final presentation - usaid internship

BRINGING DATA INTO THE DEVELOPMENT CONVERSATIONMARY ZIEMBA, DUKE UNIVERSITY ‘18, B.S. COMPUTER SCIENCE, CERTIFICATE INNOVATION AND ENTREPRENEURSHIP

“Increase the use of scientific research for improved development outcomes.”

–Global Development Lab results framework, science DO

Why I’m here

Collect data about science’s role in development to inform development decisions

Make this data available to CDR, the Lab, missions, others in USAID who would find it useful

MY QUESTION:WHAT DOES DATA SAY ABOUT THE ROLE OF SCIENCE AND RESEARCH IN DEVELOPMENT?

SUMMARY STATISTICS

Expenditures for research and development are current and capital expenditures (both public and

private) on creative work undertaken systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications. R&D

covers basic research, applied research, and experimental development.”

—World Bank GERD definition

current and capital expenditures (both public and private) .

basic research, applied research, and experimental development.”

Gross expenditure of research and development (GERD):

THE WORLD BANK - WORLD DEVELOPMENT INDICATORS

METRIC

DATE SOURCE2013

AVERAGE GROSS EXPENDITURE ON R&DBY REGION, ALL REPORTING COUNTRIES, % OF GDP

UNESCO

METRIC

DATE SOURCE2013

GERD BREAKDOWN BY CATEGORYSELECT COUNTRIES ACROSS INCOME GROUPS

El Salvador KazakhstanIceland

UNESCO (GERD); GDP/CAPITA (WORLD BANK)

METRIC

DATE SOURCE2000-2012

GROSS EXPENDITURE ON R&D AND GERDRELATIONSHIP OVER TIME, BRAZIL

Thicker line = R&D expenditure Thinner line = GDP/capita

Latin America/Caribbean (excluding high-income)

Europe and Central Asia (excluding high-income)

East Asia and Pacific (excluding high-income)Nepal (South Asia)

Ethiopia (South Africa)Egypt (North Africa)

Global CompetitivenessWorld Economic Forum: “The set of institutions, policies, and factors that determine the level of productivity of an economy, which in turn sets the level of prosperity that the country can earn.”

Survey- and data-driven numbers

1—7 scale

WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-2016

GLOBAL COMPETITIVENESS SUMMARYLAC

Latin America/Caribbean Avg.Peru

Upper-Middle Income Avg.United States

WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-2016EAST ASIA/PACIFIC

CambodiaVietnam

IndonesiaUnited States

GLOBAL COMPETITIVENESS SUMMARY

WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-2016

GLOBAL COMPETITIVENESS SUMMARYMIDDLE EAST & NORTH AFRICA

IsraelMoroccoJordan

United States

WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-2016

GLOBAL COMPETITIVENESS SUMMARYEUROPE AND CENTRAL ASIA

FranceArmenia

KazakhstanUnited States

WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-2016

GLOBAL COMPETITIVENESS SUMMARYSUB-SAHARAN AFRICA

South AfricaCote d’Ivoire

TanzaniaUnited States

WHAT I LEARNED

UNESCO/WORLD ECONOMIC FORUM

METRIC

DATE SOURCE2015-16

INSTITUTIONAL QUALITYGERD AND UNIVERSITY-INDUSTRY COLLAB. IN R&D

R-squared: 0.99R-squared: 0.72

WORLD ECONOMIC FORUM, GLOBAL COMPETITIVENESS DATASET

METRIC

DATE SOURCE2015-2016

CORRELATIONS, GLOBAL COMPETITIVENESS DATAWHAT IS RELATED TO INSTITUTIONAL QUALITY?

Availability of

Scientists and

engineers

Capacity for

innovation

Company R&D

Spending

Capacity to attract

talent

Capacity to retain

talent

Extent of market

dominance

Govern-ment

procure-ment of advanced tech.

Primary educa-

tion enrolme

nt

Quality of Math

and science educ.

Quality of

primary educa-

tion

Quality of edu-cation

system

Second-ary edu-cation enrol-ment

Tertiary educa-

tion enrol-ment

Univ.-Industry Collab. in R&D

High-income 0.492 0.823 0.807 0.282 0.385 0.4 0.26 0.117 0.399 0.474 0.522 0.19 0.09 0.86

Upper-middle income

0.405 0.518 0.611 0.178 0.194 0.526 0.169Not

signif-icant

Not signif-icant

Not signif-icant

0.152Not

signif-icant

Not signif-icant

0.681

Lower-middle income

0.542 0.678 0.765 0.272 0.296 0.496 0.523Not

signif-icant

0.502 0.443 0.622Not

signif-icant

Not signif-icant

0.511

Low income 0.642 0.479 0.607 0.275 0.41 0.407 0.363

Not signif-icant

Not signif-icant

Not signif-icant

Not signif-icant

Not signif-icant

Not signif-icant

0.651

Darker green = more closely correlated

Questions?