rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary statistics

||full sample|restricted sample|never TSC|some TSC|
|---|---|---|---|---|
|TSC government administrative data:|||||
|latrines per capita by 2001|0.00044|0.00043|0.00000|0.00110|
||(0.00009)|(0.00009)||(0.00022)|
|latrines per capita by 2002|0.00079|0.00078|0.00000|0.00199|
||(0.00014)|(0.00014)||(0.00034)|
|latrines per capita by 2003|0.00455|0.00455|0.00000|0.01195|
||(0.00048)|(0.00048)||(0.00110)|
|ASER test scores:|||||
|reads letters or better|0.748|0.748|0.759|0.731|
||(0.0078)|(0.0078)|(0.0099)|(0.0126)|
|recognizes numbers 1-9 or better|0.747|0.747|0.756|0.733|
||(0.0078)|(0.0078)|(0.0098)|(0.0123)|
|recognizes numbers 10-99 or better|0.325|0.325|0.327|0.321|
||(0.0080)|(0.0080)|(0.0104)|(0.0123)|
|ASER survey data:|||||
|female|0.444|0.444|0.441|0.448|
||(0.0027)|(0.0027)|(0.0035)|(0.0043)|
|formal house|0.293|0.294|0.275|0.321|
||(0.0091)|(0.0091)|(0.0119)|(0.0135)|
|father went to school|0.650|0.650|0.646|0.656|
||(0.0074)|(0.0074)|(0.0095)|(0.0118)|
|mother went to school|0.486|0.486|0.477|0.501|
||(0.0089)|(0.0089)|(0.0112)|(0.0148)|
|mother literate|0.334|0.334|0.320|0.355|
||(0.0079)|(0.0079)|(0.0097)|(0.0136)|
|village has a government school|0.844|0.844|0.831|0.864|
||(0.0070)|(0.0070)|(0.0091)|(0.0105)|
|_n_ (six year olds)|48,048|47,975|29, 310|18,738|
|districts|575|573|337|238|

“Restricted sample” excludes two districts with very high reported TSC intensity. The “never” and “some” TSC columns split the sample at the district level according to whether the district was reported to have any TSC latrines built in 2001, 2002, or 2003. Standard errors clustered by district reported in parentheses.

42
# **Introduction**

As low-income and middle-income countries have reduced their reliance on official development assistance (ODA) and assumed greater responsibility for direct service delivery activities, many aid agencies and development banks have refocused their efforts on helping partner countries reform laws, institutions, regulations, and customary practices in ways that they hope will promote economic development and effective governance in the long-run (Andrews 2011; World Bank 2012, 2013a; USAID 2015; Buntaine et al. 2017).

However, there is a growing gap between the stated objectives of international development organizations and the metrics of success that they use to judge their own performance. Easily observable output and outcome measures—such as the amount of economic output generated, the number of children vaccinated, and the number of kilometers of roads constructed—are useful for measuring the success of direct service delivery activities (World Bank 2013a; Glennie and Sumner 2014). But most aid agencies and development banks lack credible ways of measuring their influence on the policy priorities of partner countries (Custer et al. 2015; Kremer and Clemens 2016).

The World Bank is a uniquely important case in point. In a typical year, it lends more than $30 billion to its client countries. It also spends approximately $200 million on the provision of analytical and advisory (AAA) products to client countries each year.<sup>1</sup> The organization uses both of these instruments—lending and non-lending services—to influence the development policy priorities of low-income and middle-income countries. However, insufficiently granular data on the nature, timing, and distribution of AAA products and the policy priorities of client countries has made it difficult for policymakers and scholars to understand which World Bank instruments are most useful for effectuating development policy change.

In the absence of strong empirical evidence, policymakers have offered conjectures about

> 1These summary statistics are drawn from `https://www.worldbank.org/en/about/annual-report/ fiscal-year-data` and the World Bank’s Business Intelligence Database.

1
fact that households _believed_ school attendance to be mandatory may explain the scale of these program effects.<sup>25</sup>

# **V. Comparison between** **_Ex Ante_ and** **_Ex Post_ Estimates of Program Impact**<sup>**26**</sup>

The BFL model as described in Section III can be used to simulate school enrolment (and labor participation of children) in the presence of the CCT program, which can then be compared with the outcomes for the counterfactual – namely, the observed scenario in the absence of CCT. When the simulation is conducted with the treatment group – a sub-sample of the full baseline survey sample – the impact obtained may be considered as an estimate of the “Average Intent to Treat” (henceforth AIT) because it simulates the impact of the CCT program on the enrollment rate among all eligible children, _regardless_ of whether they accept the transfer or not. Even if a child is eligible to receive the transfer, if the amount of transfer is insufficient to move him/her from category 0 to 1 or 2, the household would not accept the transfer and consequently, the transfer is not allocated to the household in the simulation.<sup>27</sup> The AIT estimator is then given by: Where _P_<sup>_*_</sup> is a dummy variable taking the value 1 if children would be enrolled in school after receiving transfer _T_ and _P_ is a dummy variable taking the value 1 if children are currently enrolled in school. AIT* provides an estimate of the average impact of the availability of the program to eligible households in treatment communities.

Two aspects of the _ex ante_ AIT estimator must be noted. Firstly, it assumes good implementation of program in treatment communities. While the simulation accounts for eligible households opting to not receive the transfer and send their children to school, it does not account for “exclusion errors” in targeting whereby households eligible and willing to

> 25 Interestingly, these results suggest that even if the conditionalities of a CCT program are not enforced, just announcing the conditions may be enough to have some impact on behavior of participants.

> 26 Similar analysis for Nicaraguan Red de Protección Social (RPS) program was performed by the authors and main findings presented in this section also hold for the RPS program. Authors omitted the RPS analysis due to the length of the article but results are available per request (See Appendix, Table A-12 for summary results).

> 27 In maximizing its utility, an eligible household will decide to _not_ accept the transfer if _U(0)_ > _U(1)_ even when _Y-I + M.wi  ≤ Y0_ , and _U(0)_ > _U(2)_ even when _Y-I  ≤ Y0_

18
which is higher than the national average of 4.6 percent of the population being emigrants (World Bank, 2011).<sup>3</sup> Comparing the means for treatment and control groups, we see that randomization has largely succeeded in achieving balance on observable characteristics of respondents and their households. The only significant differences are in household size among the Ethiopian and Filipino samples, and in internet usage among the Moroccan sample. Given that we are testing 56 different treatment-control differences in this table, this number of significant differences is no more than we would expect by chance. As a result we can consider Group A and Group B as being comparable in each country’s case, so that any differences in responses to the list randomization question should reflect the impact of adding the illegal migration question to Group A’s set.

# **4.2 Measuring Illegal Migration**

Table 2 reports the results of the list randomization exercise. The first column reports sample size, and the second uses the control group to check what proportion of respondents answer “3” to the list randomization question, reflecting that they think all three statements in the list are true. We would be concerned if this proportion was high, since then respondents with illegal migrants would be in the position of having to answer “4” if they were in the treatment group, thereby revealing their illegal migrant status. Reassuringly we see that the proportion answering “3” in the control group is low, being 0.14 for the Philippines, less than 0.10 for Ethiopia, and less than 0.05 for Mexico and Morocco.

The next three columns then report the mean number of true statements reported in the control group, in the treatment group, and the difference. The difference is our list randomization estimate of the incidence of illegal migration. We estimate that 8.4 percent of the households in the Mexican sample have an illegal migrant abroad, as does 8.6 percent of the Philippines sample, 38.5 percent of the Ethiopian sample, and 41.5 percent of the Moroccan sample.

> 3 There are two important reasons why the rate is greater than the national average for the Filipino sample. First, the World Bank data is based on destination country census reports of where individuals live, which can miss a lot of the temporary migration from the Philippines. Second, our definition of migrant for this sample includes both a direct household member being a migrant as well as an immediate family member.

9
|**NAICS**|**Industry Name**|
|---|---|
|484110|General Freight Trucking, Local|
|484220|Specialized Freight (except Used Goods) Trucking, Local|
|488210|Support Activities for Rail Transportation|
|488510|Freight Transportation Arrangement|
|488991|Packing and Crating|
|493110|General Warehousing and Storage|
|493120|Refrigerated Warehousing and Storage|
|493130|Farm Product Warehousing and Storage|
|493190|Other Warehousing and Storage|
|541214|Payroll Services|
|541219|Other Accounting Services|
|541511|Custom Computer Programming Services|
|541512|Computer Systems Design Services|
|541513|Computer Facilities Management Services|
|541519|Other Computer Related Services|
|541611|Administrative Management and General Management Consulting Services|
|541612|Human Resources Consulting Services|
|541613|Marketing Consulting Services|
|541614|Process, Physical Distribution, and Logistics Consulting|
|561110|Office Administrative Services|
|561410|Document Preparation Services|
|561499|All Other Business Support Services|
|561910|Packaging and Labeling Services|
|562112|Hazardous Waste Collection|
|562211|Hazardous Waste Treatment and Disposal|
|811212|Computer and Office Machine Repair and Maintenance|
|811213|Communication Equipment Repair and Maintenance|
|811219|Other Electronic and Precision Equipment Repair and Maintenance|
|811310|Commercial and Industrial Machinery and Equipment (except Automotive and Electronic) Repair|

Table 4.2: Supporting industries 14
<mark>Romer, David. “In praise of confidence intervals.” In</mark> _<mark>AEA Papers and Proceedings</mark>_ <mark>, vol. 110, pp. 55-60. 2020.</mark> <mark>Santos, S.V.S., 2017. Socialização de gênero na educação infantil: continuidades e rupturas vivenciadas pelas crianças na família, na igreja e na escola.</mark> _<mark>Educação</mark>_ <mark>,</mark> _<mark>42</mark>_ <mark>(3), pp.731-750.</mark> <mark>Sheskin, Mark, Paul Bloom, and Karen Wynn. "Anti-equality: Social comparison in young children."</mark> _<mark>C</mark> ognitio_ _<mark>n</mark>_ <mark>130, no. 2 (2014): 152-156.</mark> <mark>Shonkoff, J. P., & Philips, D. A. (Eds.). (2000). From neurons to neighborhoods: The science of early child-hood development. Washington, DC: National Academy Press.</mark> <mark>Smith, Craig E., Peter R. Blake, and Paul L. Harris. "I should but I won’t: Why young children endorse norms of fair sharing but do not follow them."</mark> _<mark>PloS one</mark>_ <mark>8, no. 3 (2013): e59510.</mark> <mark>Sutter, Matthias, Claudia Zoller, and Daniela Glätzle-Rützler. “Economic Behavior of Children and Adolescents – A First Survey of Experimental Economics Results.”</mark> _<mark>European Economic Review</mark>_ <mark>111 (2019): 98–121.</mark> <mark>Taubinsky, Dmitry, and Alex Rees-Jones. "Attention variation and welfare: theory and evidence from a tax salience experiment."</mark> _<mark>The Review of Economic Studies</mark>_ <mark>85, no. 4 (2018): 2462-2496.</mark> <mark>Vandell, Deborah Lowe, Jay Belsky, Margaret Burchinal, Laurence Steinberg, Nathan Vandergrift, and NICHD Early Child Care Research Network. "Do effects of early child care extend to age 15 years? Results from the NICHD study of early child care and youth development."</mark> _<mark>Child development</mark>_ <mark>81, no. 3 (2010): 737-756.</mark> <mark>Wechsler, D. “Wechsler intelligence scale for children-WISC-IV”. [S.l.]:</mark> _<mark>Psychological Corporation</mark>_ <mark>(2003).</mark> <mark>Witt, Susan D. 1997. "Parental influence on children's socialization to gender roles."</mark> _<mark>Adolescence</mark>_ <mark>32, no. 126: 253+</mark> <mark>Zaluar, A. (2010). Youth, drug traffic and hypermasculinity in Rio de Janeiro.</mark> _<mark>VIBRANT-Vibrant Virtual Brazilian Anthropology</mark>_ <mark>, 7(2), 7-27.</mark> 35
# **Appendix: Table A.3 Regressions on Financial Development and Financial Inclusion**

This table presents OLS regressions of liquid liabilities over GDP, private credit over GDP, the percentage of adults with an account at a formal financial institution, and the percentage of adults with a loan from a formal financial institution on the set of country-level variables listed below. This table reports the models for a sample of low- and lower-middle-income countries. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

||**Liquid Liabi**|**lities / GDP**|**Private Cre**|**dit / GDP**|**Acc**|**ount**|**Lo**|**an**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Ln(Population)|0.0687**|0.0826**|0.0443*|0.0532*|0.0104|-0.0046|-0.0134|-0.0227***|
||(0.027)|(0.030)|(0.024)|(0.027)|(0.025)|(0.025)|(0.008)|(0.008)|
|Ln(Population Density)|-0.0141|0.0224|-0.0191|-0.0017|-0.0415|0.0003|0.0023|0.0135|
||(0.034)|(0.037)|(0.030)|(0.033)|(0.029)|(0.029)|(0.010)|(0.009)|
|Natural Resources|-0.1336|0.0595|-0.2944|-0.1327|0.1667|0.1980|0.2647**|0.2622**|
||(0.380)|(0.405)|(0.333)|(0.356)|(0.355)|(0.334)|(0.115)|(0.107)|
|Ln(GDP per capita)|0.0319|0.0304|0.0141|-0.0234|0.0453|0.0914|-0.0125|0.0081|
||(0.060)|(0.069)|(0.053)|(0.061)|(0.052)|(0.054)|(0.017)|(0.017)|
|Growth GDP Per Capita||1.3126||0.3826||3.7702**||1.4366***|
|||(1.827)||(1.604)||(1.574)||(0.504)|
|Inflation||-0.7141||-0.1248||0.7242||0.3467|
|||(0.962)||(0.845)||(0.835)||(0.267)|
|KKM Index||0.1459||0.1487*||0.0952||0.0201|
|||(0.090)||(0.079)||(0.076)||(0.024)|
|Manufacturing/GDP||-0.9589||0.0335||-0.8331||-0.1631|
|||(0.626)||(0.549)||(0.495)||(0.158)|
|Sec./Prim. School Enrollment||0.1745||0.2069||0.0365||-0.0633|
|||(0.203)||(0.179)||(0.157)||(0.050)|
|Road Density|-0.0006|-0.0017|0.0001|-0.0009|0.0025**|0.0009|0.0002|-0.0002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|
|Sub-Saharan Africa|-0.1752*|-0.1488|-0.1414*|-0.1163|-0.0421|0.0483|-0.0875***|-0.0654**|
||(0.089)|(0.115)|(0.078)|(0.101)|(0.078)|(0.094)|(0.025)|(0.030)|
|Constant|0.2651|0.4996*|0.1412|0.1484|0.0510|0.2283|0.1586***|0.2232***|
||(0.160)|(0.252)|(0.140)|(0.221)|(0.141)|(0.212)|(0.046)|(0.068)|
|Observations|40|40|40|40|37|37|37|37|
|Adjusted R-squared|0.2339|0.2613|0.1411|0.1716|0.1564|0.3412|0.3705|0.5215|
|Root MSE|0.1867|0.1834|0.1639|0.1610|0.1564|0.1382|0.0508|0.0443|

20
Note that the slack variables can have different upper ��� and lower values ���, as well as to change across observations. As in the binary SVM, non-linear function can be estimated by including kernels in the above minimization problem.

We have implemented this algorithm in Stata using the command _svmachines_ (Guenther and Schonlau 2016). We have included all possible predictors as inputs variables. A grid-search has been used to find the optimal parameters, that is the ones that maximize accuracy or minimized the MSE in the CV sample. In case of continuous outcome variables, we have considered 13 possible values for each parameter (the penalization term _C3_ , the kernel smoothing parameter _γ_ , and the band width _ε_ ), as well as one kernel (Guassian) for a total of 2197 combinations (1x13<sup>3</sup> ). A similar grid-search has been conducted with the binary outcome variable survival, although in that case it has been necessary to calibrate only two parameters (the penalization term _C2_ and the kernel smoothing parameter _γ_ ) combined with the same two kernels. We have considered 16 possible values for each parameter, as well as two kernels (Guassian and sigmoid) for a total of 512 combinations (1x16<sup>3</sup> ). As mentioned in Section 4.3, SVM does not produce estimated probabilities: it only predicts on which side of the margin is an observation. Therefore, when predicting firms in the top 10% in Section 5, we have estimated pseudo-probabilities through Platt Scaling (Guenther and Schonlau 2016). However, in this case data are sorted randomly using the operating system random-number generator. As a consequence, different runs may give different results, potentially making results for SVM in Table 6 not exactly replicable in other computers.

# **A.3.3 Boosted Regression**

A simple version of Boosted Regression (or Boosting) can be illustrated using the AdaBoost algorithm for binary classifications:

1. All observations have the same weight<sup>1</sup> ��

2. Estimate the first classifier (e.g. a logistic regression) with the equally weighted data 3. Compute the classification errors, increase the weights of the misclassified observations 4. Estimate the second classifier with the new observation weights 5. Repeat steps 3-4 until you have M classifiers 6. Combine all the M individual classifiers by giving more weight to the classifiers with higher accuracy.
We combine these firm-level data with country-level data to control for national characteristics, including a country’s institutional framework. The data source for each variable is indicated in section 3.5. Data for the national variables are aligned with the survey year of a country’s enterprise survey (see Appendix 2 for information on survey years). In a few cases where national data were unavailable for the specific survey year, we choose the observation of the nearest available year.

We include three different instruments for foreign output share. The selection of these instruments is based on the assumption that the instruments are correlated with the foreign output share variable, but not with domestic firm productivity in the country of interest. First, we take advantage of the fact that for many countries the Enterprise Surveys Indicator Database publishes surveys for more than one year. Based on the previous survey year for such countries, we are able to calculate the foreign output share variable as defined in equation 2 to be used as an instrument. For some countries we have to rely on the previous version of the Enterprise Surveys Indicator Database which covers the period 2002-2005.<sup>13</sup> In most cases, the time lag between the original and lagged foreign output share variables covers between two and four years. Therefore, it is safe to assume that the instrument is correlated with the foreign output share variable in _t_ , but not correlated with domestic firm productivity in _t_ .

Second, we add a sectoral measure of sector targeting by investment promotion agencies.<sup>14</sup> The measure equals 1 if a sector has been targeted by a country’s investment promotion agency in a certain year and 0 if not. As foreign output share rather reflects longer-term FDI presence, we aggregate the dummies over the period 2000 to 2004 to obtain a measure of total length of sector targeting in the early 2000s. The sum can range from 0 (no targeting over this period) to 5 (continuous targeting over this period). To control for non-linearities, we use the measure in natural logarithms, i.e. ln(total sector targeting + 1). We believe that total sector targeting is wellsuited as an instrument, as it is correlated with the foreign output share variable, but not with domestic firm productivity – especially since there is a time lag between these two measures.

Finally, following Keller and Yeaple (2009), we include sectoral tariffs obtained from the World Bank WITS Database. The data are based on the WTO NAMA method (effectively applied rates).

> 13 We thank Minh Cong Nguyen for making an older version of the Enterprise Surveys Indicator Database available to us.

> 14 The authors are grateful to Torfinn Harding, Beata Javorcik and Daniel Lederman for making this dataset available to us. For more information on the dataset, see Harding and Javorcik (2011).

15
institutional quality. The region ranks low on the social infrastructure index (3.32) relative to the EU12 (4.84) and the EAP12 (4.32). Moreover, we find a close association between the quality of the social infrastructure and the per capita stock of public capital across countries, suggesting that countries with a lower institutional quality also exhibit a lower stock of public capital (Figure 9).

**Figure 8:** _Public capital stock and institutional quality_ (average 2003-12)

<!-- Start of picture text --> 200<br>150 World EAP12 EU12 Eurasia<br>100<br>50<br>0<br>2 3 4 5 6 7<br>Public Capital Stock (per capita)<br><!-- End of picture text --> Social Infrastructure/Institutional Index (0-7) Sources: author's own calculations based on Hall and Jones (1999) for adjusted social infrastructure index; WDI and IMF WEO for the public capital stock.

In developing countries, capital formation is hampered by the quality of the public investment management system (Barma and others 2012). For example, weaknesses in the selection process of public projects could lead to oversized projects. In this case, public investment does not necessarily turn into productive physical capital. Gupta and others (2011) constructed a new public capital series that explicitly control for investment efficiency levels. They relied on the Public Investment Management Index (PIMI)<sup>6</sup> described in Dabla-Norris and others (2011), which depends on institutional factors, such as the quality of project selection, management and evaluation, and regulatory and operational frameworks.

The PIMI-index in the region ranges from 1.41 (Kyrgyz Republic) to 2.39 (Kazakhstan) on a scale from 0 to 4, suggesting poor efficiency in the public investment management process. These indices are applied to the public investment spending series. The low value of these indices means that not all public investment spending translates into productive physical capital. In fact, the cumulative public investment spending should be discounted by a factor ranging from 40 to 65 percent (depending on the country) to control for deficiencies in the public investment management process. As a result, the

> 6  The PIMI Index is the average of the score for each stage of the public investment process (project appraisal; project selection; project implementation; and project evaluation).  Countries with good investment processes have high scores.  Scores for these stages are between 0 and 1, so that the total PIMI score is between 0 and 4.

17