Prepared by Clemens Breisinger1, Mariam Raouf1, Manfred Wiebelt2, Ahmed Kamaly3, Mouchera Karara3
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
Prepared by Angga Pradesha1, Resham Thapa-Parajuli2 & Xinshen Diao1
1. International Food Policy Research Institute
2. Tribhuvan University, Nepal
Last updated: 3 May 2021
Kwaw Andam, Hyacinth Edeh, Victor Oboh, Karl Pauw & James Thurlow
One in a series of initial assessments of the economy-wide impacts of COVID-19 lockdowns in several African countries
Prepared by Dalia Elsabbagh1, Mariam Raouf1, Manfred Wiebelt2
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
Last updated: 2 September 2020
By Muhammad Saad Moeen, Zeeshan Haider, Sania Haider Shikoh, Noormah Rizwan, Amna Ejaz, Stephen Davies and Abdul Wajid Rana
This presentation has been developed under the Pakistan Agriculture Capacity Enhancement (PACE) program funded by the United States Agency for International Development (USAID)
Updated: December 30, 2020
Prepared by Angga Pradesha1, Sothea Oum2 & Xinshen Diao1
1. International Food Policy Research Institute
2. Centre for Strategy and Innovation Policy (CSIP)
Prepared by Angga Pradesha1, Resham Thapa-Parajuli2 & Xinshen Diao1
1. International Food Policy Research Institute
2. Tribhuvan University, Nepal
Last updated: 3 May 2021
Kwaw Andam, Hyacinth Edeh, Victor Oboh, Karl Pauw & James Thurlow
One in a series of initial assessments of the economy-wide impacts of COVID-19 lockdowns in several African countries
Prepared by Dalia Elsabbagh1, Mariam Raouf1, Manfred Wiebelt2
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
Last updated: 2 September 2020
By Muhammad Saad Moeen, Zeeshan Haider, Sania Haider Shikoh, Noormah Rizwan, Amna Ejaz, Stephen Davies and Abdul Wajid Rana
This presentation has been developed under the Pakistan Agriculture Capacity Enhancement (PACE) program funded by the United States Agency for International Development (USAID)
Updated: December 30, 2020
Prepared by Angga Pradesha1, Sothea Oum2 & Xinshen Diao1
1. International Food Policy Research Institute
2. Centre for Strategy and Innovation Policy (CSIP)
Myanmar: Impacts of COVID-19 on Economy, Agri-Food Systems, Jobs & Incomes
Feed the Future Myanmar Agriculture Policy Support Activity (MAPSA) Policy Note
Updated: June 4, 2020
Dalia Elsabbagh1, Sikandra Kurdi1, and Manfred Wiebelt2
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
Last updated: January 2021
A presentation from the 13th Poverty Environment Partnership meeting held in Manila, Philippines, June 2008.
Download this presentation and more from the meeting here: http://www.povertyenvironment.net/pep13
Prepared by Zouhair ElKadhi2, Dalia Elsabbagh1, Thouraya Lakoud2, Manfred Wiebelt3, and Clemens Breisinger1
1. International Food Policy Research Institute
2. Tunisian Institute of Competitiveness and Quantitative Studies
3. Kiel Institute for the World Economy
Last updated: 2 May 2020
Journal of Sustainable Regional health systems issue Ruby Med Plus
Background: Creating a culture of safety has received great attention to ensure that patients receive the safest possible care. A key
precept of patient safety programs is the removal of the “culture of blame.” Patient safety has been and still is a priority in Italian
Hospitals. The aim of this study was to measure the safety culture in teaching and non-teaching hospitals of Italy.
Methods: Data were collected from 261 staffs working in the teaching and non-teaching hospitals by means of the Italian version
of the Safety Attitudes Questionnaire-Short form 2006.
Results: Mean response rate from returned valid questionnaires was 60%. Both hospitals did not differ significantly in SAQ
dimensions except unit management. Clinical departments differ with each SAQ dimension as indicated by Kruskal Wallis test.
Regression analysis showed positive trend between safety climate and other SAQ dimensions except for stress recognition dimension.
Physicians scored high in team climate, safety climate and job satisfaction and non-physicians scored high in stress recognition
and job satisfaction. Comparing the gender scores, stress recognition and job satisfaction dimensions scored high with females and
Males scored high in team climate and job satisfaction. Both at professional and gender level hospital management scores were
low.
Conclusion: This cross sectional survey provides benchmark data for both hospital safety cultures. Results point out critical
attention to patient safety at teaching and nonteaching hospitals. Further research is needed to check safety culture impact on
patient outcomes in both the hospitals.
Keywords: patient safety, safety culture and safety attitudes
Pakistan Software Houses Association (P@SHA) has released a survey report titled “Effects of COVID19 Crisis on the IT / ITES Sector of Pakistan” on the 3rd of April, 2020. The report aims to analyze the impact of CoronaVirus Pandemic on the IT and ITeS companies of Pakistan. The survey for this report was conducted from March 25th to 28th and a total of 205 companies participated in the survey.
Myanmar: Impacts of COVID-19 on Economy, Agri-Food Systems, Jobs & Incomes
Feed the Future Myanmar Agriculture Policy Support Activity (MAPSA) Policy Note
Updated: June 4, 2020
Dalia Elsabbagh1, Sikandra Kurdi1, and Manfred Wiebelt2
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
Last updated: January 2021
A presentation from the 13th Poverty Environment Partnership meeting held in Manila, Philippines, June 2008.
Download this presentation and more from the meeting here: http://www.povertyenvironment.net/pep13
Prepared by Zouhair ElKadhi2, Dalia Elsabbagh1, Thouraya Lakoud2, Manfred Wiebelt3, and Clemens Breisinger1
1. International Food Policy Research Institute
2. Tunisian Institute of Competitiveness and Quantitative Studies
3. Kiel Institute for the World Economy
Last updated: 2 May 2020
Journal of Sustainable Regional health systems issue Ruby Med Plus
Background: Creating a culture of safety has received great attention to ensure that patients receive the safest possible care. A key
precept of patient safety programs is the removal of the “culture of blame.” Patient safety has been and still is a priority in Italian
Hospitals. The aim of this study was to measure the safety culture in teaching and non-teaching hospitals of Italy.
Methods: Data were collected from 261 staffs working in the teaching and non-teaching hospitals by means of the Italian version
of the Safety Attitudes Questionnaire-Short form 2006.
Results: Mean response rate from returned valid questionnaires was 60%. Both hospitals did not differ significantly in SAQ
dimensions except unit management. Clinical departments differ with each SAQ dimension as indicated by Kruskal Wallis test.
Regression analysis showed positive trend between safety climate and other SAQ dimensions except for stress recognition dimension.
Physicians scored high in team climate, safety climate and job satisfaction and non-physicians scored high in stress recognition
and job satisfaction. Comparing the gender scores, stress recognition and job satisfaction dimensions scored high with females and
Males scored high in team climate and job satisfaction. Both at professional and gender level hospital management scores were
low.
Conclusion: This cross sectional survey provides benchmark data for both hospital safety cultures. Results point out critical
attention to patient safety at teaching and nonteaching hospitals. Further research is needed to check safety culture impact on
patient outcomes in both the hospitals.
Keywords: patient safety, safety culture and safety attitudes
Pakistan Software Houses Association (P@SHA) has released a survey report titled “Effects of COVID19 Crisis on the IT / ITES Sector of Pakistan” on the 3rd of April, 2020. The report aims to analyze the impact of CoronaVirus Pandemic on the IT and ITeS companies of Pakistan. The survey for this report was conducted from March 25th to 28th and a total of 205 companies participated in the survey.
IMF Fiscal Monitor: Policies to support people during the COVID-19 pandemicTatianaApostolovich
The COVID-19 pandemic has struck against the backdrop of a preexisting sluggish global growth outlook, with low inflation and nominal interest rates. The pandemic has elevated the need for fiscal policy action to an unprecedented level. This issue of the Fiscal Monitor discusses the role of fiscal policy to save lives, protect the most-affected people and firms from income losses, unemployment, and bankruptcies, and reduce the likelihood that the pandemic results in a deep, long-lasting slump.
This study aims to explain the macroeconomic and welfare impacts of changes in indirect taxes brought about in response to COVID-19. We study whether the tax relief provided for in the federal budget for fiscal year 2020-21 was effective in providing relief to private enterprises and the trade sector. We also study whether production subsidies granted during the first wave of COVID-19 were effectively able to support firms in the agricultural sector. This assessment allows us to draw lessons that may be useful for designing tax benefit policies amid future waves of the pandemic or during other emergency times.
Samuel Benin
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Virtual Event - The political economy of COVID-19: Impacts on agriculture and food policies
OCT 22, 2020 - 08:30 AM TO 10:00 AM EDT
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Bofana, Jose. 2023. Mapping cropland extent over a complex landscape: An assessment of the best approaches across the Zambezi River basin. PowerPoint presentation given during the Project Inception Workshop, VIP Grand Hotel, Maputo, Mozambique, April 20, 2023
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For people who have money and are philanthropic, there are infinite opportunities to gift a needy person or child a Merry Christmas. Even if you are living on a shoestring budget, you will be surprised at how much you can do.
Donate Us
https://serudsindia.org/how-to-donate-to-charity-during-this-holiday-season/
#charityforchildren, #donateforchildren, #donateclothesforchildren, #donatebooksforchildren, #donatetoysforchildren, #sponsorforchildren, #sponsorclothesforchildren, #sponsorbooksforchildren, #sponsortoysforchildren, #seruds, #kurnool
COVID-19 in Egypt: Impacts on Production, Household Income & Food Systems
1. Clemens Breisinger1, Mariam Raouf1, Manfred Wiebelt2, Ahmed Kamaly3, Mouchera
Karara3
1. International Food Policy Research Institute
2. Kiel Institute for the World Economy
3. Ministry of Planning and Economic Development of the Government of the Arab Republic of Egypt
Last updated: 24 September 2020
Contact: Clemens Breisinger (c.breisinger@cgiar.org) or Mariam Raouf (m.raouf@cgiar.org)
COVID-19 in Egypt
Impacts on Production, Household Income &
Food Systems
Financial support from
We gratefully acknowledge the financial support of the United States Agency for International Development (USAID), which made this study possible under
the project “Evaluating Impact and Building Capacity” (EIBC) that is implemented by IFPRI. The information provided in this paper is not official U.S.
government information and does not represent the views or positions of the United States Agency for International Development or the U.S. Government.
Any opinions stated herein are those of the authors and are not necessarily representative of or endorsed by IFPRI and the Ministry of Planning and
Economic Development.
2. Impact of COVID-19 in Q4 (April-June)
• By implementing a relatively modest and partial
lockdown, Egypt’s GDP during the 4th quarter has
declined by 1.1 percent compared to the same
quarter in 2018/19.
• For the whole fiscal year 2019/2020 MOED and
IFPRI jointly estimated annul growth of 3.8
percent.
• The services sector is hit hardest, followed by
industry. Agriculture is the most resilient sector,
especially parts of the livestock sector have
posted positive growth.
• Higher-income households face the largest
income losses. Lower-income households also
see their incomes decline significantly and find it
harder to cope.
• The government has taken bold actions to
support these vulnerable groups, by allocating a
monthly allowance for seasonal workers who lost
their jobs and by increasing the number of
beneficiaries under the Takaful and Karama
program.
• Continuing to fully open and support the
economy will be critical for avoiding permanent
job losses and increases in poverty.
• The recovery process may also provide
opportunities for fostering more private sector-
driven and sustainable economic transformation.
Source: https://ebrary.ifpri.org/digital/collection/p15738coll2/id/133764/
3. Egypt has phased out most restrictions in June
Source: IFPRI Egypt COVID-19 Food Policy Monitor
4. Three scenarios for Egypt’s recovery from July to Dec.
Fast Recovery Gradual Recovery Slow Recovery
Global
Shocks
Q4
2019-2020
Partial Lockdown period
Remittances
and export
demand
decline
Without
Government
Intervention
Q1
2020-
2021
Production loss from
lockdown period reduced by
95%-98%, for most of the
sectors
Hotels loss are reduced by
only 50%
Production losses from
lockdown period reduced
by 90-95%
Production losses from
lockdown period reduced by
80-85%
Remittances
didn’t
decline,
contrary to
previous
expectations
. Hence, we
didn’t
implement
any negative
shock to
remittances
Hotels losses are reduced
by only 50%
Hotels losses are reduced by
only 50%
Q2
2020- 2021
Production losses reduced by
99%
Hotels losses are reduced by
only 50%
Production losses reduced
by 99- 95%
Production losses reduced by
90-95%
Hotels losses are reduced
by only 50%
Hotels losses are reduced by
only 50%
With
Government
Intervention
Q1
2020- 2021
Keeping the same recovery assumptions for each sector as in Scenario 1, and assuming an increase in
public investments of about 281 billion EGP (around 17.7 billion $), as targeted in the Investment
plan of FY 2020/21. This second Scenario assumes that most of these Investments are directed to
boost the construction sector, housing and infrastructure projects, especially roads and bridges under
the national road network and the completion of the third and fourth phases of the Cairo metro.
Q2
2020- 2021
*A detailed description about the size of the shocks for disaggregated sectors will be shown in the annex
5. Recovery scenarios, with and without
government intervention (GDP)
Change in quarterly GDP with fast or slow
recovery (in percent change)
(changes are relative to a no-COVID scenario)
Change in quarterly GDP with fast or
slow recovery (in percent change)
(changes are relative to a no-COVID scenario)
With Government Intervention (Investment plan)
0.8
2.7
-0.2
1.8
-1.2
1.1
-2.0
-1.0
0.0
1.0
2.0
3.0
Q1 Q2
Faster Recovery Gradual Recovery Slow Recovery
-1.2
0.5
-2.2
-0.3
-3.1
-0.9
-4.0
-3.0
-2.0
-1.0
0.0
1.0
Q1 Q2
Faster Recovery Gradual Recovery Slow Recovery
Without Government Intervention
6. Fast recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Sector GDP (in percent change)
(changes are relative to a no-COVID scenario)
With Government
Intervention
(Investment plan)
0.8
3.3
0.7
1.3
-1.4
0.8
1.1
2.5
6.4
0.1
18.9
-0.6
0.1
0.7
-21.4
-0.3
-2.2
15.4
1.0
4.9
2.1
2.8
0.3
2.6
2.4
4.3
9.6
0.8
20.4
1.9
1.5
3.6
-9.1
1.2
1.0
13.0
Q2 Q1
0.6
-0.9
-1.0
-0.9
-1.8
-0.9
-0.5
-0.9
-1.1
-0.5
-0.5
-1.8
-1.2
-1.0
-22.2
-1.2
-3.0
15.2
0.8
0.2
0.1
0.3
-0.2
0.6
0.7
0.3
0.1
0.1
0.0
0.7
0.2
2.0
-10.6
0.2
0.1
12.8
Agriculture
Industry
Mining
Manufacturing
Textile
Wood and paper
Chemicals
Machinery
Other Manufacturing
Utility
Construction
Services
Trade
Tansportation
Hotelry
Fin Services
Other Services
ICT
Q2 Q1
Without Government
Intervention
7. Gradual recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Sector GDP (in percent change)
(changes are relative to a no-COVID scenario)
With Government
Intervention
(Investment plan
0.5
2.4
0.0
0.4
-4.3
-0.9
0.8
1.3
4.9
-0.3
17.3
-1.9
-1.3
-2.7
-24.8
-1.2
-5.2
15.9
0.8
4.0
1.4
2.0
-2.0
1.1
1.8
3.2
8.3
0.5
19.3
0.8
0.2
0.9
-12.9
0.1
-0.1
16.8
Q2 Q1
0.4
-1.8
-1.7
-1.8
-4.7
-2.5
-0.7
-2.1
-2.5
-1.0
-2.1
-3.1
-2.5
-4.5
-25.6
-2.1
-6.0
15.7
0.7
-0.5
-0.6
-0.5
-2.5
-0.8
0.1
-0.7
-0.9
-0.2
-0.5
-0.4
-1.0
-0.6
-14.3
-0.8
-0.9
16.5
Agriculture
Industry
Mining
Manufacturing
Textile
Wood and paper
Chemicals
Machinery
Other Manufacturing
Utility
Construction
Services
Trade
Tansportation
Hotelry
Fin Services
Other Services
ICT
Q2 Q1
Without Government
Intervention
8. Slow recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Sector GDP (in percent change)
(changes are relative to a no-COVID scenario)
With Government
Intervention
(Investment plan
0.3
1.6
-0.7
-0.5
-7.6
-2.7
0.6
-0.2
3.6
-0.8
16.6
-3.1
-2.3
-4.2
-34.4
-2.1
-8.1
16.9
0.8
3.6
1.0
1.5
-3.7
-0.1
1.7
2.4
7.3
0.3
18.7
-0.1
-0.5
-0.3
-16.3
-0.6
-2.1
15.4
Q2 Q1
Without Government
Intervention
0.1
-2.3
-2.3
-2.6
-7.9
-4.3
-0.9
-3.4
-3.5
-1.5
-1.7
-4.3
-3.4
-5.9
-35.2
-3.0
-8.9
16.7
0.7
-0.8
-0.9
-0.9
-4.2
-1.9
0.1
-1.4
-1.7
-0.4
-0.7
-1.3
-1.7
-1.7
-17.7
-1.6
-2.9
15.2
Agriculture
Industry
Mining
Manufacturing
Textile
Wood and paper
Chemicals
Machinery
Other Manufacturing
Utility
Construction
Services
Trade
Tansportation
Hotelry
Fin Services
Other Services
ICT
Q2 Q1
9. Recovery scenarios, with and without
government intervention (employment)
Change in employment with fast or slow
recovery (in percent change)
(changes are relative to a no-COVID scenario)
Change in employment with fast or slow
recovery (in percent change)
(changes are relative to a no-COVID scenario)
-1.7
0.3
-3.1
-0.5
-4.0
-1.3
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
1.0
Q1 Q2
Faster Recovery Gradual Recovery Slow Recovery
2.6
4.9
1.2
3.8
0.0
3.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Q1 Q2
Faster Recovery Gradual Recovery Slow Recovery
With Government Intervention (Investment plan)
Without Government Intervention
10. Fast recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Household income (in percent change)
(changes are relative to a no-COVID scenario)
With Government Intervention
(Investment plan
Without Government
Intervention
0.7 0.7 0.6 0.7 0.6
2.7 2.6 2.7
2.5
2.8
0
0.5
1
1.5
2
2.5
3
Q1 Q2
-1.4
-1.2
-1.4
-1.2
-1.5
0.5 0.5 0.5 0.5 0.5
-2
-1.5
-1
-0.5
0
0.5
1
Q1 Q2
11. Gradual recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Household income (in percent change)
(changes are relative to a no-COVID scenario)
With Government Intervention
(Investment plan)
Without Government
Intervention
-0.4 -0.3 -0.4 -0.3
-0.5
1.8 1.7 1.8 1.7 1.8
-1
-0.5
0
0.5
1
1.5
2
Q1 Q2
-2.4
-2.2
-2.5
-2.1
-2.6
-0.3 -0.3 -0.3 -0.2
-0.4
-3
-2.5
-2
-1.5
-1
-0.5
0
Q1 Q2
12. Slow recovery scenario, with and without government
intervention (Sectoral Impact)
Change in quarterly Household income (in percent change)
(changes are relative to a no-COVID scenario)
With Government Intervention
(Investment plan)
Without Government
Intervention
-1.4 -1.3
-1.5
-1.3
-1.6
1.080 1.094 1.076 1.083 1.077
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Q1 Q2
-3.4 -3.2 -3.4
-3
-3.6
-1 -0.9 -1 -0.8
-1.1
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
Q1 Q2