rafmacalaba/datause-encoder-data huggingface dataset:
_VIXt−_ 1 is the option-implied volatility of the S&P 500 index, measured in logs. The VIX is often used as a measure of global risk aversion, and is a standard push factor for capital inflows, particularly to EM. _GDPGrowthit−_ 1 is real year-on-year GDP growth for country _i_ in the previous period, which is a standard pull factor driving foreign capital to a particular country. Our standard errors are clustered at the country level. Using quarterly GDP data restricts our both and time dimensions. We use a significantly sample along country balanced sample (detailed in Appendix A.4) of 55 countries (23 advanced, 28 emerging, 4 developing) over 2002q4-2014q4.

Table 3 shows our regressions. Columns (1) reports the results with total debt inflows, column (2) shows results on sovereign inflows, column (3) and (4) show results for bank and corporate sectors inflows using data from portfolio debt and other investment debt, while columns (5)-(6) add direct investment debt to total debt and corporate debt inflows, respectively.<sup>35</sup> We take direct investment debt (DID) and add it to corporate and total debt, respectively, to obtain a more complete measure of debt inflows.<sup>36</sup> For the full set of countries in Panel A, total debt inflows respond negatively to increases in the VIX. This response is driven by the private sector (banks and corporates), and holds (with an even larger magnitude) when DID is included in columns (5) and (6). The public sector flows’ response to the VIX is positive but not significant. On the business cycle front, the total and private sector flows respond positively to a domestic boom, while the public sector flows are countercyclical, but not significant. This pattern is largely the same for the

> 35Observations missing DID data over this time period, 2002q4-2014q4, are dropped in columns (5)-(6). See Appendix C for more discussion of FDI and DID.

> 36As discussed above, with the assumption that direct investment debt flows from offshore non-financial firms to onshore banks are negligible, we can allocate direct investment debt to the corporate sector.

29
# **<u>Appendix Table 1:</u> Tests of baseline balance with respect to randomized independent variables**

Ordinary least-squares regressions

|Dependent variable<br>:|Fraction|Fraction of|Fraction of|Words|Raven's Tests|Financial|Spouse minus|Number of|Male|Age|Years of|Have|Total HH|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Present<br>Biased, Non-|all tokens<br>allocated to|Decisions<br>Consistent|Recalled|Correct|Literacy<br>Questions|own<br>allocation to|Relatives in<br>the Village|||Education|Adequate<br>Maize|Wealth|
||Implemented|"sooner"|with Law of|||Correct|sooner (MK)|||||||
||Interest Rates||Demand|||||||||||
|**Panel A**||||||||||||||
|Indicator: days to first disbursement|-0.008|-0.035**|-0.009|0.026|0.062|-0.074|80.415*|-0.381|0.067|-0.030|-0.144|0.002|-58.843***|
|(targeted) <=6|(0.022)|(0.016)|(0.015)|(0.117)|(0.074)|(0.080)|(41.852)|(0.573)|(0.041)|(1.145)|(0.263)|(0.036)|(13.074)|
|R-squared|0.0002|0.0078|0.0007|0.0001|0.0010|0.0013|0.0057|0.0006|0.0039|0.0000|0.0004|0.0000|0.0169|
|N|661|661|661|661|661|661|661|661|661|661|661|661|661|
|**Panel B**||||||||||||||
|Implemented interest rate {.1,.25,.5,.75,1}|-0.033|-0.120***|0.034*|0.448***|0.075|0.012|129.492**|-1.205|-0.083|-0.996|0.558|-0.058|12.668|
||(0.033)|(0.021)|(0.020)|(0.164)|(0.109)|(0.116)|(61.027)|(0.887)|(0.061)|(1.620)|(0.381)|(0.052)|(22.587)|
|R-squared|0.0016|0.0432|0.0040|0.0113|0.0007|0.0000|0.0070|0.0027|0.0028|0.0006|0.0031|0.0019|0.0004|
|N|661|661|661|661|661|661|661|661|661|661|661|661|661|

Significance levels: * 10%, ** 5%, *** 1%. Robust standard errors in parentheses.

Notes: Unit of observation is individual included in revisit sample. In Panel A, each column presents results from regression of given dependent variable on indicator for days to first disbursement (targeted) <=6 and a constant. Panel B is similar but independent variable is interest rate on randomly-chosen choice. Constant term included in each regression but not reported.
# 2. Literature review on links between child labor and educational outcomes

The literature mostly suggests that child work has a negative effect on educational achievement (Bezerra, Kassouf and Arends-Kuenning 2009; Edmonds 2007; Ray 2003). In Brazil, Bezerra and co-authors (2009) study the impact of child labor on school achievement (grades of different subjects) looking at the effects of work intensity and different types of work, either within or outside the home. They find that children and adolescents who do not work perform better at school than those who work. In addition, intensity seems to matter as the educational performance of children working fewer than two hours a day is not affected whereas that of children working more than two hours a day is. When comparing child work within and outside the home, working outside the household significantly negatively affects test scores more than working within the household.

Similarly, when looking beyond the simple act of being enrolled and focusing on learning outcomes, the consensus is that there is a negative relationship between child labor and educational attainment (Emerson, Ponczek and Souza 2014; Heady 2003; Akabayashi and Psacharopoulos 1999). Using panel data from Brazil, Emerson and colleagues show that child work, while still at school, has a strong adverse effect on test scores in math and Portuguese; the average effect on proficiency scores of transitioning to work while in school is equivalent to between one-quarter and an entire year of learning (Emerson, Ponczek and Souza 2014).

At the same time, some research studies report little or no relationship between enrollment and child labor, suggesting that the two activities are not mutually exclusive (No, Sam and Hirakaw 2012; Ravallion and Wodon 2000). In their study on primary school dropout in Cambodia, No and co-authors find that child labor is not a major predictor of whether or not child workers will attend school, but family and school factors are. Putnick and Bornstein (2015), analyzing data from almost 200,000 families with children aged between 7 and 14 in 30 low- and middle-income countries, note that many working children continue to be enrolled in school. Their analysis confirms the findings from Ravallion and Wodon (2000), where a school subsidy program increased schooling attainment but did not decrease child labor.

The divergence in findings on the impacts of child labor on enrollment has been linked to the different definitions of child labor in the literature. Some definitions include both household and wage work, whereas others distinguish between the two.

5
Policy Research Working Paper 8217

# **Abstract**

Child malnutrition in Bangladesh exceeds WHO’s threshold for public health emergencies. Using more than 36,000 records from several waves of the Bangladesh Demographic and Health Survey, the research focuses on the socioeconomic determinants of household consumption of all animal-source foods; the socioeconomic determinants of fish consumption, given its importance in the Bangladeshi diet; and the impact of observed consumption patterns on mortality and resistance to infectious diseases for children in their first years of life. Better maternal education and family economic status significantly increase the level of animal-source food intake, but they decrease the consumption share of fish. This suggests that increased income and education impart a “status bias” toward eggs and meat, even though they are more expensive and less beneficial than fish for child health. In addition, mothers’ individual preferences for different animal-source foods, and the seasonal availability of fish during the pre- and post-partum periods have large effects on child mortality and significant effects on resistance to several common childhood illnesses. These findings highlight the importance of programs to increase supply of fish, maternal nutrition education and more public health programs to promote fish consumption.

This paper is a product of the Environment and Energy Team, Development Research Group and the Environment and Natural Resources Global Practice 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
(the ECHP) as Bentolila et al. (2010) and identifies the negative association between social networks use and wages in some countries. In the case of Ethiopia’s cut flower farms, Mano et al. (2011) find that workers recruited through social networks received discounted wages at entry, which quickly disappeared with job tenure, however.

This paper also adds to the literature by providing clean and country-level evidence. First, our estimation results are robust to controlling for firm fixed effects and firmoccupation fixed effects. In other words, the associations of social networks use with salary, turnover, and other outcomes do not change no matter whether the referred workers are compared with the nonreferred at the same firms or at different firms. Controlling for firm fixed effects is possible only with matched employer-employee data. Second, we use direct measures of social networks use, which are based on responses of employees. Besides, we check the validity of the measures with employer-side information. We do not need to rely on indirect measures such as being neighbors (e.g., Schmutte, 2015) and having worked at the same firms previously (e.g., Hensvik and Skans, 2016). Lastly, our data are nationally representative while most of the empirical evidence in the literature builds on data from small geographical areas or specific firms.

# **2 Theories in the literature**

The literature has various theoretical models of social networks in labor markets (Beaman, 2016; Topa, 2011; Ioannides and Datcher Loury, 2004 for reviews). Although those models hypothesize different roles of social networks, or different reasons behind social networks use, most of the hypotheses can be categorized into the following: transmission, screening, peer effects, and nepotism hypotheses. We briefly explain each hypothesis and related literature. We summarize the theoretical predictions under each hypothesis in table 1.

First, the transmitting information hypothesis is that social networks play the role in transmitting more information about job vacancies. Building on this hypothesis, a large number of theoretical papers study labor market consequences of social networks use (e.g., Calv ́o-Armengol and Jackson, 2004, 2007; Ioannides and Soetevent, 2006). While most of these papers are dedicated to analyzing network-level outcomes such as neighborhood and ethnicity rather than worker-level analysis, they have individual-level predictions that referred workers experienced a shorter unemployment spell and earn more than nonreferred ones. The intuitions behind the predictions are that since referred workers are connected to more contacts on average and therefore receive more job 4
Figure 1

## **DIFFERENCE BETWEEN ACCESS TO AND USAGE OF SERVICES**

<!-- Start of picture text --> = Access to financial services<br>= No access to financial services<br>Users<br>of financial services<br>No need/awareness<br>Voluntary exclusion<br>Population • Inability to use due to<br>price/income<br>• Assume to be rejected<br>People not<br>using financial services • Rejected due to high<br>risk/bad credit<br>Involuntary exclusion • Rejected due to discrimination<br>•Rejected due to price/product<br>or income/client features<br>Source: Claessens (2005).<br><!-- End of picture text --> Especially for developing countries, often market structure, incentives and features of the institutional environment affect the willingness and ability of financial institutions to provide financial services to small firms. Supply factors then can constrain access to financial services, even when sustainable demand for external financing is there (in the sense that the firm would have gotten financing if it were placed in a better institutional environment). The importance of the effects of financial, legal and other institutional environment and reforms can best be analyzed in a cross-country context, where financing (constraints) can be compared, controlling for firm characteristics (see Beck, Demirguc-Kunt and Maksimovic (2008 ) for work along these lines). At the single country level, a study of financing constraints can help identify the type of firms most constrained and the forms of financing for which constraints are more binding. To resolve the identification problem and to investigate the importance of supply versus demand factors requires though some specific approaches and some specific data. We review the approaches used to date, and then present our approach.

# **Demand side, econometric techniques**

Traditionally, financing constraints have been investigated from the demand side. The demand for sustainable external financing and the sensitivity of investment to the presence of internal funds, i.e., the degree of financing constraints, have been investigated using one of two approaches, each with their own strengths and shortcomings: investment-internal cash-flow regressions or Euler equations estimations. The first approach is based on the Q theory of investment suggested by Tobin (1969) and has been widely used in the finance literature after the influential paper of Fazzari et al. (1988). In the reduced form q-model, a measure of available internal funds (e.g., cash flows) is directly included as one of the independent variables. In order to investigate the presence of financing constraints, the sample is divided using a priori classifications of firms’ financing constraints, and the investment-cash-flow sensitivities of the different sub-samples are then compared. Higher sensitivity for the samples of a priori more constrained classified firms is interpreted as evidence of tighter financing constraints.

The empirical investigation of the sensitivity between investments and internal funds based on the q-model has, however, several problems. The a priori classification of firms in different groups is often set arbitrarily and 4
# **Empirical Results**

# **_DiD Estimation Results Using Batch 3 Villages and Those without the Project_**

Figure 5 and 6 summarize our significant findings from the DiD estimators using a fixed effects model.<sup>10</sup> We present the impact of the ECED project on enrollment in any early childhood services as well as on eight other child development outcomes (social competence, language and cognitive development, communication and general knowledge, language tasks, draw-a-house task, test of executive function using the dimensional change card sort game, conduct problems, and pro-social behavior). We only include noticeable and statistically significant findings.<sup>11</sup> We present results for (a) all children (regardless of whether they were enrolled in any ECED service at baseline), (b) children who were not enrolled in pre-existing ECED services at baseline, and (c) children from poorer households – those households that report below average wealth.<sup>12</sup>

# **_Enrollment_**

Figure 5 reports the estimated impact of the ECED project on enrollment. Each bar represents our estimates of a different group of children: all children (solid bar) and poor children (shaded bar). We further disaggregate these children on the basis of their enrollment status at baseline. The left-hand set of bars shows the estimated impact regardless of enrollment status at baseline and the right-hand set of bars shows the estimated impact for the subset of these children who never enrolled at baseline. As shown in this figure, children from poor households made more progress in ECED enrollment at follow-up than all children. Also, children who never enrolled at baseline are more likely to enroll at follow-up. As a result, the impact of the ECED project on enrollment is about 6 percentage points higher for poorer children who were never enrolled at baseline (15.6 percent) than for all four-year-old children (9.7 percent).

> 10 All other regression estimates are available upon request.

> 11 In total we analyzed 22 outcomes including ECED enrollment, child development outcomes, nutrition outcomes and parenting practices.

> 12 Household wealth is measured using principal components analysis. A wealth measure is created using the score of the first principal component derived from a series of questions on asset ownership. The resulting score is standardized to have mean 0 and standard deviation 1. Poor households are those with a standardized wealth below 0.

15
|**Table 3: Area plan**|**ted wit**|**h winter cro**<br>|**ps and NDV**<br>|**I of winter w**<br>|**heat, nation**<br>|**al and by VC**<br>|**and conflict st**<br>|**atus**<br>||
|---|---|---|---|---|---|---|---|---|---|
|||**2019**|**2020**|**2021**|**2019-21**|**2022**|**Difference**|**2019-22**|**No. VCs**|
||||||||**absolute**|**%**||
|**Panel A: Nationa**<br>|**l winter**|**crop area (**<br>|**mn. ha)**<br>|||||||
|Winter crop area||9.430|7.514|9.461|8.801|8.383|-1.047|-11.10|10,125|
|**Panel B: VC leve**|**l winter**|**crop area (**|**ha)**|||||||
|Total||931.4|742.1|934.4|869.3|828.0|-103.40|-11.10|10,125|
|Crop damage|No|890.3|704.5|892.0|828.9|795.1|-95.24|-10.70|9,748|
|from imagery|Yes|1,992.4|1,713.8|2,031.0|1,912.4|1,678.1|-314.29|-15.77|377|
|Occupied|No|818.4|645.3|803.7|755.8|721.0|-97.40|-11.90|8,894|
|public source|Yes|1,747.7|1,441.6|1,879.0|1,689.4|1,600.9|-146.75|-8.40|1,231|
|Active conflict|No|870.4|687.2|867.5|808.4|771.1|-99.32|-11.41|9,071|
|public source|Yes|1,455.9|1,214.8|1,510.0|1,393.6|1,317.4|-138.51|-9.51|1,054|
|Russian troops|No|792.4|616.3|772.6|727.1|699.9|-92.48|-11.67|8,330|
|public source|Yes|1,576.1|1,325.7|1,685.2|1,529.0|1,422.0|-154.04|-9.77|1,795|
|**Panel C: VC leve**<br>|**l NDVI**|**of winter w**<br>|**heat**<br>|||||||
|NDVIwwMay 11||0.731|0.660|0.703|0.698|0.694|-0.037|-5.06|9,302|
|NDVIwwMay 21||0.738|0.682|0.705|0.709|0.720|-0.019|-2.51|9,302|
|NDVIwwMay 31||0.794|0.654|0.790|0.746|0.722|-0.072|-9.10|9,302|
|NDVIwwJune 11||0.781|0.712|0.765|0.753|0.738|-0.043|-5.53|9,302|
|NDVI Max||0.836|0.765|0.838|0.813|0.775|-0.061|-7.29|9,302|
|Crop damage|No|0.836|0.766|0.840|0.814|0.776|-0.060|-7.18|8,826|
||Yes|0.819|0.748|0.806|0.791|0.743|-0.076|-9.31|476|

_Note:_ As explained in the text, _NDVIww_ is the NDVI for all fields categorized as winter wheat in the crop cover analysis for the year in question.

_Source:_ Own computation from crop maps for winter crop area and sentinel imagery 2019-22.

17
**Table 3:  Coefficient of Variation of capital inflows**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|1996-2000|0.119|-0.186|0.704|0.126|
||[0.83]|[0.31]|[0.93]|[0.14]|
|2001-2005|0.101|0.862|-0.908|-0.520|
||[0.52]|[1.30]|[0.80]|[0.61]|
|2006-2010|-0.012|0.591|**1.336***<sup>**,**</sup>**^^**|0.255|
||[0.10]|[0.82]|**[1.76]**|[0.31]|
|2011-2015|0.068|0.428|1.096|-0.155|
||[0.46]|[0.54]|[1.29]|[0.14]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Observations|165|140|142|147|
|R-squared|0.008|0.019|0.077|0.012|
|# of countries|34|33|34|34|

_Notes:_ The dependent variable is the coefficient of variation of capital flows of type i in country c in period p. Five separate 5-year periods are considered: 1991-1995, 1996-2000, 2001-2005, 2006-2010 and 2011-2015. The coefficients of variation are regressed on time dummies indicating the different periods, where the first period (1991-1995) is excluded. The interpretation of the coefficient is thus in relation to this first period. We exclude observations where the coefficient of variation exceeds a value of + 10 or is below - 10. Robust t-statistics are displayed in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level. Additionally, tests are conducted for whether the coefficients are significantly different from the previous period. ^, ^^, ^^^ indicate significant differences at the 1%, 5% and 10% level.

Readers may also worry that the increase in the volatility of capital outflows from emerging markets (both FDI and bank-related outflows) is driven by a few outliers, where the average outflow is small so that a limited increase in the variance can produce a large increase in the coefficient of variation. We therefore made the same statistical comparisons dropping the top and bottom 2 percent of the observations. Reassuringly, the broad patterns remained the same.

10