Sustainable Development vs. Middle-Income Trap

The article is a contribution to the discussion on the necessity to change the current model of economic growth of Poland for model of economy based on knowledge and innovation. In this way, our country will be able to overcome the threats that might push the economy into the trap of the average income, expressed in long-term slowdown in GDP growth. The endogenous growth theory, formed after 1989 and characterized by duplication of Western technology, enabled relatively rapid growth by over 20 years. Currently, Poland to ensure an economic growth is facing the need for innovative technologies and innovation. Risks associated with middle income trap are very real because of the disappearance of comparative advantage, which results from relatively low labor costs. The creation in Poland conditions to accelerate economic growth requires action to increase the propensity of entrepreneurs to reach for new technologies and innovation and attractive market offer from the national centers for research and development.

Download Full-text

Middle-Income Trap in Emerging States

Emerging States and Economies - Emerging-Economy State and International Policy Studies ◽

10.1007/978-981-13-2634-9_7 ◽

2018 ◽

pp. 153-177 ◽

Cited By ~ 1

Author(s):

Tetsushi Sonobe

Keyword(s):

Middle Income ◽

Middle Income Trap

Download Full-text

Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7110448 ◽

2018 ◽

Vol 7 (11) ◽

pp. 448 ◽

Cited By ~ 3

Author(s):

Robert Chew ◽

Kasey Jones ◽

Jennifer Unangst ◽

James Cajka ◽

Justine Allpress ◽

...

Keyword(s):

Sustainable Development ◽

Object Detection ◽

Household Surveys ◽

Building Structures ◽

Middle Income ◽

The Sustainable Development ◽

Detection Model ◽

Middle Income Countries ◽

Development Goals ◽

Low And Middle Income

While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals (SDGs) in low- and middle-income countries (LMICs). Though some countries’ statistical agencies maintain databases of persons or households for sampling, conducting household surveys in LMICs is complicated due to incomplete, outdated, or inaccurate sampling frames. As a means to develop or update household listings in LMICs, this paper explores the use of machine learning models to detect and enumerate building structures directly from satellite imagery in the Kaduna state of Nigeria. Specifically, an object detection model was used to identify and locate buildings in satellite images. In the test set, the model attained a mean average precision (mAP) of 0.48 for detecting structures, with relatively higher values in areas with lower building density (mAP = 0.65). Furthermore, when model predictions were compared against recent household listings from fieldwork in Nigeria, the predictions showed high correlation with household coverage (Pearson = 0.70; Spearman = 0.81). With the need to produce comparable, scalable SDG indicators, this case study explores the feasibility and challenges of using object detection models to help develop timely enumerated household lists in LMICs.

Download Full-text