close to the observed poverty levels. Moreover, our preferred specification allows for much indepth distributional analysis by incorporating the evolution of labor and non-labor income components. While a three-sector model also provides competitive estimates, it is evident that expanding the set of sectors allows for a more comprehensive distributional analysis. This is desirable despite the added noise, as our focus is on the accuracy of the estimates rather than the precision. For FY12, the prediction accuracy of models goes down, which is an expected outcome for predictions further into the past.<sup>16</sup>

**Table 5: Comparison between Observed and Backcasted Poverty**

|**Method**|**FY16**|**FY14**|**FY12**|
|---|---|---|---|
|Official Poverty|24.3|29.5|36.3|
|Preferred specification: equation (1)|28.8|31.4|30.7|
|PovertyGrowth Elasticity|24.0|25.5|26.8|
|Neutral distribution(0.86passthrough)|29.6|32.4|31.6|
|Democratic|29.6|34.8|39.6|
|Plutocratic|29.7|35.0|39.7|
|3-Sectors|28.7|31.2|29.8|
|11-Sectors|28.8|31.4|30.7|

Source: Authors’ estimations based on HIES 2018/19 and the Household Integrated Income and Consumption Survey (HIICS) 2015/16.

Note: Preferred specification follows equation (1), including information from nominal growth from eleven economic sectors to model labor income and household-specific inflation as described in the methodology. All other projections rely on headline inflation and nominal growth rates. Growth and poverty changes between FY16 and FY19 are used to estimate the elasticity for the Poverty Growth Elasticity and the Growth Incidence Curve for the Quintile growth contribution. Estimate poverty growth elasticity for real GDP per capita and poverty between FY16 and FY19 is -1.06. The neutral distribution model uses a passthrough of 0.86 to reflect the consumption share on total GDP; results using higher (lower) passthrough rates present marginally more (less) pronounced variations of poverty to growth. Quintile growth contribution projections include a Democratic approach, where households within a quantile receive the same per-capita income amount, and a Plutocratic Approach, where each household receives an amount of income based on its share in the total income within its quantile. Projections using sectorlevel growth rely on the sector of employment of the member with the highest reported labor income.

# **5. CONCLUSION**

Since 2019, the last year when data to estimate poverty and inequality levels were available, Pakistan has experienced large macroeconomic and natural shocks. However, the absence of new household survey information limits the ability to assess the implications of the different shocks on household welfare to formulate appropriate policy responses.

> 16 Slight deviations from the observed poverty rates are introduced by changes in the sampling frameworks due to urban households being oversampled in HIICS 2015/16 and the inclusion of the Federally Administered Tribal Areas (FATA) within the Khyber Pakhtunkhwa province. FATA areas are affected by violence and economically disadvantaged when compared with the rest of the country. We implement additional reweighting tests with no qualitative change in the results.

16