Note that it is also possible that one might observe positive migration flows into non-agricultural employment even in the case where the true average productivity gap, was negative; in such a case, movers would consist of those with particularly large and positive individual returns to non-agricultural relative to agricultural employment in that time period, or perhaps those who face sufficiently large idiosyncratic preferences for the move, say, those with negative.By this logic, fixed effects estimates will be generally larger than the average population treatment effect. This suggests that estimated gaps based on those who were initially in the agricultural sector are likely to be upper bounds on the magnitude of the true average productivity gap in the population as a whole. Hendricks and Schoellman make a closely related point, arguing that their estimates of the returns to international migration are likely to be upper bounds. In this study, this will likely be the case with the Kenya data where the entire sample lived in rural areas at baseline. In the Indonesia data , which features sorting in both directions , it is in theory possible to observe a non-agricultural premium every time an individual selects into non-agriculture and an agricultural premium every time an individual selects into agriculture. By a parallel logic to above, the selection equation in equation 6 suggests that among those initially working in the non-agricultural sector,garden grow bags we would only observe moves among those that benefit from working in agriculture, i.e. The resulting estimates would then serve as lower bounds on the magnitude of the true average productivity gain to non-agricultural employment. The IFLS provides an ideal test bed to understand the role of these biases in estimating the related urban-rural gap.
In the spirit of Young’s observation that migration flows in both directions, the data allow us to condition on individual birth location and measure the dynamic impacts on wages after migration. The bounding argument above predicts that the estimated urban-rural productivity gap would be larger when estimated for movers from rural to urban areas than it is when estimated for movers from urban to rural areas. We take this prediction to the data and find suggestive evidence for it. This model of selection implies that the true sectoral productivity gap in Indonesia is bounded by these two estimates, generated by movers in each direction. This paper uses detailed panel data from Indonesia and Kenya to estimate worker productivity gaps between the non-agricultural and agricultural sectors, as well as the closely related question of gaps between workers in urban and rural areas. The data we use from both countries is unusually rich, and the long-term panel data structure features high rates of respondent tracking over time. At 250 million, the Southeast Asian country of Indonesia is the fourth most populous in the world, and Kenya is among the most populous Sub-Saharan African countries with approximately 45 million inhabitants. These countries are fairly typical of other low income countries with respect to their labor shares in agriculture, estimated agricultural productivity gaps using national accounts data, and the relationships between these variables and national income levels.The high tracking rates of the datasets we employ allow us to construct multiyear panels of individuals’ location decisions. Moreover, both datasets include employment information on both formal and informal sector employment. The latter is difficult to capture in standard administrative data sources yet often employs a large share of the labor force in low-income countries. If informal employment is more common in rural areas and in agriculture, and is partially missed in national accounts data, this may generate an upward bias in measured sectoral productivity gaps.
Detailed employment data were collected during each survey round. In addition to current employment, the survey included questions on previous employment, allowing us to create up to a 21- year annual employment panel at the individual level from 1988 to 2008. Employment status and sector of employment are available for each year, but in the fourth IFLS round, earnings were collected only for the current job. Therefore, the panel has annual data on employment status and sector of employment from 1988 to 2008, and earnings data annually from 1988 to 2000 and in 2007-08. The IFLS includes information on the respondent’s principal as well as secondary employment. Respondents are asked to include any type of employment, including wage employment, self employment, temporary work, and unpaid family work.In addition to wages and profits, individuals are asked to estimate the value of their compensation in terms of share of harvest, meals provided, transportation allowance, housing and medical benefits, and credit; the main earnings measure we use is thus the comprehensive sum of all wages, profits, and benefits. Individuals are asked to describe the sector of employment for each job. The single largest sector is “agriculture, forestry, fishing, and hunting”: 34% of individuals report it as their primary employment sector, and 47% have secondary jobs in this sector. Agricultural employment is primarily rural: 42% versus 3% of rural and urban individuals, respectively, report working primarily in agriculture . Other common sectors are wholesale, retail, restaurants, and hotels ; social services ; manufacturing ; and construction . These non-agricultural sectors are all more common in urban than rural areas. Men are more likely than women to work in agriculture and less likely to work in wholesale, retail, restaurants, and hotels; and social services. Smaller male-dominated sectors include construction and transportation, storage, and communications . In the analysis that follows, we employ an indicator variable for non-agricultural employment, which equals 1 if a respondent’s main employment is not in agriculture and 0 if main employment is in agriculture. The main analysis sample includes all individuals who are employed and have positive earnings and positive hours worked to ensure that the main variable of interest, the log wage, is defined.
The sample includes 18,211 individuals and 115,897 individual-year observations.In addition to studying wage gaps, we explore consumption gaps to get a broader sense of welfare differences. IFLS consumption data were collected by directly asking households the value in Indonesian Rupiah of all food and non-food purchases and consumption in the last month, similar to consumption data collection in the World Bank’s Living Standards Measurement Surveys.In contrast to the retrospective earnings data in the IFLS, the consumption data are all contemporaneous to the survey. Consumption data were collected at the household level,tomato grow bags which we divide by the number of household members to obtain a per capita measure. The consumption sample includes 38,280 individual-year observations from 19,695 individuals in IFLS rounds 1–4. In the consumption analysis, we expand the sample to also include individuals without current earnings data; we also perform a robustness check on the consumption analysis using the main productivity sample. Data were collected on the respondent’s location at the time of the survey, and all rounds of the IFLS also collected a full history of migration within Indonesia. All residential moves across sub-districts that lasted at least six months are included. Figure 2, Panel A presents a map of Indonesia with each dot representing an IFLS respondent’s residential location. While many respondents live on Java, we observe considerable geographic coverage throughout the country. The IFLS also asked respondents for the main motivation of each move. Family-related reasons are most common at 46%, especially for women , who are more likely than men to state they migrated for marriage. The second most common reason to migrate is for work , with little difference by gender, while migrating for education is less common. We combine data across IFLS rounds to construct a 21-year panel, from 1988 to 2008 with annual information on the person’s location, in line with the employment panel; refer to Kleemans and Kleemans and Magruder for more information on the construction of the IFLS employment and migration panel. We utilize a survey-based measure of urban residence: if the respondent reports living in a “village”, we define the area to be rural, while they are considered urban if they answer “town” or “city.” We present the correspondence between urban residence and employment in the non-agricultural sector in Table 1, Panel A.
In 66 percent of individual-year observations, people are employed in the non-agricultural sector, and in 21 percent of the observations, they live in urban areas. One can see that a substantial portion of rural employment is in both agriculture and non-agricultural work, while urban employment is almost exclusively non-agricultural, as expected. Given the migration focus of the analysis, it is useful to report descriptive statistics both for the main analysis sample, as well as separately for individuals in four mutually exclusive categories : those who always reside in rural areas throughout the IFLS sample period , those who were born in a rural area but move to an urban area at some point , those who are “Always Urban,” and finally, the “Urban-to-Rural Migrants” . As discussed above, the fixed effects analysis is driven by individuals who move between sectors during the sample period. In the main IFLS analysis sample, 80 percent of adults had completed at least primary education, and a quarter had completed secondary education, while tertiary education remain quite limited, at less than 10 percent. Among those who are born in rural areas in columns 2 and 3 , we see that migrants to urban areas are highly positively selected in terms of both educational attainment, and in terms of cognitive ability, with Raven’s Progressive Matrices exam scores roughly 0.2 standard deviation units higher among those who migrate to urban areas, a substantial effect.Migration rates do not differ substantially by gender. These relationships are presented in a regression framework in Table 3, Panel A , and the analogous relationships for moves into non-agricultural employment are also evident . Importantly, the relationship between higher cognitive ability and likelihood of migrating to urban areas holds even conditional on schooling attainment and demographic characteristics , at 99% confidence. This indicates that sorting on difficult to-observe characteristics is relevant in understanding sectoral productivity differences in this context. It is worth noting that if we ignore migrants, individuals who are born and remain in urban areas are far more skilled than those who stay in rural areas. “Always Urban” individuals score over 0.4 standard deviation units higher on Raven’s matrices and have triple the rate of secondary schooling and six times the rate of tertiary education relative to “Always Rural” individuals. The urban-to-rural migrants in Indonesia are also negatively selected relative to those who remain urban residents, which corroborates Young’s claim. These patterns emerge in Table 2, Panel A, where the urban-to rural migrants score lower on all skill dimensions relative to those who remain urban; appendix Tables A1 and A2 report results analogous to Tables 3 and 4, among those individuals born in urban areas. The Kenya Life Panel Survey includes information on 8,999 individuals who attended primary school in western Kenya in the late 1990s and early 2000s, following them through adolescence and into adulthood. These individuals are a representative subset of participants in two primary school based randomized interventions: a scholarship program for upper primary school girls that took place in 2001 and 2002 and a deworming treatment program for primary school students during 1998–2002 . In particular, the KLPS sample contains information on individuals enrolled in over 200 rural primary schools in Busia district at the time of these programs’ launch. According to the 1998 Kenya Demographic and Health Survey, 85% of children in Western Province aged 6–15 were enrolled in school at that time, and Lee et al. show that this area is quite representative of rural Kenya as a whole in terms of socioeconomic characteristics. To date, three rounds of the KLPS have been collected . KLPS data collection was designed with attention to minimizing bias related to survey attrition. Respondents were sought in two separate “phases” of data collection: the “regular tracking phase” proceeded until over 60 percent of respondents had been surveyed, at which point a representative subset of approximately 25 percent of the remaining sample was chosen for the “intensive tracking phase” . These “intensive” individuals receive roughly four times as much weight in the analysis, to maintain representativeness with the original sample.