During the COVID-19 global pandemic, the prices of different mineral commodities decreased significantly worldwide. Between January and April 2020, the price of zinc dropped by 18.9 percent. During the same time period, the price of gold increased by some 12.8 percent.

Gold rose to 3,362.51 USD/t.oz on August 1, 2025, up 2.25% from the previous day. Over the past month, Gold's price has risen 0.15%, and is up 37.65% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on August of 2025.

Abstract:

This brief shows an overview of the potential impact that the COVID-19 emergency might cause on monetary variables with a counterfactual effect on agricultural markets. The referred sanitary emergency has unexpectedly strengthened the dollar resulting on a hamper to Ecuadorian food prices and food security. Our country (in full dept) fulfil with the necessary conditions to shift the crisis (human and economic burden) to international levels which should be considered by the private and public stakeholders.

Palabras clave: Economía agrícola, Seguridad alimentaria, COVID-19/SARS-CoV-2, desarrollo

Key Words: Agricultural Economics, Food Security, COVID-19 / SARS-CoV-2, development

The daily return is defined as R_t=ln⁡(P_t/P_(t-1) ), where P_t and P_(t-1) are the closing prices at day t and t-1, respectively. According to (WHO 2020), March 11, 2020 is the day which the global COVID-19 outbreak is considered as a pandemic. We define the dummy variable D = 0 before this date and D = 1 after that. The daily closing price for COMEX’s copper from January 02, 2018, to December 30, 2022, which was extracted from Nasdaq.

This is an archival release to document the state of the data set for this research project before it got severely derailed by the Covid-19 pandemic and the explosion in Beirut on 4 August 2020. Please consult the readme for a detailed description of the contents and workflows.

Lumber fell to 690.67 USD/1000 board feet on July 31, 2025, down 0.12% from the previous day. Over the past month, Lumber's price has risen 11.49%, and is up 37.84% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lumber - values, historical data, forecasts and news - updated on August of 2025.

This study examines the volatility of beef and lamb prices in Türkiye, as food price inflation compromises the food security of low- and middle-income households. The inflation is the result of a rise in energy (gasoline) prices leading to an increase in production costs, together with a disruption of the supply chain by the COVID-19 pandemic. This study is the first to comprehensively explore the effects of multiple price series on meat prices in Türkiye. Using price records from April 2006 through February 2022, the study applies rigorous testing and selects the VAR(1)–asymmetric BEKK bivariate GARCH model for empirical analysis. The beef and lamb returns were affected by periods of livestock imports, energy prices, and the COVID-19 pandemic, but those factors influenced the short- and long–term uncertainties differently. Uncertainty was increased by the COVID–19 pandemic, but livestock imports offset some of the negative effects on meat prices. To improve price stability and assure access to beef and lamb, it is recommended that livestock farmers be supported through tax exemptions to control production costs, government assistance through the introduction of highly productive livestock breeds, and improving processing flexibility. Additionally, conducting livestock sales through the livestock exchange will create a price information source allowing stakeholders to follow price movements in a digital format and their decision-making.

Germany Commodity Price: Precious Metals: Gold data was reported at 3,232.079 USD/Troy oz in 15 May 2025. This records an increase from the previous number of 3,180.971 USD/Troy oz for 14 May 2025. Germany Commodity Price: Precious Metals: Gold data is updated daily, averaging 1,419.728 USD/Troy oz from Jan 2013 (Median) to 15 May 2025, with 3135 observations. The data reached an all-time high of 3,422.638 USD/Troy oz in 06 May 2025 and a record low of 1,050.717 USD/Troy oz in 17 Dec 2015. Germany Commodity Price: Precious Metals: Gold data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

Germany Commodity Price: Energy Resources: Brent Crude data was reported at 64.699 USD/Barrel in 15 May 2025. This records a decrease from the previous number of 65.751 USD/Barrel for 14 May 2025. Germany Commodity Price: Energy Resources: Brent Crude data is updated daily, averaging 71.790 USD/Barrel from Jan 2013 (Median) to 15 May 2025, with 3133 observations. The data reached an all-time high of 128.852 USD/Barrel in 08 Mar 2022 and a record low of 22.784 USD/Barrel in 28 Apr 2020. Germany Commodity Price: Energy Resources: Brent Crude data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

Germany Commodity Price: Energy Resources: WTI Crude Oil data was reported at 61.800 USD/Barrel in 16 May 2025. This records a decrease from the previous number of 61.803 USD/Barrel for 15 May 2025. Germany Commodity Price: Energy Resources: WTI Crude Oil data is updated daily, averaging 66.802 USD/Barrel from Jan 2013 (Median) to 16 May 2025, with 3134 observations. The data reached an all-time high of 124.851 USD/Barrel in 08 Mar 2022 and a record low of 10.145 USD/Barrel in 21 Apr 2020. Germany Commodity Price: Energy Resources: WTI Crude Oil data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

Aluminum rose to 2,573.35 USD/T on August 1, 2025, up 0.30% from the previous day. Over the past month, Aluminum's price has fallen 1.90%, but it is still 13.69% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Aluminum - values, historical data, forecasts and news - updated on August of 2025.

United States Producer Price Index data was reported at 258.837 1982=100 in Mar 2025. This records a decrease from the previous number of 259.805 1982=100 for Feb 2025. United States Producer Price Index data is updated monthly, averaging 35.000 1982=100 from Jan 1913 (Median) to Mar 2025, with 1347 observations. The data reached an all-time high of 280.251 1982=100 in Jun 2022 and a record low of 10.300 1982=100 in Feb 1933. United States Producer Price Index data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I066: Producer Price Index: by Commodities. [COVID-19-IMPACT]

Abstract

Like the rest of the world, Sudan has been experiencing the unprecedented social and economic impact of the COVID-19 pandemic. From restrictions on movement to school closures and lockdowns, the economic situation worsened, and commodity prices soared across the country. Results from the first six rounds of the High-Frequency Phone survey indicated that household welfare was negatively affected. The situation led to the loss of employment and income, decreased access to essential commodities and services, and food insecurity, particularly among the poor and vulnerable Sudanese. Moreover, the inability to access food and medicine degraded in July/August 2021 despite a slight amelioration in February/April 2021.

After COVID-19 in 2020, Sudan experienced situations that are more likely to compromise the recovery process. Political instability, unrest, and protests occurred before and after the military takeover in October 2021. Meanwhile, Sudan Central Bank devalued the currency, which may increase the already high commodities price. Besides, Sudan encountered historic flooding since the onset of the rainy season between May and June 2022. To monitor and assess the dynamics of the impacts of the country's economic and political situation (high inflation, social unrest, food shortages, asset loss, displacement, etc.) on households' welfare, another round of the Sudan High-Frequency Phone survey took place in June to August 2022.

Similar to the six previous rounds, the survey was conducted using mobile phones and covered all 18 states of Sudan. Round 7 sample is composed of 2816 Households from both urban and rural areas of Sudan. This sample allows us to draw statistical inferences about the Sudanese population at the national and rural/urban levels. The risk of nonresponse was a concern, so efforts were made to minimize this risk, including follow-up with respondents who failed to respond and keep the interviews short (15–20 minutes) to reduce respondent fatigue.

The questions are similar to the previous six rounds of the High-Frequency Phone survey but with added context. Households are asked about the key channels through which individuals and households are expected to be affected by the exchange rate distortions, country political instability, or flooding that occurred in May/June 2022, as well as how they have recovered from the COVID-19 pandemic impacts. Furthermore, questions cover a range of topics/themes including, but not limited to, health conditions, access to health facilities, access to other social services, availability of common food and non-food items (including medicines), nutrition and food security, employment/labor, income, assets, coping strategies, remittances, subjective welfare, climate/weather events, and the safety nets assistance.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Sampling procedure

The sampling methodology adopted for the implementation of this survey is probabilistic. Each of the units in the targeted population of the study must have a nonzero and known probability of selection. The sample was stratified by rural/urban for all 18 states. The distribution of the sub-sample between states and rural/urban is proportional to the size of the individuals owning mobile phones, i.e., not equal allocation. The selection of the individual phones (the households) is random, i.e., with equal probability, using a systematic sample procedure in the list (frame) of phones. This allows for extrapolating the results of the sample to the target population and estimating the precision of the results obtained. However, the implementation of this approach requires the availability of an adequate sampling frame containing all the units of the population without omissions or duplications.

In this survey, the sampling frame is provided by the phone lists. Considerable efforts were made to compile the frame using multiple lists of phone numbers collected during the implementation of various projects/surveys during the last few years at the household level across the country. This reduces the chances of having more than one phone number per household. Moreover, the interviewers double-checked during data collection that only one number was called for each selected surveyed household. Therefore, selecting individual phone numbers is the same as selecting households. It is worth noting that for West Kordofan and Central Darfur, the proportionality of rural/urban cannot be done according to the size of phones since there are no details for rural/urban. So, the size of the rural and urban populations (projection 2020) was used instead.

In Sudan, under the present federal system, the state is considered a semiautonomous entity mandated to take care of the affairs of the citizen, provide governance, and be responsible for planning, policy formulation, and implementation of the annual program. Consequently, the sample needed to cover all 18 states of the country. The sample is conceived to provide reliable estimates for the country (urban and rural) and to give statistically meaningful results at the national level.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services, water, transportation, housing, internet, energy) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Youth module screening - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, transportation, fuel) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. Respondent were asked to think about each child in their household for the education question. The Household Questionnaire provides information on: - Demographics - Mental health of the respondent - Children education.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. One youth per household is interviewed in the youth section of the questionnaire. The Questionnaire provides information on: - Demographics - Access to basic goods (medicines, staple food) - Youth employment - Youth job search - Youth aspirations and expectations - Youth skills and mental health.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Geography - Access to basic goods and services (medicines, staple food, health, education, water, housing, electricity) - Employment - Income loss - Food insecurity experience - Welfare - Experience of Climate/Weather events - Shocks and Coping strategies

Response rate

BASELINE (ROUND 1): A total of 4,032 households were successfully interviewed during the first round of data collection (conducted during June 16–July 5, 2020). Selected households from each state include both rural and urban households, with the representation of each state in the final sample being proportional to the state’s population relative to the overall population. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 4,027 households.

ROUND 2: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the baseline of the Sudan HFS on COVID-19. 2,989 households were successfully interviewed in the second round. However, households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 3: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan HFS on COVID-19. 2,990 households were successfully interviewed in the third round. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 4: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan

Germany Commodity Price: Precious Metals: Silver data was reported at 32.585 USD/Troy oz in 15 May 2025. This records an increase from the previous number of 32.161 USD/Troy oz for 14 May 2025. Germany Commodity Price: Precious Metals: Silver data is updated daily, averaging 19.573 USD/Troy oz from Jan 2013 (Median) to 15 May 2025, with 3135 observations. The data reached an all-time high of 34.800 USD/Troy oz in 22 Oct 2024 and a record low of 11.859 USD/Troy oz in 18 Mar 2020. Germany Commodity Price: Precious Metals: Silver data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

The 2020 coronavirus (Covid-19) pandemic has had a noteworthy impact on commodities prices, including metals such as iron ore. The impact of Covid-19 on the global iron ore industry is apparent from the decline in the average year-to-date price of iron ore as of May 2020 (***** U.S. dollars per metric ton) as compared to the average price in 2019 (***** U.S. dollars per metric ton). Compared to other metals, however, iron ore prices have stayed relatively resilient, and are expected to recover further during 2020 once China's steel production increases again.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

In recent years, the international community has witnessed many crisis events, and the Russia-Ukraine war, which broke out on 24th February 2022, has increased international policy uncertainty and impacted the current world commodity and financial markets. Thus, we try to capture how the Russia-Ukraine war has affected the correlation structure of international commodity and stock markets. We study six groups of commodity daily returns and one group of stock daily returns and select the sample from 24th February 2022 to 1st June 2022 as the sample during the Russia-Ukraine war; in addition, we select the sample from 1st December 2019 to 31st December 2020 as the sample during COVID-19 control group, and the sample from 1st January 2014 to 31st December 2017 as the non-extreme event control group, to explore the correlation structure of international commodity and stock markets before the war, and to compare and uncover the impact of the uncertain event of the Russia-Ukraine war on the commodity and stock markets. In this paper, the marginal density function of each series is constructed using the ARMA-GARCH-std method, and the R-Vine copula model is built based on the marginal density function to analyze the correlation relationship between each market. From the Tree1 of the Vine copula, it is found that crude oil becomes the core connecting each commodity market and the stock market during the Russia-Ukraine war. The price fluctuations of crude oil may be contagious to agricultural and precious metal markets in the same direction, while the stock market price fluctuations are inversely correlated with commodity markets. Comparison with the selected control group sample reveals that the Russia-Ukraine war increases the correlation between the markets and enhances the possibility of risk transmission. The core of the correlation structure shifts from agricultural commodities and precious metals to crude oil after the Russia-Ukraine war.

Chile Commodity Prices: Avg: Crude Oil: WTI data was reported at 68.000 USD/Barrel in Mar 2025. This records a decrease from the previous number of 71.250 USD/Barrel for Feb 2025. Chile Commodity Prices: Avg: Crude Oil: WTI data is updated monthly, averaging 34.400 USD/Barrel from May 1983 (Median) to Mar 2025, with 503 observations. The data reached an all-time high of 133.930 USD/Barrel in Jun 2008 and a record low of 11.300 USD/Barrel in Dec 1998. Chile Commodity Prices: Avg: Crude Oil: WTI data remains active status in CEIC and is reported by Central Bank of Chile. The data is categorized under Global Database’s Chile – Table CL.P005: Crude Oil Price. [COVID-19-IMPACT]

Germany Commodity Price: Precious Metals: Palladium data was reported at 965.460 USD/Troy oz in 15 May 2025. This records an increase from the previous number of 953.345 USD/Troy oz for 14 May 2025. Germany Commodity Price: Precious Metals: Palladium data is updated daily, averaging 986.241 USD/Troy oz from Jan 2013 (Median) to 15 May 2025, with 3135 observations. The data reached an all-time high of 3,187.967 USD/Troy oz in 08 Mar 2022 and a record low of 473.056 USD/Troy oz in 12 Jan 2016. Germany Commodity Price: Precious Metals: Palladium data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

Germany Commodity Price: Energy Resources: Fuel Oil NYMEX data was reported at 2.177 USD/gal in 15 May 2025. This records a decrease from the previous number of 2.199 USD/gal for 14 May 2025. Germany Commodity Price: Energy Resources: Fuel Oil NYMEX data is updated daily, averaging 2.120 USD/gal from Jan 2013 (Median) to 15 May 2025, with 3133 observations. The data reached an all-time high of 4.572 USD/gal in 16 Jun 2022 and a record low of 0.699 USD/gal in 27 Apr 2020. Germany Commodity Price: Energy Resources: Fuel Oil NYMEX data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]