Migration in China. Mathematics & Economics Research Paper (Research Paper Sample)
The research paper should be a minimum of 10 double-spaced pages and a maximum of 15 double-spaced pages. Students can choose any topic related to migration in developing countries or from developing to developed countries. -The goal of the research paper is to employ key theoretical points discussed in the classes and to develop one’s own research ideas. The paper should focus on one central argument and examine it through empirical cases.
The research topic that I chose is the migration in China, basically is about people in China move from rural to city or from city to rural. Doing the research about that. And there are couples of reasons have been talked about in China, the instructor wants me to show something different, so be creative. I will put the course materials in file as the reference. By the way, the content is only about the migration in China, don't talk about the people in China migrate to other country or something like that. Lastly, I hope this paper is easy to read, I remember one of the previous paper order that I made, I barely understand.
Urban-Rural Migration ECON 475 Elisa Giannone Fall 2018 Why do people move to the cities? • Wages are higher in the cities; • Jobs are more secure; • Different types of jobs: • Informal or agriculture in rural areas; • Formal or other jobs in urban areas; • Better opportunities for the whole family. • What else? • How do economists formalize this idea? The Migration and Urbanization Dilemma • As a pattern of development, the more developed the economy, the more urbanized • But many argue developing countries are often excessively urbanized or too-rapidly urbanizing • This combination suggests the migration and urbanization dilemma • Urbanization: Trends and Projections Urban Population and Per Capita Income across Selected Countries Urbanization across Time and Income Levels Propor%on of Urban Popula%on by Region, 1950-2050 Megacities: Cities with Ten Million or More Inhabitants Estimated and Projected Urban and Rural Population of the More and Less Developed Regions, 1950-2050 Annual Growth of Urban and Slum Populations, 1990-2001 The Role of Cities • Agglomeration economies: Urbanization (general) economies, localization (industry or sector) economies • Saving on firm-to-firm, firm-to-consumer transportation • Firms locating near workers with skills they need • Workers locating near firms that need their skills • Firms benefit from (perhaps specialized) infrastructure • Firms benefit from knowledge spillovers in their and related industries • (Also: consumers may benefit from urban amenities) Formal Sector or Urban Sector • The existence of a nontrivial informal sectors is a unique feature of developing economics. • Formal sector where workers and firms operate under accepted set of rules (laws) and regulations imposed by the government. • Workers are sometimes unionized. • Firms are required to pay minimum wages, conform to safety standards, provide pensions, etc. • Firms pay taxes. Formal Sector • Formal sector bears close resemblance to economic activity in developed countries. • Firms have records and firms are relatively tangible entities. • Can issue shares of stock, pay dividends, they can be audited, protected by bankruptcy laws of country. • Entry into formal sector is costly — thus expect firms of a minimum size to needed to cover setup costs (paperwork of legal entity). Informal Sector • An amalgam of small scale organizations that escape the cover of many of the regulations of the formal sector and do not receive access to privileged facilities. • No minimum wage, no retirement plans, no unemployment insurance, no safety regulations. • Generally do not pay taxes and receive no government support. • Costly to monitor and enforce regulations so governments “look the other way”. Informal Sector • Firms in this sector exist in a shadowy penumbra. Yet, enormous fraction of labor force works within the informal sector. • Usually small scale operations. • Setup costs are low. • Advanced tax payments unnecessary, though occasional bribe may be needed. Agriculture • Tax authorities have no way to observe how much output a farmer produces, so output is untaxed. • Income is taxed in U.S. • Rural areas in developing countries typically do not have public pension programs, minimum wages, unemployment insurance . . . • But, a collection of informal institutions creates substitutes for the missing sources of support. Organiza(on • Production is organized in a variety of ways. • Family farms. Own consumption and cash crops. • Large ownership cultivators (corporate farms). • Tenant farmers (lease land from landowner) • Labors work for wages or commission on the land of others. • The landless. Reasons workers in the urban formal sector are paid higher than equilibrium wages • 1. Unions • 2. government policy • 3. incentives to workers to expend effort when labor cannot be directly supervised without tremendous costs. • 4. The threat being fired. Then one would have to return to the country or find work in the urban informal sector Theory: Harris-Todaro (1970) • John R. Harris and Michael P. Todaro (1970) presented a model of rural-urban migration; • The model serves to explain why there are GAPS between cities and rural areas; Model Assump,ons • Two sectors: urban (manufacturing or formal) and rural (agriculture or informal) • Rural-urban migration condition: when urban real wage exceeds real agricultural product • No migration cost • Perfect competition • Cobb-Douglas production function • Static approach • Low risk aversion Cobb-Douglas produc0on func0on + perfect compe00on Production function: ! = #$%& Wages are equal to the marginal product of labor: ' = ()#$*+%& Perfect competition • P=Marginal Product • Wage=Marginal Product of Labor Equilibrium with Flexible wages Outline Overview Rural–urban interaction Rural–urban migration Introduction The basic model Floors on formal wages and the Harris–Todaro equilibrium Equil w Flexible W Formal Wage Agricultural wage w* L* f L* A A B C D Rural to Urban Lecture 17 Equilibrium with Flexible wages • Equilibrium requires that the “law of one price” hold. • Same wage holds in formal and agricultural market. Otherwise have persistent migra>on to arbitrage the difference. Formal Wages Inflexible • Wages in formal sector inflexible: • May be more unionized than agricultural sector. • Showcase for government policy — minimum wage, pension, unemployment insurance • Firms in Formal sector may pay a premium — seek best workers • Wages in informal and agriculture flexible and adjust to S and D. Floors on Formal Wage • Wage in formal sector at "! ̄ • Reduce labor demand in formal sector. • Full employment requires agricultural wage at " • Can not be equilibrium. Workers will migrate to Urban. • If wages at "! employment in agriculture declines. • Have unemployment U. • Unemployed must be in Urban area, otherwise drive agricultural wage down. Equilibrium with Inflexible Wages Outline Overview Rural–urban interaction Rural–urban migration Introduction The basic model Floors on formal wages and the Harris–Todaro equilibrium Figure 10-5 Formal Wage Agricultural wage Lf U LA Wbar wbar Rural to Urban Lecture 17 Equilibrium • Rigid wage in Urban formal sector thus produces an equilibrium in which workers voluntarily migrate from rural to urban areas. • But face some chance of unemployment in Urban area. • Unemployment equilibrates the market. • Worker choices: be employed in agricultural market for low wage or move to city and gamble on securing high wage. Harris Todaro Equilibrium • Probability of ge.ng job in urban area depends on ra5o of vacancies to job seekers. • Let p be the probability obtaining job in formal sector. • Let !" be the wage in the urban informal sector. Fixed. • Expected wage in urban sector: #[!%] = (!) + (1 − ()/!" • Equilibrium: #[!%] = !0. • Equilibrium requires: ( = 213 2134 25 • If employment in informal sector probabilis5c: #[!%] = (!) + (1 − ()/!" Harris Todaro Eq • People indifferent ex ante stay or leave. • Ex post not indifferent. • A par5cular alloca5on of labor an equilibrium: ! = #(%&, %() • Extend to many sub sectors of urban market key: *[,-] = ,/. Government Policy • The informal sector an outgrowth of the formal sector, slows the pace of rural–urban migration. • Yet unregulated economic activity often responsible for congestion, pollution, crime. • Government policy: accelerate absorption of labor into formal sector. Via subsidies (tax holidays), increase employment in public sector. • Immediate effect increase in demand in formal sector,"! ↑ . And $%↑. Hence, &["(] ↑. • But can not persist. Increased gap between &["(] and "* induces migration to city. Government Policy • Increase flow to city reduces chance of ge4ng job in formal sector, while ou:low from rural area increases !" . • Eventually, obtain new equilibrium. Outline Overview Rural–urban interaction Rural–urban migration Introduction The basic model Floors on formal wages and the Harris–Todaro equilibrium Government Policy Increase flow to city reduces chance of getting job in formal sector, while outflow from rural area increases wa. Eventually, obtain new equilibrium. w¯ 0 F w¯ 0 F + L0 I w¯ + L0 F w¯ 0 f + L0 I = w0 a For E[w0 u] > E[wu] require share of formal sector must increase: w¯ 0 F w¯ 0 F + L0 I > w¯F w¯F + LI Rural to Urban Lecture 17 Government Policy • Policy increased share of employment in formal sector. Reduced share of employment in informal sector. • Yet, total size of informal sector may increase. True, if total urban sector increases more than formal sector. • Commonly seen: a=empts to increase the demand for labor in the formal sector may enlarge the size of the informal sector, as migrants respond to the be=er job condi@ons. Migra@on effect may dominate the ini@al “soak–up effect.” • Not confined to employment — any enhancement that a=empts to reduce conges@on, pollu@on, improve health care might have effect of finally worsening these indicators. Todaro paradox Efficient Allocation and Migration Policy • Think of compe--ve labor market with flexible wages. Then absorp-on curves demand curves and • ! = #$% = &'()*+,-$%.. With flexible wages !+ = !/ so fully efficient. • Harris Todaro: !0 > !2 . Increase efficiency by moving worker from informal to formal sector. • Have policy to restrict migra-on (if possible) to only those with jobs in formal sector. • Employment in formal sector .3 . Everyone else, .4 5 , in agriculture. Eliminated Informal Sector • But compared to fully flexible wages, have too few people in urban area, social loss from misallocation of resources. Evidence on Rural-Urban Wage Gap in India Evidence on Rural-Urban Wage Gap in India Evidence from India’s Rural-Urban Wage Gap • In India the rural-urban wage gap is greater than 25% and it has been large for decades; • Explanations: • Market failure that prevent arbitrages; • Different sets of skills in urban vs rural areas; • Munshi and Rosenzweig: well-functioning rural insurance networks and absence of formal insurance (government safety nets and private credit). Theory in Munshi and Rosenzweig • Households: • They like to consume goods; • They dislike volatility in consumption; • If a member moves to the city, • Income will increase to ! 1 +∈ ; • Volatility will increase so consumption risk: &' = )* +* , Main Results of the model • Some redistribution is socially optimal, which implies that (relatively) wealthy households in the community should ceteris paribus be more likely to have migrant members. • Some redistribution is socially optimal, which implies that (relatively) wealthy households in the community should ceteris paribus be more likely to have migrant members. Tes$ng the Theory: Evidence on Redistribu$on • Testable Predictions: • income is redistributed in favor of poor households within the caste; • relatively wealthy households who, therefore, benefit less from the insurance network should be more likely to have migrant members; • relatively wealthy households who, therefore, benefit less from the insurance network should be more likely to have migrant members; Evidence on Redistribu0on within Castes • data from the 2005–2011 Indian ICRISAT panel survey; • 2006 Rural Economic Development Survey (REDS) has information from over 119,000 households residing in 242 villages in 17 major Indian states on the migrant status of each household; Reduced Form Evidence • !" indicated whether any male member of household i moved out from the village; • #" indicated the household average income; • #$ indicates average cast income • Test prediction 1: • !" < 0: conditional on the household’s own income, an increase in cast income implies it is relatively less wealthy and, therefore, should be less likely to have migrants members; Test prediction 2 • Proposi'on 2 indicates that households who face greater rural income risk should be less likely to have migrant members. • They test this predic'on by including the rural income risk faced by the household as an addi'onal regressor • Income risk is measured by the coefficient of varia'on of the household’s income, squared. Conclusions • This paper provides an explanation for large spatial wage disparities and low male migration in India based on a combination of wellfunctioning rural insurance networks and the absence of formal insurance • When men migrate permanently to work, they (and their rural households) cannot credibly commit to honoring their future obligations at the same level as households without migrants.
Rural-Urban Migration ECON 475 Elisa Giannone Fall 2018 Week 3 Summary from last week • The richer a country the more people move from rural to urban areas; • Over time more people are moving to cites, especially in developing countries; • There are wage gaps between urban and rural areas; • Harris-Todaro explain these wage gaps using a simple model with inflexible wages in the city; • Policy from the government: subsidy to increase migration to urban areas BUT risk to increase informal sector in urban areas (Harris-Todaro paradox) Do people move? • There is evidence that people move less than they should; • Why people do not move? • Lack of Network; • Social/Family Ties; • Lack of Income; • Physical Barriers: walls We will explore all these causes one by one this week! Evidence on Rural-Urban Wage Gap in India Evidence from India’s Rural-Urban Wage Gap • In India the rural-urban wage gap is greater than 25% and it has been large for decades; • Explanations: • Market failure that prevent arbitrages; • Different sets of skills in urban vs rural areas; • Munshi and Rosenzweig: well-functioning rural insurance networks and absence of formal insurance (government safety nets and private credit). Rural-Urban Wage Gap 52 THE AMERICAN ECONOMIC REVIEW JANUARY 2016 The PPP-adjusted urban wage is the nominal urban wage, multiplied by the value of the consumption bundle of rural households whose heads have less than primary education and then divided by the value of the same bundle based on urban prices. As can be seen, while this correction for standard of living substantially cuts the earnings advantage from shifting from rural to urban employment, there is still a real wage gap of over 27 percent. To assess the sensitivity of our results to the choice of consumption bundle, we used the corresponding urban consumption bundle, appropriately priced for rural and urban areas, to defate the nominal urban wage. Using this destination-based defator (the Paasche index), the real wage gap is 0 5 10 15 20 25 30 35 40 45 50 China Indonesia India Figure 1. Rural-Urban Wage Gap, by Country Sources: Chinese mini-census 2006, IFLS 2007, and NSS 2004 Table 1—Rural-Urban Wage Gaps in India in 2004 Wage Nominal PPP-adjusted (rural consumption) PPP-adjusted (urban consumption) Sector (1) (2) (3) Urban 62.66 54.05 57.58 Rural 42.54 42.54 42.54 Percent gain 47.30 27.06 35.35 Notes: Wages are measured as daily wages for individuals with less than primary education. PPP-adjustment is based on rural and urban consumption bundles, respectively, for those individuals. Source: NSS Rural-Urban Wage gap over time 54 THE AMERICAN ECONOMIC REVIEW JANUARY 2016 increase. To provide additional support for the claim that the decline in the wage gap between 1999 and 2004 is not being driven by migration, we report migration rates based on decadal population censuses over the 1961–2001 period. Following Foster and Rosenzweig (2008), migration rates are computed for the cohort of males aged 15–24 (who are most likely to move for work) within each decade by comparing their numbers, residing permanently in rural and urban areas, at the beginning and the end of the decade.9 These migration rates are plotted in Figure 3, where no spike in migration is visible in the 1991–2001 period. Despite the persistently large (real) wage gaps that we have documented, rural-urban migration in India has remained low for decades, reaching a maximum of 5.4 percent in the earliest period and dropping below 4 percent in recent decades.10 It is possible that the wage gap we quantify (conditional on education) merely refects sorting on unobserved skill, and a large difference in the skill intensities of production between rural and urban areas of India, as suggested by Young’s (2013) model. We do not think sorting on skill explains the large wage gap in India. First, agriculture became more skill-intensive as a result of the Green Revolution in many 9This method requires that mortality rates are similar across urban and rural populations. In the age group 15–24, mortality is very low. The method also assumes that defnitions of rural and urban remain constant across the decade. The urbanizing of the population by redefnition, as described above, will infate the migration rates computed using the cohort method. The rates that are computed are thus likely to be upper bounds on true migration. The 2001 census indicates that movement due to marriage by women accounts for roughly 45 percent of all permanent migration in India, while employment, business, and the movement of entire families accounts for just 39 percent of migration (similar statistics are obtained in the 1991 round). We consequently focus on male out-migration when measuring the spatial mobility that is associated with the rural-urban wage gap. 10Although the detailed information needed to compute the migration rate from 2001 to 2011 is currently unavailable, provisional fgures from the latest 2011 census indicate that the proportion of the population that is urban rose by only 3.8 percentage points between 2001 and 2011, to 31.6 percent (Offce of the Registrar General and the Census Commissioner 2011). 15 20 25 30 35 40 45 50 55 60 65 1983 1993 1999 2004 2009 Figure 2. Real Rural and Urban Wages in India Source: NSS 1983–2009 Change in Rural-Urban Wage Gap VOL. 106 NO. 1 MUNSHI AND ROSENZWEIG: NETWORKS AND MISALLOCATION 55 parts of India starting in the 1970s and prior to the economic reforms of the 1990s (Foster and Rosenzweig 1995). In contrast, TFP growth in manufacturing was close to zero or even declining during this period (Balakrishnan and Pushpangadan 1994; Saha 2014). Young’s model would predict that the wage gap would therefore have declined in that period. It did not. Second, Young’s model implies that migration rates from rural to urban and from urban to rural areas should both be high where wage gaps are high to achieve the appropriate mix of skills in both sectors. But in India, both urban and rural out-migration rates are low. An independent measure of migration can be constructed from the nationally representative India Human Development Survey (IHDS) conducted in 2005, which covers both rural and urban areas. The survey provides information on the number of years that each sampled household has been residing in the current location. We assume that a household has in-migrated if it has resided in that location for less than ten years. Based on this defnition, and restricting attention to households with male heads aged 25–49, the IHDS can be used to compute urban-rural and rural-urban migration rates. These statistics are 1.06 percent and 6.48 percent, respectively. Using the same defnitions applied to the male subsample of the 2005 Indian Demographic and Health Survey (DHS), the rates are 5.55 and 5.34 percent. There is thus no evidence that the exceptionally large wage gap in India is accompanied by a commensurate fow of workers, in either direction, refuting the counterargument that these gaps simply refect differences in (unobserved) skill.11 Even with the DHS statistics, which are substantially 11Young (2013) reports balanced urban-rural and rural-urban migration rates above 20 percent in his sample of 65 countries. He uses DHS data and pools information on men and women. Men make up 10 percent of the 0 0.02 0.04 0.06 0.08 0.1 0.12 1961–1971 1971–1982 1982–1991 1991–2001 Figure 3. Change in Rural-Urban Migration Rates in India, 1961–2001 Source: Indian Population Census 1961–2001 Change in Percent urbanized Population VOL. 106 NO. 1 MUNSHI AND ROSENZWEIG: NETWORKS AND MISALLOCATION 57 (7.9 percent), friends (7.8 percent), and employers (5.6 percent). Table 3A reports the proportion of loans in value terms, both by source and purpose, using data from the 1982 REDS. As can be seen, caste loans are disproportionately used to cover consumption expenses and for meeting contingencies such as illness and marriage. For example, although loans from caste members were 14 percent of all loans in value, they were 23 and 43 percent, respectively, of the value of all consumption 0 5 10 15 20 25 30 35 40 45 China Indonesia India Nigeria 1975 2000 Figure 4. Change in Percent Urbanized, by Country, 1975–2000 Source: UNDP 2002 Table 2—Participation in the Caste-Based Insurance Arrangement Survey year 1982 1999 (1) (2) Households participating (percent) 25.44 19.62 Income of senders 5,678.92 19,956.29 (7,617.55) (22,578.95) Percent of income sent 5.28 8.74 Income of receivers 4,800.29 10,483.84 (4,462.63) (13,493.68) Percent of income received 19.06 40.26 Observations 4,981 7,405 Notes: Standard deviations in parentheses. Participation in the insurance arrangement includes giving or receiving gifts and loans. Participation measured over the year prior to each survey round. Income is measured in 1982 rupees. Source: REDS 1982 and 1999 Theory in Munshi and Rosenzweig • Households: • They like to consume goods; • They dislike volatility, V, in consumption; • If a member moves to the city, • Income will increase to ! 1 +∈ %ℎ'(' ! )* +',- ./-*0+12)/- ,-3 ∈ )* 2ℎ' 4,)- 5(/+ 0(6,- %,4' -'2 2ℎ' 7/** )- (0(,7 )-./+' 30' 2/ 3'1,(20('; • Volatility will increase so consumption risk: 9: = <= >= ? • Preferences: VOL. 106 NO. 1 MUNSHI AND ROSENZWEIG: NETWORKS AND MISALLOCATION 61 The central assumption in our analysis is that men migrating independently (and permanently) to the city cannot be monitored effectively by their rural communities and so will be excluded from rural-based insurance networks. By the same argument, caste networks will not be able to function effectively if their members are spread thinly over a very wide rural area. However, we also note that migration can be sustained without the loss of network insurance if members of a caste move together as a group. The group can then monitor its members in the city. A caste could use an analogous strategy to support cooperation and reduce information problems when its members are spread over a wide area. A single caste will not have a presence in each village, but instead will cluster in select villages. This clustering shows up clearly in the 2006 REDS census, where the mean number of castes per state is 64, while the mean number of castes per village is 12. With 340 households on average in a village, this implies that a caste will have about 30 households in those villages where it is represented.19 II. The Theory Our theory describes how the existence of well-functioning rural insurance networks can lead to low migration. The theoretical structure we develop will be taken to the data, allowing us to quantify the magnitude of the mobility restrictions. It will also be used to generate testable predictions that distinguish it from alternative explanations for the low mobility in India. A. Income, Preferences, and Risk-Sharing The basic decision-making unit is the household, which consists of multiple earners. The household belongs to a community within which all its social activities take place. Each household derives income from its local activities. Income varies independently across households in the community and over time. In addition, one or more members of the household receive a job opportunity in the city. The key decision is whether or not to send them to the city. We assume that the household has logarithmic preferences. This allows us to express the expected utility from consumption, C, as an additively separable function of mean consumption, M, and normalized risk, R ≡ V/M2 , where V is the variance of consumption:20 EU(C) = log(M) − _1 2 _ V M2 . 19The pattern of spatial clustering we have documented has theoretical foundations. Jackson, RodriguezBarraquer, and Tan (2012) examine reciprocity in societies where any two individuals interact too infrequently to support exchange but where the possible loss of multiple relationships (in the event of default) can be used to support cooperation. They show that robust networks in such settings are social quilts: tree-like unions of completely connected subnetworks. Based on the statistics reported above, caste networks appear to exhibit precisely these properties. 20This expression is obtained by evaluating log consumption at mean consumption, M, and ignoring higher-order terms. For the Taylor expansion to be valid, with CRRA preferences, consumption must lie in the range [0, 2M]. This implies that its coeffcient of variation must be less than 0.31. The panel data that we use, described below, satisfes this condition for 90 percent of households with food consumption and 70 percent of households with overall consumption (which includes durables). Condition for staying in the village • With logarithmic preferences, the household will choose to participate in the rural insurance network and remain in the village if • I is the subindex for participating in the insurance network in the rural area • A is the subindex for moving to the city • ! is the risk parameter for moving to the city VOL. 106 NO. 1 MUNSHI AND ROSENZWEIG: NETWORKS AND MISALLOCATION 63 income will increase to MA (1 + ϵ̃ ), where ϵ̃ denotes the gain in income from urban wages net of any loss in rural income due to their departure. This gain in income must be traded off against the increased consumption risk that the household will face. With network insurance, (normalized) consumption risk is denoted by RI ≡ VI/MI 2 . When the household sends migrants to the city, it loses the services of the network and the corresponding risk is βRA, where RA ≡ VA/MA 2 . The standard presumption is that the income diversifcation that accompanies migration will reduce the income risk that the household faces. Then β < 1 even if a household with migrants has no alternative mechanism through which it can smooth its consumption. As formal (nonnetwork) insurance becomes available, the risk-parameter β will decline even further. However, we continue to assume that migration increases the consumption risk that the household faces, RI < β RA. This is the wedge that restricts mobility and allows a wage gap to be sustained in our theory. Note that this key insight of our theory would apply with any model of ex post risk-sharing, as long as the reduced access to the network resulted in increased consumption risk for households with migrants. With logarithmic preferences, the household will thus choose to participate in the rural insurance network and remain in the village if (1) log(MI) − _1 2 _ VI MI 2 ≥ log(MA) − _1 2 β _ VA MA 2 + ϵ, where ϵ ≡ log(1 + ϵ̃ ). 22 Given the standard assumption in models of mutual insurance that there is no storage and no savings, full risk-sharing and log preferences imply that each household’s consumption will be a fxed fraction of the total income, ∑i yis, that is generated by the N households in the insurance network in each state s of the world. Let mean rural income, MA, be the same for all households to begin with. The income gain from migration, ϵ, is assumed to be uncorrelated with rural income and is private information, so it follows that total income will be distributed equally among the members of the network. Taking expectations, or variances, over all states, the equal-sharing rule implies that (2) MI = E( _1 N ∑ i yis) = _1 N (NMA) = MA (3) VI = V( _1 N ∑ i yis) = _1 N2 (NVA) = _ VA N . Mean consumption with insurance, MI, is equal to mean consumption under autarky, MA. However, the variance of consumption with insurance, VI, is less than the variance of consumption under autarky, VA, for N ≥ 2. 22If the terms in inequality (1) describe per-period utility, then both sides of the inequality would be multiplied by 1/(1 − δ) for an infnitely lived household with discount factor δ. This would have no effect on the results that follow. Main Results of the model • Some redistribution is socially optimal, which implies that (relatively) wealthy households in the community should ceteris paribus be more likely to have migrant members. • Households that face greater rural income risk are ceteris paribus less likely to have migrant members. Testing the Theory: Evidence on Redistribution • Testable Predictions: • income is redistributed in favor of poor households within the caste; • relatively wealthy households who, therefore, benefit less from the insurance network should be more likely to have migrant members; • relatively wealthy households who, therefore, benefit less from the insurance network should be more likely to have migrant members; Evidence on Redistribution within Castes • data from the 2005–2011 Indian ICRISAT panel survey; • 2006 Rural Economic Development Survey (REDS) has information from over 119,000 households residing in 242 villages in 17 major Indian states on the migrant status of each household; 70 THE AMERICAN ECONOMIC REVIEW JANUARY 2016 Nonresident household members who are temporary migrants are included in the household roster, as is standard in most household surveys. To test how relative income affects migration, we construct a measure of the household’s average income over time. This will depend on its wealth (productive assets) as well as the number of earners. A shortcoming of the REDS data is that it provides incomes only in the year preceding the survey and includes transfers. To address this limitation, we impute average income for each household using the ICRISAT panel dataset and a vector of household and village-level variables that are common to both the REDS and ICRISAT datasets. Both datasets provide household-level information on total land area (together with a binary variable indicating whether the household is landless), irrigated area, soil type (red, black, sandy) and soil depth, household size, the number of earners, and the occupation of the household head. Each dataset also provides, at the village level, a time-series of rainfall; daily rainfall for all seven years for the ICRISAT survey and monthly data over an eight-year period starting in 1999 for the REDS, from which we construct village-level mean annual rainfall and the variance of annual rainfall. The land characteristics, taken together, determine the value of land owned by the household. Although land wealth accounts for 85 percent of household wealth in rural India (Rosenzweig and Wolpin 1993), land sales are extremely infrequent (Foster and Rosenzweig 2002). This implies that land wealth is largely inherited and can be treated as predetermined, at least from the perspective of current household members. The household’s permanent labor income is determined by the number of earners and the occupation of the household head.30 When imputing average income for REDS households with permanent male migrants, we included those migrants among the earners. We frst estimate, using ICRISAT data, the relationship between average annual household income over all seven years excluding all transfers and the vector of which accounts for two-thirds of the missing households, from attrition. Assuming that two-thirds of missing Indian households also migrated, this implies that the annual rate of permanent household migration is less than 1 percent. 30Occupational choices are very limited in rural India. In the 2006 REDS census, 34 percent of the heads were cultivators, i.e., landowners, and 38 percent were agricultural laborers and laborers not classifed. The next most popular occupation—shopkeepers—accounted for just 2 percent of the household heads. Not surprisingly, the most important determinants of household income are landholding size and number of male earners. Table 5—Income and Consumption within the Caste ICRISAT REDS 2006 Relative income Relative consumption Consumptionincome ratio Relative income Relative consumption Consumptionincome ratio Migration (1) (2) (3) (4) (5) (6) (7) Relative income class 1 0.119 0.460 3.871 0.316 0.843 2.665 0.032 2 0.281 0.625 2.224 0.416 0.854 2.052 0.034 3 0.373 0.626 1.680 0.513 0.871 1.697 0.051 4 0.510 0.673 1.319 0.627 0.887 1.413 0.046 5 1.000 1.000 1.000 1.000 1.000 1.000 0.051 Notes: Income classes are defned by quintiles within each caste. Income and consumption are measured relative to the highest (ffth) income class. REDS 2006 income and consumption are inputed from ICRISAT data. REDS data consists of 100 castes, while ICRISAT data consist of 7 castes. Sample-size restriction is at least 30 households per caste with REDS data and 20 households per caste with ICRISAT data. Reduced Form Evidence • !" indicated whether any male member of household i moved out from the village; • #" indicated the household average income; • #$ indicates average cast income 72 THE AMERICAN ECONOMIC REVIEW JANUARY 2016 on the household’s own income, an increase in average caste income implies that it is relatively less wealthy within its caste. To test Proposition 1 we thus estimate a regression of the form (9) Mi = π0 + π1 yi + π2 _ yi + ϵi , where Mi indicates whether any male member of household i had moved permanently from the village, yi is the household’s average income over time, and _ y is the corresponding average statistic for its caste, which is constructed by averaging incomes over all households in the caste. As discussed above, this information is available from the 2006 REDS census. Conditional on average caste income, an increase in a household’s income implies that it is relatively wealthy and, therefore, should be more likely to have migrant members. However, household income could directly determine migration, as discussed above, and so the π1 coeffcient cannot be used to test the theory. The key test of Proposition 1 is π2 < 0; conditional on the household’s own income, an increase in caste income implies it is relatively less wealthy and, therefore, should be less likely to have migrant members. Table 6, column 1 reports the estimates of equation (9). Coeffcient standard errors are bootstrapped to account for the use of imputed incomes. As predicted by Proposition 1, the estimated coeffcient on caste income, π̂ 2, is negative and signifcant. This result provides support for the theory in which the migration decision is made in the context of a caste network, and networks redistribute income in favor of the poor. The positive and signifcant coeffcient on own household income, π̂ 1 in column 1, is also consistent with the theory but, as noted, there are other interpretations.33 Proposition 2 indicates that households who face greater rural income risk should be less likely to have migrant members. We test this prediction by including the rural income risk faced by the household as an additional regressor in Table 6, column 2. Income risk in our theory is measured by the coeffcient of variation of the household’s income, squared. We construct the variance of the household’s income over time using the same method that was used to impute average income.34 Using the constructed variance to compute income risk, we see in Table 6, column 2 that households facing higher rural income risk are indeed less likely to have migrant members. While this result is consistent with our theory in which migration results in the loss of risk-reducing network services, it is inconsistent with standard models of individual migration in which adverse origin characteristics lead to higher out-migration rates. 33Our analysis with 2006 REDS data restricts the sample to castes with at least 30 households in the census. This ensures that there will be a suffcient number of households in each income class in the structural estimation, where castes are divided into four to six income classes. The reduced-form results in Table 6 are robust to restricting the sample to castes with at least 10 households in the 2006 REDS census. 34The specifcation in the frst step, using ICRISAT data, is the same, except that the household characteristics are interacted with the variance of village rainfall in the ICRISAT villages. The set of household and village level regressors once again have suffcient power, with an R2 around 0.3. The estimated coeffcients from the frst step are subsequently used to predict the variance of income for each of the REDS households using their characteristics and village-level rainfall variances. We estimate the relationship between log variance and the household and village characteristics in the frst step. Predicted log variance for the REDS households can then be transformed to the variance of income, ensuring that no negative values are obtained. Test prediction 1: • !" < 0: conditional on the household’s own income, an increase in cast income implies it is relatively less wealthy and, therefore, should be less likely to have migrants members; Test prediction 2 • Proposition 2 indicates that households who face greater rural income risk should be less likely to have migrant members. • They test this prediction by including the rural income risk faced by the household as an additional regressor • Income risk is measured by the coefficient of variation of the household’s income, squared. VOL. 106 NO. 1 MUNSHI AND ROSENZWEIG: NETWORKS AND MISALLOCATION 73 Recall that the relationship between relative income and migration in Proposition 1 was derived conditional on rural income risk. The relationship between income risk and migration in Proposition 2 was derived conditional on household income (and the household’s position in the caste income distribution). The specifcation in column 2 allows us to (simultaneously) estimate these conditional effects, as required by the theory. The point estimates indicate that the magnitude of these effects are large. A one standard deviation decrease in the risk measure doubles the migration rate (from a baseline of 3.1 percent). A one standard deviation increase in own income increases the migration rate by 10 percent, while the same increase in caste income reduces the migration rate by 30 percent. The theory does not specify what constitutes the domain of the network. Although the organization of Indian society, with individuals marrying strictly within their caste, leads us to posit that rural insurance networks are organized around the caste, they could potentially be organized at the level of the village, as assumed in previous studies on risk-sharing in India, e.g., Townsend 1994; Ligon 1998. To address this possibility, we include mean village income as an additional regressor in Table 6, column 3. Consistent with Mazzocco and Saini (2012) who report full risk-sharing at the caste level, but reject full risk-sharing in the village with ICRISAT data, we see that the coeffcient on mean caste-income is stable and remains highly signifcant, whereas the corresponding coeffcient on village income is small and imprecisely estimated. One remaining possibility is that the estimated village-income coeffcient is biased because village income is correlated with village infrastructure, which directly determines migration. To address this possibility, we include Table 6—Reduced-Form Migration Estimates Migration (1) (2) (3) (4) (5) (6) Household income 0.0059 0.0051 0.0026 0.0025 0.0021 0.0021 (0.0024) (0.0024) (0.0033) (0.0033) (0.0030) (0.0033) Caste income −0.016 −0.018 −0.022 −0.024 −0.025 −0.017 (0.0043) (0.0055) (0.008) (0.0107) (0.0107) (0.014) Income risk — −0.00038 −0.00037 −0.00053 −0.00053 −0.00053 (0.00015) (0.00016) (0.00017) (0.00017) (0.00011) Village income 0.007 0.006 — — (0.011) (0.013) — — Village/caste income 0.0073 0.0088 (0.013) (0.027) Village fxed effects No No No No No Yes Infrastructure variables No No No Yes Yes No Joint sig. of infrastructure variables χ2 — — — 16.14 16.59 — — — — [0.0011] [0.00090] — Observations 19,362 19,362 19,362 19,362 19,362 19,362 Notes: Bootstrapped standard errors in parentheses are clustered at the caste level in columns 1, 2, and 6 and two-way clustered at the caste and village level in columns 3–5. Income measured in lakhs of rupees, (1 lakh = 100,000). Infrastructure variables: whether there is a bank, secondary school, health center, or bus station in the village, as well as distance to the nearest town. χ2 p-value reported in square brackets. Sample-size restricted to castes with at least 30 households. Source: REDS census 2006 Conclusions • This paper provides an explanation for large spatial wage disparities and low male migration in India based on a combination of wellfunctioning rural insurance networks and the absence of formal insurance • When men migrate permanently to work, they (and their rural households) cannot credibly commit to honoring their future obligations at the same level as households without migrants. Summary from last week • The richer a country the more people move from rural to urban areas; • Over time more people are moving to cites, especially in developing countries; • There are wage gaps between urban and rural areas; • Harris-Todaro explain these wage gaps using a simple model with inflexible wages in the city; • Policy from the government: subsidy to increase migration to urban areas BUT risk to increase informal sector in urban areas (Harris-Todaro paradox) Do people move? • There is evidence that people move less than they should; • Why people do not move? • Lack of Network; • Social/Family Ties; • Lack of Income; • Physical Barriers: walls We will explore all these causes one by one this week! Why People do not move? First reason we explored on Tuesday: • The get insurance in their rural villages. • They dislike volatility of consumption, therefore, moving to the city is costly for them • As a result, wealthy people will move, less wealthy will stay and take advantage of the insurance system. • This paper provides an explanation for large spatial wage disparities and low male migration in India based on a combination of well- functioning rural insurance networks and the absence of formal insurance • When men migrate permanently to work, they (and their rural households) cannot credibly commit to honoring their future obligations at the same level as households without migrants. Today: Hardness to Move: “Physical Barriers” • There are several walls in the world: • Occupied Palestinian Territories and Israel • Mexico-US • … • They prevent migration from one side to the other Evidence on the Walls over Time • Are there more or less walls than before? Allen and Morten (2018): Migration and Walls • Between 2007 and 2010 the United States constructed a border fence along one third of its southern border. • They ask two questions: • (i) how did migrants respond to this increase in the cost of migrating between the United States and Mexico, • (ii) What were the overall effects of building this fence, for the labor market in the United States as well as in Mexico? Empirical Context and Data • The 2006 Secure Fence Act was signed by President George Bush on October 26, 2006. • The Bill authorized the construction of reinforced fencing on locations of the border in California, Arizona, Mexico and Texas. • Between 2007 and 2010, 700 miles of fence was constructed long the 2000-mile US-Mexico border. Fence location at the border Border Fences Locations affected by the fence Main Findings of the Paper • show evidence of a decrease in migration on both the intensive margin (with migrants substituting towards destinations that were less affected by the wall), • by looking at Mexican census data, on the extensive margin (where locations who had more exposure to the wall had a large growth in the local labor force than less exposed locations). Data • geocode the locations of the fence along the border by digitizing an engineering report that displays all the fence locations at a 1:50,000 scale • this allows to identify which portions of the fence were constructed during the 2007-2010 expansion • Two Data sources on migrants: • primary source for observing bilateral migration flows between the US and Mexico is the database of Mexican Consulate ID cards. • Survey of Migration at Mexico’s Northern Border Data Issues • It is hard to measure migration, especially illegal migration; • One challenge: measures of number of migrants crossing the border is not the same as the number of migrants successfully living in a destination • Dataset that include information on immigrants: • “Matricula Consular Database”,: ID card given to Mexicans residing in the US; • observe close to one million new cards being issued every year. • Survey of Migration at Mexico’s Northern Border (EMIF Norte, • survey takes place in a selected sample of border cities that are traditionally used as crossing points. • designed to measure the size and characteristics of the flows of migrant workers between Mexico and the United States Cross-Check of the Matricula ID and US data on Mexicans Comparison Matricula and Population Comparison matricula data and ACS data Empirical evidence • Fact 1(a): The fence reduced bilateral migration... • Fact 1(b): ...and changed where people crossed the border • Fact 2(a): The fence affected the number of migrants differentially across the U.S... • Fact 2(b): ... and differentially affected the number of non- migrants across Mexico Fact 1(a): The fence reduced bilateral migration... • Estimate the following equation: • Where N_tod is the number of migrants between o and d in year t • controlling for origin-year, destination-year, and pair fixed effects, they look to see if travel time predicts bilateral migration flows. Fact 1(b): ...and changed where people crossed the border • run the following regression: !"#$ = &"#$' + )"#$ + *"#$ use the EMIF survey data to document that the fence expansion actually changed migrants decisions Fact 2(a): The fence affected the number of migrants differentially across the U.S... • examine the effect at the destination level • run the following regression: • where var_it is either population or wages. • All regressions control for a PUMA fixed effect • PUMA is a geographic unit of analysis Fact 2(b): ... and differentially affected the number of non- migrants across Mexico. • run the parallel regressions at the Mexican municipality level • If a Mexican municipality is more exposed to the fence, this means that it was more difficult for migrants to move to the US • run the regression IV regressions with matricula data IV regressions with matricula data Theoretical Model • General equilibrium model • Many locations • Multiple types of labor • Costly movement of people and goods between locations • Objectives of the model: 1. it provides a theoretical explanation consistent with the stylized facts 2. it provides a theoretical explanation consistent with the stylized facts Conclusions • The increase in the fence act decreased migration to the US from Mexico • It changed the destination and the origin of there migrants where going and coming from Next week • What are the consequences of migration? • Crime? • Pollution? • Voting decision? We will explore some of these in more details; • There is also seasonal migration that is quite large in the data: • We will analyze how that affects the economy as well.source..
Migration in China
Migration in China
Due to extreme rural-urban migration from the recent decades, rapid urbanization has taken place in China. The rates of migration have increased rapidly with long-term migration being the main trait. In China, migration characteristics are decided by personal decisions and joint decisions within households which agree sending their members that own comparative advantages in service and manufacturing, mainly young and male, to go to cities and work. Major destinations are big cities and coastal regions characterized by developed service and manufacturing activities. Migration in China is driven mainly by the aspiration to have higher incomes and acquire job opportunities that are better. Moreover, urban amenities and public services account partly in the flow of China’s population. However, there exist institutional barriers, such as hukou system and the segmentations in labor markets in urban areas, public services and social services which act as a hindrance inter-regional and rural-urban migration (Chan, 2012).
China’s population has witnessed urbanization rapidly in the recent 30 years of economic breakneck. During that period, the population in urban areas has increased from 26% to 56% and currently, it is estimated that more than 200 million migrants are working in China’s biggest cities. Many urban migrants move there with an aim of working for one to two decades and later return to their homes. This is motivated by the hukou system which s household registration system in China. The system links individual’s right to receive state welfare with the places that they were born. This means that, people can access state-subsidized government assistance such as healthcare and education only in the provinces that are their homes. In case they move to other places, they should either transfer the hukou or pay premiums in order to access the services. A majority of individuals are unable and unwilling to change their hukou and therefore end up being burdened with the urban residence status and costs of living which are inflated. Therefore, their plan is to save and eventually leave the city and go back to their birth land.
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