rafmacalaba/datause-encoder-data huggingface dataset:
# Synergies in Child Nutrition: Interactions of Food Security, Health and Environment and Child Care <u>,</u>

## **Emmanuel Skoufias**

**Poverty & Equity Global Practice**

**The World Bank Group**

_<u>JEL Classification:</u>_ <u>I14, I15, I18, I38, J13</u> _<u>Keywords:</u>_ Child Nutrition, Stunting, Food Security, Health Services, Environment, Child Care.
In this section we provide overviews of a handful of recent or ongoing field experiments on migration: studies of savings among migrants in the U.S. and research on barriers to migration (for internal migrations in Bangladesh and international migrants from the Philippines).

# _Studies of migrant savings_

While remittances bring numerous benefits to households in developing countries, to date we know very little about how migrants make their remittance-sending decisions. In particular, it is unknown whether migrants desire greater control over how family members back home use the remittances they receive. This question is relevant not only for migration studies but also for the large and active literature in development economics on intra-household resource allocation. What’s more, a better understanding of these questions could have substantial impact on public policy, by suggesting policies to further stimulate remittance flows and potentially channel them towards more productive uses in migrant source countries.

Ashraf, Aycinena, Martinez, and Yang (2010), henceforth AAMY, address some of these questions via a randomized controlled trial among migrants from El Salvador who are living and working in the Washington, D.C. metro area. The research aims to shed light on the extent to which migrants’ lack of direct control over the use of remittances affects remittance flows, and on the impact of new financial products that could increase migrant control.

In particular, AAMY focus on improving the ability of migrants to ensure that remittances are deposited and accumulated in savings accounts in the home country. In survey data collected as part of the study, Washington, D.C.-based migrants from El Salvador report that they would like recipient households to save 21.2% of remittance receipts, while recipient households prefer to save only 2.6% of receipts. Migrants often intend the savings to be for the use of the recipient household in the future, but such savings also can be intended for the migrant’s future use. In the latter case, migrants may send their own funds to be saved in El Salvador because they perceive savings held in the U.S. as relatively insecure (particularly for undocumented migrants who fear deportation and loss of their assets).

16
greenhouse gas (GHG) emissions,<sup>3</sup> reduced pressure on foreign exchange reserves, employment opportunity for unskilled agricultural labor, and reduced soil erosion in the hills (Mofijur et al., 2012; Garg et al., 2011; Eijck and Romijn, 2008; Achten et al., 2007). However, the critical question here is: would jatropha-based biodiesel be economically viable in Nepal? Does it produce more economic rent from the land than the crops that have been grown traditionally? Can it be economically produced from low-quality (or marginal) lands which are not suitable for producing other crops?

# **Figure 1: Transport sector energy consumption by fuel types in Nepal (2008/09)**

<!-- Start of picture text --> Light diesel 0.1% Liquefied petroleum<br>gas (LPG) 1.0%<br>Aviation turbine fuel<br>(ATF) 11.9%<br>Electricity 0.1%<br>Gasoline 19.9%<br>High Speed Diesel Total consumption = 20.8<br>(HSD) 67.0% Million GJ)<br><!-- End of picture text --> Source: Water and Energy Commission Secretariat (WECS), 2010

> 3 Jatropha biodiesel is considered as a potential activity to be implemented under the Clean Development Mechanism (CDM) which allows developing countries to sell carbon credits from their eligible GHG mitigation activities in the international markets.

3
# **4.1 GDP, inflation and remittances**

Growth is expected to be moderate over the medium term for both scenarios (Table 1). Sustained, but lower growth is projected due to the more challenging international economic environment and the expected return to the estimated potential growth rate. Internal demand is projected to remain the key driver of economic growth, mainly explained by private consumption. Investment is expected to remain strong, supported by large FDI inflows. As in previous years, net exports are projected to contribute negatively to growth, especially during year 1. CPI inflation is expected to start declining in year 1, mainly due to the projected deceleration of economic activity, and to gradually approach the monetary authority’s target range of 4 to 6 percent thereafter.

Under the benchmark scenario, therefore, a real GDP growth rate of 4.0 percent is assumed for year 1 and 2, close to the estimated potential GDP growth. Commerce, tourism and transport are expected to follow a similar trend as the past years, although somewhat slower, and to expand at the fastest rate among sectors, while the primary sector experiences slower growth rates. In line with a slowdown of the economy, overall inflation is expected to decline to 7.1 percent in year 1 and to 6.1 percent in year 2. Food inflation will similarly decline to 6.5 percent in year 1 and 6.4 percent in year 2.

Under a more pessimistic crisis scenario, however, a recession similar to that experienced in 2001/02 is assumed with a contraction of real GDP by 7.7 percent in year 1, followed by a faster recovery than during the Argentine crisis with an increase in real GDP of 0.8 percent in year 2. A particularly sharp decline is assumed for manufacturing and commerce, tourism and transport, which are more immediately affected by the downturn. Overall inflation is projected to follow a similar trend to 2001/02 as well, peaking at 13.9 percent in year 1 and declining to 9.2 percent in year 2.

**Table 1: Projections of GDP growth rate and inflation under the benchmark and crisis scenario,**

**<u>year 1 - year 2 (all in percent)</u>**

|||GDP|||CPI||F|ood CP|I|
|---|---|---|---|---|---|---|---|---|---|
||Benchmark|Crisis|Difference|Benchmark|Crisis|Difference|Benchmark|Crisis|Difference|
|2011|6.5|6.5|-|8.1|8.1|-|9.6|9.6|-|
|baseline<br>year (2012)|3.9|3.9|-|8.1|8.1|-|8.4|8.4|-|
|year 1|4.0|-7.7|-11.7|7.1|13.9|6.8|6.5|7.0|0.5|
|year 2|4.0|0.8|-3.2|6.1|9.2|3.1|6.4|6.7|0.3|

Source: BCU, INE and WB staff calculations; GDP = GDP at factor cost; 2011 and 2012 are actual data.

Remittances have not played an important role in the Uruguayan economy so far. Compared to other Latin American countries, remittances have not been a major contributor to the Uruguayan economy, representing less than 1 percent of GDP, reaching about USD120 million in 2012. Similar trajectories have therefore been assumed for remittances for year 1 and year 2, following the past trend, with slightly lower values for the crisis scenario.

9
12 sake), with the exception of the logit model with fixed effects which includes only year dummies. Although the significance of the coefficient varies across estimators, the direction of the effects of the various variables is in most cases consistent across estimators. One of the clearest results is that size (proxied here by the logarithm of the real capital stock at the beginning of the period, LRKAP1) is very important in determining access to long term credit. More specifically, the probability in obtaining long term credit is greater for larger firms. This result is consistent with one of the prediction in the model by Diamond (1991) that shows that for firms with low credit rating (presumably the small ones in our case), an increase in quality is associated with gaining access to long term debt.<sup>7</sup> In his model the basic trade off is between the benefit of short term debt, because it allows firms to take advantage of favorable news certain, and the liquidation risk they have to bear, since opportunistic lenders may try to appropriate the surplus by forcing the firm into bankruptcy. However, great caution is needed before linking the empirical results for Ecuador to the theoretical models that have been proposed in the literature. One problem is that lenders are assumed to be profit maximizing in the model, which may or may not be an accurate assumption for Ecuadorian intermediaries in their role of providers of directed credit.

Since we have used the real value of fixed assets as a proxy for size, the positive effect of size also reflects the fact that the availability of collateral is a prerequisite to obtain long term debt. Finally, the positive effect of the size variable could capture the 7See Schiantarelli and Sembenelli (1996) for a more detailed discussion of the theoretical models of maturity choice.
Table A.12: Backlash

||**Negative experience**<br>**with FP**|**Satisfaction with**<br>**sex life**|**Marital**<br>**satisfaction**|
|---|---|---|---|
||(1)|(2)|(3)|
|Own Voucher|-0.022|0.213|0.140|
||[0.027]|[0.196]|[0.209]|
|BAF Voucher|0.010|0.297|0.198|
||[0.032]|[0.210]|[0.202]|
|Observations|494|504|510|
|Endline control mean|0.076|8.207|8.763|
|p-value: Own = BAF|0.358|0.718|0.810|

_Notes:_ All columns include balancing controls (i.e., the baseline variables used to balance randomization– whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-in-law and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

Table A.13: Heterogeneous treatment effects of voucher by baseline financial constraints

||**Visited the**<br>**ADC**|**Visited any**<br>**clinic**|**Using a modern**<br>**FP method**|
|---|---|---|---|
||(1)|(2)|(3)|
|Any Voucher|0.190***|0.154***|0.036|
||[0.029]|[0.042]|[0.034]|
|Any Voucher _×_ Poor|0.134|0.078|0.191**|
||[0.084]|[0.111]|[0.094]|
|Observations|593|591|586|
|Endline control mean|0.015|0.193|0.123|
|p-value:||||
|Any Voucher + Any Voucher _×_ Poor = 0|0.000|0.025|0.010|

_Notes: Any Voucher_ equals one if the woman received either an Own or a Bring-a-friend voucher, and zero otherwise. All columns are fully-interacted regressions, where all covariates are interacted with Poor at baseline. The main effect of Poor at baseline is also included as a control variable. All columns include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

60
**Appendix table 1: Determinants of output and input intensity for owner-cum-sharecroppers (household fixed effect** **<u>estimates)</u>**

||**Value of**<br>**output per**<br>**hectare (log)**|**Family labor**<br>**per ha (log)**|**Total labor**<br>**per ha (log)**|**Pair of oxen**<br>**days per ha**<br>**(log)**|**Quantity of fe**<br>**– Random**<br>**Coef.**|**rtilizer per ha**<br>**effect tobit**<br>**Marginal**<br>**effect**<br>**(dlny/dx)**|
|---|---|---|---|---|---|---|
|||**All**|**plots**||||
|Ownership dummy|0.055**|0.149***|0.142***|-0.006|59.557***|0.379***|
||(1.96)|(6.02)|(5.70)|(0.21)|(6.14)|(6.13)|
|Parcel size in hectares|-0.544***|-0.710***|-0.707***|-0.640***|-5.334|-0.034|
||(18.16)|(26.73)|(26.63)|(22.53)|(0.55)|(0.55)|
|Observations|7569|7569|7569|7569|7569||
|Number of household|591|591|591|591|591||
|R-squared|0.28|0.21|0.21|0.21|||
|||**Teff p**<br>|**lots only**<br>||||
|Ownership dummy|0.096**|0.105***|0.107***|-0.078*|78.015***|0.530***|
||(2.34)|(2.85)|(2.93)|(1.66)|(6.28)|(6.24)|
|Parcel size in hectares|-0.513***|-0.648***|-0.642***|-0.672***|-20.782|-0.141|
||(9.40)|(13.23)|(13.30)|(10.88)|(1.43)|(1.43)|
|Observations|1998|1998|1998|1998|1998||
|Number of household|369|369|369|369|369||
|R-squared|0.35|0.16|0.17|0.24|||

**Note** _._ Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. The reported marginal effects for the fertilizer regression are provided in the form of _dlny/dx_ such that we can interpret the coefficient of the ownership dummy similar to that of the labor intensity regressions as the percentage deviations of input intensities on owner-cultivated plots relative to sharecropped plots. Plot level characteristics and time-varying district ( _woreda_ ) dummies are included, but not reported.

25
a detailed description of the BACI data set.

**Import tariffs** : We use annual data on bilateral applied import tariffs by country and product (SITC 2-dig., Rev. 4) from the TRAINS database of the United Nations Conference on Trade and Development. While these data span our sample period, the extent of time coverage differs considerably across countries.

**Initial GDP per capita** : We use data on GDP per capita of each developed country (in constant 2000 USD) from the World Development Indicators of the World Bank. We use values for the first year the country is observed in the estimation sample.

# **B.2 Variable definitions and summary statistics**

This section describes in detail the variables used in the econometric analysis. Table A2 provides summary statistics on each of these variables.

- _log_ (1+ _exp_ ): logarithm of one plus the FOB value of bilateral exports in thousands of US dollars of developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

- _log_ (1 + _imp_ ): logarithm of one plus the FOB value of bilateral imports in thousands of US dollars from developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

- _log_ (1 +<sup>_<u>robots</u>_</sup> _hours_<sup>):logarithmofoneplustheratiobetweenthestockofrobotsandthenumberof</sup> working hours (in millions) in developed country _n_ in sector _i_ in year _t_ . The stock of robots was estimated suing the perpetual inventory method based on the observed stock of robots in the IFR data and using a depreciation rate of 10%.

- _log_ (1 +<sup>_<u>obs.robots</u>_</sup> _hours_<sup>):logarithmofoneplustheratiobetweenthestockofrobotsandthenumber</sup> of work hours (in millions) in developed country _n_ in sector _i_ in year _t_ . The stock of robots was directly observed in the IFR data set.

- _Initial GDPpc_ : GDP per capita (in constant 2000 USD) of developed country _n_ in the first year the country is observed in the estimation sample.

- _Replaceability_ : proportion of US jobs in 1980 that were in occupations that are replaceable by robots.

- _Global robot stock_ : global stock of robots. The stock of robots was estimated using the perpetual inventory method based on the observed stock of robots in the IFR data and using a depreciation rate of 10%.

- _log_ (1 + _material inputs_ ): logarithm of one plus the expenditure on material inputs in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _IT capital_ ): logarithm of one plus the expenditure on IT capital in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _non_ - _IT capital_ ): logarithm of one plus the expenditure on non-IT capital in developed country _n_ in sector _i_ in year _t_ .

- _log_ (1 + _tariff_ ): logarithm of one plus the ad valorem import tariff applied by developed country _n_ to developing country _m_ in sector _i_ in year _t_ .

53
important items of expenditure of the ECT are medical care (32 and 24 percent respectively) and nonfood consumption (14 and 31 percent respectively). The fact that the main use of the ECT transfer was for food consumption is in line with the fact that the COVID-19 pandemic affected households’ food insecurity (Londoño-Vélez & Querubin, 2020), especially in Sub-Saharan Africa (Aborode et al., 2021). Food consumption and investment in businesses being the two main items of expenditure of the ECT also points out the porosity of the household and labor spheres for informal workers who use this type of transfer to respond to immediate consumption needs and to alleviate credit constraints. Moreover, the increase in non-food consumption and investment in an existing business between the two rounds may reflect the fact that by the time of the second round of data collection, some of the governmentmandated COVID-19 restrictions had started to be lifted, allowing individuals to earn labor income and use the transfer for a larger set of expenses.

**_Table 2_** _: Use of ECT-R1 and ECT-R2_

|Transfer used for|Share of ECT-<br>R1<br>beneficiaries|N (ECT-R1)|Share of ECT-<br>R2<br>beneficiaries|N (ECT-R)|Two-sample<br>t-test P-value|
|---|---|---|---|---|---|
|Food|75.56%<br>(0.024)|311|80.20%<br>(0.028)|197|0.224|
|Invest in existing business|72.99%<br>(0.025)|311|86.29%<br>(0.025)|197|0.000|
|Medical care|31.51%<br>(0.026)|311|24.37%<br>(0.031)|197|0.083|
|Non-food consumable items|14.47%<br>(0.020)|311|31.47%<br>(0.033)|197|0.000|
|Paid debt|11.25%<br>(0.018)|311|3.05%<br>(0.030)|197|0.000|
|Start new business|9.97%<br>(0.017)|311|3.55%<br>(0.036)|197|0.007|
|House rent|8.04%<br>(0.015)|311|4.57%<br>(0.015)|197|0.128|
|Home improvement|2.89%<br>(0.009)|311|4.57%<br>(0.015)|197|0.321|

_Source_ : Authors’ calculations from CIMS (2020). Note: Standard errors in parentheses.

11
4 policies during the last twenty years4/. Simulation results indicate that, in the case of Argentina, the quantity of private investment is little responsive to movements in interest rates. While this finding may not be entirely unexpected (e.g. Khatkhate (1988)), it appears that this insensitivity is'not due to the low values of some important parameters, but rather to the interactions of many opposing forces allowed by the model. In particular, we will demonstrate that the positive effect suggested by McKinnon and Shaw may be offset sLultanseously by a crowding out effect and a shift that could occur iu the portfolio of the private sector.

The paper proceeds as follows. In section 1 we present a simplified portfolio model. Although this approach is derived from a Tobin-Sidrauski framework, a liquidity constraint is Introduced into the model in accordance with the McKinnon-Shaw hypothesis. The demand for capital accumulation by the users of capital goods is specified in section 2 using a modified version of the flexible accelerator theory of investment. Section 3 is devoted to the demonstration that the positive Impact of a rise in interest rates on the supply of domestic credlts to finance private investment might be reduced or even reversed when the public sector is introduced Into the analysis. The equations constituting the complete model are presented in Table 2. The empirical results for Argentina are presented and discussed in section 4. Finally, section 5 contains our conclusion.

# 1. A Siple Portfolio Model

An increase in real interest rates following a financial liberalization generates, in general, a portfolio shift in favor of bank deposits. The authors in favor of the HcKinnon-Shaw hypothesis assume that this portfolio shift is coming out from unproductive assets such as cash and gold. This seems, however, a drastic simplification of the reality because it is not at all obvious that deposits are closer substitutes to these unproductive assets than to capital goods in LDCs. To illustrate this point, private Investment in LDCs is mostly the demand for capital accumulation by the owner of capital (see Khatkhate (1988, p.580)). This form of savings tends to decline when real interest rates rise and correspondingly saving in financial assets goes up. Unless the latter effect dominates significantly the former effect, private real investment may not register an upwards change with the rise in the real interest rate. In this section, we address the critical issue of whether the positive effect suggested by HcKinnon and Shaw is strong enough to offset the shift from capital goods into mon.tary assets that could occur in the portfolio of private agents.

A/Specification using cross-section data from LDCs, while providing many more observations, assumes _aa_ similarity of behaviors which is questionable.
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|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
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|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
|Kariba Dam Rehabilitation Project(RI)|Anna Cestari|
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|Income Support Program for the Poorest|Aneeka Rahman|
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|Income Support Program for the Poorest|Aneeka Rahman|
|Income Support Program for the Poorest|Aneeka Rahman|
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|Income Support Program for the Poorest|Aneeka Rahman|
|Income Support Program for the Poorest|Aneeka Rahman|
|Income Support Program for the Poorest|Aneeka Rahman|
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|Nigeria - Program to Support SavingOne Million Lives|Ayodeji Gafar Ajiboye|
|Nigeria - Program to Support SavingOne Million Lives|Ayodeji Gafar Ajiboye|
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