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Understanding household labor and land allocation decisions under agro-environmental policies is challenging due to complex human-environment interactions. Here, we develop a spatially explicit agent-based model based on spatial and socioeconomic data to simulate households' land and labor allocation decisions and investigate the impacts of two forest restoration and conservation programs and one agricultural subsidy program in rural China. Simulation outputs reveal that the forest restoration program accelerates labor out-migration and cropland shrink, while the forest conservation program promotes livelihood diversification via increasing non-farm employment. Meanwhile, the agricultural subsidy program keeps labor for cultivation on land parcels with good quality, but appears less effective for preventing marginal croplands from being abandoned. The policy effects on labor allocation substantially differ between rules based on bounded rational and empirical knowledge of defining household decisions, particularly on sending labor out-migrants and engaging in local off-farm jobs. Land use patterns show that the extent to which households pursue economic benefits through shrinking cultivated land is generally greater under bounded rationality than empirical knowledge. Findings demonstrate nonlinear social-ecological impacts of the agro-environmental policies through time, which can deviate from expectations due to complex interplays between households and land. This study also suggests that the spatial agent-based model can represent adaptive decision-making and interactions of human agents and their interactions in dynamic social and physical environments.

Payments for Ecosystem Services (PES) is increasingly used in developing countries to secure the sustainable provision of vital ecosystem services. The largest PES programs in the world are embedded in China's new forest policies, which aim to expand forest cover for soil and water conservation and improve livelihoods of rural people. The objective of this study is to identify the complex pathways of impacts of two PES programs - the Conversion of Cropland to Forest Program (CCFP) and the Ecological Welfare Forest Program (EWFP) - on household livelihood decisions, and to quantify the direct and indirect impacts along the identified pathways. We fulfill this objective by developing an integrated conceptual framework and applying a Partial Least Squares-Structural Equation Model (PLS-SEM), based on household survey data from Anhui, China. Labor allocation (for on-farm work, local paid work, local business, and out-migration) and land use decisions (i.e., rent in, maintain, rent out, or abandon cropland) for participating households are key to understand PES program effects on livelihoods. Results show that the PES programs have only small direct effects but significant indirect effects via the mediating factor of capital assets. Moreover, group heterogeneity analysis shows that lower-income households do not benefit any more than the better-off households from the PES, while households with medium wealth increase dependence on agriculture. In addition, household demographics, individual attributes, and geographic settings differ in their impacts on labor allocation and land use decisions. We conclude that CCFP and EWFP programs would be more efficient in conserving the environment while improving the economic welfare of lower-income households if capital assets were taken into account in the design of compensation schemes.

This paper brings together development accounting techniques and the dual economy model to address the role that factor markets have in creating variation in aggregate total factor productivity (TFP). Development accounting research has shown that much of the variation in income across countries can be attributed to differences in TFP. The dual economy model suggests that aggregate productivity is depressed by having too many factors allocated to low productivity work in agriculture. Data show large differences in marginal products of similar factors within many developing countries, offering prima facie evidence of this misallocation. Using a simple two-sector decomposition of the economy, this article estimates the role of these misallocations in accounting for the cross-country income distribution. A key contribution is the ability to bring sector-specific data on human and physical capital stocks to the analysis. Variation across countries in the degree of misallocation is shown to account for 30-40% of the variation in income per capita, and up to 80% of the variation in aggregate TFP.