Chapter 67 Rural Poverty and Income Dynamics in Southeast Asia

Green Revolution, Human Capital, and Off‐Farm Employment: Changing Sources of Income among Farm Households in Central Luzon, 1966–1994

Nonfarm Employment and Food Security: Rural Households in Bangladesh

So What If There Is Income Inequality? The Distributive Consequence of Nonfarm Employment in Rural China

The main aim of this research is to examine the impact of cooperative membership on adoption of modern technology by bee farmers in Kenya. This study’s data comes from a bee farmers survey in Baringo County, Kenya using a sampling size of 201 households. Probit, logit and Instrumental variable models were incorporated to test for the estimation of relationships and the information generated imported into the SPSS computer application (SPSS 2015) and STATA MP 17 for statistical analysis. The results show that education has a negative significant predictor of cooperative membership at a 5% significance level. Attaining secondary levels did not contribute to farmers’ decision to join cooperative membership. Older farmers were more likely to join cooperatives, unlike the young farmers. Using neighbors in cooperatives as an instrumental variable, the findings indicate that farmers with neighbors who were already in cooperative membership were more likely to join cooperatives. In addition, the majority of adopters of modern bee farming technologies had access to both credit and markets and reported to have experienced an increase in income from bee farming. The adoption of modern beekeeping technologies, such as protective gears, and honey extractors, enabled the farmers to produce more and better-quality honey, which fetched higher prices in the market. Cooperative membership increased the probability of adopting modern beekeeping technology. As such, the study recommends that cooperatives should provide more incentives and support to their members, such as training, subsidies, quality assurance, and market linkages to promote the adoption of modern bee farming technologies.

Discussions of rural development policy are for the most part focused on the tenurial, institutional, technical, infrastructural, and economic aspects of agricultural development. In contrast, nonfarm activities in agricultural regions receive little attention, and a number of models of agrarian economies with nonfarm activities have even predicted a decline of such activities with agricultural development.' In this paper we show that nonfarm activities in agricultural regions expand quite rapidly in response to agricultural development and merit special attention in the design of rural-and also of urban-development strategies. The poorest groups of the world's rural population include those who depend on nonfarm activities as a source of employment and income. Nonfarm activities also supply a range of goods and services to agriculture and the rural population, contributing to the growth of agricultural output and the improvement in living conditions in rural areas. Finally, the concentration and growth of nonfarm activities in rural towns localizes employment opportunities for people who leave agriculture and acts to stimulate a degree of decentralization of urban growth.

A salient theme in D. Gale Johnson’s work is the importance of agricultural development for general prosperity and for economic diversification (e.g., Johnson 2000). Johnson has also noted that most of the world’s poor are engaged in farming, so that a key focus of development policy is to raise the incomes of farmers. From a global perspective, increasing the productivity of agriculture, given the fixity of land, is necessary for both poverty reduction and the development of the nonagricultural sector. At the level of the world, agricultural productivity gains, poverty reduction, and the growth of the nonfarm sector are complements. However, the question remains whether these observations imply that every poor country should focus its public resources on agricultural development in order to raise the incomes of people now engaged in farming and whether such a policy is necessary for obtaining economic diversity. In this article, we use the experience of India over the past 30 years to address the issue of whether agricultural technical change actually leads to economic diversification and income growth within the rural sector in the context of an open-economy country in which there are cross-area trade and capital flows. We focus in particular on the rural sector because this is the sector in which linkages between agricultural and nonagricultural sectors are thought to be the strongest. We exploit the fact that India has maintained a policy of openness with respect to agricultural technology over this period, permitting and actively supporting agricultural development, and has moved to a reformed regime in which goods are traded and capital is more mobile in the 1990s. Evidence on the relationship between agricultural growth and nonfarm

The significance of rural income generation is obvious for rural well-being enhancement and rural development in particular and widely national development. The research carried on income pattern analysis enabling to detect the ways to achieve the rural income generation enhancement and so far, the rural development and rural well-being improvement. The discussions about income diversification aim have mainly been divided on one hand in income maximization household objective by increasing output, on the other hand, risk and seasonality of income management. Nevertheless, in all cases, income diversification has been seen as an income increasing factor of income, poverty reduction, and well-being enhancement. The study findings shows that The income level is quite different by sources of income of household, a high income level is achieved in beekeeping at level of 5134.04 thousand BIF, but only 5% of households are involved in beekeeping, whereas the lowest income average is realized in livestock selling, 12.88 thousand BIF and 83% of households benefit from the income of livestock selling. The income from agriculture is also low, it is 112.1 thousand BIF and 100% of the 2,560 households surveyed are involved in agricultural income generation. It is also shown that there is a high share of agricultural income in the total rural income, 67% of rural income is from agriculture. The income diversification level was calculated by using the Berry index. The average Berry index of 0.06 shows a low-income diversification level in the rural households of Burundi. The analysis of income diversification by considering the household income diversification index has proven a higher income for household that their income sources are diversified. The household average income for the household with sources of income diversified is 2.7 times higher than those with one source. The analysis of determinants of income diversification participation realized using probit model has shown that household income, agricultural household landholding, the age of household head, belonging to agricultural producers’ organization, reading skills at least of the national language, and access to market positively induce the income diversification participation, while female household headed status and the non-governmental organizations interventions negatively impact the income diversification participation. The intensity of income diversification analysis done by Tobit model shows that income diversification intensity is positively influenced by household income level, producers’ organization participation, access to market, age of household head and negatively affected by the female household headed status, and non-governmental organizations interventions. Based on the findings of the study, the following recommendations and policy implications are possible to improve the rural well-being and poverty reduction by increasing rural income through income diversification facilitation. The research findings showed that rural income diversification is positively related to market access. The policy aiming at rural income enhancement by increasing rural income source diversification might take into consideration the increasing availability of the market and linkages of rural households to the market by any means. The increase in the market orientation of the rural household might be achieved by increasing the availability of the market information system. Policy making might focus on increasing factors that increase the market orientation decisions of rural household by the increase of the rural market place, making market information available and enhancing the transport means of rural products. The policy targeting rural income enhancement and sources of diversification should be achieved by reducing the vulnerability of young farmers and households headed by females by supporting rural young farmers and female farmers. The increasing economic empowerment of young farmers and female headed households should be promoted. The policy makers should also focus on increasing education skills at least the reading skills of the national language of rural farmers since reading skills have been proven to have a positive influence on rural income source diversification. The adult literacy programs might continue and try to reach the majority of rural uneducated farmers.

Rainwater harvesting is increasingly viewed as a major strategy for enhancing agricultural productivity and boosting farm income in many drought-prone areas. While this technology is being promoted in many developing countries, there is conflicting evidence in the literature about its impact on welfare of farm households. This study uses propensity score matching and discrete choice regression techniques to assess the impact of rainwater harvesting ponds on farm household income and factors that influence adoption of such technologies in Rwanda. It finds that households with rainwater harvesting ponds have significantly higher income than their counterparts of comparable observable characteristics. It further finds evidence that increase in farm income occurs via increased input use and that household size, asset endowments and participation in farmer organizations condition adoption of rainwater harvesting ponds. The study concludes that adoption of rainwater harvesting technologies has positive benefits on farm households. It discusses the policy implications that adoption of rainwater harvesting ponds presents a pathway for reducing rural poverty.

Shenggen FanInternational Food Policy Research Institute and Institute of AgriculturalEconomics of the Chinese Academy of Agricultural SciencesLinxiu ZhangCenter for Chinese Agricultural Policy of the Chinese Academy of SciencesXiaobo ZhangInternational Food Policy Research InstituteI. IntroductionChina is one of the few countries in the developing world that has madeprogress in reducing its total number of poor over the past 25 years.

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world.

The adoption and use of modern technologies are an increasingly important discourse in the context of the manufacturing paradigm. This study aims to identify the holistic factors that influence digital transformation (DT) to meet firm performance (FP) goals. This study also examines how internal and external factors influence firms’ adoption of modern technologies to achieve a competitive edge. This study used a self-administered survey; data were gathered from (n = 487) innovative Pakistani manufacturing firms. The research model was proposed, and the hypotheses were tested using structural equation modeling. Moreover, the robustness of the structural test can be increased using critical performance map analysis. The empirical findings indicate that factors such as TOE, TAM, and institutional theory influence organizations’ adoption of DT. Similarly, DT has a significant and positive association with FP. BDAC positively mediates the relationship between DT and FP, and digital literacy moderates the relationship between BDAC and FP. This research enhanced the current knowledge. It provided valuable insights into promoting the DT adoption environment for digital technologies within manufacturing firms, while advancing the theoretical perspective on technology adoption within the context of dynamic capability theory. It offers a clear pathway for harmonizing and effectively integrating technology to increase firms’ performance.

Determinants of the adoption of modern technology in the handloom industry in Assam

In many African countries, a high proportion of the population resides in the rural areas and derives their incomes mainly from agriculture. This notwithstanding, there is a growing evidence that incomes from nonfarm employment sources are increasingly becoming important among the rural population. Using Botswana's rural household survey data, this paper examines the relative contributions and implications of agriculture and nonfarm employment to rural household incomes. Contrary to the popular held view that most rural households derive their income mainly from agriculture, the findings of this analysis clearly indicate that nonfarm employment is the dominant source of income in the study area. It accounted for 53% of the average total farm-household income of P4787 (1USS = P2.49). Remittances alone contributed a substantial share of 37% and constitute an important source of income for most households in the area. On average, 40% of each household's labour force were migrant workers. As regards employment in the area, agriculture employed more than 88% of the active labour force in the villages. The relatively low contribution (47%) from agriculture implies that the potential for savings, investment and development of entrepreneur skills among most people in the area is limited. This situation is likely to encourage rapid rural-urban migration of a considerable proportion of the youth in the area. It suggested that development strategy of Botswana government should concentrate on sustainable agricultural growth and establishment of rural enterprises to increase both income and employment sharer of agriculture and non-agricultural activities, respectively, as well as rural entrepreneurship

Technology adoption and value chains in developing countries: Evidence from dairy in India

Purpose: Returns on education was found to be worker, scale and allocative effects with positive higher payoff to agricultural productivity but its effects on non-farm employment among agricultural households who obtain income through non-farm activities to complement proceeds of agricultural activities are not yet known. Therefore, this study critically investigates the real cost effect of education on involvement in non-farm employment among rural households in South-western Nigeria. Research Method: The study drew a sample of 411 rural farm households through a multi-stage sampling technique from three states in southwest Nigeria and the data obtained were analyzed using the Mincerian equation, logistic model, inverse of Herfindahl index, calculation of expected annual rural income earning and rates of return to schooling. Findings: Results indicate that mean age and education of the rural household heads were 49.9 years and 8.8 years respectively while the household incomes were diversified up to 2.82 level and the non-farm sources contributed an average of 67% of the total income. Education is found to have considerable returns of N4706.30 (US$31.95) to gross household income and an additional year of schooling from other members of the households returns of N12519.90 (US$85) to the households’ income. Education of the household heads increases the probability of farm households participating in rural non-farm employment but the probability of participation reduces with increased level of education of the household heads. Tertiary education has the highest opportunity costs of schooling (N352200.04) per annum and the lowest rate of return to schooling (0.57%) from rural non-farm employment. Research Limitations: The study revealed the real cost of rural farm households’ educational level on the type of non-farm employment and income generated through it. The study presents information on south-western zone of Nigeria. Originality/Value: The practical value of this research is that decline rates of return to higher education reveal the rural non-farm employment being not the prime incentive for rural farm households’ members in obtaining higher education in rural southwest Nigeria. Therefore, implications are drawn for an integrated approach to higher education which may yield agricultural and non-agricultural transformation in rural Nigeria.