OCCUPATIONAL CHOICE AND ENERGY ACCESS – ELECTRICITY FOR MORE AND BETTER JOBS

Electricity plays a crucial role in health, safety, and participation in the modern world. The channels by which is contributes to economic development are still not fully understood. Especially in rural communities in low-income countries with high shares of subsistence farming, electricity access is often seen as game changer towards more diversified jobs and higher earnings. This paper analyses the effect of electricity access on occupational choices in six Sub-Saharan African countries, using a unique dataset from household surveys combined with electricity modules. It finds that, while the effect of electricity access on the decision for non-farm economic activities is positive, other factors such as educational levels, age, access to loans or land ownership also affect the decision. Overall, wages and earning in non-farm activities are higher in electrified regions and in some sectors, electrification also helps to close the gender pay-gap. However, wages and incomes in rural areas stay below the respective payments in urban areas, so that electricity access and non-farm activities will not keep workers from migrating and seeking better jobs in cities.


Introduction
Access to energy and electricity plays a crucial role in economic development, health, safety, and participation in the modern world.Increasingly urbanized and industrialized economies demand more and more electricity, one of the great challenges facing developing countries.Modern societies depend on reliable and secure electricity supplies to foster economic growth and community prosperity.Access to affordable and clean energy is one of the seventeen Sustainable Development Goals (SDG).As such, it is measured and monitored in the SDG7 Tracking report, jointly issued by the SDG7 custodians2 .The message from the latest report is mixed.Although the world has progressed towards full access and has reduced the number of people lacking access from an estimated 1,1 billion in 2010 to 675 million in 2021, some worrisome observations persist.
Firstly, the speed of improving energy access has slowed down lately, partly due to the challenges from the pandemic and its aftermath, partly because the population remaining without electricity access is increasingly harder to reach.To reach the target in 2030, the annual rate of growth in electricity access would have needed to be 1 percentage point per year from 2021 onward.Secondly, people without electricity access are increasingly concentrated in Sub Saharan African countries and in rural areas.If progress does not accelerate, the share of the global population projected to have access to electricity by 2030 will be 92 percent, leaving some 660 million people unserved, of whom approximately 85 percent will be in Sub-Saharan Africa (IEA et al. 2023).
The impact of energy access on development, though intuitively understood, is hard to measure, as is its impact on more and better jobs.The literature to this regard is still inconclusive, and the existing empirical results show, depending on the measurement method applied, positive to neutral effects.Increasingly the literature agrees that mere connection of a village or a household to a grid or mini grid will not automatically lead to large development effects, just having the electrons arrive does not seem to do the trick.A better understanding of development outcomes needs to identify economic activities and business models leading to the generation of incomes of men and women in rural and urban settings.
Non-farm activities in rural settings are less extensive researched (see next section) and often overlooked.
The traditional view on rural economic activities focuses on farming and produce related activities.Rural income is understood as farm income and linked to agricultural activities and institutionally in the decision space of the Ministry of Agriculture.Industry, energy and manufacturing are institutionally allocated with the Ministry of Economic Development, Infrastructure, or Energy often focusing on urban spaces (Reardon (1999)).Rural non-farm income can play an important part in structural change, increase food security, prevent rapid or excessive urbanization, and contribute to resilient rural solutions, when goods and services are produced locally or regionally.
Infrastructure improvement, including access to electricity, can support the decision of workers to pursue non-farm activities, either in addition to waged or subsistence farm work or as their sole source of income.Developing non-farm economic activities, including post-harvest processing, storage, food production, but also venturing into other economic activities and services will become easier and more attractive, if the small entrepreneurs have access to reliable and affordable electricity.This way, electricity access yields productive uses, beyond lighting, or the potential use of household appliances or communication devices.While there does not seem to be a universal definition of productive use of (renewable) energy, most definitions include agricultural, commercial and industrial activities involving electricity services as a direct input to the production of goods or provision of services (SE4All 2014).
This paper explores the connection between non-farm rural activities and electricity access, making use the concept of productive use of energy, using microdata from household surveys under the World Bank's Multi-Tier Framework on energy access and analyzes how the probability of taking up non-agrarian economic activity is influenced by access to electricity.Similar analysis for India (Khurana and Sangita 2022) shows promising results, based on panel data across different Indian states.The data used in this paper not only include the binary choice of an individual between farm and nonfarm activities, but also allow to explore explicit information on waged activities and self-employed and the respective economic sector where this activity happens.Six LIC African countries (Nigeria, Niger, Rwanda, Zambia, Ethiopia and Liberia) are included in the dataset.
The remainder of this paper is organized as follows.The next section gives an overview of the literature which explores energy access and its economic benefits.The data sets and the modelling approaches are explained in section three, section four has results and section five concludes.

Literature
Development follows a trajectory of structural transformation of the economy, according to Kuznets' modern theory of economic growth.Major aspects of structural change include the shift away from agriculture to non-agricultural pursuits.While this is often viewed as a movement of people from rural to urban regions, this paper tries to highlight nonfarm activities in rural areas.These can be seen either a first step towards later endeavors in cities by generating experience with non-farm activities and at the same time staying in the known environment, or as an opportunity to improve close rural value chains and hence create more and better incomes and jobs.The literature also discusses rural nonfarm activity as a common occurrence, which also takes place parallel to farming activities, also to increase resilience against shocks (Khurana and Sangita 2022).Owners of agricultural lands can engage in further economic activities; non-landowners can provide the services needed by the respective community.These include several manufacturing, service, and agro-processing activities.Energy access serves as a prerequisite to achieve the Sustainable Development Goals (SDG) (Rahman et al. 2022).
An under-researched topic in the classical development literature is the role played by energy access in many LDCs and even more so in rural areas.The World Bank (Bhatia and Angelou 2015) finds the concept of access to energy does not lend itself to an easy definition.While the arrival of the Kilowatt-hour, a household electricity connection, an electric pole in the village, or an electric bulb in the house increase security, extend the number of hours in which education or leisure can be pursued, the evidence for development outcomes is mixed at least.Socioeconomic development requires increased use of energy services across households and productive engagements.Access to energy for productive engagements can lead to higher income levels, productivity, and employment in comparison to access-less activities.
From the energy side, the World Bank has developed a definition of energy access, capturing the quality, stability, voltage level, and affordability of electricity in a Multi-Tier Framework and developed questionnaires which are integrated in household Standard of living surveys and provide the data base used in this paper below.
A Global Environment Facility (GEF) and UN Food and Agricultural Organization (FAO) working definition stating that "in the context of providing modern energy services in rural areas, a productive use of energy is one that involves the application of energy derived mainly from renewable resources to create goods and/or services either directly or indirectly for the production of income or value" is used by (Cabraal, Barnes, and Agarwal 2005).It illustrates the on the one hand that the type of energy source enters the access literature, and on the other hand expands productive use beyond income generating services.For instance, (Kanagawa and Nakata 2008) analyze the effects of electric lighting appliances on literacy in an unelectrified province in India and are able to show that complete household electrification will be could increase the literacy rate to 74.4% from 63.3%.
There are several interdependencies between electricity access and use, and economic development (World Bank Group 2018).In both rural and urban regions, lack access to reliable, affordable electricity is seen as the major obstacle to business activities in manufacturing (up to 32 percent of all manufacturing enterprises, more than 40 percent of all large firms for instance in Nigeria) ((World Bank Group n.d.)).
Access and use may link to jobs and income growth and vice versa.Moreover, access may reach regions which are deemed promising to contribute to economic growth for reasons which are not connected to energy, such as other commodities, special economic areas etc.
Grid connection can increase nonfarm incomes of rural households by about 9 percent, as shown for India during 1994-2005 (Chakravorty, Pelli, and Marchand 2014).However, a grid connection and more stable supply of electricity adds to this another 19 percentage points.The effects seem to be larger at the rural level, as (Grimm, Hartwig, and Lay 2013) find mixed results for the urban area.Based on a data set of informal firms in seven West African cities, electricity access is not significant to enterprise performance and the authors conclude that the informal sector in urban settings requires a closer look.However, for certain trades, for instance tailoring, electricity access enabled the use of sewing machines and a measurable uptake of productivity and incomes.
To make electricity access an enabler of nonfarm rural incomes, more than the arrival of the kilowatt-hour is needed (Blimpo and Cosgrove-Davies 2019).Complementary factors next to infrastructure are access to finances and loans, skills, and access to markets, as data on Rwanda find.Access to credit and public services spurs the impact of electricity in boosting household incomes from farm and nonfarm sources.
Skills training programs and the removal of barriers to market access will increase entrepreneurial activities so that electricity services can be better exploited for productive use (ibid.).The authors conclude that rural areas have large untapped economic potential that could be freed through the provision of electricity, among other enabling factors.Enabling factors can also be identified in an impact evaluation of 30 small-scale energy development projects.Materials and information to support the productive use of energy -such as training, equipment or market research seem to be crucial in an energy project for productive activities to develop on a wider scale (Terrapon-Pfaff et al. 2018).Although nonfarm enterprises are widespread in rural Sub-Saharan Africa, the motivation for households to operate enterprises, how productive they are, and why they exit the market are neglected questions according to Nagler and Naude ( 2016).Drawing integrated surveys on agriculture and using discrete choice and selection models using data from Ethiopia, Niger, Nigeria, Malawi, Tanzania, and Uganda, they find access to credit and markets, household wealth, and the education and age of the household head positively affecting the likelihood of operating an enterprise.
Electricity positively affects formation and performance of rural non-farm entrepreneurial ventures in India (Khurana and Sangita 2022), and access to household electricity impacts rural households' decisions to take up entrepreneurial activities, operated within or outside of household premises.Moreover, enterprises in the same economic sector differ in income if they have access or not, with the enterprises with access generating systematically more income, very similar to the example of tailors in West Africa.Electricity access in the Indian panel data set is found to yield 35.2% higher incomes on an average (Khurana and Sangita 2022).These findings are confirmed for the impact of rural electrification through extension of existing grid on rural micro-enterprises in Niger Delta, Nigeria (Akpan, Essien, and Isihak 2013).The data have been sampled from entrepreneurs using surveys and structured interviews.Successful productive nonfarm use not only benefits the entrepreneurs' households but spill over to the respective community (Kooijman-van Dijk and Clancy 2010).And while in most villages household profited who had been better off already, the benefits indirectly reached a larger group of people.
Energy access benefits for women are often solely discussed in terms of clean cooking fuels.Putting the gender lens to energy access and productive use needs to acknowledge that women often work in smaller less energy intensive enterprises, and hence might draw less benefits from productive use supporting policies (Pueyo and Maestre 2019).In terms of practical application, the barefoot college has trained 2,200 Rural Women as solar engineers in 93 Countries, with Barefoot Solar Programs and brought solar lighting systems to 18,047 Households ("Barefoot College InternationalSolar Barefoot College International" 2021).In South Africa, the mass roll-out of electricity to rural households has led to a significant increase in female employment (Dinkelman 2011).
For Nepal, energy access does not immediately lead to productive use (Bastakoti 2003).Productive use benefits are employed to showcase the benefits of renewable energy sourced electrification efforts globally ( IT Power 2009).The literature review by Willcox et al. (2015) tries to follow the development of enterprises after energy access over time and to find out, if new electrified enterprises crowd out existing ones.In other words, electricity access can yield new electricity-powered technology using and previously impossible activities or improve, replace, or shift towards a previously pursued activity more efficiently.
The authors found that the fraction of surveyed enterprises that had been created since the implementation of electricity access programs for instance in India almost tripled.In the literature they reviewed, electricity access is found in combination with more diverse activities of microenterprises.Kirubi et al. (2009) give detailed examples how electricity increases the productivity of rural nonfarm activities.Their paper analyses how rural electrification can contribute to rural development.In their detailed case study of a community-based electric microgrid in rural Kenya access to electricity enabled the use of electric equipment by SMEs and led to higher incomes.
The empirical evidence seems to suggest that electricity access together with further enabling factors can lead to nonfarm economic activities in rural areas which yield higher incomes.The analysis in this paper contributes to the literature by findings on a sectoral level, showing how the likelihood of choosing a nonfarm activity, the selection of waged or self-nonfarm employment and incomes in different sectors, such as manufacturing, education and selected services are positively affected by having access to energy.

Data and Method
The analysis is based on the results of the household surveys carried out under the Multi-Tier Framework (MTF) initiative of the World Bank and partners.The surveys were carried out once per country over the last four years; hence the full data set combining all countries is treated as cross-sectional.The sampling strategy stratifies the sampling frame by spatial component and access status.This strategy aimed for a 50/50 ratio between urban and rural 3 as well as between electrified and non-electrified.The total number of individuals included in the surveys exceed 133 thousand, with questions asked on the community, the household, and the individual level.For the analysis here, most questions of interest relate to the individual level.Table 1 gives an overview of the data and compares them with shares of the population who has electricity access in the World Development Indicator Database 4 .Energy access includes both grid access and off-grid solutions, while grid access and single home solar solutions prevail, mini-grid solutions are hardly visible.For Niger, Rwanda, and Nigeria the sample contains 50 percent households without access and 50 percent of the households drawn from rural areas, in the other countries the number of access-positive communities is either larger or smaller than 50%.The share of access communities in the sample should not be interpreted as electricity access statistics.As the last line in the table below shows, access statistics report different shares for the respective countries.However, for the analysis in this paper, this has no effect, because non-farm activity will be the dependent variable and electricity access is an independent variable in the estimates.
For Nigeria, the survey was carried out in the North-West geopolitical zone, in the states of Kaduna, Kano, Katsina, Kebbi, Jigawa, Sokoto, and Zamfara.The sample was distributed across the seven study states according to their populations based on available data from the most recent census.Urbanity split was not available at state level (ESMAP-MTF n.d.).The data from Nigeria are kept in the sample, however, for the first step in the analysis where energy access and household entrepreneurship in all locations is analyzed.Table 2 gives an overview of the sample size and distribution by locality for the remaining six countries which have information on urban/rural.
3 Peri-urban is counted as rural.The same caveat as above holds true, as the sampling strategy strived where possible to sample equally from urban and rural areas.The World development indicators report for Niger and Rwanda rural share of more than 82 percent.
Figure 1 compares the shares of the population by educational attainment and their work for monthly wages or as day laborer or unpaid family member for regions with electricity access and without.It shows clearly that more people have a higher education, and a waged job in regions and towns with electricity access.Constrained to rural areas, the results become more distinct: more people have a waged job in rural areas with electricity access.Moreover, more people in rural areas chose a non-farm activity to make a living if they have access to electricity than in areas without access.Twice as many people have a waged non-farm job across the whole sample and more people chose a self-employed non-farm job if they have access to electricity.In Kenya and Rwanda, the effects seem to be more pronounced, with almost three times as many people pursue non-farm waged activities and twice as many pursue non-farm self-employment in rural areas with access compared to rural areas without access.To better understand to analyze what has driven nonfarm activities among those who have done so and how is the decision or the probability to do so affected by energy access, the approach firstly determines how the choice between non-farm and farm activity is affected by individual characteristics and energy access.In a next step, this choice is explored further, and the analysis looks at the different non-farm economic sectors and explores the relation of occupational choices by economic sector.Are non-farm activities concentrated in trade, manufacturing or services and which socioeconomic characteristic affects this choice?How does having electricity access affect the sector selection?The third step then turns to incomes and analyses the effect that electricity access has on the livelihoods of workers which chose to work in non-farm activities in rural areas.Do people earn more in these activities?How about the gender wage gap, and is there a difference between electrified regions and those without electricity access?
The variables of interest are shown in Table 3.The dependent variable in model 1 is the binary choice variable of taking on a nonfarm activity.A categorial variable stands for the occupational choice in model 2, with eight occupational categories and a category of students, volunteers, and other occupations, followed by retired and unemployed.Model 3 will turn to the continuous income variable and explain differences in incomes from different economic activities by location, energy access and other independent variables.The variable sector indicates in which economic sector or industry the occupational choice is realized.The socioeconomic characteristics include assets, household incomes, education, family size, access to loans and the type of loans, being formal bank loans or informal loans from family members are included in the list of explanatory variables.Khurana and Sangita (2022) find these variables being enablers of non-farm business enterprises in India.
Model 1 looks at the binary variable Nonfarm activity, which is the answer of the interview partner to the question if they are pursuing a nonfarm activity for income generation and estimates the probability of doing so depending on a set of explanatory variables. .
The base outcome has been selected, because we are interested in the choice of nonfarm activities and suppose the default situation is a farm activity in the rural region, self-employed (subsistence) farm activity is selected as the base outcome.This way we test if energy access in combination with selected household characteristics increases the probability of selecting a nonfarm activity over subsistence farming.
The third model reflects on earnings and electricity access and follows the identification in (Khurana and Sangita 2022).The hypothesis is that nonfarm businesses in electrified villages yield higher incomes than in unelectrified villages.For this model, the sample is constrained to individuals participating in nonfarm businesses.The results of a simple OLS regression may be biased for the probability of having selected the nonfarm activity.The typical solution to this is using the two-step estimation procedure suggested by Heckman.The Heckman (1979) model was developed for a wage equation, where the sample only included people willing to work for the respective wages.In this approach, the first stage is the selection model, explaining the selection of wage with at least one more variable which may affect the choice of being in a nonfarm business.The second stage will be an OLS to understand how various independent variables including electricity access impact the incomes.The selection equation is given in stage 1 and the regression equation to explaining income after the nonfarm activity has been selected is found in stage 2, with x and z being sets of explanatory variables and z has at least one variable which differs from the explanatory variables in stage 2.
Stage 1: For the selection stage, land ownership and access are the explaining variables, with land ownership being expected to have a negative sign, under the assumption that if you own farmland, the probability to seek a nonfarm job declines it is not influencing the level of income generated from this choice.Stage 2 then uses the household and individual characteristics, such as age, schooling, gender or access to loans, energy access and characteristics of the selected sector.If the regressions are constrained to certain economic activities, several details determining the choices of certain activities emerge.Note that the Stage 2 parameters can be interpreted directly in the log-linear model and not in relation to other choices as in the multinominal logit model 2.
This is a simplified approach, which ignores biases from possible placement effects of access to electricity.Companies/governments may well decide to electrify/provide access to electricity where there is a large chance of off-farm activities booming.So unobserved factors which may stimulate off-farm activity (beyond electricity) may be captured as if they are driven by the electricity access variable.(Bacon and Kojima 2016) have suggested to use instrumental variable (IV) estimation, propensity score matching (PSM), and panel data analysis allowing for heterogeneity between households and found that studies using these methods have found clear evidence that the electrification status of households is endogenous, and that ignoring such endogeneity can over-estimate benefits.The estimation in model 3 tries to avoid this bias by comparison of wages holding the occupation constant and allowing all other independent variables to vary.An alternative would be to include an indication in the first stage of the Heckman selection.The results, however, were insignificant.

Results
Turning to the results, model 1 tells us that electricity access increases the probability of a household or individuum choosing to go into a nonfarm business activity.Table 4 shows the results for a logistic regression for two versions of model 1, with the levels of significance denoted by one, two or three asterisks.The first version (v.1) shows the result for a logistic regression with energy access and a constant being the only explaining variables.The regression runs over all observations of Nonfarm activity.Access is significant and increases the likelihood of observing a household being involved in a nonfarm activity.
The second version of the model restricts the observations to rural households and adds more explaining variables.The sample becomes smaller with the restriction and the inclusion of additional variables, because not all surveys included each question.Access stays significant, as is gender, land ownership and access to a loan, though at a lower level of significance.Gender carries a positive sign, and is coded with increasing values for females, hence being a woman yields a higher probability to work in nonfarm activities.This result becomes more understandable once we look at different economic sectors further below.Owning farmland shows a negative impact and high significance, reflecting that households who can work their own land might be less inclined to start a nonfarm business.If people went to school and or have access to loans seems to increase the likelihood of going into nonfarm activities, but not on a significant level.The second model aims to explain the occupational choice in more detail.The variable mainocc5 distinguishes five occupational choices, namely waged employment, nonfarm and farm, self-employment nonfarm and farm and other.The latter comprises students, retired persons and too old to work, day laborers, housewives and volunteers.The multinominal regression fitted to model 2 takes self-employed farm work as default outcome and estimates the probability of engaging in other activities relative to the probability to engage in self-employed farm work.Table 5 gives the estimation results for different versions of the model.The versions follow a similar logic as in model 1.The regression is run with the full sample to understand if access plays a role.Access significantly increases the probability of selecting any occupational choice compared to subsistence farming.It influences taking up nonfarm waged and nonfarm self-employed activities.One can think of energy access generating more business opportunities and hence also more demand for waged labor might occur.Restricting the observations to rural households makes the positive effect on waged farm activities more pronounced.Including more socioeconomic characteristics of the households gives a more complete picture, although the multinominal regression has its known limits regarding interpretations.The likelihood of a male person, not owning land, who went to school and has access to informal loans and to electricity is higher to go into waged nonfarm activities than into self-employed farming.Schooling lowers the likelihood to go into waged farming, while being female and/or older as well as having own land increases the likelihood to find this person in waged farming.Selfemployed nonfarm activities are the second strongest category positively affected by energy access.The relative results are consistent with this interpretation if another category is selected as base outcome.
What about incomes?Do households taking up a nonfarm activity have higher earnings if electrified as in the example from tailors in Burkina Faso showed (Grimm, Hartwig, and Lay 2013), where electricity access led to higher productivity and higher incomes?To answer these question model 3 is specified and estimated.Version one again only looks at the access variable to see if it makes sense to proceed.Nonfarm is included already in this step, to see if incomes are higher in nonfarm activities when there is access.The selection is based on owning farmland, and the coefficient is negative and significant.In version 1 energy access yields higher incomes with high significance.Adding socioeconomic variables in version 2 of this model shows the positive effect of effect remaining and being increased.Having attended school also yields higher income levels, as does being male (shown as a negative sign of the coefficient of gender, because gender equals 1 for males and 2 for females).Access to informal loans seems to suffice to engage in higher income generating activities.
The impact of energy access differs by economic sector to which the activity can be attributed.The surveys distinguish 18 economic activities, which have been aggregated into 10 sectors (agriculture, mining, manufacturing, construction, trade, transport, accommodation and restaurants, services, technical support services, education).Simple summary statistics show that having anergy access affects incomes in the sectors differently and in some sectors, access may contribute to closing the gender pay gap.In most sectors and across the gender variable, incomes are higher in locations with energy access.Agriculture seems an exception worth noting, more data are needed.A possible explanation would be that more productive processes in agriculture have not caught on and investment needed in modern production processes have a higher financial and skills gap than other sectors.Incomes are much higher in manufacturing for males in locations with energy access, as in technical services (PST).Incomes for women are lower than for men, with one exception.In locations with energy access, incomes for women in education do exceed male incomes.These findings need to be substatiated with more detailed data and cases from the respective country and over time.However, the cross sectional snapshot also shows, that energy access only brings wages in rural areas close to wages in urban areas (Figure 3).More needs to be done for structural change to be a move from agriculture to manufacturing or services and not necessarily a physical relocation.

Conclusions and Outlook
This paper made use of a large data set collected across six African countries under the Multi-Tier Framework for energy access by the World Bank Group.Data were collected on household characteristics, village characteristics, economic activities and energy access and use.This contribution used data on household incomes, their farm and nonfarm economic activities, the type of activity (self-employed or waged) and the economic sector of these activities.Under different models and specifications, it could be shown, that the probability to take on nonfarm activities increases with access, that these activities take place both as waged employed activities and as self-employed activities and that incomes are higher if the location provides access to electricity.
Other variables affect this choice.People who own land are less likely to take up a nonfarm job.The dataset does not include information about yields from farmland, and incomes are only reported for waged activities or self-employed nonfarm activities.Hence, no conclusions about nonfarm activities as a hedge against yield fluctuations and the effect of electricity on these decisions can be derived.Schooling has mixed effects; it does increase wages significantly and affects the decision to go into nonfarm activities positively but is insignificant.Nonfarm waged employment is more likely to be taken on by men while women tend to self-employment if leaving the farm sector.The gender wage gap continues to exist, even in electrified communities, with one notable exception being education.In electrified locations average female wage in education exceeds the male average.One explanation could be that a proportion of males finds other waged employment in electrified locations which provide better incomes than working in education.
The database contains very few examples of access provided by a mini-grid or a stand-alone solar system.In most cases, access came through the grid.However, including potential productive use and higher incomes from nonfarm businesses can make the business case also for mini-grids more sustainable.
Examples can be found, among other under the World Bank's Carbon Initiative for Development using results-based climate finance to demonstrate how development benefits can be delivered while reducing harmful emissions from diesel generators and or fuel-based pumps.
Enterprises contribute the necessary baseload demand which can bring up total demand to a level where the investment in access becomes feasible.Decentralized renewable energy solutions can improve climate change resilience, create employment opportunities in operating, installing, and maintaining the mini-grid and enable further economic activities with a stable electricity supply.A focus on potential productive uses improves the understanding of future electricity demand and potential cost recovery from connection to the mini-grid and eventually to the grid.
Against these results, it is worrisome, that Low Income Countries (LIC) seem to fall behind, not only in the rate of improving electricity access to unelectrified regions, but also regarding electrification of their economies.While High Income Countries (HIC) have converged to 25 percent electricity in final energy consumption, low-income countries require more energy per per-capita-GDP (PPP, 2017 USD) and use less modern energy such as electricity.Looking at the share of electricity in total final energy over decades and income status of a country (Figure 4), clearly shows that LIC are increasingly left behind.For LMICS the average value has decreased in the last decade and the spread has notably widened.The gap between all groups and LIC has widened over time, indicating that these countries are increasingly being left behind.Statistically, the country groups develop over time, countries move upwards in category while they develop, and the LIC group becomes smaller.Still, the widening gap tells a story about the poorest of the poor.

Figure 1 :
Figure 1: Educational attainments and waged work shares in regions with and without electricity access.

Figure 2 :
Figure 2: Comparing incomes in from economic activities by gender and energy access status (income in USD of year of survey)

Figure 3 :
Figure 3: Incomes by location and sector, USD of survey year.

Figure 4 :
Figure 4: Share of electricity by income status and decade.
Level Dialogue on Energy for improved availability and quality of energy data, a household energy access questionnaire, the Core Questions on Household Energy Use, has been developed by the World Health Organization (WHO) and the World Bank's Energy Sector Management Assistance Program (ESMAP), in close collaboration with the World Bank's Living Standards Measurement Study (LSMS) and other contributors.The household energy access questionnaire consists of a series of cohesive modules that can be incorporated into existing household surveys (World Bank and World Health Organization 2021).The questionnaires, respective country reports and the data are available to the public.For this paper, data for all African countries available are pooled, except for Malawi, due to harmonization challenges.The responses to the household surveys in Ethiopia, Kenya, Liberia, Niger, Nigeria, Rwanda, and Zambia yield of energy access, and the data have not yet been explored from a labor, jobs, or occupational choice perspective.This contribution tries to close this gap.
This initiative was launched in June 2015 by the Energy Sector Management Assistance Program (ESMAP) and extends the definition of energy access beyond a yes/no indicator.The definition encompasses access to electricity and modern energy cooking services and defines it as the ability to obtain energy that is adequate, available when needed, reliable, of good quality, affordable, formal, convenient, healthy, and safe for all required energy applications across households, enterprises, and community institutions.To measure the achievement towards SDG7 in a more comprehensive way, and in response to the key recommendation from the UN Higha unique database of household characteristics, such as education (duration and level), gender, age, marital status, characteristics of the dwellings, energy access by type and quality and nonfarm and farm activities, as well as incomes generated from these activities.The country reports focus more on the technical details

Table 1 :
Sample by country and energy access Source: World Development Indicators, MTF survey microdata, own calculations.

Table 2 :
Sample by country and locality 4World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources.It presents the most current and accurate global development data available, and includes national, regional and global estimates.https://databank.worldbank.org/source/worlddevelopment-indicators

Table 3 :
Dependent and explaining variables in the models Source: World Bank Group, own calculations.
With   as the binary dependent variable,   characteristics of the individual and characteristics of the household, including electricity access.Model 2 makes use of data regarding different categories of farm and nonfarm work.The hypothesis tested here is that the probability of taking up a waged activity, nonfarm or farm, as well as to engage in self-employed nonfarm activities are influenced by electricity access, among other variables.The dependent variable is a categorical variable with nine different categories and the base outcome is subsistence self-employed agricultural activity, crop or livestock.A multinominal logit model is fitted, which describes the probability to select one of the other options over the base outcome as

Table 4 :
Model 1 -Logit results for nonfarm enterprises

Table 5 :
Model 2 -Multinominal regression estimates for occupational choice