Trends in poverty, urbanization and agricultural productivity in Zimbabwe

Transcription

1 Trends in poverty, urbanization and agricultural productivity in Zimbabwe Preliminary findings Rob Swinkels (World Bank) and Percy Chipunza (Ministry of Lands, Agriculture and Rural Resettlement) June 14, 2018 Based on ZIMSTAT data Maps by Herbert Zvirereh 0

2 Introduction Urban areas are important centers of growth and drivers of poverty reduction. This happens through agglomeration effects leading to structural transformation. So what do we know about urbanization, poverty and structural transformation in Zimbabwe? We take a brief look at spatial trends and emerging evidence on agricultural productivity Data used: the 2002 and 2012 population census, other spatial datasets, the labor force surveys (1999, 2004, 2011 and 2014), and the PICES/ APM 2017 survey. Presentation Title 1

3 Globally, urbanization rate is strongly related to welfare GDP per capita Per capita GDP and urban population rate 2016 (all countries in the world) Zimbabwe Sub-Sahara Africa Others Urban population rate (%) Given its current GDP per capita, Zimbabwe s urbanization rate is relatively high globally, but typical for Africa 2 Urbanization and poverty reduction in Zimbabwe

4 Urbanization in Zimbabwe appears to have stalled, Urban population (as % of total population) Zimbabwe Sub-Saharan Africa ,000,000 12,000,000 10,000,000 8,000,000 6,000,000 4,000,000 2,000,000 Population size, urban and rural 2002 and Sub-Saharan Africa (IDA & IBRD countries) Source: World Development Indicators Zimbabwe Rural Urban Source: based on ZIMSTAT data (census 2002 and 2012) 3 Note: all of Harare province (including Harare rural) was regarded as urban

5 In 2012, 23 percent of the Zimbabweans said they lived in a different district in the country in 2002 Rural ureas, 12% Domestic migrants in 2012 and their origin (as proportion of total population) Harare, 3% Bulawayo, 1% Other cities, 2% 12 percent of migrants came from rural areas But 10 percent came from urban areas Migration pattern is mixed: urban -> rural and rural -> urban are about equal Municipalities, Based on ZIMSTAT data (Population census 2002 and Presentation Title

6 Domestic migration Net rural to Harare migration was nil (migration equals outmigration back to rural) Other cities also lost people, mostly to rural But net migration from rural to small towns was positive Number of domestic migrants in 2012 by location type, and by where they lived in 2002 Rural to municipalities and towns Rural to other cities Rural to Harare Rural to Rural Harare to municipalities and towns Harare to other cities Harare to rural Other cities to municipalties and towns Other cities to other cities Other cities to Harare Other cities to rural Municipalities and towns to other municipalities and Municipalities and towns to other cities Municipalities and towns to Harare Municipalities and towns to rural 0 100, , , , , , ,000 Number of people 5 Source: based on ZIMSTAT data (census 2012)

7 Urban population growth is especially low in the two primary and four secondary cities (city councils), and relatively high in the small towns (town councils). 3,000,000 2,500,000 2,000,000 1,500,000 1,000, ,000 0 Population in large cities, secondary cities and small towns in % 12% Population growth (%) 26% 40% Primary cities City councils Municipalities Town councils 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Population 2002 Population 2012 Growth (right axis) 6 Presentation Title

8 Four of Zimbabwe s six major cities are in areas with relatively low population density Kadoma Harare Kwekwe Mutare Bulawayo Gweru 7 Presentation Title

9 Poverty did not reduce Multi-dimensional poverty rose and then dropped Percentage 50 Multi-dimensional poverty trends Multi-dimensional poverty (k=4) Multi-dimensional poverty (k=3) Source: Stoeffler, Alwang, Mills and Taruvinga (2015) using PICES 2001, 2007 and 2011/12 New poverty estimates expected this year from PICES 2017 k= number of deprivations Presentation Title 8

10 Poverty has become more rural - In 2001, 88 percent of the extreme monetary poor lived in rural areas - In 2011 this was 92 percent In most of Sub-Saharan Africa poverty is becoming more urban Proportion of extreme poor 2001 (%) Urban Proportion of extreme poor 2011 (%) Urban Rural 2001 (no of extreme poor) Urban Rural 2011 (no of extreme poor) Urban 2011 (no of people) Urban Rural Rural Rural Source: based on ZIMSTAT data (PICES 2001 and 2011) Presentation Title 9

11 The extreme monetary poor are concentrated in the northwest of the country, with small pockets in the northeast and southeast *Hot spot analysis identifies where high and low poverty are spatially clustered. To be a statistically significant hot spot, ward level poverty will need to be high and be surrounded by other wards with high poverty. The local sum for ward poverty and of its neighboring wards is compared proportionally to the sum of all poverty; when the local sum is very different from the expected local sum, and when that difference is too large to be the result of random chance, a statistically significant z-score results. Source: ZIMSTAT poverty map based on populationcensus 2012 and PICES About half the extreme monetary poor are in the north west where poverty depth is also highest

12 Ward poverty rates are lower when population density is high Urban poverty is much lower, partly due to smaller household sizes Household size Household size and extreme poverty rate 30% 25% 20% 15% 10% 5% 0% Extreme poverty rate Avg household size Extreme poverty rate Presentation Title 11

13 Rural poverty likely also linked to poor connectivity.road density is low in high poverty areas, despite high population density Population density 2012 Source: Population census 2012 and ZIMSTAT poverty map 2012 Presentation Title 12

14 Connectivity is low in the northwest where poverty is high (based on travel time and population density) Presentation Title 13

15 Small towns have more poor people in their immediate vicinity than larger urban settlements Number of extremely poor people 400, , , , , , ,000 50,000 0 Number of poor by distance from urban centers Primary cities City Councils Municipalities Town councils Number of extremely poor people within 5km within 10km within 15km within 50km Investing in the development of small towns may have high poverty impact Presentation Title 14

16 During pabor moved into agriculture, where average labor productivity is 6 percent of the industry average, and 11 percent of the services average in 2014 Agriculture added 960,000 jobs since 1999 Average labor productivity in agriculture fell by 55 percent between Source: WB Jobs Diagnostic, based on ZIMSTAT data (Labor force surveys), and World Development Indicators

17 Workers went back to self-employment in agriculture - reverse structural transformation Source: ZIMSTAT Labour Force Surveys Source: WB Jobs Diagnostic, based on ZIMSTAT data (Labor force surveys), and World Development Indicators

18 Male workers and youth entered into selfemployment in agriculture when paid employment fell For youth the increase in employment rate came from agricultural self-employment. partly due to drop in rural unemployment and inactivity after 1999 Urban paid employment stayed the same, Rural paid employment fell for youth. Urban self-employment for youth also fell. Source: WB Jobs Diagnostic, based on ZIMSTAT labor force survey data

19 More people in Zimbabwe are self-employed in agriculture than in other countries Source: WB Jobs Diagnostic, based on ZIMSTAT labor force survey and WDI data

20 Density of agriculture own account workers & unpaid family workers 2012 is very similar to poverty density Density of agric employers and paid agric workers

21 Density Paid Employment in Agro-processing (male) 2012 Source: ZIMSTAT Central Business register 2013

22 Conclusions Urbanization in Zimbabwe stalled during 2002 to 2012 Many of the extreme poor live within 5-15 km from a small town Lots of people moved back to low productivity agriculture Country is over self-employed, especially in agriculture. To reduce poverty, need to: Raise labor productivity in agriculture Understand main constraints for this (better evidence) Create more wage jobs in agriculture and in secondary town -> resurrect agro-processing in small towns -> design support programs that address core constraints and improve monitoring of their effectiveness 21 Presentation Title

23 The PICES household agricultural productivity module 2017 The PICES-PICES 2017 survey provides unique dataset for generating further analysis to support evidence-based policy making ZIMSTAT will release micro-data to researchers To be completed by Percy Chipunza..