opportunities. This includes opportunities to build assets (human capital) in childhood and opportunities to command a return on these assets through a fair access to the marketplace for jobs in adulthood. For those vulnerable to falling 8