1. 1 'POVTIME': module to compute aggregate intertemporal poverty measures Carlos Gradín Universidade de Vigo

2. Description ‘povtime’ computes aggregate intertemporal poverty measures (poverty accounting for time) in a balanced panel of individuals. The program computes the family of FGT-type intertemporal poverty measures proposed in: –Gradin, Del Rio, and Canto ("Measuring Poverty Accounting for Time", Review of Income and Wealth, 58(2): 330-354, 2012). Other measures that can be interpreted as particular cases of this general family: –Foster (“A Class of Chronic Poverty Measures” in Poverty Dynamics: Interdisciplinary Perspectives, OUP, 2009) and –Bossert, D'Ambrosio and Chakravarty ("Poverty and Time", Journal of Economic Inequality, 2012. 2

3. Measuring poverty Poverty in a cross-section of individuals y=(y 1, y 2,..., y q, y q+1,..., y N ) Poverty index: P(y; z) Stata modules: povdeco, apoverty, sepov 3 PoorNon poorz FGT(0) = Headcount rate (H=q/N) FGT(1) = Poverty gap ratio (HI) FGT(2) = Poverty severity

4. Measuring longitudinal poverty Poverty in a (balanced) panel N Individuals observed T times Poverty index?: P(y; z) Stata modules: povtime 4

5. 5 i) summarize the complete individual information in time individual intertemporal poverty index ii) then construct an aggregate poverty index that takes into account a social preference for equality among individuals

6. 6 Gradín, Cantó and del Río (RIW, 2012) P satisfies all desirable properties for Foster (OUP 2009) Bossert, D’Ambrosio and Chakravarty (JOEI 2012)

7. Advantages A code that allows for measuring agggregate poverty in a panel –complementing existing codes for measuring poverty in a cross-section –following various measures recently proposed in the literature, –in a way consistent with how poverty is measured in a cross-section. Easy to undertake in-depth analysis –robustness (dominance analysis), –decomposition into components (incidence, intensity, inequality), –analysis of the distribution of individual poverty indices, etc. Easy to obtain inference using bootstrapping 7