rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 446 clusters surveyed in 2004,<br>4352 0-3 children eligible for<br>longitudinal tracking<br>296 clusters excluded from<br>the longitudinal sample*<br><!-- End of picture text --> <!-- Start of picture text --> 1504 Children in 1262 households in<br>150 sites included in cohort in 2004<br>1332 Children located in 2007 results for<br>1128 households in 2007 1272 children in<br>analysis<br><!-- End of picture text --> <!-- Start of picture text --> 1239 Children located in 2011 results for<br>1044 households in 2011 1227 children in<br>analysis<br>119 children not<br>located in either<br>year (7.9%).<br>55/119 deceased<br><!-- End of picture text -->
# **Child Labor, Schooling, and Child Ability**<sup></sup>

Richard Akresh University of Illinois at Urbana-Champaign Damien de Walque The World Bank Emilie Bagby Mathematica Policy Research Harounan Kazianga Oklahoma State University _Keywords_ : Fetal origins hypothesis; Education; Child Labor; Sibling Rivalry _JEL classification_ : I21, J12, O15, J13

* The data used in this paper were collected for a project evaluating social protection programs in Burkina Faso, which benefited from support by Marie-Claire Damiba, Seydou Kabré, and Victorine Yameogo from the Secrétariat Permanent du Comité National de Lutte contre le SIDA et les Infections Sexuellement Transmissibles (SP-CNLSIST) in Burkina Faso and Hans Binswanger, Nono Ayivi-Guedehoussou, Ousmane Haidara, Timothy Johnston, Mead Over, and Tshiya Subayi-Cuppen at the World Bank. Robert Ouedraogo, Jean-Pierre Sawadogo, Bambio Yiriyibin and Pam Zahonogo from the University of Ouagadougou, Department of Economics supervised the data collection. The project is funded by the NBER Africa Project and the following World Bank trust funds grants: Spanish Strategic Impact Evaluation Fund (SIEF), Bank-Netherlands Partnership Program (BNPP), Gender Action Plan (GAP), Knowledge for Change Program (KCP), WB-DFID Evaluation of the Community Response to HIV and AIDS, and Luxembourg Poverty Reduction Partnership (LPRP). The authors also thank German Caruso and Moussa Kone for research assistance and Christopher Udry, Aziz Ouedraogo, Zah Marie Sawadogo, Dembélé Youssouf, and Samuel Alnaa for assistance procuring the rainfall data. We are grateful to Adam Wagstaff for useful comments. 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, its Executive Directors, or the countries they represent.

Contact Information: Richard Akresh, University of Illinois at Urbana-Champaign, Department of Economics, 1407 West Gregory Drive, Room 214, Urbana, IL 61801. Email: akresh@illinois.edu; Emilie Bagby, Mathematica Policy Research, 1100 1<sup>st</sup> Street, N.E., 12<sup>th</sup> Floor, Washington, D.C., 20002. Email: <u>ebagby@mathematica-mpr.com;</u> Damien de Walque, The World Bank, Development Research Group, 1818 H Street, N.W., Washington, D.C., 20433. Email: ddewalque@worldbank.org; Harounan Kazianga, Oklahoma State University, Department of Economics, 324 Business Building, Stillwater, OK 74708. Email: harounan.kazianga@okstate.edu 32
studies also have to deal with potential simultaneity between infrastructure and income levels. For example, richer or faster-growing countries are likely to devote increased resources to infrastructure development. Failing to control for these and similar forms of reverse causality implies that estimates of the output elasticity of infrastructure may be confounded with the income elasticity of the demand for infrastructure services, and hence may suffer from upward biases.<sup>4</sup> Finally, studies using cross-section or panel macroeconomic data typically fail to account for the potential heterogeneity in the output elasticity of infrastructure across countries or states, which could arise from technological features such as network effects, scale economies and other factors that may affect the output elasticity of public capital.<sup>5</sup> This paper estimates the contribution of infrastructure to aggregate output using a production function framework including as inputs infrastructure assets, human capital, and noninfrastructure physical capital. We use a large cross-panel dataset comprising 88 countries and over 3,500 country-year observations, drawn from countries with very different levels of income and infrastructure endowments.

One distinguishing feature of our approach is that, in contrast with much of the earlier literature, we use physical measures of infrastructure rather than monetary ones – such as a public investment flow or its accumulation into a public capital stock. We do this for two reasons. First, as an abundant literature has argued, public expenditure can offer a very misleading proxy for the trends in the public capital stock, as the link between spending and capital is mediated by the extent of inefficiency and corruption surrounding project selection and government procurement practices, which can vary greatly across countries and over time (e.g., Pritchett 2000; Keefer and Knack 2007). Second, our interest here is infrastructure capital, rather than broader public capital, and in many countries the two can be very different owing to the involvement of the public sector in non-infrastructure industrial and commercial activities (a common occurrence in virtually all countries over a good part of our sample period), and due

> 4 A way out of this problem is to use a full structural model in the empirical estimation. In this vein, some empirical studies have used stripped-down versions of Barro‘s (1990) framework (e.g., Canning and Pedroni 2008). An alternative is to use some kind of instrumental variable approach, ideally featuring outside instruments for infrastructure. For example, Calderón and Servén (2004) employ demographic variables as instruments -- alone or in combination with internal instruments -- in a GMM panel framework. Roller and Waverman (2001) follow a similar approach.

> 5 In this vein, Gregoriou and Ghosh (2009) estimate the growth effects of public expenditure in a panel setting, and find that they exhibit considerable heterogeneity across countries.

3
- 0.31 , and that the function _h_ is always negative. Hence, equation (7) is positive whenever past NTL levels are above 0.31 and the _Net Inflow_ is large enough.

- Using the 2km radius, a marginal increase in Net Inflow would increase NTL level in areas with already some level of human activity (NTL > 0.56) as soon as the inflow of displaced persons is high enough; while the 5km radius infirm this finding. Using the 5km NTL average, there exists no level of net inflow Net Inflow high enough so that an increase in net inflow would raise the NTL levels, even in areas with some level of human activities

In outbound areas, a marginal increase in the net inflow of displaced person would raise the NTL levels whenever the total outflow is negative enough in areas with past NTL levels high enough (above 0.56 for the 2km analysis, and above 0.31 for the 5km analysis).

_Table 5.0.2 Regression of NTL on net inflows of persons in settlement, 2km and 5km radius_

||x=<br>|Y = NTL(x-<br>2Km<br>|km radius)<br>x=<br>|5km<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|VARIABLES<br>|Linear|Quadratic|Linear|Quadratic|
|α1;Lagged NTL<br>|0.878***|0.839***|0.816***|0.916***|
||(0.00591)|(0.0212)|(0.00741)|(0.0260)|
|α4;(Lagged NTL)<sup>2</sup><br>||0.0320*<br>(0.0184)||-0.155***<br>(0.0242)|
|α2;Total Inflow<br>|4.90e-07***|-3.44e-06***|-4.42e-08|-5.10e-06***|
||(8.45e-08)|(6.25e-07)|(7.70e-08)|(4.37e-07)|
|α3;(Lagged NTL)*Total Inflow<br>|-7.66e-07***<br>(1.66e-07)|1.97e-05***<br>(2.79e-06)|6.86e-07***<br>(1.29e-07)|1.87e-05***<br>(1.63e-06)|
|α5;(Lagged NTL)<sup>2</sup>* Total Inflow<br>||-2.37e-05***<br>(2.92e-06)||-5.75e-06***<br>(1.35e-06)|
|α6;(Total Inflow)<sup>2</sup><br>||9.40e-12 ***<br>(3.46e-12)||1.88e-11***<br>(2.49e-12)|
|α7;(Lagged NTL)*( Total Inflow)<sup>2</sup><br>||-5.95e-11***<br>(0)||-6.05e-11***<br>(9.03e-12 )|
|α8;(Lagged NTL)<sup>2</sup>*( Total Inflow)<sup>2</sup><br>||7.66e-11***<br>(0)||0<br>(0)|
|α9;Population in 2017<br>|3.70e-06***|3.77e-06***|9.56e-06***|7.15e-06***|
||(4.18e-07)|(4.29e-07)|(6.58e-07)|(6.42e-07)|
|α10;% of agriculture in 2020<br>|-3.25e-05|-3.06e-05|6.02e-05*|0.000108***|
||(2.38e-05)|(2.38e-05)|(3.47e-05)|(3.35e-05)|
|α0;Constant|-0.0240***|-0.0125*|-0.00620|-0.0233***|
||(0.00397)|(0.00669)|(0.00525)|(0.00811)|
|Observations|8,497|8,497|8,497|8,497|
|Number of settlements|293|293|293|293|
|District Fixed Effect|Yes|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
By mid-1980s, underktanding the exact role of incentives in attracting FDI became a new research agenda. One direction has been to explore the reaction of multinational companies to changes in tax policy when they differ in their activities, motivations, market structure an/or financing. Others have searched to examine which tax instruments may have the greater effect on the behavior of international investors.

# A. Tax Behavior of Multinational Companies

While most early studies examine the impact of taxes on the average foreign investors, there were many reasons to believe that this impact differs greatly depending on the characteristics of the multinational company. International investors often have at their disposal numerous alternative methods of structuring and financing their investments, arranging their transactions between related parties located in different countries, and returning profits to investors. These alternatives have important tax implications, and there is considerable anecdotal evidence that tax considerations strongly influence the choices that firmns make.

One of the earlier findings of the literature is that the impact of tax rates on investment decisions is generally higher on export-oriented companies than those seeking the domestic market or location-specific advantages. In surveys, these firms are those with managers that have responded more favorably to tax incentives (see Reuber (1973)7 and Guisinger (1985)). This finding is not really surprising because export-oriented firms such as garmnent manufacturers are operating in highly competitive markets with very slim

> 7 For his study, Reuber separated investors into different groups according to their type investment (market seeking or export oriented). He used 80 investment projects in various industries of thirty developing countries, made by companies from various national origin. The companies surveyed were asked to identify, among various incentives, which one was deemed so important that its absence would have caused the abandonment of the project or major changes in it. Among export oriented projects, 48% named fiscal incentives (including tax holidays, duty remission and acce!erated depreciation). Among market oriented projects, 56%.of responses named protection of the market as the prevailing factor. By using a different approach to define the problem, Reuber showed that investment incentives might not matter to all investors but they do matter to some investors having a specific investment strategy ( e.g. export platform). Another merit of the study is to pose the question "do incentive matter?" in terms of the location of the projects rather than a broader decision to carry out FDI.

9
- (1) What is the likelihood of a WTO finding of a legal inconsistency of any particular measure?

- (2) How large is the lost foreign market access at stake?

- (3) Was this WTO violation a sufficiently important economic cause of lost foreign market access?

- (4) What is the legal-political _likelihood_ as well as the _form_ of compliance of the respondent with any WTO rulings?

If resources were not a constraint, the idea would be to take every red-flagged measure in the GTA database, as well as every imposed antidumping, countervailing duty, or safeguard measure found in the Global Antidumping Database, and to evaluate each according to the answers to these four questions. Specifically, an analyst would use legal expertise to clarify the likelihood that the existing WTO jurisprudence would determine that there has been a violation of commitments. Then she would take the information on the HS codes and match them to trade flow data to determine how much market access is at stake. Third, additional economic expertise would be used in an attempt to clarify the extent to which the new trade barrier was the cause of the lost market access as opposed to being due to some other cause.<sup>18</sup> Finally, legal and political expertise would be 18 Admittedly, disentangling the economic impact of any potential WTO violations from other causes may be quite challenging given the environment generated by the timing of the global economic crisis. For example, there was tremendous volatility and turmoil in markets during 2008–2009, and in many instances it may be difficult to distinguish the impact of a WTO-violating policy from other ‘natural’ economic activity that was simultaneously taking place – e.g., the negative demand shock associated with the 18
<!-- Start of picture text --> /<br>GA". Oe Ps t — 2h Sow KE<br>AS AS LOSSPRR<br>NSS BER SSEEERYT<br>SS et ee ee (EE<br>% {><br><!-- End of picture text --> <!-- Start of picture text --> —— —e— eo — eo —e eo<br>® 0 —e— —o- —9o— -@- —e—<br>oe ee —e— ee oe ee<br>—e— oe eo =<br><!-- End of picture text -->
<!-- Start of picture text --> All Votes 2009-2010<br>.“fo .le<br>0 1 2 3 0 1 2 3<br>Hazard Ratio Hazard Ratio<br>N=3533 with 33237 failures. N=2680 with 2491 failures.<br>Prior to 9/2010 2/2010-5/2010<br>-fe -Hb<br>0 1 2 3 0 1 2 3<br>Hazard Ratio Hazard Ratio<br>N=2100 with 1972 failures. N=/85 with 743 failures.<br><!-- End of picture text --> Figure 4: Cor Proportional Hazard Models: Panels display estimated hazard ratios with 95% confidence intervals. From the northwest to the southeast, panels show results for increasingly restricted samples, closing the window around the votes taken in the months immediately before and after the press conference.
uncontrolled countries, followed by a narrowing as more and more countries decontrolled. This alerts us to the possibility that iinterest rate controls may not have been fully effective in the early years, at least for these countries. Bear in mind, however, that the sample of countries may suffer from selection bias to the extent that reporting of statistics to _IFS_ may be correlated with degree of regime liberalization.

# _Bank spreads increase_

Bank quoted gross intermediation spreads (as measured by subtracting quoted deposit from quoted lending rates) increase sharply with the general increase in rates during the 1980s. In industrial countries the increase is from 2.8 per cent in the mid-1970s to 5.2 per cent in the early 1990s; in the developing countries the spreads are wider: increasing from 4.1 per cent to 6.7 per cent. During the 1 990s, these spreads remain high in developing countries, whereas they decline in the industrial countries.<sup>9</sup> Except for one year, the median quoted rates for the developing countries are higher than for the industrial countries. The gap becomes quite wide by the late 1990s. Once again, this result cannot be extrapolated to an increase in bank profitability for a variety of reasons. For one thing, the single rates used do not purport to be average rates, but quoted rates for instruments of standard quality. Furthermore, the risk profile of borrowers and the extent to which quoted rates bundle the cost of other banking services to customers may have changed systematically over time.

# **2. Short-run dynamics of wholesale rates - overview**

We have nearly complete monthly data since 1980 on wholesalel° interest rates and inflation for some 28 significant" developing countries. In a later section we will have something to say about nominal rates, but here the focus is on rates adjusted for exchange rate

> 9 To what extent this recent reversal in industrial countries reflects disintermediation from banking is not clear: it is widely believed that industrial country banks have lost some market power in the past decade, which would have narrowecl spreads. But although it is their most creditworthy customers that they have lost to securities markets, the shifting composition of their loan portfolio towards lower quality is unlikely to influence the _quoted_ spreads, which are usually for standard borrower categories.

> **0** We use the term to imply either money market or treasury bill rates.

> 1 The number would be 43 before excluding microstates and multiple members of currency unions (see Data Annex)

7
Estimate N = 960 with servations on each of 120 schools. Based on simulated data with identical structure to WSDP sample. Average and variance of impacts estimates and standard errors calculated using Arellano and Bonhomme's (2012) mean group and robust variance estimators. See Section 4.4.4 for additional details. In this case, the estimated mean and variance of treatment effects are both statistically sigproportion of schools which were hurt range from 0.49 to 1, which is consistent with roughly half of schools being hurt/bene t by the program to everyone benefitting, but include the is 0.1 so that the deconvolution assumptions are violated, <!-- Start of picture text --> st<br>° j\\<br>} J‘<br>> f \<br>aa \<br>3 . \<br>\<br>\<br>— / \:<br>v/, \<br>\<br>=<br>o<br>-4 2 0 2 4 6<br>Treatment Effect<br>——-_ True Distribution p=0<br>——-— p01 p=0.8<br><!-- End of picture text --> but the correlation between the treatment effect and the omitted variable is relatively weak. Since the omitted variable is mean zero, the estimated average effect remains unbiased. observation is from an “older” grade (which are randomly selected since this coefficient does not enter the
|P048521|1999|1999||1 Closed|Jordan|
|---|---|---|---|---|---|
|P048521|1999|2000|3/13/2000|3 Closed|Jordan|
|P048521|1999|2001|2/21/2001|5 Closed|Jordan|
|P048521|1999|2002|5/16/2002|8 Closed|Jordan|
|P048521|1999|2003|4/15/2003|10 Closed|Jordan|
|P048521|1999|2004|3/31/2004|12 Closed|Jordan|
|P048521|1999|2005|3/31/2005|14 Closed|Jordan|
|P048521|1999|2006|11/15/2005|15 Closed|Jordan|
|P048521|1999|2006|6/26/2006|16 Closed|Jordan|
|P048521|1999|2007|12/13/2006|17 Closed|Jordan|
|P048522|1997|1997||0 Closed|Yemen,Republic of|
|P048522|1997|1998||3 Closed|Yemen,Republic of|
|P048522|1997|1999||7 Closed|Yemen,Republic of|
|P048522|1997|2000|6/22/2000|9 Closed|Yemen,Republic of|
|P048522|1997|2001|6/26/2001|12 Closed|Yemen,Republic of|
|P048522|1997|2002|12/5/2001|13 Closed|Yemen,Republic of|
|P048588|1999|1999||1 Closed|Philippines|
|P048588|1999|2000|6/16/2000|4 Closed|Philippines|
|P048588|1999|2001|6/13/2001|6 Closed|Philippines|
|P048588|1999|2002|6/16/2002|8 Closed|Philippines|
|P048588|1999|2003|6/18/2003|10 Closed|Philippines|
|P048588|1999|2004|6/22/2004|12 Closed|Philippines|
|P048588|1999|2005|6/23/2005|14 Closed|Philippines|
|P048588|1999|2006|6/21/2006|15 Closed|Philippines|
|P048588|1999|2007|6/27/2007|16 Closed|Philippines|
|P048588|1999|2008|6/24/2008|17 Closed|Philippines|
|P048588|1999|2009|4/30/2009|18 Closed|Philippines|
|P048606|1997|1997||0 Closed|South Africa|
|P048606|1997|1998||2 Closed|South Africa|
|P048606|1997|1999||5 Closed|South Africa|
|P048606|1997|2000|2/27/2000|7 Closed|South Africa|
|P048606|1997|2001|6/29/2001|9 Closed|South Africa|
|P048606|1997|2002|4/30/2002|11 Closed|South Africa|
|P048606|1997|2003|5/28/2003|13 Closed|South Africa|
|P048606|1997|2004|5/28/2004|15 Closed|South Africa|
|P048606|1997|2005|5/28/2004|15 Closed|South Africa|
|P048606|1997|2021|7/15/2020|16 Closed|South Africa|
|P048651|1999|1999||3 Closed|Honduras|
|P048651|1999|2000|3/24/2000|5 Closed|Honduras|
|P048651|1999|2001|6/1/2001|7 Closed|Honduras|
|P048651|1999|2002|6/25/2002|9 Closed|Honduras|
|P048651|1999|2003|6/26/2003|10 Closed|Honduras|
|P048652|2001|2001|6/27/2001|1 Closed|Guatemala|
|P048652|2001|2002|6/2/2002|3 Closed|Guatemala|
|P048652|2001|2003|6/9/2003|6 Closed|Guatemala|
|P048652|2001|2004|6/2/2004|8 Closed|Guatemala|
|P048652|2001|2005|4/15/2005|10 Closed|Guatemala|
increase the demand for Kenyan labor in these sectors.<sup>20</sup> We obtained estimates of the share of expatriate labor or specialized technology not available to Kenyan firms that is used by multinational service providers in Kenya from the survey mentioned above. We found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or specialized technology. In particular, the estimated share of foreign inputs used by multinationals in Kenya is: telecommunications, 10 percent plus or minus 2 percent; financial services, 3 percent, plus or minus 2 percent; maritime transportation, 3 percent, plus or minus 2 percent; and air transportation, 12.5 percent, plus or minus 2.5 percent.

**Tariff and Sales Tax data** .  Most-Favored Nation (MFN) tariff rates at the eight digit level were taken from the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php.</u> These tariff rates were then aggregated to the sectors of our model, using simple averages. Since these are MFN tariff rates, they exceed the collected tariff rates due to tariff preferences to regional partners and due to other preference items or tariff exemptions. Thus, they exaggerate the protection received by Kenyan industry and agriculture. Consequently we obtained the value of overall customs duties and other taxes applied only on imports.<sup>21</sup> We then took the ratio of the total taxes on imports to the total value of imports to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent. That is, on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production. We then scaled all the MFN tariffs in our model so that the weighted average import tax is 8.4 percent.

Our estimates for sales tax data are taken from Kiringai. Thurlow and Wanjala (2006).

# **IV. Results**

> 20 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement. 21 Economic Survey (2006, pp. 103, 115).

16
# **5. Data**

This paper focuses on the cohort of women born between 1950 and 1973 and their use of health services for pregnancy and childbirth. Data used for the analysis are from the Indonesia Family Life Survey (IFLS), an ongoing longitudinal survey of individuals, households and communities. Three waves of IFLS have already been conducted in 1993, 1997 and 2000. A total of 10,435 households were surveyed in the most recent wave. The instrument used in all three waves followed a similar structure and repeated the same questions allowing comparison across the waves. Using information collected in the three waves, I have constructed a pooled dataset of all women born between 1953 and 1974 who were included in the IFLS survey. For each woman, the first pregnancy that was reported in the IFLS survey was selected for analysis so that the dataset consists of only one pregnancy for each woman. Women, therefore, form the unit of analysis for this study.

Data used for the analysis are drawn primarily from the Ever-Married Woman Information (EMWI) book of IFLS, which collected retrospective life histories on marriage, children ever born, pregnancy outcomes and contraceptive use from all evermarried women aged 15 to 49 years. The childbirth section of this book collected data on pregnancy-related care, such as type of provider sought for delivery and ante-natal care and, frequency and timing of ante-natal care visits for all pregnancies that took place during the five year period preceding each survey round. As the first round of IFLS was conducted in 1993, detailed health care use data are available for all births that took place from 1988 onwards. As its name implies, the EWMI book collected pregnancy data only 12