Summary statistics of variables
Table 1 depicts the summary statistics of the variables used in the CMP model. As shown in the table, the average maize labour productivity is 0.09Mt/man-day whereas the maximum productivity obtained by a farmer was 0.54Mt/man-day. On average, a farmer spends Gh¢2,016.73 on fixed inputs, 396.1Kg of fertilizer, 88.8Kg of maize seed and 4.1litres of weedicides to cultivate 1.2 Ha of maize. This finding underscores the fact that most rural farmers are still engaged in subsistence level farming which confirms the findings of Ibrahim and Shaibu [8]. Whilst the average age of a farmer is 51.3years, the average farming experience was 38.1years. Hence, the average of the workforce in farming in the study area is very high. This implies that farmers in Ghana are ageing. This can be attributed to the fact that the youth do not see farming as an attractive occupation. The reason can also be that most of the youth have received some level of formal education and hence migrate to cities in search for ‘non-existing’ white-collar jobs. The mean household size is 7.9 which means that farm households will often depend on the workforce of the family labour for their farming activities. This is in line with Ibrahim and Shaibu [8], who state that farm households depend on a pool of family labour for farm operations.
This could be due to the fact that the youth of today are not interested in farming. The number of extension visits per farmer is very low thus 0.7 times per annum. Out of 194 farmers interviewed, 53% were males. Though women are noted to be largely engaged in farming than men in Northern Ghana they do not have access and control over land due to the nature of the land tenure system. This confirms the findings of Fon [20] that a majority (75.7%) of women are believed not to have access and control over land. This is one of the reasons why the percentage of women engaged in maize farming in the study area is low.
The percentage of household heads with at least primary school education was 56%. The sanitation and hygiene are poorly observed by the respondents with 64% and 67% having bushes and stagnant water around their houses respectively. This suggests that most farmers will spend more in clearing the bush to reduce breeding of mosquitoes, which can raise their averting expenditure.
Simple arithmetic summation was used to estimate the averting expenditure among maize farmers in the Bunkpurugu-Nakpanduri District. As depicted in Table 1, the mean annual malaria averting expenditure is GH¢ 284.6. The results corroborated with the findings of Gunda et al. [21] that household’s expenditure on malaria is GH¢554.40 (for both prevention and treatment). The minimum and maximum expenditures of households on preventing malaria are GH¢24.00 and GH¢990.00 respectively. The vast difference between the minimum and the maximum averting expenditure is attributed to some of the demographic characteristic differences such as bush, stagnant water around houses, household size, income among others.
Determinants of averting expenditure on malaria and labour productivity
In order to identify factors influencing malaria averting expenditure as well as maize production labour productivity, CMP framework with ordinary least square regression model was used (see Table 2). This was done to solve the problem of endogeneity of averting expenditure variable. The atanhrho_12 is negative but insignificant. This implies that there are no unobserved factors affecting malaria averting expenditure as well as maize production labour productivity. Hence there is no selectivity bias. The 1% significance of the likelihood ratio test suggests that there is a correlation between the error terms of the malaria expenditure and the maize labour productivity models. Therefore, the two models could not have been estimated individually [22].
Socio-economic determinants of averting expenditure on malaria
Results from the analysis showed that off-farm income, presence of pregnant women in a household and number of children in school are statistically significant at 5% each. Whilst presence of bushes around the houses was statistically significant at 1%, household size was statistically significant at 10%. This suggests that off-farm income, household size, number of children in school, presence of pregnant women and bushes around the houses are significant determinants of averting expenditure on malaria.
The coefficient of off-farm income implies that if a household’s off-farm income increases by GH¢1.00, the amount of money spent by the household in averting malaria will increase by GH¢0.014 ceteris paribus (holding other factors constant). This is in line with the findings of McElroy et al. [23] that, household level of expenditure in preventing malaria will increase as their income level increases.
Also, the coefficient of household size is 7.32 which means that if one person is added to the household, their averting expenditure will increase by GH¢7.32 all other things being equal. This makes economic sense because as household size increases, their averting materials will equally increase as well and hence the results. In addition, the coefficient of households with bushes is 50.68. This implies that households that are having bushes around their houses are more likely to spend GH¢50.68 more than their counterparts ceteris paribus. This is because bushes breed mosquitoes and households will put more effort to get rid of it and hence spending more to avert than houses without bushes. The findings of this study corroborate with the work of Mbako et al. [24] that clearing bushes around houses sustainably empowers and strengthens rural communities to reduce mosquito breeding sites. The clearing of bushes around houses require financial expenditure hence the positive direction of its effects of annual malaria averting expenditure.
The coefficient of presence of pregnant woman or women in a household is 45.18. This suggests that households with pregnant women are more likely to spend GH¢45.18 more than households without pregnant women. This confirms the findings of Sabin et al. [25] that most pregnant women are willing to try new methods of malaria prevention although cost related barriers to such methods were stressed. This is because pregnant women are more susceptible to malaria, and hence households need to employ all means to get rid of mosquitoes and as a result spend more to avert than households without pregnant women. Lastly, if the number of school children in a household increases by one, the amount of money the household will spend to avert malaria will increase by GH¢11.21. School children sometimes educate the parents about the importance of malaria averting as compared to prevention.
Effects of malaria averting expenditure on maize labour productivity
The results show that malaria averting expenditure, farming experience, age of household head, and ownership of motorbike were the significant socio-economic factors that influence labour productivity.
The coefficient of malaria averting expenditure suggests that if farmers increase their malaria averting expenditure by GH¢1.00, their maize labour productivity will increase by 0.024% Mt/man-day ceteris paribus. Malaria is the common disease during farming season, which has a lot of effects on farm labour productivity. Farmers who avert malaria are at less risk to malaria infection and are likely to be more productive as compared to their counterparts who did not avert malaria. This is based on the positive relation of malaria averting expenditure with maize labour productivity which agrees with the findings of Kioko [10] that households afflicted with malaria have lower crop output compared with households that are not afflicted with malaria. This further confirms the findings of Ibrahim et al. (2017) that for every naira spent on malaria prevention, farmers’ productivity will increase by N1.85k.
Farming experience variable suggests that as number of years of farming increases by one, the maize labour productivity of the farmer decreases by 0.19% Mt/man-day ceteris paribus. Experience is an important factor in determining labour productivity and as a result of that, its contribution cannot be overlooked. This might be that farmers who are relatively older may be less productive in maize farming. As farmers’ advance in age, they become weak and are no more effective as they were young and hence the negative relationship of farming experience with labour productivity. This agrees with the findings of Guo et al. [26] that an increase in age is not conducive to improving agricultural output. This is at variance with the findings of Afari [27] that farmers who are more productive may have spent a greater part of their formative years on the farm and have at least learnt a lot of skills (at least in traditional way) in making good use of available inputs at their disposal. The co-efficient of age implies that the labour productivity of farmers will increase by 0.16% Mt/man-day for a 1-year increase in the age. Also, farmers who own motorbikes are relatively highly productive than their counterparts. As shown in Table 2, a farmer who owns a motorbike is 1.78% more labour productive than his or her counterpart. This is because a motor bike provides a faster means of transport to farmers thereby sparing them more time for farm work. With motorbikes, the energy that could have been used to pedal bicycle or walk to the farm is spared for working on the maize farm.
For production variables, whilst the level of significance of quantity of fertilizer is 1%, seed, and weedicides are statistically significant at 5% each. A 1kg increase in the quantity of fertilizer applied will increase maize labour productivity by 0.01% Mt/man-day. Similarly, maize labour productivity increases with quantity of weedicides and seeds used.
Farmers perceive benefit of averting expenditure on malaria
Table 3 depicts the percentage distribution of farmers’ responses on perceived benefits of averting malaria. From the results, farmers perceived that spending money or energy to prevent the malaria helps to increase the number of times one goes to farm. Out of 194 respondents, 59.5% strongly agreed to this. Whilst 43.5% and 51.5% of the respondent respectively strongly agreed and agreed that when one spends money to avert malaria infection, he/she saves income. This is because the person would not have to spend money to cure himself or herself from malaria. Most of the farmers were of the view that when they avert malaria, they will save their income for other purposes.
In addition, averting malaria helps one to increase labour productivity. As shown in Table 3, a total of 96.5% of the farmers interviewed perceived that averting of malaria keeps them healthy to work in the farm and get higher output. This is corroborated by the perception that averting prevent them from spending money to treat malaria. Farmers’ explanation was that one can only spend money in treating malaria if the person is infected with malaria but if one averts malaria, one will be free from malaria attack and hence does not need to spend money on that. The general body weakness caused due to malaria infection is undisputable and it always makes the victim inactive over time. Other important benefits of spending money or energy to avert malaria infection include good health and prevention from other diseases. Malaria is the common disease that affects farmers during the farming season. This is the time most hospitals record high malaria cases.
