Geographical information is a crucial element for the optimal targeting of poverty reduction policies and programs, especially for a vast country with a large and heterogeneous demographic range like Indonesia. However, the official poverty information from Statistics Indonesia at best provides reliable data down to the kota/kabupaten (city/district) level. In light of the need for reliable poverty figures at kecamatan (subdistrict) and village levels, The SMERU Research Institute is continuously updating its Poverty Map of Indonesia. This map is SMERU's third effort to provide poverty statistics down to the village level and not only provides poverty figures, but also combines these with additional socioeconomic information to improve poverty analysis for its users.
History of Poverty Map
With support from the Ford Foundation, SMERU developed the Poverty Map of Indonesia 2000; the first poverty map of Indonesia which estimated poverty rates down to the village level. The estimation combined information obtained from household surveys-collected from National Socioeconomic Survey (Susenas)-with information collected through the population census (Sensus Penduduk) and village census/Potensi Desa (Podes). The model used in the map to estimate poverty rates down to the village level is a provincial urban/rural model (totaled to 53 estimation models). Map user evaluation reveals that the map has been widely used by various institutions, including government institutions at national and regional levels, research institutions, nongovernmental organizations, private sectors, and donors.
The dynamic nature of poverty at various geographic levels requires the frequent updating of the poverty figures over time, so that the poverty estimates remain useful and appropriate for interventions. With support from the Ford Foundation and UNICEF, SMERU updated its 2005 poverty map by combining the latest 2010 Population Census with 2010 Susenas and 2011 Podes. The map also incorporates data on child poverty, as well as additional socioeconomic indicators and physical conditions of the areas, such as natural resources, local economic characteristics, infrastructure, and access to health and educational services. The model used to estimate poverty rates in the Poverty and Livelihood Map of Indonesia 2010 is a kabupaten/kota model (totaled to 497 estimation models). The poverty estimates were not only calculated based on national poverty line (kabupaten/kota specific) but also used kabupaten/kota specific internationally comparable poverty lines (kabupaten/kota specific) that are referenced against the US$2 (PPP) per person per day.
Indonesia Poverty Map 2015
SMERU realizes that a lot of people move in and out of poverty over the course of time. Therefore, real-time and continuous updating of household welfare information is critical for the monitoring of the latest developments in the economy, as well as devising timely policy responses. To accommodate this, SMERU has taken up the challenge of updating the poverty map during intercensal periods. With support from the Ford Foundation, the update of small area estimations during the interval between censuses is conducted by combining the latest 2010 Population Census, 2010 Susenas, 2015 Susenas, and 2014 Podes.
In doing this, SMERU attempted to estimate the 2015 poverty figures of people in 2010 using counterfactual consumption data before applying small area statistics to estimate poverty rates down to the village level. The model used to estimate poverty rates in the Poverty and Livelihood Map of Indonesia 2015 is a provincial urban/rural model (totaled to 65 estimation models). Besides using the official provincial urban/rural poverty lines, SMERU also provides poverty figures based on internationally comparable lines that are referenced against the new US$3.1 (PPP) per person per day.
Data on livelihood indicators are also updated. The updated poverty data is displayed on the map and the free editable map of OpenStreetMap (OSM) Indonesia is used as its base map. It aims to provide a clear visualization of the data and a more interactive poverty map for the user. In addition, the map also features qualitative information for selected villages. This is the very first effort to overlay poverty statistics with qualitative information and it is expected to facilitate improved poverty analyses for the users. For the time being, qualitative information is available for only 50 villages. We expect input from various stakeholders regarding qualitative information from other villages as well as data related to the livelihood conditions of the communities.
Website Cover Photo: Hafiz Arfyanto