metadata: authors: 0: author: Bentze,Thomas Patrick
1: author: Wollburg,Philip Randolph
count: World
docna: 0: docna: A Longitudinal Cross-Country Dataset on
            Agricultural Productivity and Welfare in Sub-Saharan Africa
docty: Policy Research Working Paper
abstracts: cdata!: Since 2008, the World Bank's Living
            Standards Measurement Study&#x2013;Integrated Surveys on
            Agriculture (LSMS-ISA) program has supported the collection
            of nationally representative, longitudinal, multi-topic
            household survey data to inform researchers and policy
            makers of living standards in Sub-Saharan Africa. The
            surveys maintain a distinct focus on the agricultural
            sector, collecting detailed plot-level data and information
            about agricultural activities, while measuring socioeconomic
            conditions of thousands of smallholder farmers and
            households across multiple countries. This paper presents a
            harmonized panel dataset (HP) from LSMS-ISA surveys from
            2008 to 2021 in seven Sub-Saharan African countries:
            Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and
            Uganda, from 2008 to 2021. It includes more than 200,000
            agricultural plot observations, more than 400,000
            individuals, and about 59,000 households. The HP allows for
            in-depth analysis of farm, household, and individual
            dynamics over time and across countries. It is ideal for
            researchers interested in studying the dynamics between
            agriculture, economic development, and welfare outcomes in
            Sub-Saharan Africa.
display_title: A Longitudinal Cross-Country Dataset
            on Agricultural Productivity and Welfare in Sub-Saharan Africa
listing_relative_url: /research/2024/11/34423791/longitudinal-cross-country-dataset-agricultural-productivity-welfare-sub-saharan-africa
topicv3: Growth, Poverty and Inequality m1327633 1091,Agricultural Productivity and Markets
            m1504065 2883,Household Surveys m1504071 2761,Rural Development m1504505 2667
publishtoextweb_dt: 2024-11-18T00:00:00Z
dois: 10.1596/1813-9450-10976
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 42
author_keys: 0: author_key: 2033483
1: author_key: 1464872
model_extractions: Policy Research Working Paper 10976 A Longitudinal Cross-Country Dataset on Agricultural Productivity and Welfare in Sub-Saharan Africa _Thomas Bentze Philip Wollburg_ Development Economics Development Data Group November 2024
Policy Research Working Paper 10976

# **Abstract**

Since 2008, the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) program has supported the collection of nationally representative, longitudinal, multi-topic household survey data to inform researchers and policy makers of living standards in Sub-Saharan Africa. The surveys maintain a distinct focus on the agricultural sector, collecting detailed plot-level data and information about agricultural activities, while measuring socioeconomic conditions of thousands of smallholder farmers and households across multiple countries. This paper presents a harmonized panel dataset (HP) from LSMS-ISA surveys from 2008 to 2021 in seven Sub-Saharan African countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda, from 2008 to 2021. It includes more than 200,000 agricultural plot observations, more than 400,000 individuals, and about 59,000 households. The HP allows for in-depth analysis of farm, household, and individual dynamics over time and across countries. It is ideal for researchers interested in studying the dynamics between agriculture, economic development, and welfare outcomes in Sub-Saharan Africa.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tbentze@worldbank.org, _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
A Longitudinal Cross-Country Dataset on Agricultural Productivity and Welfare in Sub-Saharan Africa

# **Thomas Bentze**<sup>**1**</sup> **, Philip Wollburg**<sup>**1,2**</sup>

**JEL codes** : O12, I32, P36

**Keywords:** Agricultural Productivity; Household Welfare; Panel Data; Household surveys; Economic Development;

> 1 <mark>The World Bank, Living Standards Measurement Study, Development Economics Data Group, Rome, 00184, Italy.</mark> 2 <mark>Wageningen University, Development Economics Group, Wageningen, 6706KN, The Netherlands.</mark>

> **Disclaimer:** The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# 1) Background and Summary

The agricultural sector concentrates large fractions of the labor force and economic output in many lowincome countries. In Sub-Saharan Africa, the world’s poorest region, the agricultural sector accounts for about 50% of the labor force (World Bank, 2021). The agricultural sector, and consequently domestic food production, is dominated by smallholder farmers in the region. Many of the world’s extreme poor rely on the agricultural sector for their income and food security. The importance of the agricultural sector for poverty alleviation, food security, structural transformation, and economic development more broadly has been widely recognized (Dercon and Gollin, 2014; Gollin, 2010). As a result, studying the sector has been of considerable interest to development research, policy interventions, and investment decisions (Adamopoulos and Restuccia, 2020; Gollin et al., 2014, 2013; Gollin and Udry, 2021; Restuccia and Rogerson, 2013).

However, the data landscape was long characterized by a persistent lack of high-quality data, requisite to informing research and policy interventions (Carletto, 2021). The World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) program was designed to address this gap. The LSMS-ISA program consists of nationally and sub-nationally representative, longitudinal and multi-topic household surveys, with a distinct focus on agricultural production. Starting in 2008, the surveys were implemented by National Statistical Offices (NSOs) with the support of the World Bank’s Living Standards Measurement Study (LSMS) team. LSMS-ISA data is widely considered the premier source of survey micro-data on agricultural production and productivity in Sub-Saharan Africa – and its relationship to livelihoods, household income, poverty, and food security (Wollburg et al., 2024a, 2024b).

In this paper, we present a harmonized panel of LSMS-ISA surveys from seven Sub-Saharan African countries, namely Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania and Uganda. This harmonized panel dataset (hereafter HP) covers over 200,000 agricultural plot observations, over 400,000 individuals and 58,000 households, over the time period of 2008 to 2021. The data are nationally representative of these countries, which comprise 39% of the population and close to a third of the poor in Sub-Saharan Africa (Azevedo, 2011; World Bank, 2022). The data are also representative of the household and smallholder agriculture sector of the study countries. The longitudinal nature of the data allows tracking households, farms, and individuals across time. The HP provides longitudinal records for close to 58,000 agricultural and non-agricultural households and just over 400,000 individuals, allowing researchers to analyze patterns, dynamics, and trajectories over time.

The HP datasets are compiled from the public data releases of the LSMS-ISA surveys, which can be downloaded from the World Bank’s Microdata Library (see Table 1). The HP consists of four datasets, one containing records of households, one of individuals, one of agricultural plots, and one of crops on each plot. The preparation of the HP consisted of constructing, cleaning, and harmonizing close to 150 agricultural, household, and individual indicators with the objective of creating a data asset that is ready for analysis. The datasets are fully mergeable with publicly available raw LSMS-ISA datasets such that users can add additional variables from the LSMS-ISA surveys, and tailor the dataset to according to their research needs. Household, community, and farm locations are georeferenced, so that the datasets can be enriched with geospatial information. The HP datasets allow for highly disaggregated analyses at the 2
<!-- Start of picture text --> 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>4<br>Ea Es wave 3 wave 4 wave<br>NPS,<br>wave 3<br><!-- End of picture text -->
The surveys administered a household questionnaire, an individual questionnaire, an agriculture questionnaire, and a community questionnaire. The HP dataset, with its focus on agricultural production and productivity, includes variables predominantly from the agriculture, household, and individual questionnaires (see ‘Survey instruments’ below). The survey questionnaires were administered in faceto-face interviews and recorded as Computer Assisted Personal Interviews (CAPI) using the Survey Solutions platform in Ethiopia (waves 4 and 5), Malawi (waves 3 and 4), Mali (wave 2), Nigeria (waves 3 and 4), Tanzania (waves 3, 4 and 5) and Uganda (waves 7 and 8). In Uganda, CAPI software CWEST was used in wave 2, and SurveyBe in waves 3 to 5. In all other rounds of the surveys, Paper Assisted Personal Interviews (PAPI) or Computer Assisted Field Interviews (CAFE) were used.

Outside of the survey questionnaires, additional variables were recorded and are provided as part of the publicly available datasets. First, land areas of agricultural plots and/or parcels were in many cases measured using handheld GPS devices, since respondent-reported land areas were shown to be unreliable (Carletto et al., 2013). Furthermore, households, agricultural plots, and communities were georeferenced via GPS during the survey operations, which enables the integration of survey data with geo-spatial data.

Sampling, tracking, and survey weights The surveys supported by the LSMS-ISA are designed to be nationally representative of the country’s population of households. To achieve this, a stratified two-stage probability sampling approach is employed, with census Enumeration Areas (EAs) selected as primary sampling units with probability proportional to size. Survey strata consist of urban/rural levels and administrative areas. In each selected EA, all households are listed and then randomly selected from the complete list. Population and housing censuses are used as sampling frames. Once households have been sampled and interviewed, sampling weights are constructed and provided to data users to allow for the calculation of nationally and subnationally representative estimates. These weights reflect a given household’s inverse probability of selection into the sample. For each survey, that is for any country-wave, the household weights approximately sum to the total population of households in the country. Weights were adjusted to account for attrition, inclusion of split-off households (i.e. new households that form by splitting from an existing household) and were post-stratified to ensure that they sum up to known population totals.

The surveys included in the HP are panel surveys that follow different units from round to round across time. Households are tracked through time in Ethiopia, Niger, and Nigeria, individuals are tracked in Malawi, Uganda, and Tanzania. Where individuals are tracked, the households they reside in are also tracked. Enumeration Areas (EAs) are tracked in Mali, with resampling of households within EAs in each wave. Agricultural plots cannot be tracked through waves (or even across seasons, in the case of Uganda), but parcels are tracked across waves in Ethiopia and Malawi, and across seasons within waves in Uganda. Parcels are defined by the World Program for the Census of Agriculture as a “piece of land of one land tenure type entirely surrounded by other land, water (...) or other features not forming part of the holding”, whereas plots are a section of the parcel “on which a specific crop or crop mixture is cultivated”(Food and Agriculture Organization of the United Nations, 2015). In Malawi, we assume that “gardens” are equivalent to parcels. In Tanzania, “plots” have a definition that is akin to parcels, and parcels are not explicitly tracked.

<mark>In some cases, such as in Malawi or Tanzania, individuals that split from a sampled household to form a new household are followed, and we had to make a choice as to which household inherited the ID of the</mark> 4
<mark>previous wave. For example, split-off households in Malawi inherit IDs if they are within a certain distance from their previous location (200m). If multiple split offs satisfy this condition, the previous household head is tracked. If no prior head is found, the household with the most tracked individuals is selected to inherit the household ID. If none of these conditions are met, no household inherits the ID. A similar approach was adopted in Tanzania, but without the distance requirement. In general, split-off households inherit the EA and stratum IDs of the household they originated from.</mark> In the following, we discuss in more detail the survey waves included in each country and their respective survey design aspects:

- <mark>In Ethiopia, data from the Ethiopian Social Survey (ESS) were assembled across five survey periods: 2010/2011, 2012/2013, 2014/2015, 2017/2018 and 2021/2022. The panel was fully refreshed in wave 4, and households are therefore not tracked across more than three waves. Split-off households (see Section SI.I) were not tracked in Ethiopia. The first wave of the panel survey (ESS 2010/2011) is only designed to be representative of rural areas and small towns, and the sample was expanded to urban areas from wave 2 onwards (ESS 2012/2013). Furthermore, waves 1 to 3 of the sample were designed to be representative of the most populous regions of the country</mark> (Central Statistical Agency and Living Standards Measurement Study (LSMS), World Bank, 2021) <mark>. In wave 5, the survey was renamed to the Ethiopia Socioeconomic Panel Survey (ESPS).</mark>

- <mark>In Malawi, data from the I</mark> ntegrated Household Panel Survey (IHPS) were assembled across four periods: 2009/2010, 2012/2013, 2015/2016 and 2018/2019. All split-off households were tracked in Malawi. A random half of EAs were dropped from the sample in wave 3 due to budgetary constraints (National Statistical Office, 2020). This panel runs alongside the Integrated Household Survey (IHS), a cross-sectional survey program. The original households sampled into the IHPS are a subset of the IHS 2010/2011.

- <mark>In Mali, data from the</mark> _<mark>E</mark> nquête Agricole de Conjoncture Intégrée_ (EACI) was assembled from two periods: 2014 and 2017. The smallest tracking unit in Mali is the EA, and households are therefore not followed through time. The survey covers all regions and urban/rural areas, except Kidal (Ministry of Rural Development, 2019).

- <mark>In Niger, data were drawn from the</mark> _<mark>E</mark> nquête National sur les Conditions de Vie des Ménages et Agriculture_ – ECVM/A) across two periods: 2011 and 2014. Households, including split off households, were tracked across these waves (Ministry of Finance and National Institute of Statistics, 2016).

- <mark>In Nigeria, data were assembled from the General Household Survey (GHS) across four periods: 2010/2011, 2012/2013, 2015/2016 and 2018/2019. A partial refresh of the panel was undertaken in wave 4. Split off households were not tracked in Nigeria. While the survey is representative at the regional and urban/rural levels, some areas could not be visited in wave 4 due to security concerns (2018/2019) and this wave is therefore only representative of areas that were accessible</mark> (National Bureau of Statistics, 2021a).

5
- In Tanzania, data were assembled from the National Panel Survey (NPS) across five periods: 2008/2009, 2010/2011, 2012/2013, 2014/2015 and 2019/2021. Split off households were tracked in Tanzania (National Bureau of Statistics, 2021b). A “refresh” sample was added to the panel in 2014/2015 and interviewed again in 2020/2021 as part of wave 5. Also in 2014/2015, a representative sub-sample of the panel which started in 2008 was selected to form the “extended” sample, which was re-interviewed in 2019/2020 as part of a survey with sex disaggregated data (NPS-SDD). For practical purposes, we denote the NPS-SDD, along with the 2020/2021 survey of households as part of the refresh sample, as “wave 5”.

- In Uganda, data were assembled from the Uganda National Panel Survey (UNPS) across seven periods: 2009/2010, 2010/2011, 2011/2012, 2013/2014, 2015/2016, 2018/2019, 2019/2020. In Wave 4 (2013/2014), one third of the sample was refreshed. Split off households were tracked for a subset of the total sample, consisting of 20% of the baseline households. At the time of writing, wave 6 was not available to the public and is therefore absent from the HP.

More detailed survey-specific information, including logistical and operational details and step-by-step descriptions of the calculations of sampling weights for each survey can be found in the Basic Information Documents for each survey which are publicly available on the World Bank’s Microdata Library. We provide links to these documents in Table 1.

6
Table 1: Links to raw data and supporting documents

|**Country**|**Wave**|**Link to the raw data**|**Link to supporting documents**|
|---|---|---|---|
|||https://microdata.worldbank.org/index.php/catalog/2053/data-|https://microdata.worldbank.org/index.php/catalog/2053/related-|
||1|dictionary|materials|
||2|https://microdata.worldbank.org/index.php/catalog/2247/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2247/related-<br>materials|
|Ethiopia|3|https://microdata.worldbank.org/index.php/catalog/2783/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2783/related-<br>materials|
||4|https://microdata.worldbank.org/index.php/catalog/3823/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3823/related-<br>materials|
||5|https://microdata.worldbank.org/index.php/catalog/6161/data-<br>dictionary s|https://microdata.worldbank.org/index.php/catalog/6161/related-<br>materials|
||1 and<br>2|https://microdata.worldbank.org/index.php/catalog/2248/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2248/related-<br>materials|
|Malawi|3 and<br>4|https://microdata.worldbank.org/index.php/catalog/3819/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3819/related-<br>materials|
|li|1|https://microdata.worldbank.org/index.php/catalog/2583/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2583/related-<br>materials|
|Ma|2|https://microdata.worldbank.org/index.php/catalog/3409/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3409/related-<br>materials|
|Ni|1|https://microdata.worldbank.org/index.php/catalog/2050/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2050/related-<br>materials|
|ger|2|https://microdata.worldbank.org/index.php/catalog/2676/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2676/related-<br>materials|
||1|https://microdata.worldbank.org/index.php/catalog/1002/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/1002/related-<br>materials|
||2|https://microdata.worldbank.org/index.php/catalog/1952/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/1952/related-<br>materials|
|Nigeria|3|https://microdata.worldbank.org/index.php/catalog/2734/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2734/related-<br>materials|
||4|https://microdata.worldbank.org/index.php/catalog/3557/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3557/related-<br>materials|
||1|https://microdata.worldbank.org/index.php/catalog/76/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/76/related-<br>materials|
||2|https://microdata.worldbank.org/index.php/catalog/1050/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/1050/related-<br>materials|
||3|https://microdata.worldbank.org/index.php/catalog/2252/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2252/related-<br>materials|
|Tanzania|4|https://microdata.worldbank.org/index.php/catalog/3455/data-<br>dictionary <br>and<br>https://microdata.worldbank.org/index.php/catalog/2862/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3455/related-<br>materials <br>and<br>https://microdata.worldbank.org/index.php/catalog/2862/related-<br>materials|
||5|https://microdata.worldbank.org/index.php/catalog/5639/data-<br>dictionary<br>and<br>https://microdata.worldbank.org/index.php/catalog/3885/data-|https://microdata.worldbank.org/index.php/catalog/5639/related-<br>materials<br>and<br>https://microdata.worldbank.org/index.php/catalog/3885/related-|
|||dictionary|materials|
||1|https://microdata.worldbank.org/index.php/catalog/1001/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/1001/related-<br>materials|
||2|https://microdata.worldbank.org/index.php/catalog/2166/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2166/related-<br>materials|
||3|https://microdata.worldbank.org/index.php/catalog/2059/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2059/related-<br>materials|
|Uganda|4|https://microdata.worldbank.org/index.php/catalog/2663/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/2663/related-<br>materials|
||5|https://microdata.worldbank.org/index.php/catalog/3460/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3460/related-<br>materials|
||7|https://microdata.worldbank.org/index.php/catalog/3795/data-<br>dictionary|https://microdata.worldbank.org/index.php/catalog/3795/related-<br>materials|
|||https://microdata.worldbank.org/index.php/catalog/3902/data-|https://microdata.worldbank.org/index.php/catalog/3902/related-|
||8|dictionary|materials|

Survey instruments 7
The survey questionnaires of LSMS-ISA supported surveys typically consist of:

- An **agricultural questionnaire** , which is most often divided between post-planting and postharvest questionnaires. These questionnaires usually elicit information on land ownership and use; land use and agriculture income tax; farm labor; use of inputs; GPS land area measurement and coordinates of household fields; agricultural capital; irrigation; and crop harvest and utilization. An additional livestock questionnaire usually collects information on animal holdings and costs, production, and costs and sales of livestock by products. While base units of observation may change between modules and surveys, these data are typically observed at the parcel, plot, or plot-crop levels – or livestock type level in the case of the livestock modules.

- A **household questionnaire** , which contains information about the household (household modules) and the individual household members (individual modules). The household questionnaire typically starts with a roster of all members of the household. Information about the household members, collected at individual level includes demographics; education; health (including anthropometric measurement for children); financial inclusion; labor. Information about the household, collected at the household level includes food and non-food expenditure; consumption; household nonfarm activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; credit, tax, and income; and other sources of household income.

- A **community questionnaire,** which contains information on infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail prices. While this information is not included in the HP, data users can, in most cases, merge in community-level data using EA (enumeration area) identifiers.

In addition to the survey questionnaires, a set of **geospatial variables** are derived by the data producers at the household and plot levels and provided to data users as part of the raw data files. These typically include measures of distance, climatology, soil and terrain. A selected set of these geospatial variables are included in the HP’s plot and household-level datasets. Household GPS locations are also provided as part of the public data releases on which the HP data is based. Note that the household geolocations are slightly offset from the true locations, in order to preserve the anonymity of households and survey villages. An offset range of 0-2 km is used for urban areas, while a range of 0-5 km is used in rural areas, where communities are more dispersed, and risk of disclosure may be higher. An additional 0-10 km offset for 1% of rural clusters (10% in Mali) effectively increases the known range for all rural points to 10 km while introducing only a small amount of noise (Central Statistical Agency and Living Standards Measurement Study (LSMS), World Bank, 2021; Ministry of Finance and National Institute of Statistics, 2016; Ministry of Rural Development, 2019; National Bureau of Statistics, 2021a, 2021b; National Statistical Office, 2020). The publicly available raw data is typically structured according to questionnaire modules (e.g. agriculture questionnaire, module 1).

Harmonized Panel datasets and variable creation 8
The survey data described above was processed and harmonized to create the HP. The HP consists of four datasets recorded at different units of observation.

- Plot-crop-level dataset (based on the agriculture questionnaire);

- Plot-level dataset (based on the agriculture questionnaire);

- Household-level dataset (based on the household and individual questionnaires/modules);

- Individual-level dataset (based on the individual questionnaire/modules).

We group variables in the HP into four categories: (i) unit and linking identifiers (ii) crop production and agricultural variables that are defined at the plot-crop, plot or household levels (iii) socio-economic variables that are at the household or individual level and (iv) geospatial variables that are integrated using geocoordinates. A more comprehensive description of each variable can be found in the variable directories provided alongside the dataset (see Supplementary Tables 1 to 5). The variable directories also flag cases where entire variables are missing in some country-years due to missing data or absent questions in the raw data files, and describe additional data cleaning operations for each variable.

# _Unit and linking identifiers_

The HP provides two sets of key identifiers: unit identifiers and linking identifiers. Unit identifiers are created during data processing and consist of numeric identifiers which can be used to track units longitudinally. All unit identifiers have variable names that end with _id_obs_ (such as _plot_id_obs_ or _hh_id_obs_ ). Linking identifiers are included to allow merging with the raw data, so they are the same as in the raw LSMS-ISA files that can be downloaded from the World Bank’s Microdata Library. All linking identifiers have variable names that end in _id_merge_ (such as _plot_id_merge_ or _hh_id_merge_ ). To find the names of the specific variables in the raw data that can be merged with the linking identifiers, users may consult the following entries of the variable directories (Supplementary Tables 1 to 5): _plot_id_merge, hh_id_merge, indiv_id_merge, plot_manager_id_merge, respondent_id_merge, ea_id_merge_ .

Moreover, we include a common set of identifier and geographic variables, across the all four datasets of the HP. They are the EA and strata IDs, household weights, urban/rural status, geocoordinates and administrative-level codes and names, where applicable. It is important to note that the identification of administrative levels is sometimes only possible via numeric codes, which are not necessarily tracked through time (these cases are flagged in the variable directories).

# _Construction of the harmonized plot-crop and plot datasets_

This section describes plot-crop and plot-level datasets in the HP, which mostly contain data deriving from the agricultural questionnaires (see Figure 2). They consist of plot-crop and plot-level data from households which report cultivating crops in the main agricultural season, and therefore do not contain the entire population of households interviewed in each survey. Users can refer to the household or individual-level datasets (described below) for the entire sample of households.

The HP plot-crop dataset consists of a limited set of agricultural variables that were recorded at the plot and crop level of observation in the raw data, i.e. for each cultivated crop on each of the household’s agricultural plots. This dataset records production by crop on each plot, which can be used for more detailed analysis of production than the aggregated plot-level dataset can. The variables included are harvest output quantity and value, seed input quantity and value, harvest and planting months, use of improved seeds, pesticide use and crop shocks. The latter include a catch-all crop shock variable, along 9
<!-- Start of picture text --> LSMS-ISA data source Raw data level of Dataset and level of<br>observation observation inthe<br>Harmonized Panel<br>Geospatial aianiial<br>Agricultural 7A<br>questionnaire<br>= ES ae Household , 2<br>ihe<br>: Plot-crop<br>J<br>f<br>re<br>Household ipffi<br>Household rr Household<br>questionnaire and asset<br>Individual Individual<br>Commu nity a hae<br>questionnaire<br><!-- End of picture text -->
<mark>Other inputs, recorded at the plot level, include land input (cultivated plot and farm area in hectares); labor inputs including labor days spent on the plot by family members, total labor days spent on the plot including by hired labor and free labor from non-family members, hired labor value; use of organic and inorganic fertilizer inputs, inorganic fertilizer quantity in nitrogen equivalent, inorganic fertilizer value. We also include information on the number of seasonal crops grown on the plot, which was the “main crop” (i.e. the crop type with the highest value of production on the plot) and the share of output attribute to the “main crop” type. Agricultural asset indices are computed using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household agricultural assets (either from agriculture or household questionnaire). A regression method is used to predict factor scores</mark> (Rencher, 2002).

<mark>Variables on the production environment and practices include crop shocks; irrigation status of the plot; use of erosion protection techniques; use of a tractor on the plot and plot ownership modalities, including plot ownership and possession of a formal title for the plot; livestock ownership; perennial crop production; number of plots under management (cultivated or not); and number of plots left fallow by the household in the current agricultural season; beginning and end dates of the current agricultural season.</mark> <mark>We draw on the household questionnaire to create variables describing plot manager characteristics (the plot manager is the household member mainly responsible for the cultivation of the plot), which include plot manager age, education, and gender.</mark> <mark>All variables described above are based on farmer self-reports. In addition, we include key geospatial variables, which are provided as part of the publicly available data files. These include agro-ecological zones, distance of the household from the nearest market and population center as well distance of the plot from the household (in kilometers), plot slope, elevation, a wetness index and soil quality indicators. The soil quality indicators contain binary variables for nutrient availability, nutrient retention, rooting conditions, oxygen availability, excess salts, toxicity, and workability. These variables are equal to one if there is no or a slight constraint to the soil in each dimension. To summarize these variables into a single dimension, we compute a soil fertility index using a principal component analysis (PCA), which also employs a regression method to predict factor scores</mark> (Rencher, 2002) <mark>. All the geospatial variables outlined above are obtained from remote sensing products and integrated via the geocoordinates of the interviews (without offset). They are provided alongside the raw survey data, and their integration was therefore conducted prior to the creation of the HP. More information, along with their origin, can be found in the surveys’ online documentation (see Table 1).</mark> <mark>A few crucial processing steps, undertaken while creating the HP, must be highlighted:</mark>

- A fraction of plot areas is self-reported, and not measured with handheld GPS devices. Due to the importance of that variable to calculate key agricultural variables such as yields, and known inaccuracies of self-reported plot areas, we impute missing GPS area values. We employ a predictive mean matching method with five nearest neighbors, using self-reported areas along with administrative divisions to obtain linear predictions which serve as our distance variable (Little, 1988).

11
- For the agricultural variables, we focus on the “main” (or long) growing season of each country, that is, the datasets contain agricultural inputs and outputs that were used and produced during the main agricultural season. As such, the minor agricultural seasons, for the surveys sometimes record agricultural production data, are not covered in the HP. The exception is Uganda, which has a bimodal rainfall pattern in many areas. Here, two growing seasons are observed in each survey wave.

- The plot and plot-crop datasets contain only plots observations for years in which (i) they are cultivated in the long or main growing season and (ii) they contain a recorded harvest quantity for at least one crop. Perennial crops such as fruit or tree crops are excluded from the dataset. A harvest quantity of zero is only included if there is recorded evidence of an exogenous shock on the plot, such as a natural disaste <mark>r. Other cases, such as non-response, are dropped from the dataset. A</mark> s a result, the plot and plot-crop samples contain a subset of all the plots listed in raw data.

- <mark>A set of key agricultural input and output variables are valued both in LCUs as well as US dollars, to facilitate cross-country analysis. The valuation consists of calculating the sale or purchase price of the various categories of each input and output variable in each wave. Precise crop variety categories such as maize or teff are used for the calculation of output values. Categories of fertilizer types are used to calculate inorganic fertilizer prices, categories of men, women and children workers are used to calculate hired labor wages, and categories of crop types and improved status of seeds are used to calculate seed values. To account for the highly noisy nature of the price estimates, median EA-level input and output prices are calculated within each category (e.g. median EA-level maize price per kg, median hourly wage of male agricultural workers). If the number price observations are below a threshold of 10, median prices are calculated across the next larger administrative unit. If the number of observations in that larger administrative unit is also below 10, the next larger unit is chosen, and this process is repeated up to the national level. The prices are then multiplied by the output and input quantities recorded on the plot, which have themselves been converted to standardized quantity units such as kilograms. The resulting variables are in local currency units (LCUs). We provide another set of variables which are converted into USD using an exchange rate at the year of the survey and deflated to 2020 dollars values. T</mark> he CPI and exchange rate data are drawn from <mark>World Bank collections of development indicators and consist of yearly time series, made available through the World Bank Open Data Initiative.</mark>

- “Main crop” types are defined to facilitate inter-country comparisons with the plot-level dataset. Each crop is classified into the following categories: barley, wheat, rice, sorghum, maize, millet, perennials (including fruit and tree crops), legumes, root crops, nuts and a catch-all “other” category. In case users want to define their own categories, the exact names of the crops (i.e., as they appear in the raw data) are maintained in the plot-crop dataset.

- The plot-level dataset in Uganda is effectively at the _parcel_ level, as GPS measurements were obtained at this level of granularity. This also allows users to merge across seasons, since parcels

12
can be tracked across seasons, unlike plots. This is not the case for the plot-crop-level dataset which is at the _plot_ and _crop_ level.

- In both Mali and Niger, perennial crops were provided at the household and crop level. In order to retain this information to some degree, and for merging purposes, each household-crop pair was associated with a bespoke ID. These lines can be identified by cases where the plot link identifier begins with “missing_”. To limit confusion in the plot dataset, however, these observations were not taken into account.

_Construction of the harmonized household and individual datasets_ This section describes the HP household and individual datasets, which mostly contain variables deriving from the household questionnaires (see Figure 2). <mark>An important aspect of these datasets is that they include</mark> _<mark>all</mark>_ <mark>households that are listed in the household questionnaire cover module, regardless of their agricultural activities. They therefore include close to 30,000 households in the seven countries which are not in the plot-crop or plot-level datasets.</mark> The household-level dataset <mark>consists of variables from the household questionnaire that are key to analyzing livelihood and welfare outcomes. Included are access to electricity, household dependency ratios – defined as the ratio of individuals younger than 15 or older than 65 to the other members of the household, whether the household operates a nonfarm enterprise, and household size. Critically, the dataset also contains information on household consumption. The variables included are the total consumption value of the household in LCU and in USD and the consumption quintile of the household (where the quintiles are defined within country-waves). The consumption aggregates are provided in LCU by the data producers are part of the publicly available survey data. More information on the steps taken to calculate the consumption aggregate can be found in the surveys’ online documentation (see Table 1). We also compute a household asset index using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household assets. A regression method is used to predict factor scores (</mark> Rencher, 2002) <mark>. We further compute the household dietary diversity index, following guidelines from the Food and Agriculture Organization (FAO). It consists of the sum of distinct food groups consumed by the household over the past 7 days (</mark> Kennedy et al., 2011) <mark>. Finally, we include information on whether the household suffered any shocks in the last 12 months prior to the interview. These variables are all drawn from self-reported data. In addition, a geospatial variable capturing population density is also included in this dataset.</mark> <mark>Finally, the individual-level dataset includes information about the household members, which is derived from the household questionnaire. A first set of demographic variables includes age, sex, educational attainment including indicators for any formal and primary education. A variable which identifies the members’ relationship to the household head has been included in text format, due to the absence of a unified set of codes. On time use and work/employment, we have indicators capturing any work done during the reference period, the number of hours worked overall, on a farm, in a small enterprise, or in a wage job over a reference seven-day period (either the last seven days, or a typical seven days in the year). Additional variables were added to identify the industry of members’ wage jobs, if applicable. The</mark> 13
<mark>industries include agriculture, fishing, manufacturing, mining, construction, and services. These variables are all based on respondent reporting. Moreover, anthropometric data were collected by measuring children’s height and weight during survey fieldwork. Using this information, a series of variables were computed for children that range from 0 to 5 years of age: length/height-for-age, weight-for-height, BMIfor-age and weight-for-age Z-scores. These estimates are calculated using the 2006 WHO child growth standards, and an indicator for wasting was created for children who have a weight for height values under -2 standard deviations from the WHO child growth standards (</mark> Onis et al., 2006).

# Coding steps

The construction of HP datasets was done in Stata. It consists of a series of structured Stata scripts (“dofiles”), made available in a dedicated public GitHub repository (Bentze, 2024). A consistent processing protocol was executed in each wave. As a first step, plot-crop, plot, individual, and household frames were created, using the crop harvest modules (for the plot-crop and plot frames), the cover information of the household questionnaire (for the household frame), and the household roster (for the individual frame). Following a modularized approach, each variable in the HP was then cleaned and processed separately. This approach guarantees a stable sample size and facilitates reuse by data users, who can download the code and include new variables as they see fit (see “Usage Notes” below). The coding operations described above are undertaken separately for each country-wave pair, in do files which are given the following naming structure: country ISO code, survey name, and wave number. For example, the do file for the first wave of the Ethiopia ESS is named _ETH_ESS1.do_ .For each of the plot-crop, plot, household, and individuallevel datasets, survey waves were then appended (i.e. combined) for each country, and panel unit IDs were created. During this process, variables were merged to their appropriate frames. Finally, variables were converted from LCU to USD, where relevant. These steps were undertaken is a series of do files named according to the following structure: ISO code followed by “append” (e.g. _ETH_append.do_ ). Finally, country-level datasets were appended to obtain the final cross-country HP datasets. The flow of Stata dofiles is summarized in Figure 3.

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<!-- Start of picture text --> Variable extraction and cleaning, Merging of variables into frames and Data appended across countries<br>creation of frames harmonization of IDs, data<br>ETH_ESS1.do<br>} emesudo | appended across waves<br>ETH_ESS3.do<br>= —___<br>—><br>e<br>e<br>e<br>%<br>”<br>UGA_UNPS8_S2.do -<br>Se o “<br>|| Examplewith dataofprocesseda user-madefromfile raw "Ys! o<br>LUNPSdateeets na<br><!-- End of picture text -->
- A plot and crop-level dataset in which units are uniquely identified by the crop name variable, plot unit ID, survey wave ID and season ID.

- A plot-level dataset in which units are uniquely identified by plot ID and season ID.

- A household-level dataset in which units are uniquely identified by household ID, survey wave ID and season ID.

- An individual-level dataset, in which units are uniquely identified by individual ID, survey wave ID and season ID.

<mark>All datasets include a household ID. Individual IDs as well as plot IDs are nested within household IDs (see Usage Notes). All datasets are provided as Stata data files, and code is publicly available as Stata do-files on GitHub</mark> (Bentze, 2024).

# <mark>4) Technical Validation</mark>

The LSMS-ISA surveys have undergone rigorous technical validation and methodological innovation throughout their lifetime. For example, different approaches to land area measurement and labor time use have been tested in the context rigorous field experiments, and their insights drawn upon to ensure state-of-the-art data collection methods ( see, for example, Carletto et al., 2013).

Moreover, processes prior to publishing the raw LSMS-ISA data, substantial efforts were carried out by the World Bank LSMS-ISA team to clean and correct errors stemming from data collection, editing and entry. Errors were often corrected at various stages of the fieldwork. In PAPI and CAFE interviews, Stata and CSPro software were used to identify potential errors such as out of range values and inconsistent skip patterns. When deemed necessary, paper questionnaires were reviewed, and data was revised. In some cases, data entry was performed multiple times to better identify data entry errors. The use of CAPI software such as _Survey Solutions_ allowed for error and inconsistency flags in the field, and for real-time data availability which enabled high-frequency quality checks.

The harmonized variables created for and included in the HP were also validated. This involved inspecting key summary statistics of each variable: mean, median, minimum, maximum, as well as number of missing values, to detect any problems or irregularities. In case of any suspicious values, these values were further inspected and corrected if necessary. Each correction is noted in the directories of variables (Supplementary Tables 1 to 5).

# <mark>5) Usage Notes</mark>

# _Using the datasets together_

Each dataset contains both unit and linking identifiers. They can be used to merge the four datasets which comprise the HP. Observations can be uniquely identified by combining identifiers with the survey _wave_ variable and the _season_ indicator. The latter distinguishes observations between the two seasons in Uganda. Household IDs are present in all datasets, and users can therefore perform one-to-many joins between the household dataset (using _wave_ and _household_id_obs_ as keys) and any other dataset.

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<!-- Start of picture text --> Plot crop dataset<br>Key: wave, season, hh_id_obs,<br>plot_id_obs, crop_name<br>\ Many-to-one<br>Many-to-one Key: wave,Household season,datasethh_id_obs < Key: wave,Individual season,dataset hh_id_obs,<br>One-to-many indiv_id_obs<br>y, Many-to-one<br>Plot dataset<br>Key: wave, season, hh_id_obs,<br>plot_id_obs<br><!-- End of picture text -->
The do files are designed to allow users to add variables according to their needs. As explained above (see “Coding Steps”), variables are created in discrete modules within each country-wave pair. They can then be stored in a user-defined location that is dedicated to temporary files. Users can therefore add their own variables by writing their own scripts to pre-process the data, and incorporate them into the final dataset by plugging their custom-made variables into the relevant dataset and country (see Figure 3 for an example). The only requirement for merging in new data is that the linking identifiers match. Users may refer to the variable directories to identify the variable name of the linking identifier in the raw data (Supplementary tables 1 to 5). The LSMS-ISA raw data, which is publicly available (see Table 1), is typically structured according to questionnaire modules (e.g. module 1 from household questionnaire). Users need to consult the survey questionnaires to identify which modules contain the variables of their interest and pull the variables from the corresponding raw data file. Once a user-made variable has been created from the raw LSMS-ISA data, and it is uniquely identified by the linking identifier, it can be merged in the appropriate dataset (either plot-crop, plot, household, or individual), within one of the do files prefixed with “Append_”. Users may follow the example of how other variables are merged into the HP data frames to do this. Once the new variable is successfully merged in the dataset, the remaining do files can be executed without further modification.

# _<mark>Performing analysis with the HP</mark>_

<mark>The LSMS-ISA surveys are designed to be nationally representative. To obtain nationally representative estimates, users need to make use of the sampling weights, EA identifiers, and strata identifiers. As mentioned above, these three variables are present in all four datasets of the HP. Statistical software typically provide packages that enable use of these variables to estimate nationally representative indicators, such as Stata’s</mark> _<mark>svy</mark>_ <mark>set of commands which allow the running of statistical models with complex survey data. Users must use the sample weights (</mark> _<mark>pw</mark>_ <mark>) as probability weights to obtain valid point estimates, EA identifiers (</mark> _<mark>ea_id_obs</mark>_ <mark>) and strata identifiers (</mark> _<mark>strataid</mark>_ <mark>) to obtain valid standard errors.</mark> <mark>As noted above, variables that identify panel units end with the</mark> _<mark>_obs</mark>_ <mark>suffix. Users are advised to use these variables when performing panel analysis. Panel identifiers exist for EAs (</mark> _<mark>ea_id_obs</mark>_ <mark>), parcels (</mark> _<mark>parcel_id_</mark>_ <mark>obs), households (</mark> _<mark>hh_id_obs</mark>_ <mark>)</mark> _<mark>,</mark>_ <mark>individuals (</mark> _<mark>indiv_id_obs</mark>_ <mark>), plot managers (</mark> _<mark>plot_manager_id_obs</mark>_ <mark>), and respondents (</mark> _<mark>respondent_id_obs</mark>_ <mark>). Moreover, while EA and stratum variables remain fixed, households may move during the lifetime of the panel. A variable named</mark> _<mark>geocoords_id</mark>_ <mark>is provided to identify each unique set of geocoordinates. The time dimension is identified by</mark> _<mark>country</mark>_ <mark>,</mark> _<mark>wave</mark>_ <mark>(denoting the survey round), and</mark> _<mark>season</mark>_ <mark>variables. The latter is necessary to distinguish between seasons in Uganda.</mark> <mark>Variables identifying the administrative area codes of the households’ residences were also added to the dataset (</mark> _<mark>admin_1, admin_2, admin_3</mark>_ <mark>). However, codes may have changed throughout the lifetime of the panel (for instance, when administrative boundaries were redrawn) such that we cannot guarantee persistent identifiers. When possible, administrative area names were also included (</mark> _<mark>admin_1_name, admin_2_name, admin_3_name</mark>_ <mark>).</mark> <mark>We have deliberately refrained for extensive outlier correction, as these may depend on users’ analytical preferences. Users are therefore advised to check for extreme and outlier values, and winsorize or trim variables as they see fit. Moreover, missing values were not imputed, except for GPS plot areas (see the Methods section above). Users may wish to implement imputation methods that suit their research needs.</mark> 18
# <mark>Bibliography</mark>

Adamopoulos, T., Restuccia, D., 2020. Land Reform and Productivity: A Quantitative Analysis with Micro Data. Am. Econ. J. Macroecon. 12, 1–39. https://doi.org/10.1257/mac.20150222

- Azevedo, J.P., 2011. WBOPENDATA: Stata module to access World Bank databases. Bentze, T., 2024. Github repository: LSMS-ISA-harmonised-dataset-on-agricultural-productivity-andwelfare: https://github.com/lsms-worldbank/LSMS-ISA-harmonised-dataset-on-agriculturalproductivity-and-welfare.

- Carletto, C., 2021. Better data, higher impact: improving agricultural data systems for societal change. Eur. Rev. Agric. Econ. 48, 719–740. https://doi.org/10.1093/erae/jbab030

- Carletto, C., Savastano, S., Zezza, A., 2013. Fact or artifact: The impact of measurement errors on the farm size–productivity relationship. J. Dev. Econ. 103, 254–261. https://doi.org/10.1016/j.jdeveco.2013.03.004

- Central Statistical Agency, Living Standards Measurement Study (LSMS), World Bank, 2021. Ethiopia Socioeconomic Survey (ESS), ESS Panel II, 2018/2019.

- Dercon, S., Gollin, D., 2014. Agriculture in African Development: Theories and Strategies. Annu. Rev. Resour. Econ. 6, 471–492. https://doi.org/10.1146/annurev-resource-100913-012706

- Food and Agriculture Organization of the United Nations, 2015. World programme for the census of agriculture 2020.

- Gollin, D., 2010. Chapter 73 Agricultural Productivity and Economic Growth, in: Handbook of Agricultural Economics. Elsevier, pp. 3825–3866. https://doi.org/10.1016/S1574-0072(09)04073-0

- Gollin, D., Lagakos, D., Waugh, M.E., 2014. Agricultural Productivity Differences across Countries. Am. Econ. Rev. 104, 165–170. https://doi.org/10.1257/aer.104.5.165

- Gollin, D., Lagakos, D., Waugh, M.E., 2013. The agricultural productivity gap. Q. J. Econ. 129, 939–993.

- Gollin, D., Udry, C., 2021. Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture. J. Polit. Econ. 129, 1–80. https://doi.org/10.1086/711369

- Kennedy, G., Ballard, T., Dop, M.C., 2011. Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations, Rome.

- Little, R.J.A., 1988. Missing-Data Adjustments in Large Surveys. J. Bus. Econ. Stat. 6, 287–296. https://doi.org/10.1080/07350015.1988.10509663

- Ministry of Finance, National Institute of Statistics, 2016. Basic Information Document: 2014 National Survey on Household Living Conditions and Agriculture (ECVM/A-2014).

- Ministry of Rural Development, 2019. Basic Information Document: Enquete Agricole de Conjoncture Intégrée aux conditions de vie des ménages (EAC-I 2017).

- National Bureau of Statistics, 2021a. Basic Information Document: Nigeria General Household Survey– Panel 2018/19.

- National Bureau of Statistics, 2021b. Basic Information Document: National Panel Survey (NPS 20192020), Extended Panel with Sex-Disaggregated Data.

- National Statistical Office, 2020. Basic Information Document: Malawi Integrated Household Panel Survey (IHPS) 2019.

- Onis, M. de, Elaine Borghi, Onyango, A., Amani Siyam, Alain Pinol, 2006. Length/height-for-age, weightfor-age, weight-for-length, weight-for-height and body mass index-for-age; methods and development, WHO child growth standards. WHO Press, Geneva.

- Rencher, A.C., 2002. Methods of multivariate analysis, 2nd ed. ed, Wiley series in probability and mathematical statistics. J. Wiley, New York.

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Restuccia, D., Rogerson, R., 2013. Misallocation and productivity. Rev. Econ. Dyn. 16, 1–10. https://doi.org/10.1016/j.red.2012.11.003 Wollburg, P., Bentze, T., Lu, Y., Udry, C., Gollin, D., 2024a. Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa. Proc. Natl. Acad. Sci. 121, e2312519121.

https://doi.org/10.1073/pnas.2312519121 Wollburg, P., Markhof, Y., Bentze, T., Ponzini, G., 2024b. Substantial impacts of climate shocks in African smallholder agriculture. Nat. Sustain. https://doi.org/10.1038/s41893-024-01411-w World Bank, 2022. Poverty and Inequality Platform (database). World Bank, 2021. World Bank Open Data.

# Acknowledgments

We are grateful to the World Bank LSMS team and the National Statistical Offices for their efforts to collect and publish the LSMS-ISA data and to support this data harmonization effort. We would like to thank, in particular, Alemayehu Ambel, Asmelash Haile Tsegay, Manex Bule Yonis, Wondu Yemanebirhan Kassa (Ethiopia), Heather Moylan, Wilbert Drazi Wondru (Malawi), Marco Tiberti, Ismael Yacoubou Djima (Mali, Niger), Akiko Sagesaka, Ivette Contreras, Gbemisola Oseni, Kevin McGee (Nigeria), Akuffo Amankwah, Amparo Palacios Lopez (Tanzania), Giulia Ponzini (Uganda). We would also like to thank Siobhan Murray for geospatial support, and Gero Carletto and Talip Kilic for their guidance and comments. This work was supported by the World Bank Research Support Budget grant “On agricultural productivity measurement in Sub-Saharan Africa”.

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# Supplementary information

Supplementary Table 1. Directory of identifiers (present in all datasets)

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_country_|Country name|||
|_wave_|Wave number|||
|_household_id_obs_|Household ID<br>(panel<br>identificator)|Variable created by assigning<br>a numeric code to each<br>unique household. The first<br>number of the code<br>determines the country.||
|_household_id _merge_|Household ID<br>(to merge with<br>raw data)|ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH:_household_id_(wave 1)_,_<br>_household_id2_(wave 2)_,_<br>_household_id2_(wave 3)_,_<br>_household_id_(wave 4)_, household_id_<br>(wave 5)<br>MWI:_case_id_(wave 1),_y2_hhid_<br>(wave 2)_, y3_hhid_(wave 3)_, y4_hhid_<br>(wave 4)<br>MLI: concatenation of_grappe_and<br>_menage_(wave 1), concatenation of<br>_grappe_and_exploitation_(wave 2)<br>NER:_hid_(wave 1)_,_concatenation of<br>_GRAPPE, MENAGE_and<br>_EXTENSION_(wave 2)<br>NGA:_hhid_(wave 1)_, hhid_(wave 2)_,_<br>_hhid_(wave 3)_, hhid_(wave 4)<br>TZA:_hhid_(wave 1)_, y2_hhid_(wave<br>2)_, y3_hhid_(wave 3)_, y4_hhid_(wave<br>4)_, sdd_hid_(wave 5)<br>UGA:_Hhid_(wave 1)_, HHID_(wave<br>2)_, HHID_(wave 3)_, HHID_(wave 4)_,_<br>_HHID_(wave 5)_, hhid_(wave 7)_, hhid_<br>(wave 8)|
|_season_|Season ID<br>(UGA)|Variable created to<br>distinguish between seasons<br>in UGA, which has a bimodal<br>seasonal pattern, and<br>households are therefore<br>observed twice within each<br>wave|<br>This variable is set to 1 by default in<br>all other countries.|
|_ea_id_obs_|EA ID|Variable created by assigning<br>a numeric code to each<br>unique EA. The first number<br>of the code determines the<br>country.|Longitudinal ID to track units<br>through time|
|_ea_id_merge_||ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables:|

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||||ETH:_ea_id_(wave 1)_, ea_id2_(wave<br>2)_, ea_id2_(wave 3)_, ea_id_(wave 4)_,_<br>_ea_id_(wave 5)<br>MWI:_ea_id_(wave 1)_, ea_id_(wave<br>2)_, ea_id_(wave 3)_, ea_id_(wave 4)<br>MLI:_not uniquely identified by ea,_<br>_ea_id in ag questionnaire_(wave 1)_,_<br>_no community questionnaire, ea_id in_<br>_ag questionnaire_(wave 2)<br>NER:_not uniquely identified by ea,_<br>concatenation of_ms00q10 ms00q11_<br>_ms00q12 ms00q14 (wave 1) not_<br>_uniquely identified by ea,_<br>concatenation of_MS00Q10_<br>_MS00Q11 MS00Q12 MS00Q14_<br>(wave 2)<br>NGA: concatenation of_lga and ea_<br>(wave 1), concatenation of_lga and ea_<br>(wave 2), concatenation of_lga and ea_<br>(wave 3), concatenation of_lga and ea_<br>(wave 4)<br>TZA: concatenation of_region,_<br>_district, ward_and_ea_(wave 1),<br>carried over from wave 1 for waves<br>in extended panel. Concatenation of<br>_hh_a01_1 hh_a02_1 hh_a03_1_<br>_hh_a04_1_(wave 4, refresh), carried<br>over for wave 5 refresh.<br>UGA:_comm_(wave 1), carried over<br>from wave 1 (waves 2, 3, 4)|
|---|---|---|---|
|_pw_|Household<br>weight|||
|_strataid_|Strata ID|Variable created by assigning<br>a numeric code to each<br>unique stratum. The first<br>number of the code<br>determines the country.||
|_urban_|Is this an<br>urban EA?|||
|_admin_1_|Administrative<br>level 1||Source:https://data.humdata.org|
|_admin_2_|Administrative<br>level 2||Source:https://data.humdata.org|
|_admin_3_|Administrative<br>level 3||Source:https://data.humdata.org|
|_admin_1_name_|Name of<br>administrative<br>level 1|||
|_admin_2_name_|Name of<br>administrative<br>level 2||ETH: not available<br>MLI: not available|
|_admin_3_name_|Name of<br>administrative<br>level 3||ETH: not available<br>MLI: not available|

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|_lat_modified_<br>_lon_modified_|EA Longitude<br>(WGS84)<br>Modified<br>EA Latitude<br>(WGS84)<br>Modified||“Modified” refers to the offset of the<br>coordinates.<br>TZA: in waves 4 (extended panel<br>only) and 5, only households who<br>have not moved could be assigned<br>geocoordinates.<br>UGA: missing in waves 4, 5, 7, 8|
|---|---|---|---|
|_geocoords_id_|Geocoordinate<br>ID|Variable created by assigning<br>a numeric code to each<br>unique pairs of<br>geocoordinates. The first<br>number of the code<br>determines the country.|TZA: in waves 4 (extended panel<br>only) and 5, only households who<br>have not moved could be assigned<br>geocoordinates.<br>UGA: missing in waves 4, 5, 7, 8|

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Supplementary Table 2. Directory of variables in the plot-crop-level dataset

|**Variable name**|**Label**<br>|**Data processing notes**<br>|**Additional notes**|
|---|---|---|---|
|_crop_name_|Crop name|Absent of a harmonised list of<br>codes for crops, this variable<br>is coded a string.||
|_plot_id_obs_|Plot ID (panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>plot. The first number of the<br>code determines the country.<br>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset**||
|_plot_id_merge_|Plot ID (to<br>merge with<br>raw data)|ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH: concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 1)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id_(wave 2)_,_concatenation<br>of_holder_id_,_parcel_id_and_field_id_<br>(wave 3)_,_concatenation of_holder_id_,<br>_parcel_id_and_field_id_(wave 4)_,_<br>concatenation of_holder_id_,_parcel_id_<br>and_field_id,_OR concatenation of<br>_holder_id_, and_field_id_for<br>households answering section 12c<br>(wave 5)<br>MWI: concatenation of_case_id_and<br>_plot ID_(wave 1), concatenation of<br>_y2_hhid_and_plot ID_(wave 2)_,_<br>concatenation of_y3_hhid_,_garden ID_<br>and_plot ID_(wave 3)_,_concatenation<br>of_y4_hhid_,_garden ID_and_plot ID_<br>(wave 4)<br>MLI: concatenation of_grappe_<br>_menage, bloc_and_parcelle_(wave 1<br>concatenation of_grappe_ _menage,_<br>_bloc_and_parcelle_(wave 2)<br>NER: concatenation of_hid, field_<br>_number_and_parcel number_(wave 1)_,_<br>concatenation of_GRAPPE,_<br>_MENAGE, EXTENSION, champ_and<br>_parcelle_(wave 2)<br>NGA: concatenation of_hhid_and_plot_<br>_ID_(wave 1)_,_concatenation of_hhid_<br>and_plot ID_(wave 2)_,_concatenation<br>of_hhid_and_plot ID_(wave 3)_,_<br>concatenation of_hhid_and_plot ID_<br>(wave 4)<br>TZA: concatenation of_hhid_and_plot_<br>_number_(wave 1)_,_concatenation of|

24
||||_y2_hhid_and_plot number_(wave 2)_,_<br>concatenation of_y3_hhid_and_plot_<br>_number_(wave 3)_,_concatenation of<br>_y4_hhid_and_plot number_(wave 4)_,_<br>concatenation of_sdd_hhid_and_plot_<br>_number_(wave 5)<br>UGA: concatenation of_Hhid, parcel_<br>_ID_and_plot ID_(wave 1)_, HHID ,_<br>_parcel ID_and_plot ID_(wave 2)_,_<br>_HHID, parcel ID_and_plot ID_(wave<br>3)_, HHID, parcel ID_and_plot ID_<br>(wave 4)_, HHID, parcel ID_and_plot_<br>_ID_(wave 5)_, hhid, parcel ID_and_plot_<br>_ID_(wave 7)_, hhid, parcel ID_and_plot_<br>_ID_(wave 8)|
|---|---|---|---|
|_parcel_id_obs_|Parcel ID<br>(panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>parcel. The first number of<br>the code determines the<br>country<br>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset.**Parcel ID is set to<br>missing to prevent confusion|<br>Only tracked through waves in<br>Ethiopia and Malawi.<br>NGA, TZA, MWI w1: absent|
|_parcel_id_merge_|Parcel ID (to<br>merge with<br>raw data)|<br>ID to merge with raw LSMS<br>data<br>UGA: plot is equal to the<br>parcel ID in the**Plot-level**<br>**dataset.**Parcel ID is set to<br>missing to prevent confusion|NGA, TZA, MWI w1: absent<br>Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH: concatenation of_holder_id_, and<br>_parcel_id_(waves 1 to 5)_,_absent for<br>households answering section 12c<br>(wave 5)<br>MWI: concatenation of_y2_hhid_and<br>_garden ID_(wave 2)_,_concatenation of<br>_y3_hhid_,_garden ID_(wave 3)_,_<br>concatenation of_y4_hhid_,_garden ID_<br>(wave 4)<br>MLI: concatenation of_grappe_<br>_menage, bloc_(wave 1 concatenation<br>of_grappe_ _menage, bloc_(wave 2)<br>NER: concatenation of_hid, field_<br>_number_(wave 1)_,_concatenation of<br>_GRAPPE, MENAGE, EXTENSION,_<br>_champ_(wave 2)<br>UGA: concatenation of_Hhid, parcel_<br>_ID_(wave 1)_, HHID , parcel ID_(wave<br>2)_, HHID, parcel ID_(wave 3)_,_<br>_HHID, parcel ID_(wave 4)_, HHID,_<br>_parcel ID_(wave 5)_, hhid, parcel ID_<br>(wave 7)_, hhid, parcel ID_(wave 8)|

25
|_harvest_end_month_|Harvest end<br>month|If different crops harvested at<br>various times, latest date is<br>chosen.<br>ETH: Dates had to be<br>converted, so there is a one-<br>week discrepancy between the<br>months in the data and the<br>months in our Gregorian<br>calendar.|UGA: absent wave 1|
|---|---|---|---|
|_planting_month_|Month of<br>planting|If different crops planted at<br>various times, earliest date is<br>chosen.<br>ETH: Dates had to be<br>converted, so there is a one-<br>week discrepancy between the<br>months in the data and the<br>months in our Gregorian<br>calendar.|TZA: absent<br>UGA: absent wave 1, wave 2|
|_harvest_kg_|Total harvest<br>in kg|ETH: using conversion factor<br>database, or enumerator<br>conversion if not available.<br>NGA: In w4 and w3, full<br>harvest quantity was imputed<br>based on realized harvest at<br>the time of the interview +<br>expected harvest after the<br>interview.<br>MLI: full harvest quantity<br>imputed based on a “% area<br>left to harvest” variable.<br>MWI: Post planting harvest<br>may have been reported in<br>post-harvest period.|Summed up to the plot level in the<br>plot dataset|
|_harvest_value_LCU_|Value of plot<br>harvest, in<br>LCU|<br>Harvest valued using median<br>prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin<br>1 in some surveys) under the<br>condition that there are more<br>than 10 observed sales in the<br>area. If there are less than 10<br>sales, prices are calculated at<br>a higher geographical level.<br>Prices are calculated<br>independently for each crop<br>type.<br>UGA: sale prices multiplied<br>by 100 in wave 5 and divided<br>by 100 in wave 1 season1.|Summed up to the plot level in the<br>plot dataset|
|_harvest_value_USD_|Value of plot<br>harvest, in<br>USD|,<br>Harvest values<br>(_harvest_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World|Summed up to the plot level in the<br>plot dataset|

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|||Bank databases (Azevedo et<br>al 2011)||
|---|---|---|---|
|_seed_kg_|Quantity of<br>seeds (in kg)|.,<br>NER: Seeds are not at the plot<br>level, they are at the<br>household level. We divide<br>seeds among plots based on<br>the distribution of plot areas.|Summed up to the plot level in the<br>plot dataset<br>TZA: Absent in wave 1 and wave 2<br>UGA: Absent in wave 1, wave 2,<br>wave 3|
|_seed_value_LCU_|Value of<br>seeds in LCU|Seeds valued using median<br>prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin<br>1 in some surveys) under the<br>condition that there are more<br>than 10 observed sales in the<br>area. If there are less than 10<br>sales, prices are calculated at<br>a higher geographical level.<br>Prices are calculated<br>independently for each crop<br>type and separately for<br>improved and traditional<br>seeds, where possible<br>TZA: No question asking for<br>the quantity of seeds used, in<br>w1 and w2, only the value of<br>seeds bought. Median prices<br>could therefore not be<br>computed, and seed values are<br>taken as they are reported.<br>Seeds only taken into account<br>if they were bought.<br>MLI: all seeds are assumed to<br>be purchased (no question<br>asking for the quantity<br>purchased). This might lead to<br>an underestimation of seed<br>prices as a portion may be<br>obtained for free as gifts or<br>leftover from the previous<br>season.<br>UGA: all seeds are assumed<br>to be purchased (no question<br>asking for the quantity<br>purchased). This might lead to<br>an underestimation of seed<br>prices as a portion may be<br>obtained for free as gifts or<br>leftover from the previous<br>season. In w1 w2, w3 no<br>question asking for the<br>quantity of seeds used, only<br>value. Median prices could<br>therefore not be computed,<br>and seed values are taken as<br>they are reported. Seeds only|Summed up to the plot level in the<br>plot dataset|

27
|||taken into account if they<br>were bought||
|---|---|---|---|
|_seed_value_USD_|Value of<br>seeds in USD|<br>Seed values<br>(_seed_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et<br>al., 2011)||
|_improved_|Does this plot<br>contain<br>improved<br>seeds?<br>(default:<br>traditional)||NGA: absent in w1, w2<br>TZA: absent in w1, w2<br>UGA: absent in w1, w2, w3|
|_used_pesticides_|Were<br>pesticides<br>used on this<br>plot?|||
|_crop_shock_|Did a shock<br>affect crops in<br>the current<br>season?||UGA: crop shocks are missing in w7,<br>w5|
|_pests_shock_<br>_rain_shock_<br>_drought_shock_<br>_flood_shock_|Were crops<br>affected by<br>pests in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>rains in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>drought in the<br>current<br>agricultural<br>season?<br>Were crops<br>affected by<br>floods in the<br>current<br>agricultural<br>season?||Flood shock is absent in MWI w1,<br>MLI all waves,<br>Rain shock is absent in NER all<br>waves,, UGA all waves<br>Rain and flood shocks could not be<br>distinguished in TZA<br>Shocks only reported on plots with<br>full losses in NGA waves 1 to 3, and<br>UGA<br>UGA: crop shocks are missing in w7,<br>w5|

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Supplementary Table 3. Directory of variables in the plot-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_main_crop_|Crop with the<br>highest value on<br>the plot|Crops have been categorized in<br>10 groups for comparability:<br>-<br>Barley<br>-<br>Legumes<br>-<br>Maize<br>-<br>Millet<br>-<br>Nuts<br>-<br>Rice<br>-<br>Sorghum<br>-<br>Tubers<br>-<br>Wheat<br>-<br>Other||
|_plot_id_obs_(see above)|Plot ID (panel<br>identificator)|||
|_plot_id_merge_(see above)|Plot ID (to merge<br>with raw data)|||
|_parcel_id_obs_(see above)|Parcel ID (panel<br>identificator)|||
|_parcel_id_merge_(see above)|Parcel ID (to<br>merge with raw<br>data)|||
|_plot_area_GPS_|Area (in hectares)<br>of plot|When plot areas are absent, they<br>have been imputed using multiple<br>imputation approach which relies<br>on self-reported areas and<br>administrative areas.<br>We employ a predictive mean<br>matching method with five<br>nearest neighbours, using self-<br>reported areas along with<br>administrative divisions to obtain<br>linear predictions which serve as<br>our distance variable (Little,<br>1988).|UGA: 80% missing<br>in wave 7|
|_yield_kg_|Yield quantity<br>(harvest in kg/ha)|Ratio between plot-level<br>_harvest_kg_and_plot_area_GPS_||
|_yield_value_LCU_|Yield value<br>(harvest value/ha),<br>inLCU|Ratio between plot-level<br>_harvest_value_LCU_and<br>_plot_area_GPS_||
|_yield_value_USD_|Yield value<br>(harvest value/ha),<br>inUSD|Ratio between plot-level<br>_harvest_value_USD_and<br>_plot_area_GPS_||
|_farm_size_|Farm size (ha)|Sum of the size of all plots (<br>regardless of whether they are<br>cultivated-could include plots||

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|||with perennial crops, or fallowed<br>plots)||
|---|---|---|---|
|_plot_manager_id_obs_|Unique plot<br>manager ID|<br>Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_id_in the individual-<br>level dataset|UGA: absent when<br>there are multiple<br>managers in wave<br>7|
|_plot_manager_id_merge_||Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_merge_in the<br>individual-level dataset||
|_formal_education_manager_|Does the plot<br>manager possess<br>any formal<br>education?|||
|_primary_education_manager_|Did the plot<br>manager complete<br>primary school?||“Primary<br>education” consists<br>of:<br>-<br>6 years in<br>NGA<br>-<br>6 years in<br>MALI<br>-<br>7 years in<br>TZA<br>-<br>8 years in<br>ETH<br>-<br>6 years in<br>NIGER<br>-<br>8 years in<br>MALAWI<br>-<br>7 years in<br>UGANDA<br>NGA: not asked for<br>old members who<br>are not in school<br>(w1, w2)|
|_age_manager_|Age (in years) of<br>the plot manager|Capped at 100||
|_female_manager_|Is the plot<br>manager female?||NER: plot manager<br>= hh manager if<br>managed jointly|
|_married_manager_|Is the plot<br>manager married?||UGA: w7 has no<br>ID information on<br>plots managed<br>jointly. The<br>resulting sample<br>could be biased.|
|_respondent_id_obs_||This ID can be merged with<br>_indiv_obs_id_in the individual-<br>level dataset|TZA: absent in<br>wave 5<br>UGA: absent in<br>wave 1, wave 2|
|_respondent_id_merge_||This ID can be merged with<br>_indiv_obs_merge_in the<br>individual-level dataset|TZA: absent in<br>wave 5|

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||||UGA: absent in<br>wave 1 wave 2|
|---|---|---|---|
|_formal_education_respondent_|Does the plot<br>respondent<br>possess any<br>formal education?||,<br>TZA: absent in<br>wave 5<br>UGA: absent in<br>wave 1, wave 2|
|_primary_education_respondent_|Did the plot<br>respondent<br>complete primary<br>school?||TZA: absent in<br>wave 5<br>NGA: not asked for<br>old members who<br>are not in school<br>(w1, w2)<br>UGA: absent in<br>wave 1, wave 2<br>“Primary<br>education” consists<br>of:<br>-<br>6 years in<br>NGA<br>-<br>6 years in<br>MALI<br>-<br>7 years in<br>TZA<br>-<br>8 years in<br>ETH<br>-<br>6 years in<br>NIGER<br>-<br>8 years in<br>MALAWI<br>-<br>7 years in<br>UGANDA|
|_age_respondent_|What is the age of<br>the respondent?|Capped at 100|TZA: absent in<br>wave 5<br>UGA: absent in<br>|
||||wave 1, wave 2|
|_female_respondent_|Is the respondent<br>female?||<br>TZA: absent in<br>wave 5<br>UGA: absent in<br>wave 1, wave 2|
|_married_respondent_|Is the respondent<br>married?||TZA: absent in<br>wave 5<br>UGA: absent in<br>wave 1, wave 2|
|_nb_seasonal_crop_|Number of<br>seasonal crops<br>grown on plot|||
|_intercropped_|<br>Is any crop<br>intercropped?|||
|_contains_BARLEY_<br>_contains_LEGUMES_|Plot contains<br>BARLEY|||
|_contains_MAIZE_<br>_contains_MILLET_|Plot contains<br>LEGUMES|||

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|_contains_OTHER_<br>_contains_RICE_<br>_contains_SORGHUM_<br>_contains_TUBERS_<br>_contains_WHEAT_<br>_contains_NUTS_|Plot contains<br>MAIZE<br>Plot contains<br>MILLET<br>Plot contains<br>OTHER<br>Plot contains<br>RICE<br>Plot contains<br>SORGHUM<br>Plot contains<br>TUBERS<br>Plot contains<br>WHEAT<br>Plot contains<br>NUTS|||
|---|---|---|---|
|_share_value_BARLEY_<br>_share_value_LEGUMES_<br>_share_value_MAIZE_<br>_share_value_MILLET_<br>_share_value_NUTS_<br>_share_value_OTHER_<br>_share_value_RICE_<br>_share_value_SORGHUM_<br>_share_value_TUBERS_<br>_share_value_WHEAT_|Share of plot value<br>derive from<br>BARLEY<br>Share of plot value<br>derive from<br>LEGUMES<br>Share of plot value<br>derive from<br>MAIZE<br>Share of plot value<br>derive from<br>MILLET<br>Share of plot value<br>derive from NUTS<br>Share of plot value<br>derive from<br>OTHER<br>Share of plot value<br>derive from RICE<br>Share of plot value<br>derive from<br>SORGHUM<br>Share of plot value<br>derive from<br>TUBERS<br>Share of plot value<br>derive from<br>WHEAT|Shares do not take perennial<br>crops into account.||
|_maincrop_valueshare_|Share of plot value<br>attribute to main<br>crop|||
|_inorganic_fertilizer_<br>_organic_fertilizer_|Has at least one<br>inorganic fertilizer<br>been used on this<br>plot?<br>Has at least one<br>organic fertilizer<br>been used on this<br>plot?||NGA: no dedicated<br>section to organic<br>fertilizers before<br>w3|

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|_nitrogen _kg_|Nitrogen<br>equivalent of<br>applied inorganic<br>fertilizer (kg)||ETH: UREA (46 -<br>0 - 0), DAP (18 -<br>46 - 0), and NPS<br>(19 - 46 - 7).<br>NGA: UREA and<br>NPK (20- 10 – 10<br>appears to be the<br>most prevalent<br>blend).<br>Mali: UREA, DAP,<br>NPK and cereal<br>complex ( 15 – 15<br>– 15) are included.<br>A few less<br>prevalent inorganic<br>fertilizers are left<br>out.<br>TZA: CAN and SA<br>|
|---|---|---|---|
||||also used.|
|_inorganic_fertilizer_value_LCU_|Inorganic fertilizer<br>value, in LCU|Inorganic fertilizers valued using<br>median prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin 1 in<br>some surveys) under the<br>condition that there are more than<br>10 observed sales in the area. If<br>there are less than 10 sales, prices<br>are calculated at a higher<br>geographical level. When<br>possible, separate prices were<br>calculated for units that couldn’t<br>be converted into kilograms.|ETH (w1), TZA<br>(all waves): all<br>fertilizers are<br>assumed to be<br>purchased (no<br>question asking for<br>the quantity<br>purchased). This<br>might lead to an<br>underestimation of<br>fertilizer prices as a<br>portion may be<br>obtained for free as<br>gifts or leftover<br>from the previous<br>season. Moreover,<br>purchased<br>fertilizers are at the<br>plot holder level in<br>ETH (wave 1), and<br>purchases are<br>assumed to be<br>equally distributed<br>among plots.|
|_inorganic_fertilizer_value_USD_|Inorganic fertilizer<br>value, in USD|Fertilizer values<br>(_inorganic_fertilizer_value_LCU)_<br>are converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et al.,<br>2011)||
|_total_family_labor_days_|Total family labor<br>days on the plot||UGA: Absent in<br>wave 7.|
|_total_hired_labor_days_|<br>Total hired labor<br>days on the plot||UGA: Absent in<br>wave 7, season 2.|

33
|_hired_labor_value_LCU_|Vale of hired<br>labor, in LCU|Hired labor value valued using<br>median prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin 1 in<br>some surveys) under the<br>condition that there are more than<br>10 observed wages in the area. If<br>there are less than 10 wages,<br>prices are calculated at a higher<br>geographical level. When<br>possible, separate wages were<br>calculated for men, women and<br>children.|TZA: overall<br>median wages<br>calculated, then<br>applied to total<br>number of labor<br>days (no<br>man/child/woman<br>wage distinction).<br>Moreover, “free”<br>labor is not<br>distinguished from<br>paid labor, and<br>wages may<br>therefore appear<br>lower than reality.|
|---|---|---|---|
|_hired_labor_value_USD_|Vale of hired<br>labor, in USD|Hired labor values<br>(_hired_labor_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et al.,<br>2011)||
|_total_labor_days_|Total labor days<br>on the plot||Family labor days<br>+ hired labor days<br>+ “other” labor<br>days (non-hired<br>and non-family)<br>NGA: no PP<br>harvest in wave 1.<br>No “other” labor<br>until wave 4.<br>TZA: no “other”<br>labor.<br>UGA: no family<br>labor in wave 7. No<br>“other” labor after<br>wave 3|
|_irrigated_|Is the plot<br>irrigated?|||
|_tractor_|Did the household<br>use a tractor in<br>this season?||ETH: absent in<br>wave 1<br>UGA: absent in<br>waves 1 and 2|
|_plot_owned_|Farmer declares<br>owning the plot||Owned=<br>purchased,<br>inherited, granted<br>by local leaders...|
|_plot_certificate_|Possession of a<br>certificate for the<br>plot||<br>MWI: absent in<br>wave 1 and wave 4|
|_erosion_protection_|Is the plot<br>protected from<br>erosion?||NGA: absent in<br>w1, w2. Only for<br>plots with erosion|

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||||problems in wave<br>3.<br>UGA: only for<br>plots with erosion<br>problems from<br>wave 4 onwards|
|---|---|---|---|
|_livestock_|Is the respondent<br>engaged in<br>livestock<br>activities?|||
|_ag_asset_index_|Agricultural assets<br>index|Index based on a PCA of all<br>agricultural assets owned by the<br>household|UGA: missing in<br>wave 1|
|_perennial_crops_hh_|Does this<br>household grow<br>perennial crops?|||
|_nb_fallow_plots_|Number of fallow<br>plots under<br>household<br>management|||
|_nb_plots_|Number of plots<br>under household<br>management|||
|_share_kg_sold_|<br>Share of harvest<br>output (in kg) sold|Set to missing if reported share<br>above 100% percent. Set to<br>missing if household does not<br>harvest any crops.||
|_agro_ecological_zone_|Agro-ecological<br>Zones||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_popcenter_|HH Distance in<br>(KMs) to Nearest<br>Population Center<br>with +20,000||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_market_|HH Distance in<br>(KMs) to Nearest<br>Market||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above<br>Also missing in<br>MWI, MLI, NER,<br>and all of TZA and<br>UGA|
|_elevation_|Elevation (m)||From the<br>geovariables files,<br>which are missing<br>inNERw2,TZA|

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||||w4 and w5, UGA<br>wave 4 and above|
|---|---|---|---|
|_twi_|Potential wetness<br>index||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_nutrient_availability_<br>_nutrient_retention_<br>_rooting_conditions_<br>_oxygen_availability_<br>_excess_salts_<br>_toxicity_<br>_workability_|Nutrient<br>Availability<br>Nutrient Retention<br>Rooting<br>conditions<br>Oxygen<br>availability<br>Excess salts<br>Toxicity<br>Workability||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_soil_fertility_index_|<br>Soil fertility index|PCA of soil variables above||
|_plot_slope_|Plot Slope<br>(percent)||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_plot_dist_household_|Plot Distance in<br>(KMs) to HH||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above<br>Only present in<br>ETH, MWI.|

Supplementary Table 4. Directory of variables in the household-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_hh_size_|Household<br>size|||
|_hh_shock_|Was the<br>household<br>negatively<br>impacted by a<br>shock over the<br>past 12<br>months?||MALI: 3 year recall period.<br>TZA: 2 year recall period in w4, w5.|

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|_hh_electricity_access_|Does the<br>household<br>have access to<br>electricity?||Houses which have a generator/ solar<br>panels but aren’t connected to a grid<br>are also recorded as having access to<br>electricity|
|---|---|---|---|
|_hh_dependency_ratio_|Household<br>dependency<br>ratio||Individuals that are younger than 15<br>or older than 65 are classified as<br>“dependents”|
|_hh_formal_education_|Does anyone<br>in the<br>household<br>posses any<br>formal<br>education?|||
|_nonfarm_enterprise_|Someone in<br>household<br>owns a non-<br>farm<br>enterprise|||
|_totcons_LCU_|Consumption<br>aggregate per<br>capita, in<br>LCU||MWI: absent in w3, w4<br>MLI: absent in w2|
|_totcons_USD_|Consumption<br>aggregate per<br>capita, in<br>USD|Consumption aggregates<br>(_totcons_LCU)_are converted<br>to current USD and deflated<br>to 2020 dollar values, using<br>exchange rates and a deflator<br>extracted from World Bank<br>databases (Azevedo et al.,<br>2011)|TZA: consumption aggregates for the<br>refresh sample, wave 5, include rent<br>and durables, which is not the case<br>elsewhere.|
|_cons_quint_|Household<br>consumption<br>quintile||Quintile of_totcons._This amounts to a<br>within wave quintile|
|_hh_asset_index_|Household<br>asset index|Index based on a PCA of all<br>assets owned by the<br>household||
|_HDDS_|Household<br>dietary<br>diversity<br>index|Index derived from a<br>household consumption<br>module. See the manuscript<br>for more information|MLI: absent in w2|

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Supplementary Table 5. Directory of variables in the individual-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_indiv_id_obs_|Individual ID<br>(panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>individual. The first number<br>of the code determines the<br>country.||
|_indiv_id_merge_|Individual ID<br>(to merge<br>with raw data)|<br>ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH:_individual_id_(wave 1)_,_<br>_individual_id2_(wave 2)_,_<br>_individual_id2_(wave 3)_,_<br>concatenation of_household_id_and<br>_individual_id_, (wave 4)_,_<br>concatenation of_household_id_and<br>_individual_id_(wave 5)<br>MWI:_PID_(wave 1),_PID_(wave 2)_,_<br>_PID_(wave 3)_, PID_(wave 4)<br>MLI: concatenation of_grappe_<br>_menage,_and_numero d’ordre_(wave<br>1), concatenation of_grappe_<br>_exploitation_and_numero d’ordre_<br>(wave 2)<br>NER: concatenation of_hhid_and<br>_numero d’ordre_(wave 1)_,_<br>concatenation of_GRAPPE,_<br>_MENAGE, EXTENSION_and_numero_<br>_d’ordre_(wave 2)<br>NGA: concatenation of_hhid_and_id_<br>(wave 1)_,_concatenation of_hhid_and<br>_id_(wave 2)_,_concatenation of_hhid_<br>and_id_(wave 3)_,_concatenation of<br>_hhid_and_id_(wave 4)<br>TZA: concatenation of_hhid_and<br>_member number_(wave 1)_,_<br>concatenation of_y2_hhid_and_indidy2_<br>(wave 2)_,_concatenation of_y3_hhid_<br>and_indidy3_(wave 3)_,_concatenation<br>of_y4_hhid_and_indidy4_(wave 4)_,_<br>concatenation of_sdd_hhid_and<br>_sdd_indid_(wave 5)<br>UGA: concatenation of_Hhid,_and<br>_PID_(wave 1)_, HHID_and_PID_(wave<br>2)_, HHID_and_PID_(wave 3)_, HHID_<br>and_PID_(wave 4)_, HHID_and_PID_<br>(wave 5)_, hhid_and_PID_(wave 7)_,_<br>_hhid_and_PID_(wave 8)|
|_relationship_head_<br>_age_|HH Member<br>Relationship<br>to HH Head<br>Age (inyears)|Absent of unified codes, this<br>variable is coded as a string<br>Capped at100||

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|_married_|Is the<br>individual<br>married?|||
|---|---|---|---|
|_female_|Is the<br>individual a<br>female?|||
|_formal_education_|Any formal<br>education?|||
|_primary_education_|Completed<br>primary<br>education?||“Primary education” consists of:<br>-<br>6 years in NGA<br>-<br>6 years in MALI<br>-<br>7 years in TZA<br>-<br>8 years in ETH<br>-<br>6 years in NIGER<br>-<br>8 years in MALAWI<br>-<br>7 years in UGANDA<br>NGA: not asked for old members<br>who are not in school (w1, w2)|
|_height_|Individual<br>height||Absent in MLI and NER|
|_weight_|Individual<br>weight||Absent in MLI and NER|
|_wasting_|Child with<br>wasting||Absent in MLI and NER, TZA wave<br>4, wave5|
|_haz06_|Height-for-<br>age Z-score|These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_waz06_|Weight-for-<br>age Z-score|<br>These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_whz06_|Weight-for-<br>length/height<br>Z-score|These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_bmiz06_|BMI-for-age<br>Z-score|<br>These estimates are calculated<br>using the 2006 WHO child<br>growth standards|Absent in MLI and NER, TZA wave<br>4, wave5|
|_farm_work_|Individual has<br>worked in<br>own farm in<br>(past) 7 days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_SOB_work_|<br>Individual has<br>worked in<br>own business<br>in (past) 7<br>days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_wage_work_|Individual has<br>worked for<br>own wage in<br>(past) 7 days|in last 7 days, or typical 7<br>days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_farm_hrs_|<br>Number of<br>hours spent in<br>own farm ag<br>work in (past)<br>7 days||In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|

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|_SB_hrs_|Number of<br>hours spent in<br>own business<br>work in (past)<br>7 days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|---|---|---|
|_wage_hrs_|Number of<br>hours spent in<br>wage labor in<br>(past) 7 days|In MLI, NER and NGA (w1, w2),<br>this information is only available for<br>individuals’ “main job”|
|_ind_ag_|Any wage<br>work in<br>agriculture||
|_ind_fish_|Any wage<br>work in<br>fishing||
|_ind_mining_|Any wage<br>work in<br>mining||
|_ind_manuf_|Any wage<br>work in<br>manufacturing||
|_ind_const_|Any wage<br>work in<br>construction||
|_ind_serv_|Any wage<br>work in<br>services||
|_working_age_|Working age<br>household<br>member<br>(according to<br>questionnaire)|The cutoff changes across surveys|

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