Is accuracy all we should care about?

Criteria for evaluating different methods
1. Accuracy

2. Simplicity

3. Credibility

4, Ease of implementation

5. Stability


1000 features vs. GDP

Mean Absolute Deviation
(pct. points)


Complex models

Features

¢ World Development Indicators

e World Economic Outlook

© Google Earth Engine (nighttime lights, rainfall, land surface
temperature, impervious surface, cropland, normalized
difference vegetation/snow/water index)

Algorithms:

Lasso

Post-lasso

CART random forests

Conditional inference random forests
Gradient boosting


Training data

e Use 2000+ poverty estimates from the World Bank's Poverty and
Inequality Platform

F4 "o9 "03 "oa "os "oe "07%

95 96 97 98 9970001 "02 03 0405 "06 "07 "0809 10 11 12 13 14 15 16 17 18 19 20 21 22

Lithuania (0 03 (05 02 14 18 15 13 08 22 15 07 09 07 13.07 13 1 09 05
Bangladesh [8] 34 |] Bi |
Turkmenistan ‘50°

Loss function: Mean absolute deviation in pct. points

= true *
Loss = S Weountry * [poverty cuntrytime — poverty country,time


Simple models

e Leverage that there is a tight relationship between income from
national accounts and income measured in household surveys

Poverty line

20

O19 5 15
Daily consumption per capita (2011 USD PPP)

HB 2016 (last survey) HEB 2021 (nowcast)


Nowcasting Global Poverty

Daniel Gerszon Mahler

2022-10-14



No data vs. old data

105

cs a ct

Mean absolute deviation
(percentage points)

Nn

0 T T T T T
4 ~6
Extrapolation time

— GDP growth
— Best model that predicts poverty rates directly


Feature importance

Growth in HFC per capita
Growth in GDP per capita

Growth in GNI per capita
Growth in Employment rate -- Percent of total

population

Change in Employment rate ~ Percent of total
population

Change in General government gross debt - Percent
of GDP

Volume of Imports of goods -- Percent change

Change in Unemployment, total (% of total labor.
force) (modeled ILO estimate)

Growth in General government gross debt -~ Percent
of GDP

Growth in PPP conversion factor, private
consumption (LCU per international $)

0 0.25

0.50
Variable importance

0.75

1.00


Simple models

e Leverage that there is a tight relationship between income from
national accounts and income measured in household surveys

Poverty line

20

O19 5 15,
Daily consumption per capita (2011 USD PPP)

HB 2016 (last survey)


Objective

e Test various methods to nowcast extreme poverty in all
countries of the world as of the present year.

e Test how complex machine learning models with new data
sources compare against more simple nowcasting models


Recent advances

e Recent papers used satellite imagery and OpenStreetMap to
predict timely granular estimates of poverty (or wealth) with
high accuracy.

PNAS a

Microestimates of wealth for all low- and be” t be os
middle-income countries

ao”



Motivation

© Timely poverty estimates are vital to allocate resources and
track SDG 1.1.

IMF approves aid for world’s 28 poorest countries

e Yet, on average across the developing world, the most recent
survey with poverty data is from 2015.
¢ 16 countries have no poverty estimate at all.


Final reflections

e Non-traditional data sources may give higher accuracy in
nowcasts of SDG indicators.

e But sometimes the increased accuracy may not be worth the
trouble.

e It may come down to the particular application and the purpose
of providing up-to-date data.