** Article & Dataset Currently Under Review **

Dataset Overview

Our household dataset is associated with a pre-print article "Impacts of large-scale land acquisitions on smallholder agriculture and livelihoods in Tanzania". The household survey is designed for the purposes of policy evaluation with selection of households based on proximity to large-scale land acquisitions (treatment) and a set of households in similar socio-ecological contexts with no association to large-scale land acquisitions (control). Households were selected as a random sample in 35 villages surrounding LSLAs who provided responses to a questionnaire covering household income, assets, farming practices, health, food-security, and energy-use.

Two datasets are provided. First, the "hh_dataset_rep.csv" providing household responses for variables used in this study. Second, the "hh_crops_rep.csv" provides detail on crops cultivated by each household, self-reported yields and farm-gate prices. Each variable is described in the "variable_descriptoin.xlsx". In addition to the datasets, we provide replication code for this study "lsla_mechanisms_rep.Rmd" as an R-Markdown file.

Article Abstract

Improving agricultural productivity is a major sustainability challenge of the 21st century. Large-scale land acquisitions (LSLAs) have important effects on both well-being and the environment in the Global South, but their impacts on agricultural productivity and subsequent effects on farm incomes or food-security are under-investigated. Prior studies lack data or methods to investigate the mechanistic nature of household change in agricultural practices that may vary due to LSLA conditions. To overcome this challenge, we use a novel household dataset and a quasi-experimental design to estimate household level changes in agricultural value driven by LSLAs in Tanzania. In addition, we use a causal mediation analysis to assess how contract farming arrangements, land loss, and adoption of new farming technologies around LSLAs influence agricultural productivity. We find that households near LSLAs produced 19.2% (95% CI: 3.5 – 37.2%) higher agricultural value, primarily due to increased crop prices and farmer selection of high-value crops. Importantly, effect sizes are positively and negatively mediated by different mechanisms. The presence of contract farming explains 18.1% (95% CI: 0.56%, 47%) of the effect size in agricultural value, whereas land loss reduces agricultural value by 26.8% (95% CI: -71.3%, -4.0%). We also estimate whether improvements in food-security and household incomes occur in proximity to LSLAs, as anticipated with higher agricultural value. However, we do not find increases in agricultural income and food security, which may be due to higher crop prices in proximity to LSLAs. Our results stand in contrast to assumptions that technological spillovers occur through LSLAs and are principal drivers of agrarian change, holding important implications for agricultural transformations. Instead access to output markets through contract farming enables greater agricultural value whereas land loss negatively affects the agricultural value of households. Governance strategies should focus on limiting negative impacts related to the loss of smallholder land rights enabling greater access to contract farming.

The sampling frame was a list of SHCFs who obtained credit from Commodity Fund in 2007/2008 financial year in Kiambu County. It was thought after twenty-three years (2007/2008 to 2019/2020), the farmers would be in a position to give a valid perception on the impact that the agricultural credit had had on their coffee productivity. Further, the study only considered farmers who borrowed credit between KShs. 100,000 and KShs. 1 million. This is because those farmers who had more than 1 million had coffee farms between 4 to 8 acres and maintained their own farming records. This reduced the number of SHCFs from 3,589 to 87. The FCS of these SHCFs were identified from CF database and this formed a basis of random sampling 87 farmers who did not borrow credit for the study period – either from CF or other formal financial institutions. Thus the total sample size was 174. The summary of the data is as follows with Theme 1 to Theme 4, Risk perception and Regressand are binary.

Theme 1 (demand for inputs) had six response variables: payment of leasing land (FDI1); buying of land (FDI2); accessing both printed and electronic information (FDI3); acquisition of agrochemicals and fertilizers (FDI4); acquisition of tree seedlings (FDI5); acquisition of manure (FDI6).
Theme 2 (demand for labor) had five variables: increased use of child labor on the coffee farm (FDL1); increased use of labor from other members of your family apart from children on the farm (FDL2); increased use of hired labour (FDL3); increased use of ox-plough (FDL4); and increased use of tractor (FDL5).
Theme 3 (Efficiency of production) had five variables: increased use of optimal combination of inputs (FIE1); increase in area of farming of coffee (FIE2); replacement of old trees with improved varieties (FIE3); increased access to extension services (FIE4); and increase of the cost of labour (FIE5)
Theme 4 (returns) had five variables: annual profit per acre (FRT1); increase in numbers of shares for farmers in SACCO (FRT2); increase in farmers’ wealth (FRT3); and investing in other business (FRT4).
Risk perception: had two response variables: risk of making loss (RISKL) and risk of loan default (RISKD)
Regressand: impact of agricultural credit on coffee productivity (FPOR)

"The Farm Service Agency (FSA) offers farm operating loans to farmers who are temporarily unable to obtain private, commercial credit at reasonable rates and terms. Operating loans are used to purchase items such as livestock and feed, machinery and equipment, fuel, farm chemicals, and insurance; pay family living expenses and general farm operating expenses; and make minor improvements or repairs to buildings and fencing.

Both guaranteed loans and direct loans are available through this program. FSA guaranteed loans provide lenders (e.g., banks, Farm Credit System institutions, credit unions) with a guarantee of up to 95 percent of the loss of principal and interest on a loan. The maximum FSA guaranteed operating loan is $1,302,000 (adjusted annually based on inflation).

Applicants unable to qualify for a guaranteed loan may be eligible for a direct loan from FSA. Direct loans are made and serviced by FSA officials, who also provide borrowers with supervision and credit counseling. The maximum amount for a direct farm operating loan is $300,000.

FSA also provides Microloans, which are direct operating loans designed to meet the unique financial operating needs of many socially disadvantaged and beginning farmers, niche farm operations, the smallest of family farm operations, and those serving local and regional food markets, including urban farmers. The maximum loan amount for a Microloan is $35,000.

The repayment terms vary according to the type of loan made, collateral securing the loan, and the applicant's ability to repay. Term operating loans are normally repaid within 7 years and annual operating loans are generally repaid within 12 months or when the commodities produced are sold."This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Farm Operating Loans (Direct and Guaranteed) For complete information, please visit https://data.gov.

Cavity-nesting birds are a diverse and charismatic community, with a common need for tree cavities that makes them vulnerable to land management by humans. However, little research has formally integrated human social aspects into management recommendations for the conservation of cavity-nesting birds. In agroecosystems, people's management decisions modify and define the habitat availability for native cavity-nesting species. These behaviors during adulthood are related to people's worldviews and are shaped, in part, by childhood experiences. On-going forest loss may reduce opportunities for children to interact with and learn from cavity-nesting birds and their habitats. We used a social-ecological framework to assess rural children's knowledge and representations of native cavity-nesting birds and their habitats in agroecosystems of the threatened Atlantic Forest of Argentina. We employed “freelists” and "draw-and-explain" strategies with 235 children from 19 rural schools, and then compared results with a 4-year dataset of trees (n = 328) and tree-cavity nests (n = 164) in the same study area. Children listed a high diversity (93 taxa) of native cavity-nesting birds, especially parrots (Psittacidae), toucans (Ramphastidae), and woodpeckers (Picidae), which they mostly recognized as cavity-nesters. However, children drew agricultural landscapes with few of the habitat features that these birds require (e.g., tree cavities, native forest). Exotic trees were overrepresented in drawings (40% of mentions) compared to our field dataset of nests (10%) and trees on farms (15%). Although children mentioned and depicted a high diversity of native cavity-nesting birds, our results may reveal a problematic extinction of experience regarding how these birds interact with their habitat. To strengthen children's contextualized knowledge and promote their long-term commitment to the conservation of cavity-nesting species, we recommend fostering meaningful experiences for children to interact with native cavity-nesting birds and recognize their habitat needs. A version of this article translated into Spanish is available in the Supplementary Material 1. Methods Study area and socioecological context We worked in high altitude terrain within the department of San Pedro, Misiones province (26° 36'S, 54° 01'W; 500-700 m a.s.l., 1200-1400 mm annual rainfall). The study area encompassed much of the remaining extent of Araucaria mixed rainforest in Argentina. This forest is composed of >100 tree species, including Nectandra spp. and Ocotea spp. (laureles), Balfourodendron riedalianum (guatambú) and Araucaria angustifolia (Paraná pine), a critically endangered species (Cabrera 1976, Kershaw and Wagstaff 2001, Thomas 2013). The study area covers two Important Bird Areas: San Pedro (AICA AR123) and Cruce Caballero Provincial Park (AICA AR122; Bodrati and Cockle 2005, Bodrati et al. 2005, Birdlife International 2019). Here, researchers have recorded at least 75 bird species in 21 families that are known or strongly suspected to nest in tree cavities (Bonaparte 2024). Twenty-four of these species are endemic to the Atlantic Forest and seven are internationally threatened or near-threatened. In well-preserved Atlantic Forest, many cavity-nesting species select cavities in large, live, native trees for nesting (Cockle et al. 2011). However, in family agroecosystems, dead trees with cavities excavated by woodpeckers become increasingly important to the cavity-nesting community, probably because they replace the resource of large native trees with decay-formed cavities that are scarce in agroecosystems (Bonaparte et al. 2020). The study area encompassed both public and private lands and comprised a mosaic of small and medium-sized family farms (mean ± SD = 36 ± 24 ha). This mosaic is characterized by patches and corridors of forest, as well as open paddocks, annual and perennial plantations, and both native and exotic tree plantations, interspersed with three provincial parks that have varying histories of selective logging and other land uses (Varns 2012). Scattered native and exotic trees are common in plantations, in pastures, and around residential areas; provide diverse ecosystem services to agricultural families; and constitute important habitat elements for many cavity-nesting bird species (Bonaparte et al. 2020). Traditionally, people that live and farm in rural areas of Misiones call themselves "colonos", and the rural areas they inhabit are referred to as "colonia". The “colono” families have varied origins (many are immigrants from Europe, Brazil, or Paraguay). In many cases, they arrived in Misiones during the 20th century with permits to occupy small plots on fiscal lands or as occupants of private lands. In our study area, 67% of the human population resides in rural areas (IPEC 2015) and their main productive activity is family agriculture, with no salaried labor (or little when it exists) and low accumulation potential (Baranger et al. 2008). Their production may be destined for family consumption, informal sales, and industry-oriented sales (Furlán et al. 2015). Study design and participants There are some difficulties in assessing children's ideas because they may lack the vocabulary they need to express themselves, or because they are sometimes shy and it is difficult for an unfamiliar person to access their opinion (Sullivan et al. 2018). However, there are several tools adapted for children of different ages that help researchers understand how they see and what they know about the landscape around them. A widely used tool in ethnobiology is the "freelisting" method, hereafter referred to as freelists. This method highlights elements within a given domain that are locally important or significant to respondents (Puri 2010). From freelist data (see below), researchers can calculate relative salience (a statistic that includes rank and frequency) of items within a given domain across all respondents (Quinlan 2005). Another tool used to assess children's representations and interpretations of their environment is the "draw-and-explain" method (Moseley et al. 2010), which seeks to access, in an easy and familiar way, children's ideas and visual representations of a given place (Barraza and Robottom 2008, Franquesa-Soler and Serio-Silva 2017). The combined assessment of these two activities constitutes a mixed approach that allowed us to obtain quantitative and qualitative information about children's knowledge, observations of their environment, and the most salient, important, and familiar elements of their surroundings. In this study we used a mixed methods approach composed of two steps. The first step consisted of two independent activities, specially adapted for rural students in the last three grades of formal primary education in Argentina (10 to 13 years of age). The activities developed with the participant students consisted of a freelisting method (Puri 2010) and a drawing activity (“draw-and-explain” method; Moseley et al. 2010), carried out at school. The second step consisted of comparing the results obtained from the activities with the participants with field data on the cavity-nesting bird community in the area and the characteristics and species of trees they use for nesting (e.g., tree species used as nest trees). Prior to starting the data collection at each school, we held a private, in-person meeting with the principal or teacher in charge. During these 15- to 30-minute meetings, we provided a formal letter describing our objectives, methodology, scope of the study, and expected forms of disseminating results. We then verbally described the details written in the letter, explained the planned activities, and answered questions about the research and logistics. Finally, we verbally requested their free, prior, and informed consent to carry out the activities (Newing 2010), and agreed on a date to visit the school and perform the activities. During April and May 2019, we visited 19 rural schools. Previously, we visited one additional school as a pilot to test and adjust the activities with 18 students; the results of the activities in the pilot school are not presented here. All schools visited were rural public schools with 12 to 120 students each. Participants were 236 students aged 10 to 13 years (mean ± SD = 11.6 ± 0.8; 9% 10 years old, 37% 11 years old, 43% 12 years old, and 11% 13 years old), in the last three years of formal primary education in Argentina. We decided not to gather data on the gender of study participants because our research was not focused on gender-related questions (Radi 2021). Collecting these data a posteriori based on participants' first names leads to misgendering and reinforces harmful cisnormative constructs. We consider the participant group in this study, students of public rural primary schools, to be representative because there are no private schools in the area and we did not observe gender bias in the groups of students attending classes. Description of methodologies at each school Upon arrival in the classroom, we conducted a playful icebreaker and gave a brief introductory talk (Barreau et al. 2016). During the introductory talk, we described in a general but clear way our objectives and the activities we would conduct with the participants, trying not to bias their upcoming answers. We asked the participants to complete the activities individually. Additionally, we informed them that our proposal was neither a school assignment nor mandatory, so they could opt out of the activities if they wished. The first activity we developed at each school was the freelist to assess the salience and knowledge of native birds. For this, we provided each participant with a pencil and a sheet of paper with spaces to write their name, age, and grade, followed by ten numbered rows to write the names of bird species. We instructed the children to complete

Reference Sullivan J.A., Samii, C., Brown, D., Moyo, F., Agrawal, A. 2023. Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania. Proceedings of the National Academy of Sciences 120, e2207398120. https://doi.org/10.1073/pnas.2207398120 Abstract Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (P = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities. Replication Data We include anonymized household survey data from our analysis to support open and reproducible science. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. For the 2018 surveys, several anonymized extracts are provided including an imputed (n=10) dataset to fill in missing data that was used for the main analysis. This data can be found in the hh_data folder and includes:

hh_imputed10_2018: anonymized household dataset for 2018 with variables used for the main analysis where missing data was imputed 10 times hh_compensation_2018: anonymized household extract for 2018 representing household benefits and compensation directly received from LSLAs hh_migration_2018: anonymized household extract for 2018 representing household migration behavior following LSLAs hh_rsdata_2018: extracted remote sensing data at the household geo-location for 2018 hh_land_2019: anonymized household extract for 2019 of land variables Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in lsms_data folder). We've provide sub-modules from the LSMS dataset relevant to our analysis but the full datasets can be access through the World Bank's Microdata Library (https://microdata.worldbank.org/index.php/home). Across several analyses we use the LSLA boundaries for our four selected sites. We provide a shapefile for the LSLA boundaries in the gis_data folder. Finally, our data replication includes several model outputs (found in mod_outputs), particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our main_analysis.Rmd script. Replication Code We provide replication code in the form of R Markdown (.Rmd) or R (.R) files. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article. Scripts include:

main_analysis.Rmd: main analysis supporting the finding, graphs, and tables reported in our main manuscript compensation.R: analysis of benefits and compensation received directly by households from LSLAs landvalue.R: analysis of household land values as a function of distance from LSLAs migration.R: analysis of migration behavior following LSLAs selection_bias.R: analysis of LSLA selection bias between control and treatment enumeration areas

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Data & Code will be made available by August 25th.

Article Abstract

Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100-million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (p = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.

Replication Data

We include anonymized household survey data for replication of our analysis. In particular, we provide i) an anoymized household dataset collected in 2018 (n=994) for households nearby (treatment) and far-away from (control) LSLAs and ii) a household dataset collected in 2019 (n=165) within the same sites. This data can be found in the hh_data folder.

Our analysis also incorporates data from the Living Standards Measurement Survey (LSMS) collected by the World Bank (found in lsms_data folder).

Finally, our data replication includes several models outputs, particularly those that are lengthy to run in R. These datasets can optionally be loaded into R rather than re-running analysis using our main_analysis.Rmd script.

Replication Code

We provide replication code in the form of an R Markdown (.Rmd) file. Alongside the replication data, this can be used to reproduce main figures, table, supplementary materials, and results reported in our article.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2018/19 is the fourth round of the survey with prior rounds conducted in 2010/11, 2012/13, and 2015/16. GHS-Panel households were visited twice: first after the planting season (post-planting) between July and September 2018 and second after the harvest season (post-harvest) between January and February 2019.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Agricultural plots
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The original GHS-Panel sample of 5,000 households across 500 enumeration areas (EAs) and was designed to be representative at the national level as well as at the zonal level. The complete sampling information for the GHS-Panel is described in the Basic Information Document for GHS-Panel 2010/2011. However, after a nearly a decade of visiting the same households, a partial refresh of the GHS-Panel sample was implemented in Wave 4.

For the partial refresh of the sample, a new set of 360 EAs were randomly selected which consisted of 60 EAs per zone. The refresh EAs were selected from the same sampling frame as the original GHS-Panel sample in 2010 (the “master frame”). A listing of all households was conducted in the 360 EAs and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximated 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS-Panel households from 2010 were selected to be included in the new sample. This “long panel” sample was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across the 6 geopolitical Zones. The systematic selection ensured that the distribution of EAs across the 6 Zones (and urban and rural areas within) is proportional to the original GHS-Panel sample. Interviewers attempted to interview all households that originally resided in the 159 EAs and were successfully interviewed in the previous visit in 2016. This includes households that had moved away from their original location in 2010. In all, interviewers attempted to interview 1,507 households from the original panel sample.

The combined sample of refresh and long panel EAs consisted of 519 EAs. The total number of households that were successfully interviewed in both visits was 4,976.

Sampling deviation

While the combined sample generally maintains both national and Zonal representativeness of the original GHS-Panel sample, the security situation in the North East of Nigeria prevented full coverage of the Zone. Due to security concerns, rural areas of Borno state were fully excluded from the refresh sample and some inaccessible urban areas were also excluded. Security concerns also prevented interviewers from visiting some communities in other parts of the country where conflict events were occurring. Refresh EAs that could not be accessed were replaced with another randomly selected EA in the Zone so as not to compromise the sample size. As a result, the combined sample is representative of areas of Nigeria that were accessible during 2018/19. The sample will not reflect conditions in areas that were undergoing conflict during that period. This compromise was necessary to ensure the safety of interviewers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 4 consists of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets.

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicits information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; and household fishing activities. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicits information on access to infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collects different information during each visit, but for the same plots and crops.

Cleaning operations

CAPI: For the first time in GHS-Panel, the Wave four exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires, household, agriculture and community questionnaires were implemented in both the post-planting and post-harvest visits of Wave 4 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Survey Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given tablets which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 4 was highly automated. Each field team was given a mobile modem allow for internet connectivity and daily synchronization of their tablet. This ensured that head office in Abuja has access to the data in real-time. Once the interview is completed and uploaded to the server, the data is first reviewed by the Data Editors. The data is also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file is generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files are communicated back to respective field interviewers for action by the interviewers. This action is done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following

The purpose of the 2010 land use/land cover data set is to provide a generalized view of how developed land has changed throughout the state, primarily capturing the conversion of resource land to development and characterizing the type of development (e.g. very low density, low density, medium density or high density residential development, commercial, industrial, institutional). Urban Land Uses: 11 Low-density residential - Detached single-family/duplex dwelling units, yards and associated areas. Areas of more than 90 percent single-family/duplex dwelling units, with lot sizes of less than five acres but at least one-half acre (.2 dwelling units/acre to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, yards, and associated areas. Areas of more than 90 percent single-family/duplex units and attached single-unit row housing, with lot sizes of less than one-half acre but at least one-eighth acre (2 dwelling units/acre to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing, garden apartments, high-rise apartments/condominiums, mobile home and trailer parks; areas of more than 90 percent high-density residential units, with more than 8 dwelling units per acre. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services, including associated yards and parking areas. 15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, research laboratories, and parking areas. 16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, public and private colleges and universities, military installations (built-up areas only, including buildings and storage, training, and similar areas), churches, medical and health facilities, correctional facilities, and government offices and facilities that are clearly separable from the surrounding land cover. 17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Urban areas whose use does not require structures, or urban areas where non-conforming uses characterized by open land have become isolated. Included are golf courses, parks, recreation areas (except areas associated with schools or other institutions), cemeteries, and entrapped agricultural and undeveloped land within urban areas. 191 Large lot subdivision (agriculture) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres, with a dominant land cover of open fields or pasture. 192 Large lot subdivision (forest) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres, with a dominant land cover of deciduous, evergreen or mixed forest. Agriculture: 21 Cropland - Field crops and forage crops. 22 Pasture - Land used for pasture, both permanent and rotated; grass. 23 Orchards/vineyards/horticulture - Areas of intensively managed commercial bush and tree crops, including areas used for fruit production, vineyards, sod and seed farms, nurseries, and green houses. 24 Feeding operations - Cattle feed lots, holding lots for animals, hog feeding lots, poultry houses, and commercial fishing areas (including oyster beds). 241 Feeding operations - Cattle feed lots, holding lots for animals, hog feeding lots, poultry houses. 242 Agricultural building breeding and training facilities, storage facilities, built-up areas associated with a farmstead, small farm ponds, commercial fishing areas. 25 Row and garden crops - Intensively managed truck and vegetable farms and associated areas. Forest: 41 Deciduous forest - Forested areas in which the trees characteristically lose their leaves at the end of the growing season. Included are such species as oak, hickory, aspen, sycamore, birch, yellow poplar, elm, maple, and cypress. 42 Evergreen forest - Forested areas in which the trees are characterized by persistent foliage throughout the year. Included are such species as white pine, pond pine, hemlock, southern white cedar, and red pine. 43 Mixed forest - Forested areas in which neither deciduous nor evergreen species dominate, but in which there is a combination of both types. 44 Brush - Areas which do not produce timber or other wood products but may have cut-over timber stands, abandoned agriculture fields, or pasture. These areas are characterized by vegetation types such as sumac, vines, rose, brambles, and tree seedlings. Water: 50 Water - Rivers, waterways, reservoirs, ponds, bays, estuaries, and ocean. Wetlands: 60 Wetlands - Forested or non-forested wetlands, including tidal flats, tidal and non-tidal marshes, and upland swamps and wet areas. Barren Land: 70 Barren land 71 Beaches - Extensive shoreline areas of sand and gravel accumulation, with no vegetative cover or other land use. 72 Bare exposed rock - Areas of bedrock exposure, scarps, and other natural accumulations of rock without vegetative cover. 73 Bare ground - Areas of exposed ground caused naturally, by construction, or by other cultural processes. Transportation: 80 Transportation - Miscellaneous Transportation features not elsewhere classified.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUseLandCover/MapServer/1**Please note, due to the size of this dataset, you may receive an error message when trying to download the dataset. You can download this dataset directly from MD iMAP Services at: https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUseLandCover/MapServer/exts/MDiMapDataDownload**

The purpose of the 2010 land use/land cover data set is to provide a generalized view of how developed land has changed throughout the state, primarily capturing the conversion of resource land to development and characterizing the type of development (e.g. very low density, low density, medium density or high density residential development, commercial, industrial, institutional). Urban Land Uses: 11 Low-density residential - Detached single-family/duplex dwelling units, yards and associated areas. Areas of more than 90 percent single-family/duplex dwelling units, with lot sizes of less than five acres but at least one-half acre (.2 dwelling units/acre to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex, attached single-unit row housing, yards, and associated areas. Areas of more than 90 percent single-family/duplex units and attached single-unit row housing, with lot sizes of less than one-half acre but at least one-eighth acre (2 dwelling units/acre to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing, garden apartments, high-rise apartments/condominiums, mobile home and trailer parks; areas of more than 90 percent high-density residential units, with more than 8 dwelling units per acre. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services, including associated yards and parking areas. 15 Industrial - Manufacturing and industrial parks, including associated warehouses, storage yards, research laboratories, and parking areas. 16 Institutional - Elementary and secondary schools, middle schools, junior and senior high schools, public and private colleges and universities, military installations (built-up areas only, including buildings and storage, training, and similar areas), churches, medical and health facilities, correctional facilities, and government offices and facilities that are clearly separable from the surrounding land cover. 17 Extractive - Surface mining operations, including sand and gravel pits, quarries, coal surface mines, and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Urban areas whose use does not require structures, or urban areas where non-conforming uses characterized by open land have become isolated. Included are golf courses, parks, recreation areas (except areas associated with schools or other institutions), cemeteries, and entrapped agricultural and undeveloped land within urban areas. 191 Large lot subdivision (agriculture) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres, with a dominant land cover of open fields or pasture. 192 Large lot subdivision (forest) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres, with a dominant land cover of deciduous, evergreen or mixed forest. Agriculture: 21 Cropland - Field crops and forage crops. 22 Pasture - Land used for pasture, both permanent and rotated; grass. 23 Orchards/vineyards/horticulture - Areas of intensively managed commercial bush and tree crops, including areas used for fruit production, vineyards, sod and seed farms, nurseries, and green houses. 24 Feeding operations - Cattle feed lots, holding lots for animals, hog feeding lots, poultry houses, and commercial fishing areas (including oyster beds). 241 Feeding operations - Cattle feed lots, holding lots for animals, hog feeding lots, poultry houses. 242 Agricultural building breeding and training facilities, storage facilities, built-up areas associated with a farmstead, small farm ponds, commercial fishing areas. 25 Row and garden crops - Intensively managed truck and vegetable farms and associated areas. Forest: 41 Deciduous forest - Forested areas in which the trees characteristically lose their leaves at the end of the growing season. Included are such species as oak, hickory, aspen, sycamore, birch, yellow poplar, elm, maple, and cypr

Abstract

The Ethiopia Socioeconomic Panel Survey (ESPS) is a collaborative project between the Ethiopian Statistical Service (ESS) and the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) team. The objective of the LSMS-ISA is to collect multi-topic, household-level panel data with a special focus on improving agriculture statistics and generating a clearer understanding of the link between agriculture and other sectors of the economy. The project also aims to build capacity, share knowledge across countries, and improve survey methodologies and technology. ESPS is a long-term project to collect panel data. The project responds to the data needs of the country, given the dependence of a high percentage of households on agriculture activities in the country. The ESPS collects information on household agricultural activities along with other information on the households like human capital, other economic activities, and access to services and resources. The ability to follow the same households over time makes the ESPS a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses of how households add to their human and physical capital, how education affects earnings, and the role of government policies and programs on poverty, inter alia. The ESPS is the first-panel survey to be carried out by the Ethiopian Statistical Service that links a multi-topic household questionnaire with detailed data on agriculture.

Geographic coverage

National Regional Urban and Rural

Analysis unit

• Household
• Individual
• Community

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the second phase ESPS panel survey is based on the updated 2018 pre-census cartographic database of enumeration areas by the Ethiopian Statistical Service (ESS). The sample is a two-stage stratified probability sample. The ESPS EAs in rural areas are the subsample of the AgSS EA sample. That means the first stage of sampling in the rural areas entailed selecting enumeration areas (i.e., the primary sampling units) using simple random sampling (SRS) from the sample of the 2018 AgSS enumeration areas (EAs). The first stage of sampling for urban areas is selecting EAs directly from the urban frame of EAs within each region using systematic PPS. This is designed to automatically result in a proportional allocation of the urban sample by zone within each region. Following the selection of sample EAs, they are allocated by urban rural strata using power allocation which is happened to be closer to proportional allocation.

The second stage of sampling is the selection of households to be surveyed in each sampled EA using systematic random sampling. From the rural EAs, 10 agricultural households are selected as a subsample of the households selected for the AgSS, and 2 non-agricultural households are selected from the non-agriculture households list in that specific EA. The non-agriculture household selection follows the same sampling method i.e., systematic random sampling. One important issue to note in ESPS sampling is that the total number of agriculture households per EA remains at 10 even though there are less than 2 or no non-agriculture households are listed and sampled in that EA. For urban areas, a total of 15 households are selected per EA regardless of the households’ economic activity. The households are selected using systematic random sampling from the total households listed in that specific EA.

The ESPS-5 kept all the ESPS-4 samples except for those in the Tigray region and a few other places. A more detailed description of the sample design is provided in Section 3 of the Basic Information Document provided under the Related Materials tab.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The ESPS-5 survey consisted of four questionnaires (household, community, post-planting, and post-harvest questionnaires), similar to those used in previous waves but revised based on the results of those waves and on the need for new data they revealed. The following new topics are included in ESPS-5:

a. Dietary Quality: This module collected information on the household’s consumption of specified food items.

b. Food Insecurity Experience Scale (FIES): In this round the survey has implemented FIES. The scale is based on the eight food insecurity experience questions on the Food Insecurity Experience Scale | Voices of the Hungry | Food and Agriculture Organization of the United Nations (fao.org).

c. Basic Agriculture Information: This module is designed to collect minimal agriculture information from households. It is primarily for urban households. However, it was also used for a few rural households where it was not possible to implement the full agriculture module due to security reasons and administered for urban households. It asked whether they had undertaken any agricultural activity, such as crop farming and tending livestock) in the last 12 months. For crop farming, the questions were on land tenure, crop type, input use, and production. For livestock there were also questions on their size and type, livestock products, and income from sales of livestock or livestock products.

d. Climate Risk Perception: This module was intended to elicit both rural and urban households perceptions, beliefs, and attitudes about different climate-related risks. It also asked where and how households were obtaining information on climate and weather-related events.

e. Agriculture Mechanization and Video-Based Agricultural Extension: The rural area community questionnaire covered these areas rural areas. On mechanization the questions related to the penetration, availability and accessibility of agricultural machinery. Communities were also asked if they had received video-based extension services.

Cleaning operations

Final data cleaning was carried out on all data files. Only errors that could be clearly and confidently fixed by the team were corrected; errors that had no clear fix were left in the datasets. Cleaning methods for these errors are left up to the data user.

Response rate

ESPS-5 planned to interview 7,527 households from 565 enumeration areas (EAs) (Rural 316 EAs and Urban 249 EAs). However, due to the security situation in northern Ethiopia and to a lesser extent in the western part of the country, only a total of 4999 households from 438 EAs were interviewed for both the agriculture and household modules. The security situation in northern parts of Ethiopia meant that, in Tigray, ESPS-5 did not cover any of the EAs and households previously sampled. In Afar, while 275 households in 44 EAs had been covered by both the ESPS-4 agriculture and household modules, in ESPS-5 only 252 households in 22 EAs were covered by both modules. During the fifth wave, security was also a problem in both the Amhara and Oromia regions, so there was a comparable reduction in the number of households and EAs covered there.

More detailed information is available in the BID.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The purpose of the 2010 land use/land cover data set is to provide a generalized view of how developed land has changed throughout the state - primarily capturing the conversion of resource land to development and characterizing the type of development (e.g. very low density - low density - medium density or high density residential development - commercial - industrial - institutional). Urban Land Uses: 11 Low-density residential - Detached single-family/duplex dwelling units - yards and associated areas. Areas of more than 90 percent single-family/duplex dwelling units - with lot sizes of less than five acres but at least one-half acre (.2 dwelling units/acre to 2 dwelling units/acre). 12 Medium-density residential - Detached single-family/duplex - attached single-unit row housing - yards - and associated areas. Areas of more than 90 percent single-family/duplex units and attached single-unit row housing - with lot sizes of less than one-half acre but at least one-eighth acre (2 dwelling units/acre to 8 dwelling units/acre). 13 High-density residential - Attached single-unit row housing - garden apartments - high-rise apartments/condominiums - mobile home and trailer parks; areas of more than 90 percent high-density residential units - with more than 8 dwelling units per acre. 14 Commercial - Retail and wholesale services. Areas used primarily for the sale of products and services - including associated yards and parking areas. 15 Industrial - Manufacturing and industrial parks - including associated warehouses - storage yards - research laboratories - and parking areas. 16 Institutional - Elementary and secondary schools - middle schools - junior and senior high schools - public and private colleges and universities - military installations (built-up areas only - including buildings and storage - training - and similar areas) - churches - medical and health facilities - correctional facilities - and government offices and facilities that are clearly separable from the surrounding land cover. 17 Extractive - Surface mining operations - including sand and gravel pits - quarries - coal surface mines - and deep coal mines. Status of activity (active vs. abandoned) is not distinguished. 18 Open urban land - Urban areas whose use does not require structures - or urban areas where non-conforming uses characterized by open land have become isolated. Included are golf courses - parks - recreation areas (except areas associated with schools or other institutions) - cemeteries - and entrapped agricultural and undeveloped land within urban areas. 191 Large lot subdivision (agriculture) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres - with a dominant land cover of open fields or pasture. 192 Large lot subdivision (forest) - Residential subdivisions with lot sizes of less than 20 acres but at least 5 acres - with a dominant land cover of deciduous - evergreen or mixed forest. Agriculture: 21 Cropland - Field crops and forage crops. 22 Pasture - Land used for pasture - both permanent and rotated; grass. 23 Orchards/vineyards/horticulture - Areas of intensively managed commercial bush and tree crops - including areas used for fruit production - vineyards - sod and seed farms - nurseries - and green houses. 24 Feeding operations - Cattle feed lots - holding lots for animals - hog feeding lots - poultry houses - and commercial fishing areas (including oyster beds). 241 Feeding operations - Cattle feed lots - holding lots for animals - hog feeding lots - poultry houses. 242 Agricultural building breeding and training facilities - storage facilities - built-up areas associated with a farmstead - small farm ponds - commercial fishing areas. 25 Row and garden crops - Intensively managed truck and vegetable farms and associated areas. Forest: 41 Deciduous forest - Forested areas in which the trees characteristically lose their leaves at the end of the growing season. Included are such species as oak - hickory - aspen - sycamore - birch - yellow poplar - elm - maple - and cypress. 42 Evergreen forest - Forested areas in which the trees are characterized by persistent foliage throughout the year. Included are such species as white pine - pond pine - hemlock - southern white cedar - and red pine. 43 Mixed forest - Forested areas in which neither deciduous nor evergreen species dominate - but in which there is a combination of both types. 44 Brush - Areas which do not produce timber or other wood products but may have cut-over timber stands - abandoned agriculture fields - or pasture. These areas are characterized by vegetation types such as sumac - vines - rose - brambles - and tree seedlings. Water: 50 Water - Rivers - waterways - reservoirs - ponds - bays - estuaries - and ocean. Wetlands: 60 Wetlands - Forested or non-forested wetlands - including tidal flats - tidal and non-tidal marshes - and upland swamps and wet areas. Barren Land: 70 Barren land 71 Beaches - Extensive shoreline areas of sand and gravel accumulation - with no vegetative cover or other land use. 72 Bare exposed rock - Areas of bedrock exposure - scarps - and other natural accumulations of rock without vegetative cover. 73 Bare ground - Areas of exposed ground caused naturally - by construction - or by other cultural processes. Transportation: 80 Transportation - Miscellaneous Transportation features not elsewhere classified. Last Updated: Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/PlanningCadastre/MD_LandUseLandCover/MapServer/1 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.The data included is here for the Borno state.

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.The data included is here for the Bauchi state.

Determinants of choice of farmers’ coping strategies.