rafmacalaba/datause-encoder-data huggingface dataset:
# _5.1 Effect of ratings on net capital flows_

The baseline regression results presented in Table 3 are estimated using a bias-corrected dynamic panel estimator. The results suggest that after accounting for the traditional indicators both absolute sovereign ratings and relative risk ratings are significant predictors of capital inflows (See Appendix Table A.3 for results without absolute or relative ratings). However, the coefficient of the absolute sovereign rating is less significant and smaller in the post-crisis period compared to the pre-crisis period (columns (1) and (2) of Table 3). Moreover, the magnitude of the relative rating variable is larger and more significant in the post-crisis period (columns (3) and (4) of Table 3). This finding is consistent with our expectation that relative risk ratings became more important especially when compared to a decline in the importance of absolute ratings in the post-crisis period.

**Table 3: Sovereign credit ratings, relative risk ratings, and net capital inflows to emerging and developing countries**

||**Abs**|**olute rating**|**Rela**|**tive to peers**|
|---|---|---|---|---|
|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.041<br>(0.038)|0.106***<br>(0.038)|0.096**<br>(0.039)|0.103***<br>(0.038)|
|S&P rating|0.519***<br>(0.081)|0.163*<br>(0.083)|||
|Relative rating|||0.141*<br>(0.083)|0.212***<br>(0.080)|
|Growth diff.|0.118<br>(0.098)|0.263***<br>(0.087)|0.201**<br>(0.099)|0.260***<br>(0.088)|
|Interest rate diff.|0.120|0.436***|0.028|0.465***|
||(0.117)|(0.117)|(0.117)|(0.118)|
|VIX|-0.030|0.055|-0.116***|0.047|
||(0.040)|(0.040)|(0.041)|(0.039)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|780|859|780|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.328|0.237|0.293|0.240|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 1998-2017 period. The pre-crisis period includes Q1 1998-Q2 2008 while the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

The findings suggest that in a situation of abundant global liquidity and strong flows to developing countries but comparative caution among investors given the adverse experiences of the crisis, the relative perception of sovereign risk (i.e. how investors perceive a country’s creditworthiness relative to its peers) played a more important role than the sovereign rating in influencing capital flows in the post-crisis period. These findings for capital flows in the post16
declined, as fiscal pressures created by funding these subsidies escalated. The point is, that a reduction in public spending on agriculture need not have a negative impact on agricultural output as long as the allocation of public spending is targeted to public goods.

Van Blarcom et.al. developed a set of guidelines for structural adjustment in agriculture. These included: (i) reduce industrial protection and remove the misalignment of the RER; (ii) reduce subsidies and effectively target the remaining subsidies to services that foster growth; (iii) increase the public goods component of public spending on agriculture (such as research and extension); (iv) privatize state-owned enterprises in conjunction with price liberalization in order to end the drain on fiscal resources; and (v) build greater transparency into the public sector's budget process so that public funds are allocated in a context of clearly defined sector strategies and explicitly stated goals.

Support services that can be transferred to the private sector should be transferred. Recurrent costs should be adequately funded, particularly operations and maintenance costs of public infrastructure. Indeed, the _World Development Report 1994_ (World Bank, 1994) identified these recurrent costs as generating some of the highest social returns of any type of public spending. One way of ensuring adequate funding for recurrent costs is through cost recovery measures. The user charges for water, power, and fertilizer often account for a small fraction of their actual cost in many countries, and should be increased to reflect the cost of providing them.

# **The North**

Agricultural policies in industrialized countries (the North) have taken fundamentally different forms than those in developing countries (the South), and the contrasts are instructive. Agricultural sectors in the North generally comprise a small proportion of the economy relative to the urban-industrial sectors. Northern agriculture has remained highly protected, while Northern industry is afforded generally low rates of protection. Furthermore, low industrial protection, coupled with lower levels of exchange rate misalignment, has resulted in a substantially lower level of indirect taxation of agriculture than in developing countries.

# **_The United States_**

The nature of the relationship between the macroeconomy and relative prices in agriculture for the US was detailed in several pioneering studies by Schuh (1974, 1989). Schuh's (1974) analysis for the US began with the 1950s. The US dollar was considered overvalued, but under the Bretton Woods agreement, the US agreed to a fixed nominal exchange rate relative to gold. The exchange rate overvaluation adversely affected the competitiveness of US agriculture, particularly for exportables, at a time of burgeoning levels of grain stocks. The US resorted to providing massive food aid, in essence as an implicit export subsidy, and by the end of the 1 960s, had added explicit export subsidies as 26
- 23 -

3. A Numerical Calibration of the Model for Chile In this section the model specified in the previous section<sup>is</sup> calibrated. The calibration procedure uses coefficients<sup>fro.ia</sup> three sources: i)econometric estimates of some key functions (like import and export 3quations, investment equationb,price equations, ii) values calculated divectly<sup>from time</sup> series or iii) assumed plausible coefficient values. To assure consistency to the base year values coming from National Ac .ounts and Balance of<sup>Payments</sup> Statistics (that base year is 1987) the constant terms of the model's equations are adjusted so as to replicate that year. The appendix documents the<sup>initial</sup> values of the variables of the model and some parameters used to estimate<sup>the</sup> growth constraints and other relationships of the model.

Let us turn now to the parameterization of the model. Taking 1981 as a year of "full" capacity utilization for the Chilean economy and assuming<sup>an</sup> annutal rate of growth of capacity output of 1.5? we obtain a rate of capacity utilization for 1987 of 0.946 (a little bit higher than the estimated for that year in Marf4n and Artiagotia, 1989). The output-capital ratio , k , is .333 so the implied ICOR is 3.0. The accelerator coefficient in the investment function, B, is 0.059 and the crowding-in parameter, a, is - 0.23 (Zucker, 1988). The parameterization of the private sector savings rate function is 9pn - 0.087 + 0.16 u , for the public savings rate is sg- - 0.046 + 0.1 u (note that negative intercepts imply marginal savings rates for private<sup>savings</sup> and public sector net revenues -hat exceed averages propensities). The<sup>foreign</sup> savings rate function, in domestic currency, is Sf - 0.136 **+** 0.34 er + 0.645 i + 0.487 u - 0.314 Y*. These equations show (recall the model is stated in terms of shares) that a 1? increase in the rate
# **A3.4  The Relationship Between Stunting and Adult Height**

An alternative approach to incorporating health into the human capital index is to use measures of stunting in childhood directly as the observed proxy for latent health. Stunting is measured as the fraction of children under five years old whose height is more than two reference standard deviations below the reference median, where the reference median and standard deviation are taken from WHO standards for normal healthy child development. Creating a link from stunting to the contribution of latent health to productivity, requires evidence on the relationship between the proportion of children who are stunted in childhood and average attained height of the population in adulthood. Combining this relationship with the estimated labour market returns to height creates a link from stunting in childhood to worker productivity in adulthood operating through the channel of increased height. This subsection discussed two complementary approaches to obtaining this relationship The first is a calibration based on a simple variant on the calculations and estimates in Galasso and Wagstaff (2016). They cite a number of cohort studies that provide evidence that having been stunted as a child reduces attained adult height by approximately 6 centimeters. Under the assumption that average adult height conditional on stunting status in childhood does not change with the stunting rate, they calculate the change in average adult height due to the elimination of stunting as this difference of 6 centimeters multiplied by the fraction of the adult population that was stunted in childhood.

This estimate may however be conservative because it assumes no change in the adult height of children who were not initially stunted, even though these children are likely also to benefit from the improvements in health that reduce the proportion of children who are stunted. These wider effects can be captured with an alternative calibration of how the mean of the distribution of adult height shifts when childhood stunting falls. Let x represent adult height and q� represent the fraction of adults who were stunted as children, i.e. q� ≡P�x� �z�� where x� denotes height in childhood when stunting is measured, and z� represents the corresponding age‐specific height threshold for stunting in childhood. Next consider three simplifying assumptions: (i) adult height is normally distributed, i.e. x~N�μ, σ<sup>�</sup> � ; (ii) the fraction of adults who were stunted as children is the same as the fraction of children who were stunted when these adults were children, i.e. q� �q≡P�x�z� , where z is the adult height threshold 43
Croke et al. (2016) children. Moreover, the TMSDG analysis is underpowered to detect these expected MDA effects. Nearly all expected MDA effects that lie in the 95% confidence interval are smaller than the 80% MDE of 0.28 kg.

Furthermore, it is commonly believed that the health impact of worms is much greater for those with moderate to severe infections than with light infections. Since TMSDG’s estimate for effects on infected children is based on studies with moderate intensity infections on average, while the majority of those infected in most MDA studies are likely to have light intensity infections (see footnote 6 above), we may actually expect the average MDA effect to be even smaller than the 0.20 kg estimated above. This underscores the points that (1) TMSDG’s MDA and screen-and-treat results are consistent with one another, and (2) TMSDG is underpowered to detect effect sizes that we would reasonably expect given TMSDG’s estimated effects on infected children.

Finally, power is also an issue in many of the individual studies which make up the meta-analysis. In low worm infection prevalence populations, very large samples are needed to have sufficient statistical power to pick up effects of mass drug administration. Since low-intensity infections are expected to have little impact, and since intensity rises non-linearly with prevalence (Anderson and May, 1985), only a small fraction of the population in low prevalence environments will have moderate to heavy infections and the necessary increase in sample size may be much larger if weight gain effects are concentrated among those with moderate-severe intensity infections.<sup>15</sup>

> 15 Anderson and May (1985) explain that the relationship between prevalence and intensity is well described by: _Mean Intensity = k*(1-Prevalence)_<sup>_-1/k_</sup> _-k_ , where _k_ is a parameter representing the degree of aggregation or dispersion of the parasite in the population. Estimates of _k_ lie in the range 0.11 to 0.81 (Anderson, Truscott and Hollingsworth, 2014). Taking the derivative with respect to prevalence implies that intensity is expected to rise more than linearly in prevalence.

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<!-- Start of picture text --> 0.7<br>Capital Stock (No Tax)<br>GHG (No Tax)<br>Debt (No Tax)<br>0.6<br>Capital Stock<br>GHG<br>Debt<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 10 20 30 40 50 60 70 80<br><!-- End of picture text --> Figure 4: Simulations of scenarios 2 and 3 falls on current generations. We set up an intertemporal model, related to Sachs (2015), that proposes burden sharing between current and future generations, by allowing for both carbon taxation and green bonds as funding sources for a low-carbon transition. The issuance of green bonds helps to improve intergenerational equity by funding immediate investment in climate mitigation, with bonds repaid by future generations which thereby participate in financing the reduced climate damage from which they will benefit. We examine three scenarios using a new numerical procedure called NMPC that allows for finite horizon solutions and phase changes. We show that the combination of carbon pricing and green bonds improves environmental effectiveness, capital accumulation and debt sustainability. The main conclusion is that, in the financing of low-carbon transitions, a mix of policies should be used that includes green bonds and carbon pricing, which seems to be politically more feasible, speeds up the transition, and exhibits greater intergenerational fairness.

# **References**

- [1] Acemoglu, D., P. Aghion, L. Bursztyn, and D. Hemous (2012): The Environment and Directed Technical Change, American Economic Review, 102(1), 131-166.

- [2] Bachelet, M. J., L. Becchetti, and S. Manfredonia (2019): The Green Bonds Premium Puzzle: The Role of Issuer Characteristics and Third-Party Verification. Sustainability, 11(4), 10-98.

- [3] Baker, M., D. Bergstresser, G. Serafeim, and J. Wurgler (2018): Financing the response to climate change: The pricing and ownership of US green bonds. Working Paper. 25194. National Bureau of Economic Research.

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**Behavioral Change Promotion, Cash Transfers and Early Childhood Development Experimental Evidence from a Government Program in a Low-Income Setting**<sup>_1_</sup>

# _Patrick Premand (World Bank)_

_Oumar Barry (Cheikh Anta Diop University)_ Keywords: Early Childhood Development, Parenting, Social and Behavioral Change Communication, Cash Transfers, Spill-overs, Africa, Field Experiment.

JEL Codes: I15, I38, J13, O15.

1 This paper is based on a collaboration between the government of Niger, the World Bank and UNICEF to implement behavioral accompanying measures to promote Early Childhood Development as part of the Niger national safety net program. The study was supported by the Strategic Impact Evaluation Fund (SIEF), the Early Learning Partnership and the Sahel Adaptive Social Protection Program at the World Bank, as well as the Niger Adaptive Safety Nets Project, managed by the Cellule Filets Sociaux (CFS) in the Prime Minister’s office of the Government of Niger. We are grateful to Ali Mory Maidoka, Bassirou Karimou, Kadi Aboubacar and Aichatou Bello at CFS, Omar Habib, Rocio Berzal, and Mariama Hassane Amadou at UNICEF, as well as Carlo del Ninno, Fanta Touré and Mahamane Maliki Amadou at the World Bank for a fruitful collaboration. Marc Smitz coordinated the baseline and follow-up surveys and provided excellent contributions to RCT implementation and data analysis. Horacio Vera Cossio provided excellent research assistance. The Nigerien National Statistical Office (INS) collected baseline data. Riseal and the Swiss Tropical and Public Health Institute (STPH) collected follow-up data, with coordination from Kaspar Wyss, Alexandra Nicola, Ibrahim Sy and Amina Garba. Charles Super oversaw the use of the child cognitive test for the follow-up survey. Follow-up survey data was collected with SurveySolutions, with the support of Misha Lokshin, Zurab Sajaia, Sondo Eloi Somtinda, Martial Kouamé and Arthur Shaw. We are grateful for inputs and comments at various parts of the study from Jenny Aker, Harold Alderman, David Evans, Emanuela Galasso, Karen Macours, Quentin Stoeffler, Charles Super, Julieta Trias, Marian Zeitlin, anonymous SIEF and DIME referees, as well as seminar participants at STPH, ETH Zürich, the University of Zürich, UNICEF Innocenti and the World Bank. The computational reproducibility of the results has been verified by DIME analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations; or those of the Executive Directors of the World Bank, the government they represent; or those of UNICEF or the government of Niger. Corresponding author: Patrick Premand, ppremand@worldbank.org.
Independently of growth, policy, or financial market developments in the United States, shocks to confidence of U.S. businesses and consumers can themselves reverberate across borders and be sources of business cycle fluctuations (Levchenko and Pandalai-Nayar 2015). Elevated uncertainty about changes in U.S. policies can reduce incentives to commit to capital investment at home and abroad, and this in turn could adversely affect long-term global growth prospects (Kose and Terrones 2015).

# **4.1 Growth spillovers**

U.S. growth shocks are expected to have sizable effects on activity in the rest of the world. Our estimates show that a 1 percentage point increase in U.S. growth could lift growth in advanced economies by 0.8 percentage point and in EMDEs by 0.6 percentage points after one year, while global growth (excluding the United States) could rise by 0.7 percentage point (Figure 8).<sup>8</sup> Growth spillovers reported here are based on a Bayesian vector autoregression model with Cholesky ordering based on a sample that covers other AEs includes such as the Euro Area (19 countries), Canada, Japan, and the United Kingdom and 19 EMDEs for 1998Q1-2016Q2. The model includes growth in the United States, other advanced economies, EMDEs and the rest of the world, as well as U.S. 10-year Treasury yields and emerging market bond spreads (World Bank 2016a; World Bank 2017).

The impact of U.S. growth shocks on investment in these economies would be approximately twice as large. NAFTA members (Canada and Mexico) would particularly benefit from trade spillovers (Yifan and Abeysinghe 2016). Terms of trade effects through commodity markets would be another transmission channel (World Bank 2016b).

# **4.2 Financial market spillovers**

The role of the United States in global financial markets goes well beyond direct capital flows to and from the United States.<sup>9</sup> U.S. bond and equity markets are the largest and most liquid in the world. Swings in U.S. sovereign bond yields are often closely mirrored in other large financial markets. Similarly, cross-border spillovers from U.S. equity markets are significant and depend more on openness to the global economy than on the size of actual

> 8This estimate for advanced economies is in line with other estimates for Canada (Swiston and Bayoumi 2008). For Mexico and Caribbean economies with strong economic ties to the United States, considerably larger spillovers in excess of 1 percentage point have been estimated (Sun and Samuel 2009; Swiston and Bayoumi 2008).

> 9See Berkmen et al. (2012); de Grauwe and Yi (2016); Frankel and Saravelos (2012); Helbling et al. (2011); Metiu, Hilberg, and Grill (2015).

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**Figure 3**

Distribution of running variable <!-- Start of picture text --> Histogram of Village Population<br>2001 Population Census Data<br>0 500 1000 1500 −100 −50 0 50 100<br>Population Normalized Population<br>.008<br>.006<br>8000<br>.004<br>Density<br>6000<br>Frequency<br>4000<br>.002<br>2000<br>0<br><!-- End of picture text --> Notes: The figure shows the distribution of village population around the population thresholds. The left panel is a histogram of village population as recorded in the 2001 Population Census. The vertical lines show the program eligibility thresholds used in this paper, at 500 and 1,000. The right panel uses the normalized village population (reported population minus the threshold, either 500 or 1,000). It plots a non-parametric regression to each half of the distribution following McCrary (2008), testing for a discontinuity at zero. The point estimate for the discontinuity is -0.01, with a standard error of 0.05.

41
differences with the same number and pattern of observations. Table 1 shows similar results than those of the baseline side. Here again, more of the control variables show no difference between the control and the experiment group, except for those mentioned above. The results are similar to those found in the original project report summary statistics. While income per capita are, on average, the same for both groups, having TV or VCR, assets that are associated in theory with wealth, are statistically different for both groups.

Table 1a shows the differences in characteristics between comparison groups for the full longitudinal sample and the longitudinal sample with follow up attrition. It is important to highlight if these differences in the main outcome indicators prevail or if they shift drastically. If attrition occurs due to random circumstances, there could still be a balance of characteristics in the initial sample of children belonging to the treatment and control groups. The only consequence of attrition would be a reduced statistical power of the surveys. If these imbalances are pronounced in the presence of attrition, most likely the attrition would be highly correlated with the treatment status which could ultimately bias the estimates. In general, the results in this table point towards attrition explained randomly rather than by a factor intrinsically related to the treatment status. This is why most indicators show similar differences between the full longitudinal sample and the sample with attrition.

Furthermore, the survey questions _days with fever_ , and _days with stomach pain_ also yielded statistically significant coefficients, without considering asset tenure to receive campaigns’ information. This can be interpreted that health related symptoms are also different for both groups, with better health symptoms experienced by the infants in the treatment group than by infants in the control group. Exploring the differences in cognitive skills between groups using panel data and adjusting estimates based on certain communications assets within the household shed light on the determinants of the changes in cognitive abilities of children in the presence of the communication campaign interventions.

Table 2 presents the descriptive statistics of the follow up cohort. That is, the table shows the means of the independent variables to test whether the outcome of health 14