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This study conducts an empirical analysis of the relationship between credit market conditions and agriculture output in Sub-Saharan Africa.

This paper uses a two-stage least square instrumental variable and difference generalised method of moments dynamic panel model because potential reverse causation and endogeneity are addressed.

The findings show that better credit market conditions contribute to agriculture productivity. The results also show that better infrastructure and availability of agriculture inputs are associated with productivity improvements. The empirical results are robust when an alternative measure of agriculture productivity is used.

An important research agenda for future studies will be to consider alternative measures of credit market conditions and other intervening variables that influence the nexus. Besides, other methods that account for cross-sectional dependence could also be considered as the impact of credit on agriculture varies across the sub-regions.

The findings make a case for enhancing credit market access to boost agriculture productivity. There is also a need to implement financial education programs for farmers and ensuring continuous engagement with farmers.

Although the issue of agriculture finance has been well documented in the literature, few studies have estimated the elasticity of agriculture productivity to changes in credit conditions. Also, our consideration of the intervening role of infrastructure amongst others is an area that has remained relatively unexplored.

The study aims to analyse the role of finance in the agro-industrialisation nexus in Nigeria using annual data on manufacturing value added, agricultural value added and volume of finance availed to the agricultural sector from 1981 to 2015.

To establish the presence of a long-run relationship, the error correction model and bounds cointegration techniques are employed. Likewise, the model is augmented to test whether the associated relationship between industrial output and agricultural output depends on access to finance by farmers with the inclusion of an interaction term.

Some salient contributions to the literature are as follows: agriculture and finance are strong and positive predictors of industrialisation in the long run; in the short run, past realisations of industrial output and finance have significant asymmetric effects on industrial output; the explanatory power of agriculture decreases with the growth of the financial system; and the long-run results validate the role of finance in the agro-industrialisation nexus.

Given these findings, achieving growth in the agricultural sector that will induce desired industrialisation should be prioritised by the government through agencies such as the central bank, financial intermediaries and other stakeholders with a view to making agricultural financing a major concern for sustainable domestic consumption and industrial growth.

This paper investigates the per-capita output club convergence in case of 120 countries for the period 1995–2015. Further, we disaggregate per-capita output into three broad sectors such as agriculture, industry, and service and investigate the convergence hypothesis.

The paper tests this hypothesis using the Phillips and Sul panel club convergence technique.

Our findings are as follows: (1) our results indicate the evidence of output divergence for the full sample; (2) when countries are divided into different clubs, the results exhibit the sign of per capita output club convergence both for aggregate and three major sectors. Further, this study confirms that industry's per capita output is the main driver for aggregate per-capita output club convergence in case of club 1. For club 2, agriculture's per capita output is a primary source for aggregate per capita output club convergence. Likewise, in the case of clubs 3 and 4, we find the service sector's per capita output is the main component for aggregate per-capita output club convergence; (3) both the service and industry sectors are major drivers for aggregate per-capita output club convergence.

This study suggests to the policymaker that sector-specific policies need to be adopted to boost the per-capita output growth by improving the performance of each of the sectors across the countries.

Notwithstanding, there are many studies that examine the output convergence using a notion of beta and sigma convergence, but studies regarding per capita output club convergence both at the aggregate and sectoral level are scanty.

The key purpose of this paper is to examine the causality and long‐run relationship between CO2 emission and agricultural output for an agriculture‐dependent developing country, namely Bangladesh.

In order to attain the objective, this study has used long‐time series data and employed advanced econometric techniques of unit root test, nonlinear least square estimation, Vector Error Correction estimation and Granger causality test.

The empirical results of the study reveal that Bangladesh agricultural output is not a Granger causal for Bangladesh CO2 emission, but the country's CO2 emission is a Granger causal for its agricultural output. The results also reveal for Bangladesh that any disequilibrium between CO2 emissions and agricultural output could take approximately 17 years to converge to the long‐run equilibrium. The results further reveal that the adjustment rate for Bangladesh agricultural output is positive and quite fast at the rate of 69 percent a year. So any disequilibrium will be corrected mostly by the adjustment in Bangladesh agricultural output.

The current CO2 emission in Bangladesh is still below the equilibrium level, which is considered to be an advantage for the country, particularly its agriculture sector which will reasonably not face any stricter CO2 emissions controlling policies and regulations in the near future.

The originality of this study lies on the extent to which an agriculture‐dependent developing country such as Bangladesh does not have greater concern about the CO2 emission for now and the near future. The originality does also lie on the fact that no other study has yet examined this issue.

This study aims to investigate the contingent roles real effective exchange rates (REERs) play in mediating the effects of oil revenue on the agriculture sector value-added in 25 major and minor oil-exporting (MIOEC) countries during the period of 1975–2014.

The panel autoregressive distributed lag (ARDL) estimator proposed by Pesaran et al. (1999) was relied upon to achieve the objectives of the study. This estimator involves a pool of small cross-sectional units over a long-time span that covers for 25 oil-exporting countries over 39 years (1975–2014).

This paper reveals the following findings. Firstly, oil revenue has a direct negative effect on agricultural value-added in the short- and long-term. This finding holds for full sample and subsamples of major oil-exporting (MAOEC) and MIOEC countries. Further assessment reveals that the magnitude of the impact is larger for MAOEC than that of the MIOEC. Secondly, the finding for the long-run effect shows that the contingent effect of real exchange rate on the nexus between oil revenue and agricultural value-added is negative and statistically significant at the conventional level for the full sample. This suggests that, in the long-run, the appreciation in real exchange rates exacerbate the negative marginal effects of oil revenue on agricultural value-added in all oil-exporting countries. However, when sub-samples of MAOEC and MIOEC are considered, the contingent effect disappeared (become insignificant) in MAOEC while it is positive and statistically significant in MIOEC. Thus, in the long-run, the appreciation in real exchange rates diminishes the negative marginal effects of oil revenue on agricultural value-added in MIOEC. While oil revenue has a direct negative effect, its effect is also moderated by the variations in REERs in MIOEC in the long-run. Finally, in the short-run, fluctuations in the real exchange rate do not matter for the nexus of oil revenue and agriculture sector in these countries whether minor or MAOEC countries.

This study contributes to the debate in the empirical literature on the Dutch disease effect and “oil curse”. Using the appropriate panel ARDL empirical framework, it provides evidence on how exchange rate variations in the oil-exporting countries influence the nature of the effects of the oil revenue on agricultural sectors in the long-run but not in the short-run. Contingent effects of REERs only appear to exist in MIOEC in the long-run.

The purpose of this paper is to examine the extent at which electricity consumption (EC) has contributed to real sector performance, to identify energy-dependent sectors of the economy for appropriate sector-specific policy interventions and to avoid energy conservation policies that may retard the growth of the real sector and economic growth in general.

This paper used time series data, covering the period between 1981 and 2015. Various time series econometric analyses such as unit root test for stationarity and vector autoregressive and vector error correction models were used to establish the long-run and short-run co-integration relationship among the variables.

This study finds that EC displays a little and insignificant impact on manufacturing sector output, as well as agriculture and service outputs. The empirical result from causality test suggests a unidirectional causality running from agriculture to EC, as well as service sector to EC, whereas bidirectional causality runs between EC and manufacturing sector. This study therefore recommends adequate power supply to the manufacturing sector, while energy efficiency policy and regulatory reform should address agriculture and service sectors.

Few studies have examined the impact of EC on disaggregated gross domestic product. This research gap has strong policy implications on Nigerian economy as the output of real sector plays vital role in driving the economy. Given the pressing needs for Nigeria to boost real sector output and be among the world’s 20 largest economies by 2030, it becomes imperative for this sector-specific research to be conducted to ensure that sectoral purpose-driven energy interventions are formulated to address power supply challenges in the real sector.

The purpose of this paper is to outline a version of SinoTERM, a multi‐regional computable general equilibrium (CGE) model of China that has been updated and disaggregated further to enhance the agricultural detail. A version of the model is publicly available and will be useful to CGE modelers studying Chinese agricultural issues (www.monash.edu.au/policy/sinoterm.htm).

The paper outlines data sources for building SinoTERM. It contains a CGE application to agriculture in China. Unlike the national input‐output table published by the National Bureau of Statistics, the master database of SinoTERM contains many agricultural sectors.

CGE models that represent a nation as a single economy may offer rich insights into winners and losers from particular policy scenarios. Multi‐regional analysis takes this a step further by comparing outcomes for regions in which particular industries are a relatively large part of the economy.

This paper builds on the first SinoTERM paper in several ways. First, the database is disaggregated further to represent tea, sugar cane and silkworms as individual sectors in the CGE database. Second, given the extraordinary economic growth in China, the national and regional database has been updated to 2006 using data from the 2007 yearbook. Third, the paper contains an application to agriculture: it examines the impacts of productivity growth in different agricultural sectors in China.

The regional CGE model used in this application could be used to explore many other policy issues concerning agriculture in China.

The purpose of this paper is to investigate the relationship between financial development and agriculture growth employing Cobb‐Douglas function by incorporating financial development as an important factor of production over the period 1971‐2011.

The autoregressive distributed lag (ARDL) bounds testing approach to cointegration with structural breaks is applied to examine long run relationship between the variables. The direction of causality is detected by vector error correction method (VECM) Granger causality test and robustness of causality analysis is tested through innovative accounting approach (IAA).

The empirical analysis confirmed that the series are cointegrated for long run relationship between agriculture growth, financial development, capital and labor. The results indicated that financial development has positive effect on agricultural growth. This implies that financial development plays a significant role in stemming agricultural production and hence agricultural growth. Both capital and labour in the agriculture sector also add to agricultural growth. The Granger causality analysis revealed bidirectional causality between agricultural growth and financial development. The robustness of these results is confirmed by innovative accounting approach (IAA).

This study has important policy implications for policy‐making authorities to stimulate agricultural growth by improving the efficiency of the financial sector.

This paper convincingly argues that there is a need for case‐by‐case study on such a project in view of each country's unique characteristics.

This paper aims to investigate the effect of rural-urban income gap on agricultural growth in China and its dynamics over time and across regions since reform and opening up.

Two types of indices are constructed to measure the rural-urban income gap: the intra-provincial index and the inter-provincial index. A provincial panel data from 1978 to 2010 and growth accounting method are used to estimate the size of the adverse effect of rural-urban income gap on agricultural growth in China.

The empirical results show that both indices of rural-urban income gaps are negatively associated with agriculture output, but the inter-provincial rural-urban income gap produces a larger adverse effect than the intra-provincial rural-urban income gap. Growth accounting analysis further shows that such adverse effects are decreasing over time and are larger in the central provinces. The results represent resource diversion effects of rural-urban income gap on agriculture.

This paper bridges the gap in existing literature on the relationship between sectoral income gaps and agricultural growth, which confirms Schultz's argument that agricultural activities are efficient even in developing countries and the rural resources diverted out by income gap are not surplus. The results imply that equalized rural-urban and regional policies are required to maintain sustainable agricultural growth in China.