rafmacalaba/datause-encoder-data huggingface dataset:
63

# Determinants of Chlld Nutrition and NortalLty

Studies in the 1970s and 1980s in demography, ecotiomics, anthropology and sociology came to the conclusion that there **was a** strong, probably causal, relationship between **increases** in mother's schooling and decreases in the Incldence of mortality among her own children, particularly in low income countries **(e.g.** Basu, 1987). Many hypotheses **are** offered as to why thls correlation is observed, **some** of which Are discussed below (e.g. Ran and Schultz, 1979; Barrera, 1988). The patterns is widely replicated because many fertility surveys provide the minium data on women: their **age,** education, children ever born and children stlll living. The universality of the relationship is reminiscent of the discovery in the 1960s of relative (logarithmic) wage differentials associated with the **years** of sctooling. The human capital interpretation of **these** latter wage-education differentials **as** an approximation of the private rate of return to schooling has gradually gained acceptance (Schultz, 1988e).

In the case of child mortality, an added year of maternal education is associated with a 5 to 10 percent reduction in child mortality. Although the level of mortality tends to be higher in rural than in urban populations of low income countries, the proportionate reduction in child mortality associated with an additional year of mother's schoolin& Is of a roughly similar magnltude in both urban and rural **areas** of many countries (Cochran at al., 1980). The proportionate mortality reducing effect **is** tauller for the father's schooling than the mother's, especially in rural populations (Mensch et al., 1986). Studies in Latin America have also noted that the child mortality differentials associated with maternal education are more moderate in Costa Rica end Cuba. The hypothesis advanced to explain **these** exceptions is
<!-- Start of picture text --> ECHAM Model<br>10000<br>1000<br>vj 100<br>><br><<br>2<br>eo 10<br>:<br>aD<br>% w= Future<br>5 4<br>a<br>0.1<br>0.01<br>1 10 100 1000<br>Return Period (Years)<br><!-- End of picture text -->
(2008) includes only fast-growing developing countries and shows that the link between total public spending and growth is overall positive with some components of public spending being particularly significant in affecting growth. For this group of countries, unproductive components of public expenditure are less significant<sup>9</sup> —or even have a negative impact on growth—while the productive component of public spending is statistically significantly.<sup>10</sup> Our paper extends this initial empirical study in a way to answer the question of whether the findings presented in the literature are sensitive to country sample selection bias. We try to accomplish this goal by extending the initial data set and including a mix of countries with different growth performances.

# **III.** **<u>DATA AND COMPARATIVE ANALYSIS</u>**

The section explains the data sample and summarizes comparative analysis.

# _III.1. Data_

Two sets of countries are included in the analysis. The first group consists of the seven fast-growing developing countries. The second set includes countries with less consistent and more volatile growth performance patterns.<sup>11</sup> The regression period is 1970-2005.<sup>12</sup> The main data source is the Government Financial Statistics (GFS) of the International Monetary Fund (IMF).<sup>13</sup>

# _III.2. Comparative Analysis_

This section investigates some country facts and findings, and compares different country characteristics on public expenditure and growth that may be helpful when interpreting the subsequent econometric results.

First, when the growth rates of GDP per capita are compared for these two groups of countries, it can be seen that, by definition, in the first set, countries have grown much faster on average between 1970 and 2005 (see Tables 1 and 2). While this group has almost 5 percent growth rate on average, the second group has a mean growth rate of 1.6 percent. As can be seen in Figure 1, the first group outperforms the second set of countries throughout the period of 1970 to 2005 except during the Asian financial crises of 1997-98, and the years 2004 and 2005 thanks to high growth performance of countries like Turkey, Uruguay, and Venezuela in the comparison set.

> 9 Defined _a priori_ using the definition by Bleaney, Gemmell, and Kneller (2001); and Kneller, Bleaney and Gemmell (1998 and 1999).

> 10 Similarly, Rogers (2008) shows that the impact of public schooling expenditures on economic growth is significantly higher in countries that are using schooling productively.

> 11 The list of countries in the fast-growing set and the comparison set is given in the introduction section. 12 The time period may change slightly from one country to another depending on data availability at the country level.

> 13 Details on variables and data sources are presented in the Appendix to this paper as well as in MorenoDodson (2008).

7
Is Workfare Cost-Effective against Poverty in a Poor Labor-Surplus Economy?

Rinku Murgai, Martin Ravallion and Dominique van de Walle<sup>1</sup>

**Keywords:** NREGA, workfare, public works, poverty, wages, forgone income, India, Bihar

**JEL:** I32, I38

**Address for correspondence :** <u>mr1185@georgetown.edu</u>

> 1 Murgai and van de Walle are with the World Bank and Ravallion is with the Department of Economics, Georgetown University and the NBER. This paper draws on data collected and analysis done for a World Bank project co-managed by Puja Dutta and Rinku Murgai. The authors are grateful to Sunai Consultancy Private Ltd and GfK Mode for support on the field work for this study. The authors are also grateful to the Rural Development Department, Government of Bihar, for providing insights into the challenges and ongoing initiatives in Bihar. Arthur Alik Lagrange and Maria Mini Jos provided very able research assistance. Useful comments were received from seminar participants at the Indian Statistical Institute, the Paris School of Economics, the Australian National University, Georgetown University and the World Bank. These are the views of the authors and do not necessarily represent those of their employers, including the World Bank or of any of its member countries.
|Table 3. Tariff sche|dules for low-income, lo<br>Type of tariff|w-consumption cust<br>Fixed charge<br>($)/month|omers<br>Block border|Price per block,<br>cents/kWh|
|---|---|---|---|---|
|Benin<br>|social tranche|n.a.||9.6|
|Botswana|n.a.|n.a.||n.a.|
|Burkina Faso|block 1, residential|0.18||14.3|
|Cameroon*|block 1 residential|12.90||8.6|
|Cape Verde|block 1, residential|—||22.5|
|Chad|block 1 residential|n.a.||15.7|
|Congo, Dem. Rep.<br>of|social tariff|0.01||4.0|
|Congo,Rep. of|n.a.|n.a.||n.a.|
|Côte d’Ivoire|block 1 residential|0.64||6.9|
|Ethiopia|block 1 residential|0.16||3.2|
|Ghana|block 1 residential|0.54||7.6|
|Kenya|block 1 residential|1.74||4.9|
|Lesotho|—|—||—|
|Madagascar|economic tariff|0.30|25|6.0|
||||> 25|27.6|
|Malawi|block 1 residential|0.92||2.0|
|Mali|social tariff|n.a.|50|13.2|
||||100|20.3|
||||200|23.9|
||||>200|27.7|
|Mozambique|block 1 residential|n.a.||4.0|
|Namibia|n.a.|n.a.||n.a.|
|Niger|—|—||—|
|Nigeria|pensioners’ tariff|0.23||3.0|
|Rwanda|—|—||—|
|Senegal|tranche 1 residential|n.a.|150|0.24|
|South Africa|block 1 residential|n.a.||—|
|Sudan|—|—||—|
|Tanzania|n.a.|n.a.||3.0|
|Uganda|block 1 residential|1.09||3.4|
|Zambia|block 1 residential|1.31||1.6|
|Zimbabwe|tranche 1 residential|n.a.||0.6|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ For details see annex 13. n.a. Not applicable. — Not available.

Another important aspect of social policy in the power sector has been the growing use of prepayment meters. These have dual advantages. On the one hand, they help low income customers to control their expenditures on electricity by spreading them out into small frequent payments that they can more readily afford. On the other hand, they eliminate the commercial risk to the utility of serving low income 9
**Figure 12: Russia’s Increased oil dependence budget (left) and export revenues (right).**

<!-- Start of picture text --> 40% 80 Oil Dependence (O&Gexp/TotalEXP)<br>30% 60 65%<br>20% 40 60%<br>10% 20 55%<br>50%<br>0% 0<br>45%<br>40%<br>35%<br>Oil revenue as % of total GG Revenues<br>30%<br>Russian Oil price in US$ per bbl (right scale)<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text -->

**_In Russia, since 2007, the fiscal stance was becoming more relaxed compared with previous years, contributing to the overheating of the economy_** _._ The projected federal government budgetary surplus for 2007 went down from 4.8 to 2.8 percent, because of expenditure increases authorized under the 2007 Budget Law amendment. Total noninterest expenditure increased from 17 to 20 percent of GDP (compare column 1 and 2, Table 2). The approved medium-term fiscal framework entailed a fiscal relaxation that under the then prevailing oil price outlook envisaged a small primary surplus (0.3% of GDP) in 2008 and essentially balanced budget in 2009-2010 (Table 2). The bulk of the planned increase in public expenditures was for infrastructure development and social spending to be implemented largely through large state corporations.

**Table 2.  Consolidated budget: revenues, expenditures, and the fiscal surplus, 2007-09**

||2007<br>(actual)|2008<br>(actual)|Federal Budget<br>Law (Nov<br>2009)|Federal<br>Budget Law<br>Revised<br>(Apr 2009)|2009<br>(actual)|
|---|---|---|---|---|---|
|**Consolidated budget**||||||
|Revenues,% GDP|40.2|38.5|n/a|n/a|33.1|
|Expenditure,% GDP|34.1|33.7|n/a|n/a|41.3|
|Surplus,% GDP|6.1|4.8|n/a|n/a|-8.2|
|Non-oil balance,% GDP|-2.9|-5.8|n/a|n/a||
|**Federal budget**||||||
|Revenues,% GDP|23.6|22.3|21.2|16.6|18.6|
|Expenditure,% GDP|18.1|18.2|17.5|24.0|25.2|
|Surplus,% GDP*|5.4|4.1|3.7|-7.4|-6.4|
|Non-oil balance,% GDP|0.6|-6.4|-5.4|-12.5|-14.3|

Source: Ministry of Finance, Economic Expert Group (EEG).* including quasi-fiscal support to banks.

**_The other macro issue, exchange rate management in oil-rich countries, has attracted a huge amount of attention._** The macroeconomic issue here is that high public spending inevitably targets goods and services where international trade offers only an imperfect substitute or none at all, such as construction and other “non-traded” goods and services. This, in turn, implies that high spending unavoidably puts upward pressure on the prices of those goods and services, with a real appreciation of the currency as an unavoidable result. Once expenditure levels are set, the authorities have no choice on the real exchange rate consequences, only on _how_ they come about. One option through which appreciation takes place is a gradual appreciation/revaluation of the nominal exchange rate, so as to effect whatever real appreciation is necessary. If that option is not chosen, domestic goods end up underpriced given the state of demand for them. The other way of effecting a real appreciation is via inflation. But it is important to realize that such

14
geographical areas are likely to have a similar set of characteristics. Our sample of households from Mogadishu belongs to four PSUs in Wave 1 and ten in Wave 2. The small size of our exposed group of households introduces an important caveat to our estimates and findings. Nonetheless, we try to ease such concerns by considering different identification strategies and robustness checks.

# 3.3 Identification strategy

Using the four groups of households identified (exposed and control households before and after the incidents), we first employ a difference-in-difference (DiD) approach with repeated cross-sections to compare outcomes of households exposed to the terrorist attack against households who were not exposed to the incidents (Imbens and Wooldridge 2007). The identification strategy exploits spatial and time variation of the data. For this, we estimate the following equation: i W E W ∗E E i i (1) ii ii ii ii aa YY = αα+ λλW + μμE EEWWEE + ββ(W EEWWEE ) + ηηLL + φφXX + uu where i refers to the outcome of interest for household i in period t, W W to the period t of data collection (Wave 1 in 2016 or Wave 2 in 2017-18). E E corresponds to the status YY of household i according to our definition of treatment, to location fixed effects for EEWWEE geography a –which refers regions or districts– and i to a vector of covariates capturing aa LL characteristics of the household and household head, dwelling characteristics, exposure XX to drought, as well as humanitarian aid received.<sup>18</sup> Moreover, the probability of being exposed to an incident is likely to be associated to the location of households and the composition of regions or districts. Hence, we focus on regional comparisons (i.e., within-location variation) through the inclusion of location fixed effects.<sup>19</sup> For the DiD analysis we use the sample of households from Mogadishu as we can include fixed effects at the district level –which is the lowest geographical level available– providing a more precise comparison of households, as opposed to including region fixed effects.<sup>20</sup> Yet, we expand the analysis to the other samples as a robustness check.

> 18 Drought exposure corresponds to drought affected status from the Standardized Precipitation Index. Humanitarian aid is the percentage of beneficiaries reached through food aid and livelihood inputs by region. 19 It is unlikely the composition of neighborhoods changed substantially after a terrorist attack in the period of analysis. Only 1.6% of the Wave 2 sample of households in Mogadishu reported they were forced to leave their previous place of residence and moved to another region.

> 20 The delimitation of PSUs was different in Wave 1 and Wave 2, in part due to the lack of census data in Somalia. As a result, PSUs from Wave 1 and Wave 2 are not comparable nor mutually exclusive. Therefore, the lowest level of aggregation comparable across surveys corresponds to districts in Mogadishu.

11
Table 6: Household current income

|D|ependent varia|ble = 1(Ever B|een Beaten)||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Work|-0.218|0.392**|0.099|0.565**|-0.280|0.087|
||[0.273]|[0.196]|[0.351]|[0.220]|[0.235]|[0.219]|
|Log(HH income)|-0.003||-0.002||-0.003||
||[0.005]||[0.005]||[0.005]||
|Work X Log(HH income)|0.029||0.041||0.048||
||[0.035]||[0.034]||[0.031]||
|Log(HH income per capita)||-0.006||-0.004||-0.004|
|||[0.006]||[0.006]||[0.006]|
|Work X Log(HH income per capita)||-0.047*||-0.019||0.005|
|||[0.027]||[0.027]||[0.029]|
|Age First Marriage|||-0.008|-0.008|||
||||[0.006]|[0.006]|||
|Work X Age First Marriage|||-0.026**|-0.025**|||
||||[0.012]|[0.012]|||
|Education|||||-0.015***|-0.015***|
||||||[0.005]|[0.005]|
|Work X Education|||||-0.021***|-0.021***|
||||||[0.008]|[0.008]|
|Observations|1,324|1,324|1,323|1,323|1,324|1,324|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1. Income is calculated by taking the sum of wage income of each household member (using the average between reported income in good and bad months if these values are different), and profit from household enterprise and agriculture (subtracting total costs from revenues for each enterprise run by or crop grown by the household)_
Manove, Michael, Jorge Padilla, and Marco Pagano, 2001, “Collateral versus Project Screening: A Model of Lazy Banks” _RAND Journal of Economics_ , 32, 726-744.

Mau, Niels Jorgen, 2015, “Municipal Bailouts in Denmark – How to Avoid Them?” _Institutions of Intergovernmental Fiscal Relations: Challenges Ahead. OECD Publishing, Paris_ 227-238.

Mian, Atif, and Amir Sufi, 2014, “What Explains the 2007-2009 Drop in Employment” _Econometrica_ , 82, 2197-2223.

Morais, Bernardo, Jose Luis Peydró, Jessica Roldán-Peña, and Claudia Ruiz, 2019, “The International Bank Lending Channel of Monetary Policy Rates and QE: Credit Supply, Reach‐for‐ Yield, and Real Effects” _Journal of Finance_ , 74, 55-90.

<mark>Nakamura, Emi, and Jón Steinsson, 2018, “Identification in Macroeconomics”</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>, 32, 59-86.</mark> Degryse, Hans, and Steven Ongena, 2005, "Distance, lending relationships, and competition." _The_ _<mark>Journal of Finance</mark>_ <mark>60, 231-266.</mark> Reinhart, Carmen, and Kenneth Rogoff, 2010, “Growth in a Time of Debt” _American Economic Review P&P_ , 100, 573-78.

Revilla, Ernesto, 2013, “Subnational Debt Management in Mexico: A Tale of Two Crises” _Until Debt Do Us Part: Subnational Debt, Insolvency, and Markets. The World Bank_ .

Santos, Tano, 2017, “El Diluvio: The Spanish Banking Crisis”, _NBER Working Paper._ Smith, Heidi., 2015, “Now for Public Debt in Mexico: Policy Lessons for the Effective Oversight of State and Municipal Government Finances”, _Wilson Center_ .

37
**Table 1. Component of the change in public net debt to GDP ratio**

|Component|Formula|Sign|Line in<br>“Table”|
|---|---|---|---|
|1) Primarybalance:|)<br>(<br>_t_<br>_t_<br>_t_<br>_g_ −|+|4|
|2) Seigniorage:|_t_<br>σ|-|7|
|4) Domestic real interest rate cost:|)<br>1(<br>)<br>1(<br>)<br>(<br>1<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_d_<br>_g_<br>_b_<br>_i_<br>+<br>⋅<br>+<br>⋅<br>−<br>−<br>π<br>π|+|11|
|5) Foreign real interest rate cost|)<br>1(<br>)<br>1(<br>)<br>(<br>)<br>1(<br>)<br>1(<br>)<br>(<br>*<br>1<br>*<br>*<br>*<br>1<br>*<br>*<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_l_<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_f_<br>_g_<br>_nfa_<br>_i_<br>_g_<br>_b_<br>_i_<br>+<br>⋅<br>+<br>⋅<br>−<br>−<br>+<br>⋅<br>+<br>⋅<br>−<br>+<br>−<br>−<br>π<br>π<br>π<br>π|+|12-13|
|3) Real GDP growth:|1<br>)<br>1(<br>−<br>⋅<br>+<br>_t_<br>_t_<br>_t_<br>_d_<br>_g_<br>_g_|-|14|
|6) Capital loss due to real exchange<br>rate depreciation|[<br>]<br>)<br>1(<br>)<br>1(<br>)<br>)<br>1(<br>)<br>1(<br>*<br>1<br>*<br>1<br>*<br>^<br>_t_<br>_t_<br>_t_<br>_t_<br>_l_<br>_t_<br>_t_<br>_f_<br>_t_<br>_g_<br>_nfa_<br>_i_<br>_b_<br>_i_<br>_e_<br>+<br>⋅<br>+<br>⋅<br>+<br>−<br>⋅<br>+<br>⋅<br>−<br>−<br>π|+|15|

# **5.2. Dynamic Consistency Measure**

The FS tool also calculates two additional sustainability indicators: the primary balance at which debt-output ratio is stabilized in each projection year and at the end of the projection period (cells:Y39; Table sheet): In addition, the FS tool also calculates the corresponding required deficit reduction, defined as the difference between projected and debt - stabilizing primary balance during each projection year and in the long run (at the end of the projection period) (cell: Y40; Table sheet). Note that a positive number indicates inconsistency between inflation targets and stable debt ratios - if public debt is to be kept constant, larger fiscal adjustment is necessary to avoid higher inflation that would otherwise close the gap by bringing in more seigniorage revenue.

# **5.3. Additional Outputs**

The template also reports the gross financing needs of the public sector (defined as the public sector deficit, plus all maturing debt). This variable is derived, for all the scenarios as well as for the sensitivity tests, in percent of GDP and in billions of dollars (sheet “Table_GF”).<sup>7</sup>

> 7 In the stress and bound tests the calculations of the gross financing needs assume the same maturity structure as in the baseline scenario.

23
our treatments had any effect on teacher absenteeism. There is no evidence that changes in teacher attendance may mediate the effects we find on child attendance and test scores, which gives support to the interpretation of these effects as resulting from a demand-side response. This also gives reassurance that the mechanism we set up to ensure that teachers were compensated for the time spent filling in the report cards (giving 250 meticais’ worth of airtime to teachers who had filled in the report cards completely at the time of each spot check) was well-calibrated.

In Columns (2) and (6), we estimate the effect of our treatments on ever having been married. Given the young age of the targeted girls (12.65, on average, at baseline), only 2.28% (2.66%) of eligible girls in the household survey were married at baseline (endline) in the control group. Given the mean and standard deviation that prevail in the control group, we lack power to detect realistic reductions in the proportion ever married. The minimum detectable effect for which we have 80% power is indeed 2.88%-points, which would require, for instance, the control group to see more than a doubling of the share ever married compared to baseline while no new girl would form a union in the treated group. While the point estimates of the effect of the information and the parents’ treatment on the proportion ever married are large in magnitude, only the effect of the information treatment is statistically significant at 10% in the baseline regressions, and it becomes insignificant when controlling for baseline characteristics (Column (6)).

The remaining columns of Table 4 show tests of whether the treatments had any effect on the share of girls with an above-median predicted score based on two separate principal component analyses (PCA). The first variable measures the self-reported quality of the monitoring exercised by parents on their children’s school attendance, while the second one measures the extent to which the girls say that they participate in decisions about their own lives. The set of interventions evaluated in this experiment had no impact on either measure.

30
- Iba ̃nez, A. M., S. V. Rozo, and D. Bahar (2020). Empowering migrants: Impacts of a migrant’s amnesty on crime reports. IZA Discussion Papers 13889, Institute of Labor Economics (IZA).

- Kaushal, N. (2006). Amnesty programs and the labor market outcomes of undocumented workers. _Journal of Human Resources 41_ (3), 631–647.

- Kaushal, N., Y. Lu, N. Denier, J. S.-H. Wang, and S. J. Trejo (2016). Immigrant employment and earnings growth in canada and the usa: Evidence from longitudinal data. _Journal of Population Economics 29_ (4), 1249–1277.

- Kling, J. R., J. B. Liebman, and L. F. Katz (2007). Experimental analysis of neighborhood effects. _Econometrica 75_ , 83–119.

- Londo ̃no-V ́elez, J. and P. Querubin (2022). The impact of emergency cash assistance in a pandemic: Experimental evidence from colombia. _Review of Economics and Statistics 104_ (1), 157–165.

- MacPherson, C. and O. Sterck (2021). Empowering refugees through cash and agriculture: A regression discontinuity design. _Journal of Development Economics 149_ , 102614.

- Mastrobuoni, G. and P. Pinotti (2015). Legal status and the criminal activity of immigrants. _American Economic Journal: Applied Economics 7_ (2), 175–206.

- Monras, J., J. V ́azquez-Grenno, and F. Elias (2018). Understanding the Effects of Legalizing Undocumented Immigrants. CReAM Discussion Paper Series 1708, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.

- Monras, J., J. V ́azquez-Grenno, and F. Elias (2020). Understanding the effects of granting work permits to undocumented immigrants. Technical report, Department of Economics and Business, Universitat Pompeu Fabra.

- Moussa, W., N. Salti, A. Irani, R. Al Mokdad, Z. Jamaluddine, J. Chaaban, and H. Ghattas

35