Abu Shonchoy http://research.economics.unsw.edu.au/ashonchoy

56/39 Barker St., Sydney, NSW 2032, Australia, (614)-017-60667 abu:shonchoy@unsw:edu:au

Education: Bachelor of Social Sciences (BSS) with Honors, Economics, Universityof Dhaka, Bangladesh 2003.

Master of Economic Policy (MEcPol), The Australian National Uni-versity, Canberra, Australia 2005

Doctor of Philosophy in Economics, University of New South Wales(UNSW), Australia. (Expected date of completion July 2009).

Research: Primary: Development Economics, Macro Economics and EconomicGrowth.

Secondary: Public Economics and Labor Economics.

Dissertation: Title: Essays in Development Economics.

Experience: Lecturer- in- Charge February 2008 to presentSchool of Economics, UNSW. Course instructor for graduate IntroductoryData and Statistics course.

Associate Lecturer July 2006 to presentSchool of Economics, UNSW, Sydney Australia. Tutor in charge of thefollowing courses:� Microeconomics 1, taught by Diane Enahoro (2008)� Macroeconomics 1, taught by Louise Zaieme (2008, 2007)� Business Economics, taught by Associate Professor Trevor Stagmen(2006, 2007) and Dr. Nigel Stapledon (2007)

� Game Theory and Business Strategy, taught by Dr. Gautam Bose(2006)

� Development Economics, taught by Dr. Gautam Bose (2006)

� Quantitative Methods A, taught by Louis Yeung (2006).

School of Economics, ANU, Canberra Australia. Tutor in charge for theENGN 3211, Investment Decisions and Financial Systems, taught by Mr.John Gage (2004, 2005).

IMF Winter Internship November 2007 - February 2008Policy development and Review Unit (PDR), International Monetary Fund(IMF), Washington DC, USA.

Lecturer July 2005 - February 2006Department of Economics and Social Sciences, BRAC University, Dhaka,Bangladesh. Course instructor for Intermediate Macroeconomics (under-graduate) and Managerial Economics (post graduate).

Academic Assistant (Economics) April 2004 - July 2005Fenner Hall, Australian National University (ANU).

Publications: �Spending and Absorption of Aid in PRGF Supported Programs�(WithBerndt Markus, Paolo Dudine and Jan Kees Martijn) IMF Working PaperWP/08/237. (2008)

�Determinants of Government Expenditure in Developing Countries, APanel Data Approach�forthcoming in BRAC University Journal. (2008)

�Seasonal Migration pattern of Labor: evidence from Kurigram district,Bangladesh�(With A.Z.A Shahriar and Sakiba Zeba), ESS Working Pa-per Series 007. Department of Economics and Social Sciences, BRACUniversity. Dhaka, Bangladesh. (2006)

Conference: �The E¤ectiveness of Microcredit in the Lean Period: Evidence fromBangladesh� presented to the conference �Bangladesh in the 21st Cen-tury�at Harvard University, Boston, USA, 13-14th June, 2008.

"The Information Economics Revolution: a conference in Honour of SirJames Mirrlees", at University of Melbourne, Melbourne, Australia, 14thand 15th March, 2007.

ICT for Human Resource Development: How to use ICT to enhance adultliteracy program of Bangladesh, Presented at the Youth and ICT SeminarSeries organized by the Development research network (DNET), DhakaBangladesh and BRAC University, Bangladesh, 2006

Computer skill:Windows and Macintosh Operating Systems, Microsoft O¢ ce utilities,LATEX, Scienti�cWorkplace, STATA, E-views, SPSS andMATLAB (work-ing knowledge)

Recognitions: Participated in the �3rd Meetings of the Winner of the Bank of SwedenPrize of Economic Sciences�by the council for the Lindau Nobel LaureateMeetings from 20th to 24th August. Only 300 young researchers from

around the world have been given this opportunity to participate in themeeting. (2008)

University International Postgraduate Scholarship (UIPA), University ofNew South Wales (UNSW).(2006)

Neil Vousden Memorial Scholarship for Master�s studies in Economics atthe Australian National University (2005)

Best paper in the �Youth and ICT Seminar Series�organized by Devel-opment research network (DNET), Dhaka Bangladesh. (2005)

Faculty Scholarship, University of Dhaka, Bangladesh. (2004)

Volunteer: Active member of UN Online volunteers group and founder member ofthe OIKOS International Bangladesh. OIKOS is an International studentorganization for Sustainable Economics and Management.

Languages: Bangla (Bengali), English. Can speak Hindi and Urdu �uently.

References: Kevin J. Fox, Professor and Head of the School of Economics, Director,Centre for Applied Economic Research, University of New South Wales,Sydney 2052 Australia. Phone 02 9385 3320, E-mail:K.Fox@unsw.edu.au.

Adrian Pagan, Professor, School of Economics, UNSW, Sydney 2052Australia. Phone 02 9385 3319, E-mail:A.Pagan@unsw.edu.au.

Denzil Fiebig, Professor, School of Economics, UNSW, Sydney 2052Australia. Phone 02 9385 3958, E-mail:D.Fiebig@unsw.edu.au.

Elisabetta Magnani, Associate Professor / Postgraduate Research Co-ordinator, School of Economics, UNSW, Sydney 2052 Australia, Phone02 9385 3370, Email:E.Magnani@unsw.edu.au.

Trevor Stegman, Associate Professor, School of Economics, UNSW,Sydney 2052 Australia. Phone 02 9385 3670, E-mail:T.Stagmen@unsw.edu.au.

31/10/2008 11:52:51 page 1/1

THE UNIVERSITY OF NEW SOUTH WALES

COURSE AND TEACHING EVALUATION AND IMPROVEMENT (CATEI)

CATEI Evaluation Report

Student Evaluation of Large Group TeachingECON5257 - Introductory Statistics and Data Analysis

Summary Report FORM B

Faculty: Business 2008 TeachingPeriod 2

Strand: Abu Shonchoy Enrolled: 121

Class Desc: Abu Syeid Mohammad Parves Shonchoy

Lecturer/Tutor: Abu Syeid Mohammad Parves Shonchoy Respondents: 72

School: School of Economics AdministrationDate:

29-OCT-08

Mode of Study: Gender: Student Origin:

Full-Time: 68 94% Female: 55 76% Local: 4 6%

Part-Time: 4 6% Male: 17 24% International: 68 94%

SA A MA MD D SD L&T GCA Mean Response% % % % % % Agree Scale Rating Rate

C S F

Q1. This lecturer communicated effectively with students (e.g.

He / She explained things clearly).

43 42 10 4 1 0 95 ; 86 ; 88 72 5.21 100%

Q2. This lecturer stimulated my interest in the subject matter

he/she was teaching

32 46 15 3 3 0 93 ; 81 ; 84 66 5.03 99%

Q3. This lecturer encouraged me to think critically 41 37 13 6 3 0 91 ; 83 ; 86 67 5.09 97%

Q4. This lecturer provided feedback to help me learn 38 35 18 7 1 1 91 ; 79 ; 82 62 4.96 100%

Q5. This lecturer encouraged student input and participation

during classes

40 33 21 4 1 0 94 ; 82 ; 86 67 5.07 100%

Q6. This lecturer was generally helpful to students 38 44 14 4 0 0 96 ; 87 ; 89 71 5.15 99%

Q7. This lecturer related the concepts of the course to practical

examples

46 43 10 1 0 0 99 ; 90 ; 91 77 5.33 100%

Q8. This lecturer was well prepared 53 38 7 3 0 0 98 ; 89 ; 92 79 5.4 100%

Q9. This lecturer was enthusiastic about the material he/she was

teaching

44 43 10 1 0 1 97 ; 89 ; 90 75 5.26 100%

Q10. Overall, I was satisfied with the quality of this lecturer's

teaching

36 46 14 3 1 0 96 ; 85 ; 87 70 5.13 100%

Aggregated Questions Rating

Strongly Agree 41

Agree 41

Moderately Agree 13

Moderately Disagree 4

Disagree 1

Strongly Disagree 0

15 30 45 60

Respondent (%)

Seasonal Migration and the Effectiveness of

Micro-credit in the Lean period : Evidence from

Bangladesh

Abu Shonchoy∗

The University of New South Wales

November 25, 2008

Abstract

Seasonal migration due to natural disasters or agricultural down-turns is a common phenomenon in developing countries. Using primarydata from a cross sectional household survey from the northern partof Bangladesh, we quantify the factors that influence the seasonal mi-gration decision. Controlling for other characteristics, we find thatindividuals with access to micro-credit did not have a significantlydifferent level of income to those who did not have access to credit.Households that took the decision to migrate combined with micro-credit earn significantly more than households with only micro-creditin the lean period. In addition, we find a significant role for networkeffects in influencing the migration decision, with the presences of kins-men at the place of destination having a significant impact. The resultshave numerous potential policy implications, including for the designof micro-credit schemes.

JEL Classification : J62, J64, J65, O15, O18, R23.Keywords : Lean period; Seasonal migration; Micro-credit; Bangladesh.

∗Acknowledgement : I am extremely thankful to Mr. Abu Z. Shahriar and Ms. SakibaZeba for permitting me to use the data and BRAC University for funding the research.Thanks to Professor Ian Walker, Professor Denzil Fiebig, Professor Kevin J. Fox, Asso-ciate Professor Elisabetta Magnani and Dr. Suraj Prasad for showing interest and givingme numerous ideas to fulfill this research. My heartfelt thanks go to them. I also bene-fited from insightful discussions from the participants of Bangladesh in the 21st centuryconference at the Harvard University, Boston, USA. For all correspondence please email:abu.shonchoy@unsw.edu.au. Usual disclaimers apply.

1

1 Introduction

The aim of this paper is to better understand the causes of seasonal migra-tion, evaluate the characteristics of seasonal migrants and to quantify theeffects of the factors influencing seasonal migration decisions. Of particularinterest, we test the effectiveness of micro-credit programs in the lean periodand the role of networks (kinship) on the seasonal migration decision.

In the standard rural-urban migration literature, researchers primarilyfocus on permanent internal migration and its economic, social and demo-graphic significance. Only a few studies have discussed temporary internalmigration which is variously known as “seasonal migration”, “circular mi-gration”, or “oscillatory migration”. Evidence of this phenomenon exists inmany regions and particularly in the developing countries of Africa (Eklan1959, 1967, Guilmoto 1998), Asia (Hugo 1982, Stretton 1983, Deshingkar2003, Rogali et. at 2002 and Rogali 2003) and South America (Deutsch2003). People move from rural areas during lean periods to nearby citiesor towns for a short period of time in an attempt to maintain their livingstandards. Lean periods can occur due to agriculture cycles or natural dis-asters, such as draught, flood, cyclone, climate change and river erosion.Thus temporary migration is an important livelihood strategy for a largenumber of poor rural people in developing countries.

In the case of seasonal downturns or shocks, a person may prefer a tem-porary over a permanent move because such a decision offers an opportunityto combine the village based existence with the urban opportunities. In theface of village-based highly seasonal labor demand, villagers may see thetemporary migration to the urban areas as relatively practical and rationalstrategy to cope with seasonal downturn and natural shocks. But the mostimportant factor, which leads to a temporary move rather than a permanentone, is the reversal of the urban-rural wage differential that occurs duringthe peak labor demand season in the agricultural sector.

Evidence from different countries suggests that temporary mobility oflabor from rural to urban areas has important socio-economic implications.Migration reduces the inequality in the rural area with a flow of remittancesback to the migration destinations. This flow is quite regular which is un-likely to occur with permanent rural to urban migration. Such a flow playsa large role for rural families who through this income can afford the neces-

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sities of life. Also the return migrants may diffuse ideas, information andknowledge which could play a vital role in rural development process.

However, temporary migrants cause congestion and other social problemsin urban areas. Policy makers do not have enough information about thenumber of people migrating temporarily to tackle these problems. Seasonalmigrants are very hard to detect and the definition is not a clear one. Hencethey are typically excluded from national surveys. As a result, it is difficultto implement effective policies to accommodate seasonal migrants.

One of the recent policy developments in developing countries has beenthe emergence of micro-credit in poverty alleviation. It is argued that ifgiven access to credit, small entrepreneurs from poor households will findopportunities to engage in viable income-generating activities and thus, getrid of the poverty by their own. Various studies on the impact of micro-creditin developing countries have found evidence of consumption smoothing, as-set building (Pitt and Khandker 1998)and reduction of poverty (Khandker2005). Conversely, using the same data set as Pitt and Khandker (1998),Morduch (1999) found that the average impact of micro-finance is “non-existent”. Similarly Navajas et. el. (2002) concluded that micro-credit islargely unsuccessful in reaching the poor and the vulnerable. Hence a nat-ural extension of this study is to explore the effectiveness of micro-crediton the poor people especially during the lean period and how micro-creditauthorities address such problems while giving loans.

We use primary data collected from the Northern part of Bangladesh.The random cross-section household survey was conducted in January 2006by Abu Shonchoy, Abu Z. Shahriar, Sakiba Zeba and Shaila Parveen as partof the project undertaken by the Economics and Social Sciences ResearchGroup (ESSRG) of BRAC University. We choose the Kurigram district ofNorthern Bangladesh because of some distinct features. Kurigram is mainlyan agri-based, natural disaster prone and severely poverty-striken area ofBangladesh.1 Due to the agricultural cycle, after the plantation of the Aman

1Rural life of Bangladesh very much evolves around the agricultural cycle and our studyarea is not an exception. As a consequence of this cycle, two major seasonal deficits occur,one in late September to early November and the other is in late March to early May.With the widespread expansion of Boro cultivation, the incidence of the early summerlean period has significantly declined. However, the autumn lean season coming afterthe plantation of the Aman crop still affects nearly all parts of the country, specially thenorthern part of Bangladesh. In local terms, this lean season is called Monga or MoraKarthik (Rahman and Hossain, 1991).

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in September-October, farmers have very little work to do on the farms.2 Asa result, every year a large number of agricultural workers become joblessand decide to migrate temporarily. Such migrants tend to get work in theurban informal sector and work mainly as day laborers. Though the urbanstandard of living is typically a bare minimum for these migrants, they preferthis option than to stay in the village with no income at all.

Pioneering work on seasonal migration in Bangladesh has been con-ducted by Shahriar et al. (2006). Unfortunately, the study did not pro-duce efficient and consistent estimates due to the use of incomplete data.However, by using an updated version of the data, the present study hasimproved on the model of Shahriar et al. and is able to provide efficientestimates and additional insights on this research.

Hence, this study provides a significant advance in the understandingof the drivers of seasonal migration and the effectiveness of micro-credit inpoverty alleviation.

2 Background

2.1 What is Seasonal Migration

The terminology of seasonal migration probably first appeared in the sem-inal paper of Walter Elkan where he observed circular migration patternsof labor in East Africa (Elkan 1967) and explained it as “Combined withthe familiar pattern of migration, all in one direction, there is another andimportant movement back to the countryside.” However, according to Desh-ingkar and Start (2003), the formal definition of seasonal migration was putforward in the 1970’s by J. M. Nelson (1976) who discussed such labors’as “sojourners”. This work raised interest in the causes and consequencesof temporary city-ward migration in the developing countries. Accordingto Nelson, a major proportion of rural to urban migration in Africa andpart of Asia is temporary in nature. Also, Zelinsky (1971) defined seasonalmigration as “...short-term, repetitive or cyclic in nature...”.

The seasonal migration of labor has been studied in many disciplinesother than Economics. Disciplines like Demography, Anthropology, Sociol-ogy have discussed such movements of labor long before it appeared in Eco-

2In more than 80% of the farms in the study area only one (Aman paddy) or two crops(Aman and Boro paddy) are produced annually.

4

nomics. Consequently, the terminology used to describe this phenomenonvaries a lot. For example, seasonal migration has also been referred to asreturn migration, wage-labor migration, transhumance, etc. to name a few.In addition, geographers have noticed this observable fact of labor movementeven in the early 1920s. As mentioned in Chapman and Prothero (1983),“The concept of circulation as the beneficial integration of distinct placesor communities dates from the 1920s mainly characterizes the work of hu-man geographers and originated with the French led by Vidal de la Blache(1845-1918). Among French geographers, circulation refers to the reciprocalflow not only of people but also of ideas, goods, services and socioculturalinfluences (de la Blache, 1926: 349-445; Sorre, 1961: Part IV)”. Chapmanand Prothero (1983) provide a comprehensive study on this literature. Also,Nelson (1976) has a detail discussion on the causes and consequences of suchmigration.

2.2 Reasons for Seasonal Migration

Other than social issues like family structures, social customs and religiousbeliefs, economic factors are the most influential reasons to migrate in thelean period. In his seminal work, Elkan (1959, page 192) refers to thesenon-economic factors as the “...most unlikely to be the whole story, and...itcan never be the most important part of the story”. On the contrary, Elkandenoted the economic factors as “...largely a rationalization of simple eco-nomic motives”. In this section we primarily focus on the economic factorsthat lead to migration (rural to urban) and reverse migration (urban torural).

2.2.1 Reasons causing rural to urban migration

Migration can be regarded as a risk diversification strategy as mentionedin Stark and Levhari (1982) and Katz and Stark (2006). During the leanperiod, the temporary mobility of labor provides some means of livelihoodin the urban areas. There are mainly four reasons why families take suchdecision in the lean period. Firstly, it is always easier and cheaper to survivein the rural than in the urban area as the prices of food grains and otherhousehold essentials are relatively cheaper. Hence, in the most cases thehead or the most capable member of the household, who are mainly men,

5

migrates to urban area. Being the lone mover from the household, a personcan cope up with the urban life and typically survives on a bare minimumin order to send remittances back to the families.

Secondly, seasonal unemployment in agriculture causes an excess sup-ply of unskilled or semi skilled workers in the rural areas. In combinationwith this, food grains and other necessary commodities become relativelyexpensive during this period as the well-offs in these regions hoard a largeamount of crops in the normal time to sell those in the lean period at a highprice. Hence the increase in price reduces the real wage of workers. Thus, itbecomes almost impossible for an ordinary agricultural worker to maintainthe general living standards during the lean period in the village and thusthey choose to migrate.

Over recent years, much public and private investments has been con-centrated in urban areas in developing countries. Little or no effort has goneinto creating affective non-agricultural sectors in the rural areas. Therefore,there exists only a few alternative means of earning in the rural area otherthan agriculture and argi-based industries. Thus pattern of temporary labormovement is nothing but the pure response to the lack of alternatives in therural areas (Hugo 1982).

Finally, there is typically significant journey exists between the migrationdestination and origin, but interestingly, the cost of the journey is usuallyvery small and unimportant for migrants. As mentioned in Hugo (1982,page 73) “...travel costs, time taken, and distance traversed between originand destination generally constitute a minor element in a mover’s overallcalculus in deciding whether or not to migrate and where”. The recent im-provement in communication in third world countries has also reduced thecost of a movement significantly (Afsar 1999). Moreover, access to formalcredit market (through micro-credit schemes operated by NGOs) gives mi-grants the option of borrowing which can reduce their immediate relocationand travel costs. Although NGO’s do not run any specific programs to pro-vide credit for the migration, it is possible to use a loan taken by the othermembers of the family and repay the loan once they have found work in themigration destination.

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2.2.2 Reasons causing reverse migration

There are some interesting facts which influences the migrants to come backto the village hence causes reverse migration. Once moved to the urban ar-eas, there are some off-setting factors like forgone income in the normal sea-son, skills, etc. which are quite important for the reverse migration (Mendola2008). Moreover, due to poverty and resource constraints, it is extremelydifficult for a migrant to devote resources to building or investing in skillsthat are required for the formal urban job markets. Hence seasonal migrantsend up seeking jobs in the urban informal sector where the wage is typicallyat a minimum and working conditions are not pleasant. Informal sectoris primarily low-skilled and usually requires manual labor (like Rickshawpulling, construction works or day laborer etc.). The wages are very poorto support a single man, let alone a family. These people get to live in theslums or in the pavements of the large stations or sometimes by the side ofstreets. Such condition of living is worse than what they had in the villages.Moreover, lack of job security, ineffective labor unions and illness relatedinsecurity also play roles towards reverse migration. Seasonal migrants aregenerally not protected against accident and do not have provision for retire-ment benefit (Elkan 1959). Therefore if a migrant unfortunately becomesill or required money, the migrant can always seek help in the village whichprovides some sort of social security by the widespread network of socialrelations hence provides incentives for the migrants to go back (Hugo 1982).

In the lean period, large numbers of people may leave the village to seekjobs in the urban sector which lead to excess supply of labor. Employersusually exploit this by decreasing the wage rate below the standard marketrate. Moreover, employers know that migrants are temporary workers, hencethere is no incentive for them to provide training or invest in this short termlabor force. The lack of formal or skill based education mean that most ofthe migrant workers remain unskilled making it extremely difficult for themto seek jobs in the formal urban labor market.

But the most important economic factor that leads to reverse migrationis the reversal of the rural-urban wage difference. For a temporary migrant,the income in the rural sector during the normal time is typically more thanthat of the the urban sector. As a result, there is a obvious incentive formigrants to come back to the rural areas in normal period after the shockgets over.

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2.3 Factors Influencing Migration Decision

A number of studies have analyzed the internal migration pattern in Bangladesh;Chaudhury (1978), Khan (1982), Huq-Hussain (1996), Begum (1999), Hos-sain (2001), Barkat and Akther, (2001), Afsar (1999, 2003, and 2005), Kuhn(2001, 2005), Islam (2003) and Skinner and Siddique (2005), to name afew. We also find some studies on circular migration like Breman (1978),Hugo (1982), Stretton (1983), Chapman (1983), Ben Rogaly (2002, 2003)Deshinkar (2003) Deutsch (2003) etc. Broadly, these studies generally fo-cus on issues such as the scale and pattern of migration, the characteristicsor selectivity of the migrants, causes of migration, the impacts of internalmigration on urbanization and the pattern of resource transfer followed byrural-urban migration. As we could not find sufficient studies on the factorsinfluencing seasonal migration decision, hence we will use the generally usedvariables which are found significant for the internal rural to urban migraionstudies.

Wage differential : John Hicks (1932) argued that the main cause ofmigration is the wage differential as mentioned in his book “The Theory ofWages” (1932, page 76). Classic migration literatures and theories (Harrisand Todaro 1970 etc.), whether internal or international, employs wage dif-ferentials as the core mechanism that leads to migration. Thus, followingthe trend, a direct relationship between wage differential of lean and normalperiod income and migration decision has been hypothesized.

Assets and access to credit : Interestingly the relationship between landholding and migration decision in the empirical studies are inconclusive andambiguous. For example, Kuhn (2005) argues that the land-holdings ofhouseholds is a key determinants of rural-urban migration and the tendencyto migrate will be greater for those who hold less land. Hossain (2001), incontrast, finds that the tendency to migration is higher for the householdswith some sort of land holding as compared to the landless. Following therecent work of Mendola (2008) who finds negative and significant relation-ship between land holding and migration decisions for temporary migrants inBangladesh. Thus we hypothesized a positive relationship between landlessindividual and seasonal migration, holding other things equal.

Though the existing evidence suggests that researchers do not have aunanimous stand on whether or not micro-credit increases the income of theparticipants significantly and but they do, more or less, agree that access to

8

micro-credit reduces the vulnerability of the participants. Therefore, NGOmembers expected earnings from staying in the village are higher than thenon-members and hence the probability to migrate is likely to be lower.

Ecological vulnerability : Natural disasters like floods and river erosionaffect the agricultural output of the rural people and decrease the incomeand living condition dramatically. Most of the developing countries are nat-ural diaster prone, hence we should take account of such issues on migrationdecision. Historically Bangladesh has been affected by cyclones, floods andriver erosion in every alternate year. Due to the flooding of the three ma-jor rivers (Brahmaputra, Dharla and Tista) hundreds of families each yearof Kurigram, the survey area used in our research, are forced to relocate.People affected by such natural calamities will temporarily move to otherareas. As a result, such variables are important determinants of any internalmigration.

Personal Characteristics: The literature shows that internal migration ismost common among the younger population (Borjas 2000, Mendola 2008,to name a few). Demographically, the internal migrants of Bangladesh aremostly concentrated in young adult ages (Chaudhury 1978) and temporarymigrants are even younger than permanent ones(Afsar 2002). Householdsurveys at migration destinations show that three fourth of temporary andhalf of the permanent internal migrants were 15 to 34 years of age. Based onthe available literature we hypothesize that, holding other things constant;those who belong to the 20-40 age cohort are more likely to migrate in thelean period as their costs of moving are low and the probability of gettingan urban job is high.

Hugo (1982) argued that men have significantly more tendency for sea-sonal migration than women. Due to limited employment opportunities,family responsibilities and religious reasons female members of a family areless likely to migrate than the adult male members. Studies on permanentmigration reveal that those who are married have closer ties with their fam-ilies and relatives hence less likely to migrate ( Lee 1985, Kuhn 2005). Onthe contrary, Hossain (2001), argued that migration propensity is higheramong married persons. When mere survival is crucial in the agriculturallean season, the responsibility to feed dependents (spouse and children) isexpected to increase the probability of individual migration (Haq-Hussain1996). Previous empirical works on migration suggested that size of house-

9

hold positively influence individual’s migration decision (Deshingkar 2003,Mendola 2008, etc.). Hence, a positive influence of household size on migra-tion decision has been hypothesized.

Education: The role of education in the migration decision has beenwidely discussed in the literature and several studies have shown that mi-grants are usually more educated than the non-migrants in the same local-ity (Chaudhury (1978) and Kuhn (2004), for example). Educated peopleare more likely to migrate as job opportunities are higher for them in theurban centers than in the rural areas. Interestingly, Huq-Hussain (1996)suggests that educational attainments are not always an influential factor inthe migration decision, particularly among poor female migrants in Dhakacity. Country level studies have also find significant association of educationon migration decision like Sahota (1968) and Yap (1976) in Brazil, Herrick(1965) in Chile, Carvajal and Geithman (1974) in Costa Rica, Falaris (1979)in Peru, Lanzona (1997) in Philippines and Greenwood (1971) in India.

Role of networks and experience: Holding other things equal, an experi-enced worker is expected to face lower search cost in the urban job market.Thus, we hypothesize that the probability to migrate will be higher if theworker has prior migration experience. The importance of a strong supportnetwork is crucial for the immigrants (Munshi 2003, Mckenzie and Rapoport2007) as well as for the migrants (Afsar 2002). Social networks provide sup-port for relocation, learning new skills, better bargaining power and pro-tection against harassment, assault and uncertainties. Afsar (2003) foundthat 60 percent of the internal migrants, who have kinsmen at the place ofdestination, managed employment within a week of arrival in Dhaka city.Hence, the presence of kinship at the place of destination are expected tohave a higher influence on the seasonal migration tendency.

3 Data Description

We collected the primary data of the study from Kurigram where about46% of the total labor force is involved in agriculture; another 30% areagricultural day laborers (Banglapedia, 2006). The study area consisted offour selected thanas3 of Kurigram district which are Chilmari, Ulipur, Ra-

3A thana is a unit of police administration. In Bangladesh, 64 districts are dividedinto 496 thanas. There are ten thanas in the Kurigram district.

10

jarhaat and Kurigram. The survey covered 17 villages from the four thanas:four from Chilmari, three from Rajarhat, four from Ulipur and six from theSadar thana. Though the villages from each thana were selected randomly,the four thanas were selected to capture heterogeneity in income, communi-cation, infrastructure facilities, catastrophic and other sociocultural factors.

The survey showed that people living in Ulipur and Chilmari were rela-tively poor compared to those living in Rajarhaat. We observed that Kuri-gram Sadar and Rajarhat had better transportation systems compared withChilmari and Ulipur. So the ability to move is relatively higher in this area.A char4 area was also surveyed in the Kurigram Sadar to capture the specialcharacteristics of char livelihood regarding the migration decision in the leanperiod. Among the four thanas, the history of these areas suggest that Ra-jarhaat suffers the least during natural disasters. On the contrary, Chilmariis the worst affected by both flood and river erosion. River erosion is quiterare in Ulipur, though flood every year ravage the area. The char area isaffected by river erosion and flood quite regularly. The Kurigram town wasalso affected by river erosion.

According to the Banglapedia (The National Encyclopedia of Bangladesh,Second Edition 2006) the population of Kurigram district is 1,782,277 ofwhich 49.62% are male and 50.93% are female. Majority of the populationare Muslim. As a result, there is only a minor religious and cultural het-erogeneity exists in the survey area which is negligible. The people of thisregion are largely illiterate, with an average literacy rate of around 22.3%.The sample area consists of 37.02% of the total population of the district.

The survey consisted of 290 random individuals who are representativeof their household. The survey questionnaire was trialed on 30 respon-dents in Chilmari and Ulipur before using for the main survey. The finalquestionnaire consisted of 12 sections. It was designed to collect individualinformation on the migration decision and factors influencing this decision.The survey sought general information like age, occupation, average incomeand the number of dependents. The questionnaire went on to ask aboutland usage, occupation at destination if migrated, NGO membership andlandownership. The questionnaire also collected information on the natureand extent of starvation throughout the year, information on natural dis-asters, death of earning family members and sudden damage of crop or

4A char is a small river island created by silt deposits and estuaries.

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livestock.Among the two hundred and ninety respondents, 68 percent were identi-

fied as migrants. The variables were categorized into three groups: variablesrepresenting economic factors, ecological vulnerabilities and personal char-acteristics. The measure of income in the lean period in this study is theearnings of the household if the respondent stays in the village or the earn-ings of the household if migrates. So, in the data set we do not have thecounterfactuals for this information.

We were not confident that individuals could predict future plans forseasonal migration. Hence, we asked respondents about their past behaviorof migration and income patterns. To capture the seasonal migration be-havior of the respondents, we used a dummy variable, which has a value ofone if the respondent migrated in the one of the last two lean periods andzero otherwise.

The variable seasonal unemployment (unemployed during lean period)is a binary variable. An individual reported to have remained unemployedduring most of the lean season is assigned one and otherwise assigned avalue of zero. In the sample, 63 percent of the respondents reported thatthey were unemployed during the lean period. A simple dummy variable isused to indicate the land ownership. A worker is assigned a value of one ifhis/her family owns any cultivable land irrespective of the size. Otherwise,he/she is assigned a value of zero. 43 percent of the respondents reportedthat they are landless.

With 1200 micro-credit institutions and 19.3 million members, the micro-credit sector of Bangladesh is one of the largest in the world. According toCredit and Development Forum Bangladesh (CDF, 2006), about 37% ofall households in Bangladesh have access to micro-credit. Credit does notrequire any collateral and is given to both individuals and groups. The majortypes of loans include general loans, program loans and housing loans. Wemeasured the access to micro-credit through NGO membership by a dummyvariable, which is coded as one for having access and zero otherwise.

River erosion and flood are the two major natural catastrophes whichhave occurred in the study area. Both of these factors were included in thestudy as dummy variables (DV). For river erosion, the DV has a value ofone if an individual family ever experienced forced displacement due to rivererosion. In our sample, 61% of the respondents faced such an experience at

12

least once in their lives. One problem of such data is identifying the placeof such experience as one could have been forcefully displaced by such anevent which is different than the survey area. Hence, such data is noisy andone has be careful in using this information. The dummy variable for floodequals one if the respondent is a victim of a flood in the year of migration,and zero otherwise. 49 percent of the respondents reported being floodvictim in the last two years.

More males than females were interviewed (89 percent versus 11 percent).Patriarchal village societies are the reasons for such a small female responserate. 70 percent of the respondents reported to be married at the time ofthe survey which is quite a high number.

[Table 1 about here]

The occupation variable was divided into two broad categories includingagricultural and non-agricultural. This is because we are interested in testingthe hypothesis that agricultural workers are the group who migrate in thelean period. Consequently, farmers were assigned a value of one and zerootherwise. The occupational composition of the respondents is as follows: 47percent of the respondents are involved in agriculture and the rest are non-farm workers like fishermen, potters, petty traders, land leasers, garmentworkers, rickshaw-pullers to petty village musicians.

A dummy variable is also used to capture information on education.An individual having some reading ability was given a value of one and zerootherwise. In the present sample, 42 percent of the respondents have at leastsome education. Interestingly 63 percent of the respondents reported havingsome prior migration experience whereas 52 percent of the respondents hadkinsmen at the urban centers at the time of survey.

The variables used in this analysis are summarized in table 8.

4 Econometric Models

4.1 Econometric Modeling of Seasonal Migration

In order to translate the seasonal migration context into an econometricmodel, we take the approach of the random utility model framework. Letus assume that, the utility from the choice of migration(j) = 1 or 0 at the

13

lean period for the individual i = 1, 2..., N can be specified in the flowingform

Uij = Vij + εij

where Vij is the systematic component of utility and the εij is the randomcomponent. The model is completed by specifying Vij , say Vij = x′

ijβ. Herethe xij are the vectors of economic factors, ecological vulnerabilities andpersonal characteristics. As a result, the utility of migration of an individuali at the time of the lean period will be

Umigration|Lean Period = x′βa + εa, (1)

and the utility of staying of individual i at the time of lean period will be

U stay|Lean Period = x′βb + εb. (2)

Now if we denote Yij = 1 if Umigration − U stay = Y ∗ij > 0 , and Yij = 0

otherwise, then the respondent’s choice of migration in the lean period willbe

Pr[Y = 1|x] = Pr[Umig > U stay]= Pr[x′βa + εa − x′βb + εb > 0|x]= Pr[x′(βa − βb) + εa − εb > 0|x]= Pr[x′β + ε > 0|x],

where the distribution of ε is N(0, 1). In this study we will estimate sucha model with univariate probit estimation techniques.

4.2 Econometric Model of Migration and access to Micro-

credit

The model of section 4.1 will produce inconsistent estimates if we assumethat the access to micro-credit is endogenous in nature and may have influ-ence over the people in the lean period and also may influence there propen-sity to migrate. Thus, a natural extension of the univariate probit model willbe to allow two simultaneous equations; one for the access to micro-creditand the other for the migration, with correlated disturbances, which canthen be estimated with a bivariate probit model. Following Greene (2003),the general specification for a two equation model where y∗

1 is the dummy for

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Micro-credit and y∗2 is the dummy for migration is as follows,

y∗1 = x′

1β1 + ε1, y1 = 1 if y1 > 0, 0 otherwise, (3)

y∗2 = x′

2β2 + ε2, y2 = 1 if y2 > 0, 0 otherwise, (4)

E[ε1|x1,x2] = E[ε2|x1,x2] = 0,

V ar[ε1|x1,x2] = V ar[ε2|x1,x2] = 1,

Cov[ε1,ε2|x1,x2] = ρ.

In this case, unless we find evidence that ρ = 0, the probit analysis in theprevious section will give inconsistent parameter estimates. Here ρ measuresthe unobserved heterogeneity which implies that the error term will share acommon component and can be expected to be correlated with each other.

Also, we will use an endogenous treatment model where the first depen-dent variable (the dummy variable which is coded one to represents the ac-cess to micro-credit) appears as an independent variable in the second equa-tion, which is a recursive, simultaneous equation model where

y∗1 = x′

1β1 + ε1, y1 = 1 if y1 > 0, 0 otherwise, (5)

y∗2 = x′

2β2 + y1γ + ε2, y2 = 1 if y2 > 0, 0 otherwise, (6)

E[ε1|x1,x2] = E[ε2|x1,x2] = 0,

V ar[ε1|x1,x2] = V ar[ε2|x1,x2] = 1,

Cov[ε1,ε2|x1,x2] = ρ.

If we find that γ is significant then we can conclude that the people whochoose to take micro-credit has systematically different pattern of migrationdecision in the lean period.

4.3 Strategy evaluation through Difference-in-Difference Method

The major problem of the lean period hardship is the significant reductionof income due to the joblessness in the agricultural industries, thus it wouldbe better if we could test the impact of migration and micro-credit on thelean period income.

One possible way to test the impact is by using natural experiment tech-

15

niques. The case explained in this study can easily be qualified as a naturalexperiment or a quasi-experiment. We know that a natural experiment oc-curs due to some random exogenous event, in our case, the two strategiesdescribed in this study can ideally be suitable for such experiment. An riskaverse individual can choose to migrate or access micro-credit as two strate-gies. A natural experiment always consists of a control group (which is notaffected by the exogenous event) and a treatment group (which is effectedby the exogenous event). Under a true experiment framework, treatmentand control groups are randomly chosen and arise from particular policy orevent change. Thus, to control for the systematic difference between thesetwo groups, we need two periods of data, one before and one after the policychange. In our case, we have the income information of two periods, one inthe normal period and one in the lean period. Thus we can easily convertthe data to use it for two period cross-sectional data sets, one before thelean period hardship and after the lean period hardship (normal period) andcan be used to determine the effect of the two strategies.

The data set we have can be easily converted to a longitudinal dataset where we asked the two period incomes (income in lean period andincome in normal period) and other demographical variables are basicallytime-invariant (like education, occupation, sex, marital status etc.). As aresult, the data gives us the opportunity to test the impact of these two poli-cies with the help of difference–in–difference method. Following Wooldridge(2003, page 458), we use C to donate the control group and T to donate thetreatment group, letting dT equal unity for those in the treatment group T

and zero otherwise. Then let us call dP a dummy variable for the lean timeperiod, then the equation of interest is

y = β0 + δ0dT + β1dP + δ1dT ∗ dP + Other Factors (7)

where y is the outcome variable of interest, which is the log of income in ourcase. To measure the effect of a strategy, without the other factors in theregression, the δ1will be the difference-in-difference estimator:

δ1 = (y2,T − y1,T ) − (y2,C − y1,C) (8)

where the bar denotes the average, the first subscript denotes the period (1for normal and 2 for the lean season) and the second subscript denotes the

16

group. Thus the sign of the δ1 shows the effect of treatment or policy onaverage outcome of y. In our case, if either the temporary internal migrationor access to micro-credit have positive impact over the income during thelean season, then the expected sign of the δ1 will be positive. The parameterδ1 is sometimes called the average treatment effect. When we add otherexplanatory variables to equation 7, the OLS estimation δ1 will no longerbe as simple as mentioned above but the interpretation will be the same.

5 Estimation

5.1 The Determinants of the Seasonal Migration Decision

We have results from two sets of Probit estimations in the Table 2. Inthe second Model, we have log of difference of income between normal andlean period as the dependent variable. But this variable can be endogenousas one can argue that income difference between the two period could beinfluenced by the prior migration decision. As a result we used seasonalunemployment as a proxy for the endogenous variable mentioned before.Out proposed proxy variable for the lean period income suits the modelvery well as both of the models have the almost same significant variables(exceptions are social security, river erosion and age variable) and hardlyany coefficients changed the sign (variable marital status). Due to the useof log of difference in the second model, we lost some degrees of freedomwhich may lead to those aforementioned exceptions in the model. Hencethe model is well estimated and robust.

The probit estimates show that seasonal unemployment and seasonalhardship in the lean period and individual characteristics like sex, age, sizeof the family, farm occupation, prior experience and kinship at the place ofdestination and education have a significant association with the migrationdecision. The marginal effect of a unit change in the explanatory variableson the decision to migrate has also been calculated. It is evident that sea-sonal unemployment in the autumn lean period is the most decisive amongthe economic factors in determining the probability of migration, increasesthe probability by 10% for a typical worker. The results, however, do showsignificant effects of wage differential (in second model) on migration deci-sion.

17

[Table 2 about here]

Another economic factor, land ownership was also found to be insignifi-cant. It was observed during the survey that there are at least three types oflandowners in the Kurigram district: a) absentee landlords who live in urbanareas and are engaged in other occupations, b) small land owners who workon their own lands and c) landless or effectively landless workers who work inother people’s farm. It was very hard from our survey to distinguish amongthese types land ownership as respondents of the survey were either veryreluctant or unaware of these information, which could eventually increasethe explanatory power of this factor as well as of the model.

The present study finds that the probability to migrate is negative for anindividual with access to micro-credit through NGO membership with typ-ical characteristics, but the relationship is not significant. Prior migrationexperience has the strongest positive impact among all the factors influenc-ing the migration decision. Migration experience and kinship at the placeof destination reduce the cost of migration by minimizing the time for jobsearching. Both of these variables were found to be significant at less thanthe 1% level which is a crucial findings of our study.

The results also show that migration propensity is significantly higheramong males. Workers of age group 20-40 have a significantly higher inten-tion to move in the lean period. The size of family is found to be significantand positively influences the probability to migrate as expected. Thus in-dicates that for a large family, the chief earner is more likely to migrate asthe migration income in the lean period is very important for the survivalof bigger family.

One important finding of our study is that farm occupation significantlymodifies the migration decision. Since the seasonal hardship results fromseasonal unemployment in agriculture, it is quite logical that the farmerswould be keener to seek an alternative livelihood strategy, preferably in thecities. The probit model suggests that the probability of migration is signif-icantly higher among the farmers. Agricultural workers are more vulnerableto seasonal unemployment in the lean period. As a result, large numberof agricultural workers chose to migrate in the lean period and the presentstudy has found a significant and positive impact of agricultural profession-als to opt for seasonal migration. Such evidence contradicts the literature onpermanent internal migration. Studying the migration in Costa Rica, Carva-

18

jal and Geithman (1974), found that income elasticities of in-migration ratesare higher for professionals, managers, white-collar and industrial workers.Thus is quite natural as higher wages for this jobs attract migrants to cities.So, it provides evidence that lean period migration is basically a shock drivenmigration where farm laborers are mostly affected. Thus they are the vastmajority of the population who choose for the temporary internal migration.

A compelling finding of the study is that regarding river erosions. Itwas found that those who experienced river erosion at least once in theirlives have lower migration propensity. This result conform to our hypoth-esis. The probit results suggest that the probability to migrate falls if theworker experienced river erosion which is marginally significant for the firstmodel. One possible explanation of this result may be due the economicvulnerability of the river erosion affected peoples. To migrate nearby cities,one needs at least some amount of asset to cover the transportation andinitial relocation cost. Hence, the people who are affected with river erosionhave already lost their valuable lands and houses, therefore they can notafford to take decision of migration in the lean period. Those who ever ex-perienced river erosion in their lives fall into the trap of chronic poverty andthey cannot cover the minimum cost of adopting an alternative livelihoodstrategy like migration. Another explanation could be the trauma effect offorced relocation due to the river erosion which may have decreased theirmigration propensity.

An interesting relationship between farmers and non-farm occupants canbe extrapolated from the figure 1. In the figure we have created a graph ofa representative individual as a base case. The individual is a male, mar-ried, with mean income, who has no migration experience, no kinship atthe destination of migration, no education, no land ownership, no access tomicro-credit, no social security, has been affected with river erosion, aged35 and a farmer. In figure 1, the predicted probabilities of two kinds ofoccupation for a range of family sizes has been provided. Interestingly forthe non farm occupants, the size of family does not increase the probabilityof migration dramatically. As seasonal hardship results from seasonal un-employment in agriculture, in the autumn lean period, agricultural workerssuffer mostly from shortage of employment. But the same is not true forthe non-farm workers. As a result, non-farm workers are less likely to mi-grate in the lean period and the predicted probability of migration for this

19

cohort does not vary much with the changing size of the family. For theagricultural worker, the probability of migration is very high and has strongupward tendency as the size of the household increases.

Another interesting relationship of education and migration probabilitieshas been shown in figure 2. For the same representative individual (withfamily size taken to be 5) education significantly affects migration probabil-ities and this impact remains almost constant even with the increase of ageto the maximum. Thus has important policy implications as it illustratesthe potential impact of education on the propensity to migrate.

Finally, we have calculated a probability table for the above mentionedbase case and calculated the predicted probability by changing units fromits base case; see table 3. The predicted probability for the micro-credit isquite an interesting one. Using the base case, we calculated that predictedprobability of migration decreases from 0.91 to 0.82 for a person who hasaccess to micro-credit through NGO membership . Thus this result suggeststhat access to micro-credit reduces the propensity to migration in the leanperiod but as we find in the table2 that this effect is not significant. Asa result, this interesting finding of the study leads us to the next sectionwhere we will investigate if there exists a systematic relationship betweenthese two variables.

[Table 3 about here]

5.2 Seasonal Migration and Access to Micro-credit

A problem our probit model may encounter is, if there exists an endogenousnon random sample selection process for the people who took NGO mem-bership to access micro-credit and the people who migrated in the lean pe-riod. Access to micro-credit and migration in the lean period are livelihoodstrategies to overcome the income shock in the lean period. But access tothe formal credit market is a long term strategy to deal with seasonal hard-ship. Whereas temporary seasonal migration is a short term strategy whichlargely depends on individuals’ attitudes towards risk (Binswanger 1981,Quizon 1984) and such strategy works as consumption smoothing techniquefor the poor rural people (Rosenzweig and Stark 1989). Hence, there mayexist selection problems with the individuals who took NGO membershipto access micro-credit and the decision to migrate. Also, we may encounter

20

the problem of unobserved heterogeneity between these two strategies.Using the model demonstrated in section 4.2 for the seemingly unrelated

regressions, we used bi-probit estimation techniques with two models. Inthe first model (equation 3 and 4), we have treated access to NGO andthe migration decision as two endogenous equations. In the second equa-tion (equation 5 and 6), following Burnett (1997), we used an endogenoustreatment model where the first dependent variable, NGO, appears as theindependent variable in the second equation, which is a recursive, simulta-neous equation model. As a result, we can also test the impact of treatment(in our case access to NGOs) on the migration decision and can test if theallocation of treatment was random or not. Also we can evaluate the co-variance of the disturbance terms of these two equations by estimating theρ.

[Table 4 about here]

From the endogenous treatment model (second model), we found thetreatment effect to be insignificant and the estimate of ρ is only -0.41 witha standard error of 0.67. The Wald statistics for the test of the hypothesisthat ρ = 0 is 0.38. For a single restriction, the chi-squared critical value is3.84, so the hypothesis can not be rejected. The likelihood ratio test for thesame hypothesis leads to the same conclusion. Now for the simultaneous bi-variate probit model (first model), the likelihood ratio test for the hypothesisρ = 0 is not significant as the χ2 test statistics is 2.83 with an associate p

-value of 0.21. However, the correlation coefficient measures the negativecorrelation between the disturbances of access to NGOs and the migrationdecision after the influence in the included factors is accounted for. But, thisrelationship between the errors is not significant and separate estimation ofthese two equations using univariate probit estimation techniques is unlikelyto create inconsistency and biasedness in the estimations.

5.3 Testing for the Effective Strategy

As we have found evidence in the previous section that access to micro-creditthrough NGOs and temporary internal migration in the lean period are thetwo alternative livelihood strategies to overcome the income shock in thelean period, a natural extension easily leads us to test these two strategiesto check which one is more effective.

21

Here, using equation 7, the difference-in-difference estimations of thepolicy variables are migration in the lean period (Migdec) and access tomicro-finance through NGOs (NGO). Now we have two treatment groups;one is the people who choose to migrate versus people who do not andthe second is the people who choose to have access to micro-credit throughNGO membership versus the people who do not. The outcome variable forour case is the log of income and the parameters of interest are those onthe interaction terms between the policy and the treatment effects whichare Lean*Migdec and Lean*NGO in the table 5. If either of the policies iseffective in increasing the income in the lean period, then the interactionterm should be positive and significant.

[Table 5 about here]

In table 5, we show two sets of estimation results for two policy variables.The difference between the two separate estimations for a single policy vari-able is that one used no control variables and the other used a full set ofcontrol variables. Interestingly, the coefficients of different estimations arealmost similar indicating robustness of our findings. As we can see fromTable 5, the coefficient on Migdec is positive but not statistically signifi-cant. This is as expected as controlling for other factors, migration doesnot increase income significantly. The same is true for the NGO variable,as access to micro-finance is a long term policy and we did not expected thevariable to have significant influence over income.

On the other hand, the variable Lean is strongly significant and negativewhich shows the severeness of the lean period shock on income. The variableis significant for all the four models. The interaction term Lean*Migdec ishighly significant and positive as expected. Surprisingly, the interactionterm Lean*NGO is negative but not statistically different from zero, whichgives us some evidence of micro-credit’s ineffectiveness in a situation likeseasonal hardship5. Temporary internal migration is a short term solutionand preferred by any individual who wants to alleviate short term hardship.In contrast, access to micro-finance is a long term policy and the impact ofsuch a policy can not be observed within a short time span. Hence, a myopic

5We also ran several regressions by controlling for household and village specific effectsbut such inclusion did not change our result. These results can be provided upon request.

22

individual will always opt for temporary migration over the micro-financeoption.

Interestingly, if we look at table 6, we can easily observe that access tomicro-credit increases the mean income of the people though the medianincome is the same. However, in the lean period (refer to table 7) householdthat took migration decision is better off than other two groups. Householdthat took no credit and did not migrate are the worst affected with theseasonal shock. Whereas, during lean period, by having only credit accessdoes not improve the income of the household significantly. So the familiesthat took both the option during lean period has more average income thanany other group and are better off. The reason for such finding is deeprooted in the micro-credit frameworks. NGO’s have very strict policy ofloan repayment and usually collected on weekly basis. Hence, in most ofthe cases the female member of the household takes the credit but thantransfer it to the male member who migrates in the urban areas and sendthe savings to replay the loans. Whereas if families do not exploit the creditopportunities described above, than the family will lose their mobility andcan not migrate during the lean season. As a result, access to credit alonecan not improve the family income in the lean period.

6 Concluding Remarks

Seasonal migration is not an efficient long-term sustainable solution to theseasonal downturn and natural shocks suffered in the agriculture sector vis-a-vis village level poverty. Temporary migration can provide short timeeconomic benefits to migrants, their families and to their villages but suchmovements may not be possible over the years. This study has found ev-idence that access to micro-credit through NGOs and temporary internalmigration in a lean period are two strategies that individuals in the ruralareas use to overcome the income shock in the lean period. We found thateconomic, ecological and individual characteristics, all play an importantfactors in migration decision. Among the economic factors, seasonal unem-ployment and wage difference have significant effects. Personal character-istics such as sex, age, farm occupation, the role of networks and previousmigration experience, are all significant at less than the 5% level of signifi-cance.

23

This study has found systemic differences between seasonal migrationand permanent internal migration. To the author’s knowledge, existingempirical studies on permanent internal migration have found significantpositive impacts of education on migration. In this study, we find a reverserelationship. Seasonal migration is temporary in nature and, as a result, in-dividuals who have relatively better education will tend to choose permanentover temporary migration .

Micro-credit schemes have increased opportunities for the rural peopleto have access to the informal credit market. However we found that, dur-ing seasonal shocks, individual with access to micro-credit did not have asignificantly different level of income to those that did not have access tocredit. We found that, households that took both the migration decisioncombined with micro-credit earn significantly more than households withonly micro-credit in the lean period. NGO’s have a very strict policy of loanrepayment and usually collect repayment on a weekly basis. In most casesthe credit is received by the female member of the household but is usedby the male member who migrates in the urban areas during lean seasonand send remittances to replay the loans. If, however, the male member ofthe household take credit during the lean period, he will lose his mobilityand cannot undertake migration due to the strict repayment rules. ThusNGOs should consider addressing such problem by relaxing the loan repay-ment scheme during the lean period. Moreover, the results suggest thatNGOs and governments should provide more support on adult educationand building capacities on diverse skills (both non-agricultural and agricul-tural) which will help poor migrants during lean seasons, and thus alleviatethe social problems associated with seasonal migration.

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28

7 Appendix

Figure 1 Migration propensity for farmer vs non-farmer with the change offamily size

Figure 2 Migration propensity with education vs no-education with thechange of age

29

Table 1 descriptive statistics

Variables Obs. Mean S.D. Min MaxMigration decision 290 0.68 0.46 0 1Income in the normal period 290 67.13 34.46 17 270Income in the lean period 290 58.46 42.60 0 200seasonal hardship 290 0.6 0.49 0 1Seasonal unemployment 290 0.7 0.45 0 1Previous migration experience 290 0.63 0.48 0 1Kinship at the migration destination 290 0.52 0.50 0 1Sex 290 0.89 0.30 0 1Age 290 39.61 12.53 18 69Marital status 290 0.70 0.45 0 1Education 290 0.42 0.49 0 1Occupation 290 0.47 0.50 0 1NGO 290 0.19 0.39 0 1Social security 290 0.13 0.33 0 1River erosion 290 0.61 0.48 0 1Flood 290 0.49 0.50 0 1Land ownership 290 0.43 0.49 0 1Family size 290 4.94 1.32 3 9

30

Tab

le2

Uni

vari

ate

Pro

bit

Mod

el

Model

wit

hse

aso

nalunem

plo

ym

ent

Model

wit

hin

com

ediff

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ce

Vari

able

sC

oeffi

cien

tsM

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inaleff

ects

Coeffi

cien

tsM

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inaleff

ects

Sea

sonalU

nem

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ent

1.2

74(0

.479)*

*0.0

97

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25(0

.376)*

*0.0

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Land

Ow

ner

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-0.4

85(0

.402)

-0.0

21

-0.6

71(0

.422)

-0.0

2Siz

eofth

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0.3

99(0

.17)*

*0.0

15

0.4

03(0

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*-0

.009

Mem

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GO

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-0.5

87(0

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0.0

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0.8

84(0

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85(0

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-0.0

31

-0.6

96(0

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*0.0

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0.8

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0.0

29

Age

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0.0

27

Sex

1.7

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9(0

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*0.1

31

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**

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28

Const

ant

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***,*

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repre

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sign

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10

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31

Table 3 Bivariate Probit estimations

Bivariate Probit Endogenous treatment model

NGO Equation Coefficients Standard errors Coefficients Standard errors

Land Ownership 0.215 (0.201) 0.213 (0.202)Size of the household -0.081 (0.092) -0.081 (0.092)Social Security 1.272*** (0.239) 1.273*** (0.24)River Erosion 0.173 (0.212) 0.169 (0.214)Seasonal Hardship -0.032 (0.208) -0.033 (0.208)Age -0.113** (0.046) -0.115** (0.047)Age Squared 0.001** (0.000) 0.001** (0.00)Sex 0.518 (0.421) 0.521 (0.421)Farm Occupation -0.417* (0.217) -0.413 (0.219)Marriage 0.386 (0.243) 0.384 (0.243)Education 0.108 (0.199) 0.113 (0.202)Constant 0.901 (1.106) 0.909 (1.125)

Migration equation

Log of income difference 0.904** (0.373) 0.892** (0.382)Land ownership -0.69* (0.413) -0.687* (0.407)Size of the household 0.398** (0.165) -0.391** (0.173)Membership of NGO 0.19 (1.441)Social security 0.631 (0.568) 0.542 (0.874)River erosion -0.701 (0.466) -0.698 (0.46)Seasonal hardship 0.853* (0.46) 0.835* (0.478)Age Dummy 0.667 (0.438) 0.644 (0.469)Sex 1.32** (0.668) 1.283* (0.727)Farm occupation 1.63** (0.547) 1.624** (0.543)Marriage -0.202 (0.492) 0.223 (0.515)Education -0.997 (0.449) -0.978** (0.466)Prior Experience 3.85*** (0.653) 3.786*** (0.839)Kinship at destination 2.241*** (0.562) 2.212*** (0.612)Constant -8.391*** (1.916) -8.249*** (2.24)

N 266 266Rho -0.328 (0.245) -0.414 (0.674)Log Likelihood -140.87 -140.86

Values in the parenthesis are the reported standard error of the estimation.***,**,* represents significant at 1, 5 and 10 percent level.

32

Table 4 Probability Score table

Individual Characteristics Migration Not-Migration

Base case 0.908 0.091Change from the baseNo seasonal unemployment 0.614 0.389With landownership 0.831 0.169Size of the household =6, not 5 0.949 0.051Membership of NGO=1, not 0 0.819 0.181Social security =1, not 0 0.979 0.021River erosion =1, not 0 0.725 0.273Seasonal hardship =0, not 1 0.662 0.338Age is 36, not 35 0.907 0.093Female not male 0.398 0.602Occupation = 0, not 1 0.333 0.667Married = 0, not 1 0.873 0.127Education = 1, not 0 0.583 0.417Prior experience of migration = 1, not 0 1.00 0.000kinsmen at destination =1, not 0 0.999 0.000

The base case is with sex=1, marital=1, occupation=1,seasonal hardship=1, mounemp=1, age=35 and family=5.

Table 5 Bivariate Probit estimations

Dependent variable: Log of income, pooled regression

Variables Policy variable: MIGDEC Policy Variable : NGO

Constant 4.03*** 3.89*** 4.03*** 3.48***(0.061) (0.144) (0.043) (0.141)

Migdec 0.08 0.11(0.073) (0.087)

NGO 0.14* 0.04(0.086) (0.082)

Lean =1 if lean -0.95*** -0.95*** -0.95*** -0.94***period, zero otherwise (0.096) (0.096) (0.068) (0.058)Lean*Migdec 1.03*** 1.03***

(0.111) (0.111)Lean*NGO -0.11 -0.11

(0.126) (0.11)

Other Controls No Full Set No Full SetN 447 447 158 158Adjusted R squared 0.29 0.30 0.65 0.73

Values in the parenthesis are the reported standard error of the estimation.***,**,* represents significant at 1, 5 and 10 percent level.

33

Table 6 Some Summary Information of Income in normal period

Normal Periodwithout NGO support with NGO support

Mean 59.63 67.95Median 60 60SD 19.64 23.28Min 20 50Max 120 150

Table 7 Some Summary Information of Income in lean period

Lean PeriodNGO=0,Migdec=0 NGO=0,Migdec=1

Mean 16.17 77.16Median 20 75SD 13.16 38.80Min 0 5Max 50 200

NGO=1,Migdec=1 NGO=1,Migdec=0

Mean 74.85 22.72Median 80 25SD 37.18 9.47Min 10 0Max 200 50

34

Table 8: Variable description

Name Description Variable

Migration Decision A dummy variable that equals one if theindividual migrated in one of the last twolean seasons and zero otherwise.

migdec

Income in the LeanPeriod

Earnings of the household if the chiefearner stayed in the village in the lean pe-riod or the earnings of the household ifthe chief bread earner migrates in the leanperiod (per day in Local currency Units,LCU).

mincomelean

Income in the Nor-mal period

Earnings of the household in the normalperiod (Non-lean period)(per day in LCU).

mincomenorm

Seasonal Unemploy-ment

A dummy variable that equals one if theworker remains unemployed during mostof the lean season, zero otherwise.

mounemp

Seasonal Hardship A dummy variable that equals one if theindividual has one meal or less on a typicalday in the lean period, zero otherwise.

season hrd

Land ownership A dummy variable that equals one if the re-spondent’s family owns any land irrespec-tive of the size of land, zero otherwise.

landownerd

Access to Micro-credit

A dummy variable, which is coded as onefor having access to Micro-credit throughany NGOs, zero otherwise.

ngo

River Erosion A dummy variable, such that an individualhas a value of one if his/her family everexperienced forced displacement by rivererosion, zero otherwise.

rivererosion

Flood A dummy variable that equals one if therespondent faced flood in the year of mi-gration, and zero otherwise.

flood

Age Actual age of the respondent. age

Sex Sex is coded as one if the respondent ismale and zero if she is female.

sex

Marital Status A dummy variable, coded as one for thosewho are married and zero otherwise.

marital

Education A dummy variable, coded as one for thosewho have any education, zero otherwise.

edu

Household size Number of family members family

Farm Occupation A dummy variable, coded as one for thefarmers, zero otherwise.

occupationdum

Kinship at the Placeof Destination

A dummy variable, coded as one for thosewho have kinsmen at the potential place ofdestination, zero otherwise.

knship

Migration Experi-ence

A dummy variable, coded as one for thosewho have prior migration experience (anyprevious experience), zero otherwise.

migexp

35