The effective tracking rate for each KLPS round is around 85 percent.Similar to the IFLS, the KLPS includes detailed information on educational attainment, labor market participation, and migration choices. Employment data was collected in a wage employment module and a self-employment module, which both are designed to include both formal and informal employment. Most individuals were quite young during data collection for KLPS round 1, and few had wage employment or self-employment to report. Full employment histories, including more detailed questions, were collected during rounds 2 and 3, and it is from these rounds that we draw the data on individual earnings, hours worked, and wages used in the present analysis. The Kenya agricultural productivity data deserves detailed discussion. Whenever total household annual agricultural sales were sufficiently large, exceeding 40,000 Kenyan Shillings , full agricultural production and profit information was collected in the self-employment module and included in the present analysis. Agricultural wage employment is also common, and these data are always included. Limited questions on subsistence agricultural production were collected in KLPS rounds 1 and 2,grow bags garden but these are insufficient to create an individual productivity measure. More detailed information on agricultural productivity is contained in round 3, and this is included in the present analysis.
To create a measure of individual productivity comparable with other sectors, we focus on agricultural activities in which the respondent provided all reported labor hours; we also restrict attention to activities in which the respondent reports being the main decision-maker, since it seems likely that they are most knowledgeable about such activities . The profit in an agricultural activity is the sum of all crop-specific production – valued either through actual sales or at the relevant crop price if consumed directly – minus all input costs and hired labor costs. The individual wage divides this net profit by the labor hours the respondent supplied to the activity. KLPS respondents reported industry for all wage and self-employment. Most individuals are engaged in relatively low-skilled work. The most common industry for wage employment is services, at 58% overall and 74% for females . In rural areas, the most common industries for wage employment are services and agriculture , while in urban areas they are services, and manufacturing and construction . The largest self-employment industries are retail and services .KLPS round 3 collected detailed consumption expenditure data for a subset of individuals. However, because it was only collected for this round, we are unable to utilize it in panel estimation. Instead, in the panel analysis we utilize a proxy for consumption, the number of meals eaten in the previous day, which is available in both KLPS rounds 2 and 3.
Reassuringly, meals eaten is strongly correlated with our primary measures of labor productivity as well as consumption expenditures per capita ; see Appendix Table A3. As with Indonesia, in the meal consumption analysis, we are able to expand the sample to also include individuals without current earnings data. KLPS respondents provide a history of residential locations since their last interview, and this data includes residential district, town, and village, allowing us to classify individuals who lived in towns and cities as urban residents. The KLPS includes information on all residential moves that lasted at least four months in duration, a slightly more permissive definition than in the IFLS, and we are able to construct a monthly residential panel from March 1998 to October 2014.18 Combined with the retrospective labor productivity data, the main analysis sample is a monthly panel with 128,439 individual-month observations for 4,537 individuals. Figure 2, Panel B presents a map of Kenya, with each dot representing a respondent residential location during 1998–2014. Most residences in western Kenya are located in Busia district , with substantial migration to neighboring areas as well as to cities. Appendix Table A4 presents the list of main towns and cities, and shows that 70 percent of urban residential moves are to Kenya’s five largest cities, namely, Nairobi, Mombasa, Kisumu, Nakuru, and Eldoret. According to survey reports, men are slightly more likely than women to migrate for employment reasons while women are more likely to migrate for family reasons, including marriage . A smaller share of moves are for education. Summary statistics on employment sector and urban residence for KLPS respondents are presented in Table 1, Panels B and C. Panel B presents data for the main analysis sample; as described above, this contains subsistence agricultural information where available .
The employment shares in agriculture is much higher in rural areas than urban , as expected, but the share in rural areas is somewhat lower than expected, likely because subsistence agricultural activities were not captured in earlier KLPS rounds. For a more complete portrait, Panel C focuses on data from the 12 months prior to the KLPS-3 survey, which contains detailed information on subsistence agriculture, and here the agricultural employment share in rural areas is much higher. Recall that the Kenya sample is all rural at baseline . Similar patterns emerge regarding positive selection into urban migration, with educational attainment and normalized Raven’s matrix scores both far higher among those who migrate to cities . In particular, there is a raw gap of nearly 0.3 standard deviation units in Raven’s matrix scores between urban migrants and those who remain rural. Overall migration rates in Kenya are similar for females and males. Tables 3 and 4 report these patterns in terms of regression estimates, for urban migration and employment in non-agricultural work, respectively. As with Indonesia, controlling for educational attainment and gender, the Raven’s score is strongly positively correlated with urban migration .GLW estimate raw and adjusted agricultural productivity gaps of 138 and 108 log points in Indonesia, respectively . The estimate of this raw gap from the IFLS is somewhat smaller at 62 log points . The most straightforward explanation for this discrepancy is an issue of measurement. GLW observe that, in an analysis of 10 countries, the average agricultural productivity gap was 17 log points smaller when estimated in Living Standards Measurement Study data that is similar to the IFLS,grow bag for tomato and which is more likely to capture earnings in informal employment.That said, the raw gap we estimate in the IFLS remains substantial. Inclusion of control variables similar to those used by GLW to adjust macro data gaps reduces the estimated agricultural productivity gap in the IFLS to 51 and 32 log points . Estimating on the sub-sample for which we have scores from Raven’s matrix tests, the gap is reduced slightly, although note the smaller sample size in this case. Limiting the analysis to those who have productivity measurements at some point in time in both agricultural and non-agricultural employment, the productivity gap drops to 16 log points , suggesting that the selection on unobservable characteristics alluded to in Section 2 may play a meaningful role. Inclusion of fixed effects reduces the gap further , and using our preferred labor productivity measure, the log wage , as the dependent variable nearly eliminates the gap altogether: the coefficient estimate falls to 0.047 in column 7, and further to 0.045 when considering the real log wage . We follow a similar approach for Kenya, where the raw agricultural productivity gap falls from 79 log points to 56 with the inclusion of GLW’s controls , and to 32.6 log points when including an individual fixed effect. Using the preferred hourly wage measure reduces the gap to 13.4 log points , it falls further when adjusted with an urban price deflator , and neither fixed effects wage estimate is significant at traditional levels of confidence.
Comparing column 1 to column 7 in Table 5, the agricultural productivity gap is reduced by 92 percent in Indonesia and by 83 percent in Kenya. The standard errors are somewhat larger for Kenya, so the upper end of the 95% confidence interval includes a sizable gap of 37 log points, consistent with some non-trivial productivity gains to non-agricultural employment. That said, even this value remains far lower than the 108 and 71 log point effects that GLW estimate for Indonesia and Kenya, respectively, once they condition on observable labor characteristics . As noted in the introduction, these results for Indonesia and Kenya are presented graphically in Figure 1, Panels A and B and compared to GLW’s estimated productivity gaps.Table 6 presents the closely related exercise of estimating the labor productivity gap between residents of urban and rural areas. While the existing empirical literature has sometimes conflated these two gaps, Table 1 shows that employment in rural areas is not exclusively characterized by agriculture. To the extent that residential migration is costlier than shifting jobs , and the urban and non-agricultural wage premia are related but distinct parameters, one might suspect that an urban wage premium might even be more pronounced than the non-agricultural wage premium. The micro-data estimates from Indonesia and Kenya appear to be consistent with this view, at least at first glance: the raw gap reported in column 1 of Table 6 are 63 and 85 log points for Indonesia and Kenya, respectively. Similar to the agricultural productivity gap, the urban-rural productivity gap falls when additional explanatory variables are added in columns 2, 3 and 4, but remains substantial and statistically significant. Focusing the analysis only on those who have earnings measures in both urban and rural areas leads to a further reduction. Finally, the urban-rural earnings gap falls to 1.8 log points with the inclusion of individual fixed effects in Indonesia, and -0.7 log point for the preferred log wage measure . The analogous urban productivity effect estimate for Kenya is slightly larger at 13.2 log points . Thus, the productivity gap in Indonesia falls by 100 percent in Indonesia , and the reduction for Kenya is 84 percent with the inclusion of individual fixed effects. Once again, these results are summarized in Figure 1 .The selection model predicts that estimated productivity gaps would be higher among rural-to-urban migrants than for urban-to-rural migrants, given plausible patterns of selection bias. Table 7 explores this hypothesis in Indonesia by separately conditioning on birth location; Panel A contains those born rural and Panel B those born in urban areas. The same pattern of declining productivity gaps in each sub-sample is observed for non-agriculture and urban as additional controls are included. In the preferred log wage specifications in columns 4 and 8, productivity gaps are indeed somewhat larger for those born in rural areas, as predicted by the sorting model. The difference between estimates for those born in rural versus urban areas is small, suggesting rather tight bounds. For instance, the estimated productivity gain to non-agricultural employment is 5.9 log points for those born in rural areas and -0.8 for those born urban . While suggestive, note that the difference between these estimates is not significant. The discussion above establishes at least an 80 percent reduction in estimated sectoral productivity gaps once individual fixed effects are included in the analysis . The wage measures presented thus far are closely related to the labor productivity parameters that are the focus of most existing macroeconomic empirical literature. However, productivity and “utility” may diverge for many reasons, including price differences across regions, amenities, unemployment, and other factors. For instance, there could be considerable individual heterogeneity in the taste for rural versus urban amenities, e.g., comforts of home, ethnic homogeneity, better informal insurance, etc., in rural areas versus cosmopolitan cities’ better public goods and more novelty . Although it is impossible to fully capture these factors and convincingly measure individual welfare, to get somewhat closer to differences in living standards, we draw on consumption data from the IFLS. As described in Section 3, four rounds of the IFLS included questions on the value of household consumption which can be converted to per capita consumption. In the main specification, we include all individuals who have such consumption data, even if they lack earnings measures. The initial consumption gap between non-agriculture and agriculture is large and similar the productivity gap at 54 log points .