Transitions between one equilibrium and another. Fertility transition in Bangladesh (Munshi-Myaux 2006 JDE)

Transcription

1 Development Economics Slides 5 Debraj Ray Columbia, Fall 2013 Transitions between one equilibrium and another An empirical example Fertility transition in Bangladesh (Munshi-Myaux 2006 JDE) Population: Quick Background Population Landmark Date Achieved Years Taken 1 billion billion billion billion billion billion billion Population growth ) economic development Solow, technical progress Economic development ) population growth demographic transition (phrase hides a lot of detail)

2 Geographical Distribution of Population World pop (m) ,171 1,608 3,023 6,750 Percentages Europe N. America Oceania L. America Africa Asia Birth and Death Rates (Per Thousand), 1995 Country pc income Birth rate Death rate Pop growth I. Mali Malawi Sierra Leone Guinea-Bissau II. Kenya 1, Nigeria 1, Ghana 1, Pakistan 2, III. India 1, Bangladesh 1,

3 Birth and Death Rates (Per Thousand), 1995, contd. Country pc income Birth rate Death rate Pop growth IV. China 2, Sri Lanka 2, V. Nicaragua 1, Peru 3, Guatemala 3, Brazil 5, Colombia 5, VI. Thailand 6, Malaysia 7, Republic of Korea 9, Cross-Sectional Overview, " 45" Birth"rate""(per"1,000)" Death"rate"(per"1,000)" 40" 35" 30" 25" 20" 15" 10" 5" 0" 0" 1000" 2000" 3000" 4000" 5000" 6000" 7000" 8000" 9000" 10000"

4 The Demographic Transition Cross-section obscures longer-term demographic transition. Three stages: Initially birth and death rates both high The former in the reaction to the latter Then death rates fall Antibiotics, DDT, sanitation, public health organizations Finally birth rates fall But why is the reaction so slow? (macro- vs micro-interia). In particular, population rates go through inverted-u. Rates Per Thousand Birth Rate Death Rate Demographic Transition in England and Wales

5 Rates Per Thousand Birth Rate Death Rate Demographic Transition in Sri Lanka Demographic Transition, Sweden

6 Demographic Transition, Mexico on Sweden Demographic Transition, Mauritius on Sweden

7 Part of the reason for high birth rates is macro-inertia: #!!" +!" *!" )!" (!" '!" ('," #'-(&" "!-#&" &!" %!" $!" #!"!" %$)" )('" +'!" #!*&" ##('" #&'!" #')+" $!#+" $$*'" $''(" $+&#" %(*)" &!&+" &'$%" &)%%" '&&&" (%!&" )'+(" *#)%" *)#(" #!$%)" ##&()" #$+*(" #%)!(" #&$'*" #)!'+" $!!%)" $'!'*" $)#%%" %!)$*" %&)*)" %()%%" %**!&" &'#&!" (!(%%" And micro-inertia has two broad sources: Demand for children (child labor, old-age security) Supply of children (opportunity cost of having them)

8 Old Age Security p = probability that child looks after you in old age. Infant and child mortality Migration q = desired threshold probability for being cared for Social security Own savings Choose n as the smallest integer such that 1 (1 p) n q. E.g., if p = 1/2 and q = 9/10, then n = 4. If q = 95/100, then n = 5. Irrational? No. Need a boy (e.g. protection of property)? n even higher. How is p estimated? Sometimes, over generations. Monica Dasgupta s story of Umed Singh. Parents had 9 kids, 6 died. Two girls and one boy (Umed) survived to adulthood. Umed became a policeman: a pensionable job. Had two girls, then three more children (two boys). All survived. Hoarding versus targeting When are fertility decisions made? Can react to infant mortality Harder to react to child mortality Impossible to react to migration

9 Opportunity Costs (a) rental income, males wages (b) female wages Does opportunity cost a ect fertility? Certainly, relative wages for women have gone up steadily. But regression of fertility on wages plagued by endogeneity. Low fertility ) higher wages (stable employment) Omitted variables may drive wages and fertility Fertility regressed on education? Similar problems. Schultz (1985) on Sweden European food grain prices ", then collapsed. (United States and Russia exporting huge amounts of grain) butter exports jumped 4-fold (tech progress in livestock breeding, dairying, transportation)

10 Sweden, contd. Dairying was women s work in Sweden. Grain production: male work. Instrument for female/male wages: relative price of butter/grain. (Accompanying instrument for male wages: pork/grain price.) 25 counties in Sweden, 6 decadal observations each, Over this period TFR fell by 28% (by 43% for women born between 1840 and 1890) : 1/4 of TFR decline explained by the 10% rise female/male wage ratio. (Schultz, JPE 1985).

11 Is Fertility Too High? Three sources: I. Incomplete information: Umed Singh example; may not know that death rates declined. II. Ex-ante versus ex-post: Too many surviving children ex-post (sounds cruel, but you get the drift) III. Externalities: Private gain/cost versus social gain/cost Externality example 1. Education or health subsidies.

12 Externality example 2. Children as Lottery Tickets. Say jobs are available for $1,000 per month. Queue for such jobs. Probability of getting job = total number of jobs total number of job seekers. An additional child = additional lottery ticket for the family. But total number of job seekers goes up. Negative externality. E ect is minuscule when one family does this. But combined overall e ect is significant and negative. ) equilibrium fertility is higher than socially optimal. Externality example 3. Family structure and fertility. Joint families: Similar argument holds when grandparents look after children.

13 Bangladesh Period Birth rate Death rate Taken from Cleland and Streathfield, BBS, World Bank Munshi and Myaux (JDE 2006) This paper provides a norm-based explanation for two features of the fertility transition that have been observed in many di erent settings: the slow response to external interventions and the wide variation in the response to the same intervention : Total fertility rate goes from 4.5 to 2.9. This is a huge drop. Norms governing fertility use and contraception. Contraception went from 40% in 1983 to 63% in 1993.

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15 0-28 Maternal Child HealthFamily Planning (MCH-FP) project Launched in 1978, 70 villages in Matlab thana, Comilla district. Intensive family planning program Community Health Worker (CHW) visited each family once every 2 weeks since start of the project in Contraceptives are provided to them free of cost. Use goes from from 40% in 1983 to 63% in 1993 TFR from 4.5 to 2.9 children over that period.

16 (&'3,'"#$%&'()$*+(,8($3"7C,/&$0&1,?DDE,!!! /($3"7, ";!W*)2*!!! D0##! T9]HX! FH;a! 6-$23BW$)20-)!7)50,)! FIF! F;X! 6-^),$0?#)*! a9gfz! XGZ! a;h! <'?3#!#0(3$0&-! X]a! Z;F! S3*),$&/4! FX! G;F! \$%)2*! TYI! T;a!!!! =;!c&-b'*)2*! a9fyx! ]];H!!!! <00,?FCGHF,?II, Khan-Bairagi (1998) 0.7 Hindus 0.65 Full Sample Contraceptive Prevalence Muslims Year Fig. 1. Contraceptive prevalence over time.

17 Strong initial hostility to MCH-FP, especially from religious leaders. Especially hostile reaction against community health workers (violating purdah) Also, pressure against contraceptive use (linked to perceived promiscuity) Women in village limited in their mobility: Schuler et al. (1997) survey of 1300 married women under 50, Ever been to market, a medical facility, the movies, and outside the village. One point for accompanied visit, 2 points for solo visit. Mean score 2.1 (out of a maximum of 8). Sample: all married women in MCH-FP area, Table 2 Descriptive statistics Full sample Hindus Muslims Illiterate Literate (1) (2) (3) (4) (5) Panel A: Individual characteristics Age (8.01) (8.00) (8.01) (8.18) (7.44) Number of children 2.41 (1.99) 2.18 (1.79) 2.45 (2.03) 2.57 (2.05) 2.14 (1.86) Education 2.12 (3.12) 1.48 (2.68) 2.26 (3.19) (2.55) Husband s education 3.21 (4.00) 3.07 (3.81) 3.24 (4.04) 1.53 (2.62) 5.91 (4.34) Panel B: Occupation of household head (%) Farming Fishing Business Housework Other Total Panel C: Asset ownership Land (hectares) 1.00 (2.55) 0.72 (1.39) 1.06 (2.74) 0.82 (2.41) 1.29 (2.74) Cows 1.06 (1.57) 0.81 (1.42) 1.11 (1.59) 0.91 (1.46) 1.28 (1.70) Boats 0.55 (0.61) 0.63 (0.76) 0.54 (0.57) 0.55 (0.61) 0.56 (0.60) No. of Observations 21, ,723 13, Panel D: Contraceptive prevalence Probability of using 0.55 (0.50) 0.59 (0.49) 0.54 (0.50) 0.53 (0.50) 0.57 (0.50) contraceptives No. of Observations 144,186 26, ,772 91,727 52,459 Means (standard deviations) in panel A, panel C and panel D. The individual is the unit of observation in panels A C. The individual-year is the unit of observation in panel D. All statistics in this table are computed over the full sample period.

18 A Conceptual Problem Linear probability model (also tried logit): y it = A + y i,t 1 + x v(i) t 1 + Z it + C v(i) t + ivt y i is 0-1 for contraceptive use by couple i, t is time, x is aggregate village-level use, v(i) is the village of person i, Z a vector of individual characteristics (such as age) including individual and time fixed e ects in some specifications. C v t is unobserved omitted variable for village v at date t. At the heart of identification problem (Manski critique) can pick up the e ects of unobserved C v t... E.g., economic growth Village-level success of the MCH-FP program. C v t C v t can be decomposed into three parts. First component only depends on the village: C1 v. Second component only depends on time: C t2. Third varies in a village-specific way over time. Components 1 and 2 dealt with by village and time fixed e ects. The last one screws everything up: identification problem.

19 Main Idea in Munshi-Myaux Paper Inter-religion communication low. So include own-group and cross-group use separately. If own-e ect strong, then pushes back the Manski critique: For critique to work, there has to be an omitted variable which is village-, time- and group-specific. y it = A + m y i,t 1 + mm x v(i),m t 1 ++ mh x v(i),h t 1 + m Z it + C v(i),m t + ivt where i is m-household, and m and h labels self-explanatory. Can get spurious e ects only if C v(i),m t and C v(i),h t orthogonal. Table 3 Partitioning the village by religion Dependent variable: contraception All villages More than 5% Hindus/Muslims More than 15% Hindus/Muslims Annual data Muslims Hindus Muslims Hindus Muslims Hindus Muslims Hindus (1) (2) (3) (4) (5) (6) (7) (8) Lagged contraceptive prevalence (own group) (0.013) (0.014) (0.016) (0.017) (0.018) (0.020) (0.023) (0.023) Lagged contraceptive prevalence (other group) (0.006) (0.007) (0.011) (0.016) (0.013) (0.024) (0.011) (0.012) Lagged contraception (0.003) (0.005) (0.004) (0.005) (0.004) (0.006) (0.005) (0.008) R Number of observations 139,875 43,101 79,927 29,771 49,730 20,756 70,787 21,419 Box Pearson Q statistic Standard errors in parentheses. Standard errors are robust to heteroskedasticity and correlated residuals within each village-period. 2 All data 6-monthly except last two columns See paper for other robustness checks: no fisherman, bari-level e ects

20 Alternative Explanations Program e ects: Cross-sectional variation: individual fixed e ects. Secular changes: time e ects. But village-specific time e ects pose a problem. varies from village to village, after all. The CHW That is where the own-religion cross-religion trick plays a role. Economic growth Religion and occupation largely uncorrelated except for fishermen. Learning about a new technology Possible, with injectibles. But authors argue against it.