rafmacalaba/datause-encoder-data huggingface dataset:
The first three columns show the estimation results for children 6 to 15 years old. Column one shows the results for the entire subsample of this age range and columns two and three show the results for boys and girls separately. In columns 4-6, we repeat the analysis for children aged between 6 and 12 years. Column 7 shows the results for women of reproductive age, i.e. between 16 and 49 years. It is apparent that the program did not have any significant impact on hemoglobin levels. Nevertheless, it can be seen that the program impact is generally larger under THR, and the standard errors are smaller in relative terms. Moreover, comparing columns 1-3 to columns 4-6, it can be seen that the impact on younger children is relatively larger. This is consistent with our previous finding that the interventions raised enrollment of younger children the most. Furthermore, while THR have relatively a larger impact, it is surprising that the impact of school meals is almost confined to younger boys as can be seen from comparing columns 5-6. This could be an indication that food redistribution within the household tends to favor boys over girls. However, these interpretations must be taken with caution since the estimates are not statistically significant.

# 6 Conclusion

In this paper, we have used a prospective randomized design to assess the impact of two school feeding schemes on educational and health outcomes of children from low income households in northern rural Burkina Faso. We considered two programs: school meals which provide lunch in school, and take-home rations which provide girls with 10 kg of cereal flour each month, conditional on 90 percent attendance rate. Because we can rely on a baseline and on a follow up surveys, we were able to use difference in difference regressions to estimate the impact of the program. Moreover, because we have a randomized experiment, we can interpret the estimated impact as causal.

After the program ran for one academic year, we found that both school feeding schemes increased girls’ enrollment by 5 to 6 percentage points. For younger siblings of 18
In Table 3, we analyze how the adverse effect of inflation on innovation depends on establishments’ access to external finance. First, we find that inflation reduces innovation also when Table 3: Harmonized firm panel for 139 countries from 2006-16: Adverse effects of inflation depend on firms’ access to finance

||||Inno|vation|||
|---|---|---|---|---|---|---|
||||RE=100%|RE_>_50%|RE_<_50%|RE_<_5%|
|infation|-.041<sup>_∗∗_</sup>|-.041<sup>_∗∗_</sup>|-.046<sup>_∗∗_</sup>|-.044<sup>_∗∗_</sup>|-.024<sup>_∗∗_</sup>|-.019<sup>_∗∗_</sup>|
||(-3.67)|(-3.56)|(-5.34)|(-3.86)|(-2.07)|(-2.02)|
|loan|.134<sup>_∗∗_</sup>|.156<sup>_∗∗_</sup>|.102<sup>_∗∗_</sup>|.132<sup>_∗∗_</sup>|.109<sup>_∗∗_</sup>|.096<sup>_∗∗_</sup>|
||(5.44)|(5.96)|(4.37)|(5.27)|(2.89)|(2.03)|
|inf_∗_loan||-.005<sup>_∗∗_</sup>|||||
|||(-1.98)|||||
|ln(empl)|.051<sup>_∗∗_</sup>|.051<sup>_∗∗_</sup>|.054<sup>_∗∗_</sup>|.051<sup>_∗∗_</sup>|.050<sup>_∗∗_</sup>|.018|
||(16.2)|(16.9)|(15.4)|(13.4)|(6.80)|(1.04)|
|ln(age)|-.033<sup>_∗∗_</sup>|-.032<sup>_∗∗_</sup>|-.038<sup>_∗∗_</sup>|-.031<sup>_∗∗_</sup>|-.031|.023|
||(-3.58)|(-3.54)|(-3.07)|(-2.01)|(-1.00)|(0.64)|
|subsidiary|.095<sup>_∗∗_</sup>|.094<sup>_∗∗_</sup>|.102<sup>_∗∗_</sup>|.100<sup>_∗∗_</sup>|.082<sup>_∗∗_</sup>|.013|
||(3.00)|(3.02)|(3.05)|(3.29)|(2.18)|(0.20)|
|ln(real GDP pc)|-.663<sup>_∗∗_</sup>|-.666<sup>_∗∗_</sup>|-.398<sup>_∗∗_</sup>|-.570<sup>_∗∗_</sup>|-.739<sup>_∗∗_</sup>|-.046|
||(-3.87)|(-3.83)|(-2.47)|(-3.66)|(-3.21)|(-1.64)|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Firms|70,499|70,499|33,193|54,826|15,656|4,900|

World Bank Enterprise Surveys establishment-level data for 139 developing countries from 2006-16; marginal effects reported. ”RE _>_ 50”: Share of working capital financed through retained earnings larger than 50%. Heteroscedasticity robust s.e. clustered at country _∗_ year level; t-statistics in parenthesis. **,* significant at 5%, 10%.

– firms have access to formal bank loans. Access to bank loans in general, not necessarily – for financing working capital expenditure has a positive direct effect on the probability that establishments innovate (column 1). However, the negative effect of inflation on innovation is stronger for those establishments that have access to a formal bank loan (column 2). This is intuitive since these establishments are more likely to innovate and thus more exposed to the associated expenditure shocks which need to be need to be precautioned against with a buffer of liquid assets whose opportunity cost increases with inflation. The relevant effects are 22
Robb, Alicia, and John Wolken. 2002. “Firm, Owner, and Financing Characteristics: Differences between Female- and Male-Owned Small Businesses.” Finance and Economics Discussion Series Working Paper 2002-18, Board of Governors of the Federal Reserve System, Washington, DC.

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UNIDO (1993), “Women in Manufacturing: Participation Patterns, Determinants and Trends,” Unit for the Integration of Women in Industrial Development, United Nations.

37
improved information criteria, ending up with PLMP and UBSA in the equation for dependent informal employment, ALMP for informal self-employment, and both ALMP and PLMP for unemployment/discouragement (all three variables are lagged by one year).

The results in Table 5 (specifications [1a], [1b], [1c], [2c]) suggest that higher spending on passive labor market measures (per percentage point of unemployment) significantly increases both the unemployment/discouragement rate and the share of informal dependent employment, other things equal; the latter effect is likely to be the consequence of the former, which, in turn works through increasing duration of unemployment (see Table 2). An increase in PLMP spending per percentage point of unemployment by 0.1 percentage point of GDP<sup>26</sup> will raise the share of informal dependent employment by about 2.5 percentage points, other things equal. On the other hand, an increase in the long-term (over a 60 months period) income replacement rate for unemployed by one percentage point reduces the share of informal dependent employment by 0.5 percentage points (part of the latter effect will of course offset the former, but social assistance spending is not included in PLMP). This is in line with Bajada and Schneider (2009), who argue that reducing replacement rates would not only leave without adequate support those unemployed experiencing financial hardship, but it is likely to have little impact on reducing participation by the unemployed in shadow economy. The co-existence of the two above-mentioned opposite effects is consistent with theoretical ambiguity (presented in Table 2) of the impact of generosity of the benefit system on the prevalence of informal employment (recall that a higher income provided during unemployment reduces pressure and willingness to accept informal job and ensures that the unemployed can afford a longer search for a good match in the formal labor market). From the policy choice standpoint, a further study to disentangle the effects of coverage, duration, and generosity of unemployment insurance and social assistance benefits on informal employment is called for.

ALMP spending significantly reduces both the share of informal self-employment and the unemployment/discouragement rate. As discussed in Table 2, ALMP coordinated with formal enterprises likely reduces their hiring and training costs, creating both scale and substitution effects in favor of formal jobs; moreover, ALMP might help the unemployed to overcome the formal employability threshold. An increase in the ALMP spending per percentage point of unemployment by 0.1 percentage point of GDP leads to a fall in the share of informal self-employment by 0.7 percentage points, other things equal.

> 26 In our sample, PLMP (respectively, ALMP) varies between 0.03% and 0.49% (respectively, between 0.06% and 0.34%) of GDP per percentage point of unemployment (see Figure 3, right).

24
Pakistan, except that diarrhea-caused U5 mortality is much smaller in China (6% versus 21%), probably reflecting better general sanitary conditions in China. We also impute unknown causes into one of the five identified causes based on predicted probabilities using a multinomial logit model. The ranking of the five causes from the imputed data is similar to that using the 2000 data for all LDCs estimated by WHO. The above comparison provides encouraging evidence suggesting that respondent-based surveys can be used to collect relatively reliable information on cause of death.

Second, does the CR approach affect policy conclusions? Using the three different hazard models, we simulate policy interventions that aim to achieve universal access to safe water, basic sanitation, use of clean cooking fuel, and universal female primary education in rural China. The comparison of results from these different models show no significant differences between the estimated environmental effects on child mortality from the CR model and those from simpler hazard models (using all-cause mortality) that do not explicitly account for cause of death. Hence, we conclude that the accuracy of overall policy conclusions about the effectiveness of environmental interventions would not be improved if one had access to cause of death information.

Third is the identification of policy interventions. The analysis shows that interventions targeted at improving access to safe water in rural China have a statistically significant impact on reducing U5 mortality probability. To achieve universal female primary education attainment can improve child survival prospect, but the education impact is significant only in one model specification (the PW model). We do not find evidence suggesting that to improve access to sanitation or clean cooking fuels in rural China can significantly reduce child mortality risk. The policy simulation also shows that targeting environmental polices (in particular private access to safe water) in poor localities or poor households can avert more U5 deaths than untargeted interventions, with elasticity for access to safe water being 6.5 and 6.2 for poor counties and households respectively, compared with 5.6 for all rural areas.

Fourth is validating causal effects. One of the potential analytical advantages of using the CR model lies in the validating causal relationships using information on cause of death. Using the CR model, we find that the probability of death from causes that should not be related to safe water, i.e., birth related deaths and neonatal tetanus, in fact 12
Figure 3: The dynamic path of global impacts <!-- Start of picture text --> (A) Dynamic real income effects (B) Dynamic trade effects<br><!-- End of picture text --> <!-- Start of picture text --> Percent Loss of preferences Percent Loss of preferences<br>Increase to the bound Increase to the bound<br>0.0 0<br>-0.1 -1<br>-0.2 -2<br>-0.3 -3<br>-4<br>-0.4<br>-5<br>-0.5<br>-6<br>-0.6<br>-7<br>-0.7 -8<br>-0.8 -9<br>-0.9 -10<br>2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> Source: Authors’ simulations. Note: Cumulative percent change relative to the baseline.

in worldwide welfare losses that amount to 0.3 percent or US$211 billion relative to the baseline after three years. An important share of these losses is likely to be concentrated in regions that benefit from important preferential tariffs in their export markets such as East Asia and Pacific and Latin America and the Caribbean and which together account for close to three-quarters of the global decline in welfare. Highlighting the importance of preferences and the value of decades of trade liberalization, the impact on global trade is estimated to be much more pronounced with a decline by 2.1 percent or more than US$606 billion relative to the baseline by 2020.

Second, what if WTO members increased tariffs up to the legally allowed bound rates coupled with an increase in the cost of traded services? This would translate into global welfare losses of 0.8 percent or US$634 billion relative to the baseline after three years. Similarly, global household consumption is found to decrease by 0.9 percent corresponding to a loss of US$445 billion. The distortion to trade flows would be significant with decline by 9 percent or more than US$2.6 trillion relative to the baseline after three years.

26
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# **1. Introduction**

Recorded remittances to Africa have dramatically increased over the past decade, with remittances to Sub-Saharan Africa increasing five-fold from US$4 billion in 2002 to $20 billion in 2008 (Sander and Maimbo, 2002; Ratha et al, 2009). While much of this improvement likely reflects improvements in measurement and a shift towards more formal remittance channels, actual flows are still believed to notably exceed this recorded amount. Although 70 percent of Sub-Saharan migrants are believed to migrate to other countries in the region (Ratha and Shaw, 2007), remittance flows are believed to be dominated by flows from outside the region. For example, one estimate suggests that slightly less than three-quarters of all remittances to Sub-Saharan Africa were sent from the United States and Western Europe.<sup>1</sup> Yet despite the increasing attention being given to remittances in international policy circles as a result of this rapid growth, the poor quality of existing data has limited what is known about the characteristics of these remitters. There are a few studies which have looked at the determinants and consequences of receiving remittances based on surveys fielded in the source countries (see Azam and Gubert, 2006 and Shaw, 2007 for recent reviews), while Gupta et al. (2007) examine the same issues from a macro perspective. Even fewer studies have occurred from the sending side, and those that do exist focus on a single destination country (e.g. Miotti et al, 2009) and often on a single source country migrant group (e.g. Osili, 2007). As a result, currently there is nothing that can examine in a systematic way which African migrants remit and which don’t, and how much they send, yet alone answer key questions of interest to policymakers such as whether the more educated remit less or whether women remit more.

This paper aims to address this information gap by providing a detailed look at the remitting behavior of African migrants in the OECD. We focus on the OECD both because of data availability and because this is the source of the majority of remittances. We put together a new database on immigrants and use this to consider remittances of over 12,000 Sub-Saharan and North African migrants in nine OECD destination countries. Only one-third of the migrants in our sample remit, with those remitting

> 1 See World Bank estimates cited in <u>http://allafrica.com/stories/200811111001.html</u> [accessed August 8, 2009].

2
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS 1450 Social Safety Net and the Poor<br>during the Transition: The Case of<br>Bulgaria|Fareed M. A. Hassan<br>R. Kyle Peters, Jr.|May 1995|F. Smith<br>36072|
|WPS1451 Tunisia's Insurance Sector|Dimitri Viftas|May 1995|P. Infante<br>37642|
|WPS1452 The 1985-94 Global Real Estate<br>Cycle: Its Causes and Consequence|Bertrand Renaud<br>s|May 1995|R. Garner<br>37670|
|WPS1453 Air Pollution and Mortality: Results<br>from Santiago, Chile|Bart Ostro<br>Jose Miguel Sanchez<br>Carlos Aranda<br>Gunnar S. Eskeland|May 1995|C. Bernardo<br>37699|
|WPS1454 Child Labor: A Review|Christiaan Grootaert<br>Ravi Kanbur|May 1995|M. Youssef<br>34614|
|WPS1455 Tentative First Steps: An Assessmen<br>of the Uruguay Round Agreement<br>on Services|t Bemard Hoekman|May 1995|F. Hatab<br>35835|
|WPS1456 Equity Markets, Transaction Costs,<br>and Capital Accumulation: An<br>Illustration|Valerie R. Bencivenga<br>Bruce D. Smith<br>Ross M. Starr|May 1995|P. Sintim-Aboa ,;<br>38526|
|WPS1457 Does Decentralization Increase<br>Spending on Public Infrastructure?|Antonio Estache<br>Sarbajit Sinha|May 1995|WDR<br>31393|
|WPS1458 Credit Policies: Lessons from East<br>Asia|Dimitri Vittas<br>Yoon Je Cho|May 1995|P. Infante<br>37642|
|WPS1459 Pension Funds in Central Europe<br>and Russia: Their Prospects and<br>Potential Role in Corporate Governm|Dimitri Viftas<br>Roland Michelitsch<br>ent|May 1995|P. Infante<br>37642|
|WPS1460 Efficiency and Equity Considerations<br>in Pricing and Allocating Irrigation<br>Water|Yacov Tsur<br>Ariel Dinar|May 1995|C. Spooner<br>32116|
|WPS1461 Stock Market Development and Firm<br>Financing Choices|Asli Demirguc-Kunt<br>Vojislav Maksimovic|May 1995|P. Sintim-Aboagv,<br>38526|
|WPS1462 Stock Market Development and<br>Financial Intermediaries|Asli Demirgu,c-Kunt<br>Ross Levine|May 1995|P. Sintim-Aboaa4vt<br>38526|
|WPS1463 Rural Nonfarm Employment: A Survey|Jean 0. Lanjouw<br>Peter Lanjouw|May 1995|J. Shafer<br>85581|
2. Step 2. Compute the relative magnitude of the enrollment elasticity in state i (RALPHi) in relation to that of a “reference state”. That is, RALPHi is equal to ALPHAi divided by the ALPHA of the reference state (Distrito Federal in this paper).

3. Step 3. Divide the given total amount of Federal transfer for the relevant educational level by total enrolment to get per student Federal expenditure for the country as a whole (EXPT).

4. Step 4. Define ZETAi = RALPHi * SHAREi and ZETAT = the sum of ZETAi over all states i except the “reference state”.

where SHARE _i_ is the share of state _i_ in total enrollment for the relevant educational level”.

5. Step 5. Compute for the amount of transfers that should be allocated to state _i_ (EXP _i_ ) using the following equation:

# EXP _i_ =  (ZETAT / EXPT)* RALPH _i_

# FORMULA 3  Multi-component method

The approach here is to divide the budget for purposes of calculation into several parts, depending on government’s policy interest. In a two component model, the budget is divided into two parts: core and supplementary funds. The core fund is divided according to number of students as in (a) above. Supplementary funds, on the other, are allocated in accordance with the number of children with special needs multiplied by the extra cost per student of meeting those needs. This need-based approach is currently used in New Zealand, Victoria (Australia) and other countries to allocate their education budget among schools. In the Mexican context, supplementary funding could be based on the number of rural, indigenous, and poor children residing in a state. As mentioned, econometric analyses of Mexican data strongly suggest that doing so would improve not only equity but also efficiency.

Although the calculation is based on various components and sub-components, it does not necessarily mean that these funds should be earmarked to finance those individual components. In fact, as argued below, giving each state those funds as a global budget or block grant could be more efficient. Hence, it is probably better to allow the states to use the grant as they see fit, for as long as it is used for basic education purposes, and they are held accountable for delivery of clearly specified monitorable outputs.

5
Table 20 Inequality in USSR Republics in 1990

||||Quintiles|||Gind<br>|Ranking<br>|
|---|---|---|---|---|---|---|---|
|Republics||||||Index|by<br>|
||I_t|2nd|3rd|4th|5th||GIni Index|
|Russia|10.1|13.1|17.9|21.9|37|26.6941|9|
|Ukraine|11|13|19.3|21.9|34.8|24.18871|1|
|Belarus|11|14|17.7|21.9|35.4|24.37788|2|
|Uzbekistan|10.5|12.2|17.4|23.3|36.6|26.9212|10|
|Kazakhstan|8.8|14.3|16.8|22.4|37.7|28.39032|13|
|Georgia|8.9|13.7|17.3|22|38.1|28.7073|14|
|Azerbaijan|9.3|11.9|17.7|22.1|39|30.05381|15|
|Lithuania|9.9|14.1|17.1|21.5|37.4|26.55661|8|
|Moldova|9.6|14.9|17.4|22.1|36|25.87432|4|
|Latvia|9.9|14.3|17.2|21.3|37.3|26.1603|5|
|Kyrghyzstan|9.3|13.3|18.8|21.8|36.8|27.13963|11|
|Tjikistan|11.9|11.9|16.9|23.3|36|25.67341|3|
|Armenia|9.7|14.3|17.6|22|36.4|26.44916|7|
|Turkmenistan|9.6|13|18.3|21.8|37.3|27.50055|12|
|Estonia|9.6|13.7|17.3|20.9|38.5|26.35545|6|
|USSR|8.8|13.9|17.7|22.2|37.4|28.22334||

42