rafmacalaba/datause-encoder-data huggingface dataset:
# International Migration, Transfers of Norms and Home Country Fertility

Michel Beine<sup>a</sup> , Frédéric Docquier<sup>b</sup> and Maurice Schi¤<sup>c</sup>

> a University of Luxemburg and CES-Ifo

b FNRS and IRES, Université Catholique de Louvain, IZA-Bonn and CReAM-London

> c World Bank, University of Chile, and IZA-Bonn

May 2009 Abstract 1
# **The Long-Run Impacts of Adult Deaths on Older Household Members in Tanzania**<sup>**1**</sup>

Achyuta R. Adhvaryu Yale University Kathleen Beegle The World Bank

> 1 This research was supported by the World Bank’s Gender and Development Group’s Gender, Poverty and Demography Initiative. We thank Martha Ainsworth, Peter Lanjouw, John McIntire, T. Paul Schultz, anonymous reviewers, and seminar participants at the Amsterdam Institute for International Development, Development Lunch Seminar at Yale, NEUDC, University of Michigan Conference on “The Impact of HIV/AIDS on Older Persons in Africa and Asia”, and the Gender, Poverty, and Demography Workshop at the World Bank for very useful comments. The views expressed here do not necessarily reflect those of the World Bank or its member countries.
Figure 9: Relative Ranking of Importance of Prenatal Care Services Notes: This graph reports the average of the relative ranking that measures the degree of priority given by clinics to seven different prenatal care procedures including initiating prenatal care prior to week 13 of pregnancy. The data were collected using a short online survey conducted in the clinics that participated in the experiment. (see Appendix D) The relative scores aimed to rank the seven practices from 1 to 7, with 1 being the highest ranking. In practice however, the survey instrument allowed the respondent to repeat numbers.

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<!-- Start of picture text --> Figure 3a: Total Outlays, % of GDP, average (non-weighted)<br>45<br>ia<br>a<br>ian25 ee<br>20<br>15<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>——ECA =——Oil&Gas Exporters -—=EU10,Cro,Tur -——LICs & LMICs<br><!-- End of picture text --> <!-- Start of picture text --> Figure 3b: Total Outlays, % of GDP, average (weighted by<br>GDP)<br>50<br>Pee<br>40 a — -<br>35 a ad<br>17<br>25<br>20<br>a5<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>——ECA =——Oil&Gas Exporters -EU10,Cro,Tur =LICs= & LMICs<br><!-- End of picture text -->
4 environments for using public spending to seek votes instead of rents. Weaker governance states may also be more effective at targeting recurrent spending on salaries and welfare transfers to “buy” votes, that is, to enforce a _quid pro quo_ between receiving a targeted benefit and voting. We find further evidence suggestive of this in that the share of spending on state administration (consisting largely of the salaries of state employees in public administration) increases with increasing turnout in states characterized as having poor governance, while the estimated correlation is in the opposite direction in other states (administrative spending falls with increasing turnout, although the correlation is not statistically significant).

In the absence of multi-year panel data on objective indicators of governance at the statelevel, there are substantial limits to the extent to which this characterization can be defended. However, we experimented with a number of other state groupings to see whether similar differences in the correlation with voter turnout would emerge. We found no other grouping of states within which the correlation with voter turnout is so systematically different, other than the grouping of all or some sub-set of five states—Bihar, Madhya Pradesh, Orissa, Rajasthan, and Uttar Pradesh—characterized as relatively weaker governance states in the Indian literature. The difference in budget composition and its response to voter turnout in these states is robust to including measures of state income, population, poverty and inequality.

This evidence across the Indian states, and its interpretation offered here, is consistent with recent cross-country research which finds greater public investment spending in countries with lower ratings for governance, political checks and balances and electoral competition (Keefer and Knack, 2007). They argue that the more plausible explanation for this pattern of variation is that pubic investment projects are vehicles for rent-seeking in weak governance environments. Other recent work on public expenditure composition in the Indian states has
Policy Research Working Paper 7091

# **Abstract**

This paper investigates the effectiveness of protected areas in slowing tropical forest clearing in 64 countries in Asia/ Pacific, Africa, and Latin America for the period 2001– 2012. The investigation compares deforestation rates inside and within 10 kilometers outside the boundary of protected areas. Annual time series of these deforestation rates were constructed from recently published high-resolution data on forest clearing. For 4,028 parks, panel estimation based on a variety of park characteristics was conducted to test if deforestation is lower in protected areas because of their protected status, or if other factors explain the difference. For a sample of 726 parks established since 2002, a test also was conducted to investigate the effect of park establishment on protection. The findings suggest park size, national park status, and management by indigenous people all have significant association with effective protection across regions. For the Asia/Pacific region, the test offers compelling evidence that park establishment has a near-immediate and powerful effect.

This paper is a product of the Computational Tools Team and Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
## **4.3 Revenue-neutral public policies to neutralize labor supply reductions**

Overall, the reduction in payroll taxes appears to counteract many of the undesirable effects of increased remittance inflows: lower rates of labor force participation and reduced export competitiveness. However, a major effect of this policy is a sharp fall in public saving and a negative effect on investment. The decrease in government saving is a major cause for concern due to the heavy public debt burden. To neutralize the undesirable effects on public saving and investment, sales tax rates are allowed to vary so that the government savings are maintained at the initial equilibrium level. Sales taxes affect consumption choices and should be preferred to direct increases of income taxes, which could potentially deter future or even current flows of remittances. Increasing taxes on international trade is not recommendable, since protectionism is likely to reduce welfare at home.

# _Table 6 Macroeconomic results: fixed budget deficit_

||Remittance effect,<br>percent change<br>from initial<br>equilibrium|Tax effect,<br>percent change<br>from remittance<br>shock|Total effect,<br>percent change<br>from initial<br>equilibrium|
|---|---|---|---|
|Remittances|10.00|0.00|10.00|
|Total household income|2.05|1.40|3.48|
|Household labor income|1.44|2.63|4.12|
|CPI|0.54|0.13|0.67|
|Real exchange rate|0.89|0.14|1.04|
|Real GDP|-0.36|0.21|-0.16|
|Private consumption|1.50|1.27|2.79|
|Investment|0.03|0.14|0.17|
|Exports|-2.74|-1.14|-3.85|
|Imports|0.83|0.65|1.49|
|Government savings|0.00|0.00|0.00|
|Unskilled labor supply|-0.76|0.60|-0.16|
|Skilled labor supply|-0.84|0.47|-0.38|
|Unskilled wage|2.32|2.40|4.78|
|Skilled wage|2.27|2.32|4.64|
|_Memo_||||
|Absolute change in unskilled labor supply|-3,814|3,006|-808|
|Absolute change in skilled labor supply|-4,552|2,500|-2,052|

The aggregate results of this third simulation are shown in Table 6. As before, the total effect of the simulation is decomposed into the remittance impact in the first column and the tax impact in the second column.<sup>22</sup> The macro results are very close to the previous simulation, although the labor supply response is slightly lower in the fixed government saving scenario. This 22 The slight differences between the first column in Table 6 and the first column in Table 4 are due to the fact that sales tax rates are now allowed to vary to keep the budget deficit constant.

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that result from withdrawing model-based entry barriers relative to those stemming from removing the World Bank’s DBI’s measure of entry taxation. Since, as we showed, the interaction between distortions exerts a nontrivial effect on the magnitudes of partial liberalizations, we compute these differentials both in the context of an active interaction, preserving the underlying idiosyncratic distortions, and in a context with no misallocation, as in the majority of existing studies on entry regulation.

Concretely, letting _τi_<sup>_E,DBI_</sup> denote the entry barrier in country _i_ according to the DBI, we compute the following two TFP differentials: The productivity elasticity of distortions _γi_ is unchanged and remains to be given by the WLS estimate of the regression defined in equation 18.

Figure 13: TFP Gains: Model-Based versus World Bank’s Doing Business Indicator Measures of Entry Barriers <!-- Start of picture text --> Remove Entry Barrier with Misallocation Remove Entry Barrier No Misallocation<br>Kenya Kenya<br>Salvador<br>ColombiaChile<br>Ethiopia GhanaSalvador India Peru BulRomaniagColombiariaHungaryLatvia Chile Mal Portugal a SpainysiaItalyFranceBelgiumFinland Ethiopia Ghana IndiaPeru Bulgaria RomaniaHungaLatvia Malaysia Po r tugalySpItalyFranceBelgiumainFinland<br>6 7 8 9 10 11 6 7 8 9 10 11<br>log GDPpc log GDPpc<br>0.25 0.25<br>0.15 0.15<br>Difference TFP gain 0.05 Difference TFP gain 0.05<br>−0.05 −0.05<br><!-- End of picture text --> Note: The left panel of the figure shows the differential TFP gain between removing entry barriers measured as in the model relative to entry barriers imputed from the World Bank’s Doing Business Indicator. Formally, the left panel illustrates _T F PT F P{{τ_ 0 _i_<sup>_E_</sup> _<u>,γ</u>_<sup>_,γ_</sup> _<u>i</u>_<sup>_i_</sup> _<u>}</u>_<sup>_}−_</sup> _T F PT F P_ <u>�</u> _τi_<sup>_E,DBI_</sup> _<u>{</u>_ 0 _<u>,γi},γi</u>_ <u>�</u> in the vertical axis, against the log of GDP per capita in the horizontal one. GDP per capita is drawn from Penn World Table version 9.0, for the year 2014. The right panel in turn computes the differential _TFP_ gain from removing each type of entry barrier assuming no underlying misallocation (i.e., ).

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<!-- Start of picture text --> Labor Participation by Gender and Governorate<br><<br>92 9<br>83<br>CH<br>8<br>§ ©<br>o<br>te<br>&- 33 =a<br>.24<br>nN<br>Duhok Erbil Sulaimaniya<br><!-- End of picture text --> <!-- Start of picture text --> Private and Public Women Workers by Governorate<br>°<br>=<br>°o<br><3<br>g<br>°<br>N<br>° Duhok Erbil Sulaimaniya<br><!-- End of picture text -->
On average the non-absent father of the child is 4.6 years older than mothers and has completed 7.7 years of schooling (with 79 percent reporting having completed primary school education).

# **2.2 Child development outcomes**

We focus on multiple dimensions of a child’s development. In doing so we also illustrate the raw differences in outcomes across maternal education and literacy status. We start by focusing on three main indicators of child health: low birthweight, wasting and stunting. For low birth weight we classify all reports of birthweight at and below 2,500 grams.<sup>6</sup> Stunted growth refers to low height for age, when a child is short for his/her age (WHO standardized population) and is an indicator of chronic malnutrition and carries long-term developmental risks. Wasting refers to low weight for height, the process by which acute food shortage and/or disease causes muscle and fat tissue to waste away. This is also known as acute malnutrition because wasting develops in a relatively short period of time in contrast to stunting. These statistics are especially pertinent since stunting in early life is associated with impaired cognitive ability, lower educational attainment, reduced future productivity, earnings potential and greater risk of poverty (Alderman, Hoddinott, & Kinsey, 2006). Indonesians whose growth was stunted in childhood were shorter (by 3.5 cm) and demonstrated lower cognitive function as young adults and had spent fewer years (by 5 months) enrolled in formal education (Giles, Satriawan, & Witoelar, 2020). Lower adult stature and cognitive ability were in turn associated with lower adult earnings in Indonesia (Sohn, 2015).

In Table A4 we report descriptive statistics. We see that 17% of the children in rural Indonesia are born weighing 2,500 grams or less. Approximately 30% of these children also grow up to be wasted (under 2 standard-deviations of the WHO reference population’s mean) and 19% to be stunted. We complement this description by looking at the weight-for-age and height-for-age z- scores for groups of children defined by the literacy status of their mothers/caregivers. Figure 2

> 6The child’s birthweight is based on recall by the mother and when possible verified using the birth registration card. The typical child was 3,106 grams at birth. Digital scales were used for weight measurement and tape measures were used for measuring respondent height. These were then compared to World Health Organization Child Growth Standards and WHO Reference 2007 composite data to assess whether respondents are stunted or wasted.

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**Table S10: Treatment Effect Estimates Using Baseline Variables as Outcomes (Expenditures)**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.057|0.030|0.003|0.028|0.011|
||(0.049)|(0.058)|(0.005)|(0.018)|(0.075)|
|Parent Enrolled*Ineligible|-0.026|-0.104|-0.003|0.003|0.054|
||(0.075)|(0.093)|(0.005)|(0.034)|(0.122)|
|Ineligible|0.003|0.009|-0.001|-0.011|-0.026|
||(0.024)|(0.029)|(0.001)|(0.011)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.674|0.565|0.007|0.016|0.192|
|P-value of Enrolled + Enrolled*Ineligible|0.644|0.370|0.935|0.343|0.552|
|Panel B: Inv. Hyp. Sin of Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.408|0.243|0.018|0.064|0.041|
||(0.368)|(0.397)|(0.027)|(0.082)|(0.445)|
|Parent Enrolled*Ineligible|-0.320|-0.766|-0.019|0.087|0.199|
||(0.568)|(0.624)|(0.028)|(0.184)|(0.720)|
|Ineligible|0.109|0.138|-0.007|-0.082|-0.106|
||(0.190)|(0.203)|(0.006)|(0.052)|(0.229)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.609|0.521|0.006|0.019|0.2|
|P-value of Enrolled + Enrolled*Ineligible|0.862|0.345|0.905|0.398|0.707|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from IV estimates where "Parent Enrolled" is instrumented with the parent's random assignment status and Parent Enrolled*Ineligible is the interaction of these two variables. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

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Ravallion, Martin and Lorraine Dearden, 1988. "Social Security in a 'Moral Economy': An Empirical Analysis for Java'. The Review of Economics and Statistics, 70:36-45.

Ravallion, Martin and Dominique van de Walle, 1988. "Poverty Orderings of Food Pricing Reforms". Development Economics Research Centre, University of Warwick, Discussion Paper 86.

and 1989. "Urban-Rural Cost-of-Living Differentials in a Developing Country". Journal of Urban Economics, forthcoming. Sen, Amartya K. 1976. "Poverty: an Ordinal Approach to Measurement". Econometrica 48: 437-446.

1981. Poverty and Famines: An Essay on Entitlement and Deprivation. Oxford: Oxford University Press.

Sundrum. R.M. 1987. Growth and Income Distribution in India. New Delhi: Sage Publications.

._ 1988. "Indonesia's Slow Economic Growth, 1981-86." Bulletin of Indonesian Economic Studies 24: 37-72.

van de Walle, Dominique. 1988. "On the Use of the SUSENAS for Modelling Consumer Behavior". Bulletin of Indonesian Economic Studies 24: 107-22.

____ , 1989. "The Welfare Analysis of Rice Pricing Policies Using Household Data for Indonesia". Doctoral Dissertation, The Australian National University.

Villasenor, Jose and Barry C., Arnold, 1989. "Elliptical Lorenz Curves". Journal of Econometrics 40: 327-338.
# **Other issues**

Another issue to consider when responding to commodity price shock is the nature of the shock especially the permanent vs. transitory aspects. It is of course hard to know a priori. Typically, one can distinguish between demand and supply. While monetary policy needs to respond swiftly, erring on the side of caution, assuming that part of the shock is temporary and part is permanent, is appropriate. Oil prices are notoriously hard to forecast (Chart 4). The market seems to learn only gradually as the evolution of futures curves (Chart 5). The uncertainty surrounding the nature of the shock calls for precautionary saving in the form of (fiscal and “financial”) buffers. Hedging reduces the need for precautionary saving (Borensztein, Jeanne, and Sandri, 2013). It is often forgotten that uncertainty about oil price also has an independent effect that affects investment and consumption decisions.

Chart 5 – Oil prices and futures during 2000s <!-- Start of picture text --> WTI Spot and Futures Prices<br>U.S. dollars a barrel<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Source: Bloomberg, L.P.<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> Note: Updated from Leduc et al. (2015). WTI = West Texas Intermediate. Futures path as of first business day at 6-month intervals.

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## Table 4.7

|**InwardandOutw**|**ardFDIFlows **<br>|**as Percentage**<br>**(Percentage)**|**ofGrossFix**<br>|**edCapitalFo**|**rmation**|l|
|---|---|---|---|---|---|---|
||1990-1995<br>(Annual||||||
|Region/Economy|average)|1996|1997|1998|1999|2000|
|Japan:|||||||
|inward|0.1|-|0.3|0.3|1.1|**0.7**|
|outward|2.2|1.8|2.1|2.3|1.9|**26**|
|EuropeanUnion:|||||||
|inward|5.5|6.5|8.1|15.7|28.5|50.1|
|outward|7.7|10.8|14.0|24.8|41.8|60.0|
|U.S.:|||||||
|**inward**|**4.3**|**7.0**|**7.8**|**11.9**|**18.0**|**17.5**|
|**outward**|**6.1**|**7.0**|**7.2**|**8.9**|**11.1**|**9.6**|
|**South,East**and South-EastAsia:|||I||||
|inward|**6.7**|**9.1**|**10.0**|10.5|11.5|14.0|
|**outward**|**3.8**|**5.3**|**5.4**|**3.9**|**4.4**|**9.0**|
|**Source:UNCTAD,WorldInvestmen**|**tReport2002,A **|**nnextableB.5**|||||

The rise in FDI inflows to East Asia in the last decade has been remarkable. (See Table S.9 in appendix.) The value of inflows has tripled from $45 billion in 1990-95 (annual average) to $136 billion in 2000. This level declined somewhat (by 29 percent to $96.4 billion) in 2001 but still represents a doubling of inflows since the beginning of the 1990s. Among regional subgroups, inflows to ASEAN declined over that period while those to ASEAN-Plus-Three rose by 80 percent, reflecting a sharp increase in flows to China along with post-crisis disinvestment in Indonesia and a significant decline in flows to Malaysia. The growth in FDI, as in trade, was spurred by significant liberalization in these areas during the 1980s and early 1990s, although this growth was reversed by the East Asian crisis of 1997-98.

In 2001, China attracted the largest share of inflows in the region, as well as in the developing world, when the value of its inflows reached a high of $46.8 billion. After doubling from $19 billion to $40 billion between 1990-95 (annual average) and 1996, its flows remained close to that level until 2001 when they increased further. During the first half of 2002, inflows rose by 19 percent over the same period in 2001, and this trend is expected to continue.37

# **Sources of FDI**

A look at the sources of these FDI flows will help in the assessment of FDI flow patterns for East Asia. We will first look at ASEAN as a group (Figure 4.5) and then at individual East Asian countries (Table 4.8).

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