The gap falls considerably when including time fixed effects and control variables in column 2, and falls to only 3.0 log points when also including individual fixed effects in column 3, an effect that is not significant. A similar pattern is presented for the urban-rural consumption gap in columns 4, 5, and 6: the gap declines from 53 log points to 3.0 log points. Note that prices may be higher in urban areas and we do not yet adjust for such differences, though a price adjustment in urban areas would presumably only lead this estimated gap to be smaller , strengthening the finding.The consumption proxy measure in the KLPS tells a similar story. The raw gap in meals eaten in Kenya between those in non-agriculture versus agricultural employment is positive and statistically significant, though smaller than the earnings gap ; differences in magnitude are difficult to interpret given the different nature of the meals measure. Mirroring the broad pattern observed for labor productivity measures, this gap falls by almost half when including controls, and is negative though not statistically significant when including individual fixed effects ; a similar pattern holds for the urban-rural gap . Another dimension of welfare relates to patterns of unemployment. Appendix Table A13 explores whether there are differences in unemployment rates and search behavior between urban and rural areas for Kenya, where this data is available. We find that unemployment is either similar in urban and rural areas or somewhat higher in urban than in rural areas conditional on individual fixed effects ,grow bag for blueberry plants strengthening the main finding that movers to urban areas do not experience large gains in total earnings.
Finally, up to this point we have only considered mean differences in productivity or consumption across sectors, but variability of outcomes could also be a determinant of individual well being, as well as of migration choices. We test whether the variability of earnings in the agricultural sector is different than variability in the non-agricultural sector and find no statistically significant differences in either Indonesia or Kenya . In the conceptual framework, the richest model of human capital allowed for individual sector-specific productivity. Analysis of these productivities has been given renewed focus in Lagakos and Waugh , who argue that self-selection on the basis of comparative advantage could play an important role. In their model, comparative advantage is positively correlated with absolute advantage, meaning that the most productive workers have the most to gain from selecting into non-agriculture. Utilizing panel data, we estimate a modified version of equation 4 replacing the individual fixed effect with an individual-sector fixed effect.We recover these estimates, and then normalize the mean of the fixed effects of permanent rural residents to be zero. Figure 3 presents the joint distributions of these estimated individual productivities by sector. Panel A includes Indonesians born in rural areas. It is apparent that rural-to-urban migrants are positively selected relative to non-migrants, with an average rural wage approximately 18 log points higher than non-migrants. These individuals experience only a 1 log point average increase in their wage upon migration to an urban area. Panel B presents the same exercise with Indonesians born in urban areas. Here, there appears to be negative selection into rural migration, with the average mover having 10 log points lower wages when still in urban areas, and an increase of only 3 log points in rural wages among moving.
Panel C presents results in Kenya that are analogous to panel A. Compared to Indonesia, there appears to be even more positive selection among urban migrants in Kenya as well as a moderate positive urban premium of roughly 17 log points, which is slightly larger than the regression adjusted estimate presented above. Note that the realizations of roughly half of migrants fall below the 45 degree line in the three panels of Figure 3, which taken literally means that they experience higher earnings in rural than urban areas. This is consistent with the empirical finding of zero or small positive sectoral productivity gaps. This exercise is meant to be descriptive, and we interpret the relationships between the estimated individual urban and rural productivities with caution here, in part because the estimates are subject to measurement error and thus the fitted regression line may experience attenuation bias. With these caveats in mind, note that all three of these plots appear to show that absolute advantage plays a role in wage determination, with positive and similar slopes across settings and different subsets of individuals. Slopes are less than 1 in the three cases, which suggests that in a relative sense, those with absolute advantage in productivity may be gaining somewhat less from migrating than those who remain in the birth region, although once again this remains highly speculative. In Indonesia, urban wages do not change substantially relative to the year prior to moving, and even five years after the urban move, migrants see no average wage gain . There are broadly similar results in Kenya relative to the month prior to the move; there is some suggestive indication of slightly rising wages in the first two years of residence in an urban area, but these are small . There is no indication of meaningful pre-move trends in either country.
In this analysis, we consider wages for individuals who made an urban move regardless of whether they remained in cities or towns, or later moved back to rural areas. The bottom halves of both panels A and B show a “survival” rate in urban areas of between 50 to 60% after five years , suggesting substantial return migration to rural areas. Naturally, one might suspect that those with the worst economic outcomes in urban areas might return home, yet this does not appear to be the case: Appendix Figure A3 separately plots post-move wages for those who remain in urban areas and those who return to rural areas, and we find no evidence of a significant divergence in earnings between these two groups . This suggests a direction for future research in uncovering the reasons for these moves, including whether non-economic factors,blueberry grow bag including family reasons and heterogeneity in the taste for urban living, are often decisive factors.24 Other scholars have argued that job experience is particularly valuable in big cities and that residence in these cities may boost individual productivity over time . We examine this issue, first repeating the main urban productivity gap analysis but including a breakdown into the five highest population cities in each country, in Table 9. In Indonesia, all five cities are larger than 2 million inhabitants, with the capital Jakarta at 10 million. Kenya’s capital Nairobi has 3.4 million people, the second largest city has nearly one million, while the other three cities in Kenya are smaller. The capitals are also the largest destinations for urban migrants in each country. The main finding is that there is no evidence for significantly larger effects in the largest cities of Jakarta and Nairobi . There is some evidence of significant positive urban productivity gains in certain cities , namely, Bandung in Indonesia and Mombasa in Kenya, for which we have found no immediate explanation; we leave an exploration of these differences between particular cities for future research. While this analysis does not find evidence of an overall big city effect, we also assess whether large city productivity effects might manifest over a longer time horizon by repeating the event study analysis over a five year time horizon separately for Jakarta and Nairobi . These figures show no clear evidence of positive dynamic effects in these large capital cities, and if anything moving to Jakarta and Nairobi appears to lead to somewhat more negative wage realizations over time, although differences with other cities are imprecisely estimated and generally not significant. The bottom line is that there is no clear evidence for larger productivity gains for movers to the biggest cities in either Indonesia or Kenya, either immediately or over a five year time horizon.Most of the biodiversity of agroeco systems is found in the soil, and the functions performed by soil biota have considerable direct and indirect effects on crop growth and quality, nutrient cycle quality and the sustainability of soil productivity. Soil biota also contributes substantially to the resistance and resilience of agroecosystems to abiotic disturbance and stress. The microbial members of soil communities are the most sensitive and rapid indicators of perturbations and land use changes. In this sense, a quantitative description of microbial community structure and diversity has aroused great interest as a potential tool for soil quality evaluation.
Agricultural land management is one of most significant anthropogenic activities that greatly alters soil characteristics, including physical, chemical, and biological properties. This fact is particularly relevant in Mediterranean environments, where unsuitable land management together with climatic constraints can contribute to increased rates of erosion and other degradation processes of agricultural land. These conditions can lead to a loss in soil fertility and a reduction in the abundance and diversity of soil microorganisms. Agricultural management influences soil microorganisms and soil microbial processes by changing the quantity and quality of plant residues entering the soil and their spatial distribution, through changes in nutrients and inputs. The excessive use of pesticides can drastically modify the function and structure of soil microbial communities, thereby altering the normal functioning of terrestrial ecosystems, which in turn has important implications for soil quality.Management practices have a direct effect on soil microbiota, and the direct seeding of extensive crops increases microbial biomass. Soils subjected to disturbance by tillage, however, can be more susceptible to reductions in soil microbiota due to desiccation, mechanical destruction, soil compaction, reduce pore volume, and disruption of access to food resources. Some organic fertilisers, such as manure and sewage sludge, promote the activities of soil microbial communities; however, repeated application of manures may pose environmental hazards, as they introduce faecal microbial flora into soil and have the potential to alter the endogenous microbial structure. To improve or maintain the soil quality and biodiversity, the development and implementation of new sustainable agricultural practices is necessary. Currently, several strategies are being tested on experimental farms to reduce the high erosion rates and improve soil quality. On rainfed agricultural lands in eastern Spain, these practices include catch crops, no-tillage or reduced tillage, chipped pruned branches, straw mulch and weed control by herbicides. Previous studies have shown that several of these strategies contribute to reductions in soil erosion and enhance both soil quality and aggregate stability. It is very important to understand the impact of these sustainable agriculture management practices on the microorganism species diversity and structure. Such data are critical to evaluate the effect that these new strategies may have on microbial communities in the light of sustainable goals because microbial communities are crucial to maintaining the soil quality and to developing a sustainable agricultural model, based not only on crop productivity but also on ecological principles. To measure changes in the soil microbial community, the phospholipid fatty acid analysis was used. This analysis uses the lipids of the microbial membranes as biomarkers for specific groups of microorganisms and also creates a profile or fingerprint of the community structure. As a consequence, rapid changes in soil microbial community structure can be detected by changes in the PLFA pattern. In addition, the total concentration of PLFAs can be used as a measure of viable microbial biomass because phospholipids are rapidly degraded after cell death. PLFA analysis has been used to assess changes in soil microbial community structure as a consequence of various perturbations or management practices. Most studies on microbial soil community structure and composition in agricultural fields have been restricted to areas using conventional management or conventional agricultural practices, such as inorganic fertilisation or the use of herbicides, and very few studies have been conducted under Mediterranean conditions. In this study, we hypothesised that different agricultural management practices can modify, to varying extents, the soil microbial community structure of a rainfed orchard. An adjacent area, which had never been cultivated and contained only wild vegetation coverage, was used as a standard for local, high quality soil to be compared with soil from the agricultural practices tested, in terms of microbial responses.