The objective is to calculate, for household interviewed in round 2, the change in log income between the two survey rounds: ii (2) where and are respectively the first and second round log per capita incomes of household ii ii2 ii1 ΔΔyy = yy −yy surveyed in round 2. Therefore, the log per capita household income in round 1 for household is ii1 ii2 yy yy ii unknown and must be estimated. These changes would be easily calculated with panel data since all ii households are interviewed in both rounds (i.e., is observed for every household interviewed in both rounds and does not require to be estimated). Given panel data is costly and scarce in most countries in ii ΔΔyy ii the region, synthetic panels allow for predicting the first round of “unobserved” incomes of households surveyed in the second. As noted above, to avoid making assumptions about the behavior of the residuals, in this approach the unobserved log income is not obtained directly from an econometric prediction, but through statistical matching between household heads from the two surveys. Thus, the predicted income from model (1) is one of the variables on which the closeness between observations is measured. More specifically, and are predicted using LASSO regression as follows: ii1 ii2 i yy L L yy L L i (3) TT LLjj LLjj 2 yy� = αα� + ββ̂ XX jj = 1, 2 where ( L L L L ) = argmin ) �∑ i i � �1�, is the penalization TT NNjj TT parameter chosen through cross-validation, and αα� LLjj, ββ̂ LLjj (ααjj,ββjj ii=1�yy �−αα1 is the ii −ββlii1XX norm of the coefficient vector + λλ�ββii λλ . ii ii This method estimates the portion of log income that is assumed to be time-invariant, at least when �ββ ββ using cross-sectional surveys close in time. Although this assumption does not always hold, there are several reasons why it may be reasonable to believe that the portion of income explained by timeinvariant covariates remains constant in close periods. First, personal characteristics such as education level, work experience, and skill level tend to be relatively stable over time and may have a persistent effect on income. Second, if individuals have limited mobility and are unable to easily change their geographic location or industry of employment (which is expected to be the case in close periods), this could limit their ability to access higher-paying jobs and result in a relatively constant portion of income being explained by time-invariant factors. Third, if economic conditions (such as the overall level of demand for goods and services) remain relatively constant over time, this could also lead to the portion of income explained by time-invariant factors remaining constant. If a purely parametric approach were used, the full welfare measure in round 1 would be completed by adding some residuals to i . That is not the case in the method presented in this paper. The main assumption behind this semi-parametric approach is that the portion of log income explained by timeyy� invariant covariates remains constant in both periods. As a result, and can be used to find the most similar household heads in both surveys (within the set of observations that share common ii1 ii2 yy� yy� characteristics) and thus build the synthetic panel. For every observation in the second round of data, a set of neighbors (in terms of and closeness) is found in the first-round data by looking for observations with the smallest absolute difference between the two linear predictions. Among these ii1 ii2 yy� yy� neighbors, one of them is randomly selected based on the sampling weights from the first round. In addition, the matching method requires that the closest neighbors are searched only within a pre-defined 6