rafmacalaba/datause-encoder-data huggingface dataset:
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual cont|rols|With individ|ual + househo|ld controls|With individ|ual + househo<br>controls|ld + regional|
||Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|Marginal<br>probit<br>estimates:|Equal<br>weight<br>secondary|Deflated<br>secondary<br>childcare<sup>a</sup>|
||secondary|childcare<sup>a</sup>||secondary|childcare<sup>a</sup>||secondary|childcare<sup>a</sup>||
||childcare|||childcare|||childcare|||
|||||(0.128)|(0.397)|(0.199)|(0.128)|(0.402)|(0.201)|
|SSC and above||||<br>-0.017|<br>-0.052|<br>-0.026|<br>-0.027|<br>-0.052|<br>-0.026|
|||||(0.107)|(0.354)|(0.177)|(0.106)|(0.351)|(0.175)|
|HH receive international remittance=1||||0.228*|0.233|0.116|0.218|0.347|0.174|
|||||(0.131)|(0.319)|(0.160)|(0.136)|(0.300)|(0.150)|
|Log of HH annual expenditure per capita||||0.070|0.024|0.012|0.067|0.059|0.030|
|||||(0.052)|(0.198)|(0.099)|(0.053)|(0.202)|(0.101)|
|HH living standard index||||-0.017|-0.050|-0.025|-0.017|-0.068|-0.034|
|||||(0.020)|(0.072)|(0.036)|(0.021)|(0.072)|(0.036)|
|Number of children aged 6 to 14 years||||0.249***|0.407***|0.203***|0.249***|0.448***|0.224***|
|||||(0.031)|(0.121)|(0.061)|(0.032)|(0.131)|(0.066)|
|Number of HH members aged 15–64 years||||-0.016|0.295|0.148|-0.018|0.352*|0.176*|
|– women||||(0.041)|(0.209)|(0.104)|(0.041)|(0.214)|(0.107)|
|Number of HH members aged 15–64 years||||-0.023|-0.042|-0.021|-0.024|-0.038|-0.019|
|– men||||(0.041)|(0.113)|(0.057)|(0.042)|(0.109)|(0.055)|
|Number of HH members aged 65+||||0.073|0.211|0.106|0.062|0.179|0.090|
|||||(0.081)|(0.404)|(0.202)|(0.081)|(0.391)|(0.195)|
|Dhaka City Corporation (=1)|||||||-0.102**|0.663***|0.332***|
||||||||(0.051)|(0.167)|(0.084)|
|Slum (=1)|||||||0.008|-0.191|-0.095|
||||||||(0.059)|(0.224)|(0.112)|
|_Ref: Birthplace: Central_||||||||||
|Northern|||||||-0.056|0.745|0.372|
||||||||(0.076)|(0.561)|(0.281)|
|Eastern|||||||0.031|0.529*|0.265*|
||||||||(0.068)|(0.294)|(0.147)|
|Southern|||||||0.078|0.435*|0.218*|
||||||||(0.061)|(0.237)|(0.118)|
|Constant||0.343|0.172||1.340|0.670||0.198|0.099|
|||(1.381)|(0.691)||(2.190)|(1.095)||(2.210)|(1.105)|
|Observations|1,076|1,076|1,076|1,076|1,076|1,076|1,076|1,076|1,076|
|R-squared||0.261|0.261||0.283|0.283||0.300|0.300|

Note:<sup>a.</sup> For the methods of calculating time spend on secondary activities using equal and deflated weights check footnote in Figure 4.<sup>a.</sup> *** 1%, ** 5% and * 10% level of significance. b. Standard errors are in parentheses. b. Survey weight applied.

27
# **Appendix B.  Synthetic Control Method Detailed Results Tables**

|**Table B1. SCM Deta**|**iled Results for All**|**IHC Firms- Cle**|**arance Time at 75**<sup>**th**</sup>|**percentile**|
|---|---|---|---|---|
|IHC firm #|6-month average<br>treatment effect:<br>change in log hours<br>at 75<sup>**th**</sup>percentile of<br>firm-month<br>distribution<br>(�_���_��<br>�<br>�<sup>)</sup>|Number of donor<br>firms used to<br>construct synthetic<br>control firm|Rank of IHC firm<br>relative to 25 placebo<br>firms|Percentile rank<br>statistic (rank/26)|
|1|0.066|11|17|0.654|
|2|-0.814|11|3|0.115|
|3|-0.183|8|6|0.231|
|4|-0.401|11|7|0.269|
|5|-0.465|46|4|0.154|
|6|0.326|5|18|0.692|
|7|-0.545|10|4|0.154|
|8|-1.386|9|2|0.077|
|9|-0.853|7|2|0.077|
|10|-0.279|10|9|0.346|
|11|0.274|22|24|0.923|
|12|-1.013|8|3|0.115|
|13|-0.577|10|3|0.115|
|14|-0.780|13|2|0.077|
|15|0.593|42|23|0.885|
|16|-0.137|8|13|0.500|
|17|-0.028|6|11|0.423|
|18|-0.398|4|7|0.269|
|19|-1.360|6|1|0.038|
|20|-0.247|7|8|0.307|
|21|-0.896|9|2|0.077|
|Mean % change in 75<sup>**th**</sup><br>pct. clearance time|-0.434||Mean percentile rank<br>statistic(�)|0.310|
|Median %<br>change in 75<sup>**th**</sup>pct.|-0.401||||
|clearance time|||||

Notes: Synthetic control firms were constructed with the benchmark specification that includes three pre-treatment values of the outcome variable as part of the set of conditioning variables. The summary statistics at the bottom left of the table are obtained as the mean and the median of the treatment effect across the 21 firms. The statistic at the bottom right of the table �̅ given 21 firms has 5% critical values of 0.377 and 0.623 and 1% critical values of 0.339 and 0.660 (based on Appendix table A1 in Dube and Zipperer (2015)).

48
DIALOGIC READING AND STORYBOOK EVALUATION IN KENYA

# **Enhancing Young Children’s Language Acquisition through Parent-Child Book-Sharing: A Randomized Trial in Rural Kenya**

Heather A. Knauer<sup>1,5</sup> , Pamela Jakiela<sup>2</sup> , Owen Ozier<sup>3*</sup> , Frances Aboud<sup>4</sup> , Lia C.H. Fernald<sup>1</sup> 1 University of California, Berkeley, School of Public Health 2 Center for Global Development 3 The World Bank, Development Research Group 4 McGill University, Department of Psychology 5 Present address: University of Michigan, School of Social Work

*Corresponding Author, email address: oozier@worldbank.org Physical address: 1818 H Street NW, Mail Stop MSN MC3-311, Washington, DC 20433

Funding Sources: This work was supported by The World Bank, Washington, DC (via three facilities: the Strategic Impact Evaluation Fund, the Early Learning Partnership, and the Research Support Budget); and by Echidna Giving, San Francisco, CA.

Acknowledgments: We thank Sheyda Esnaashari, Saahil Karpe, Rohit Chhabra, Emily Cook-Lundgren, Marilyn N. Ahun, and Jennifer Nakata for research assistance throughout the development of this project. We thank the staff of Innovations for Poverty Action (Kenya); specifically, Patricia Gitonga, Michael Meda, Jessica Jomo, and the field team they led during data collection for this project. We thank Moran Publishers, E. A., for their cooperation and assistance in producing adapted storybooks. We are especially grateful to Patricia Kariger, who led instrument adaptation, to the KLPS team, who generously shared their previously adapted instruments with us, and to Peter J. Cooper, who shared intervention slides from a dialogic reading training in South Africa. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent.

_Keywords_ : dialogic reading, word gap, early childhood, local-language storybooks, primary school readiness _JEL Code_ : I25
# 2.3 Description of the KUR borrowers

Most KUR borrowers are micro entrepreneurs: 97 percent have fewer than five full-time salaried workers and only 44 percent have any paid workers. They operate in a diverse range of sectors of activity, with the most common ones including trade (42 percent of borrowers), crop and animal production (22 percent of borrowers), and manufacturing (14 percent of borrowers). On average, businesses have been in operation for seven years; however, there is considerable variation, with some firms that were established shortly before receiving their loan and others that have been in operation for over 30 years. According to novel data collection for this study described in Section 2.4, the average firm had profits of 3.6 million IDR (US$ 240) in the month preceding the survey (December 2020), and the median firm had profits of 1.5 million IDR (US$ 100).

Although most KUR borrowers have some form of formal documentation for their businesses, most have limited financial management practices, and many do not use bank accounts for their businesses. Eightyfive (85) percent of KUR borrowers have some form of formal documentation for their businesses. Most commonly, businesses only have the Business Certificate (SKU) or the Certificate of Business Domicile (SKDU), which is issued by local authorities and recognizes that the business exists and operates in the locality. Nevertheless, fewer than half of KUR borrowers separate their business and personal finances, and only 28 percent have any written business records. Less than a quarter of firms record every purchase, can easily determine how much cash they have on hand, or can use their records to prove to a loan officer that they would have sufficient funds to repay a loan. Despite limited financial record-keeping, three-quarters of borrowers can calculate the costs of the products and services they offer.

KUR borrowers range in age from the late teens to the elderly, with an average entrepreneur age of 40 years old. Thirty-seven percent of borrowers are female, and 63 percent are male. Borrowers have education levels roughly on par with the working age population in Indonesia; however, individuals with no formal education are under-represented and those with upper-secondary levels of education are over-represented among KUR borrowers. Most do not have formal business training and have instead developed business skills through learning by doing or through the support of friends and family with businesses. A more detailed description of KUR borrowers is provided at Appendix A.

# 2.4 Data

We use four types of data to explore how KUR works from the perspective of MSME borrowers who received KUR between 2015 and 2020. First, we use administrative data from the KUR program database to explore

**12**
# INDONESIA. THAILAND. AND NIGERIA

# Table of Contents

|I.|PageNo<br> BACKGROUND OF THE RESEARCH PR<br>T................|
|---|---|
|II.|THE EXTENT OF INFRASTRUCTURE DEFICIENCIES AND<br>PRIVATE PROVSIONS .............<br>...................<br>_5_|
||A. The Data ........................................<br>_5_|
||B. Incidence of Deficiencies by Firm Size and Region ..............<br>6|
|IM.|COSTS OF PRIVATE PROVISIONS<br>..<br>10|
||A. Manufacturers' Responses to Infrastructure Deficiencies ....<br>.......<br>10|
||B. Capital Costs and Their Incidence ........................<br>14|
||C. The Private Cost as A Measure of WiLlingness to Pay for<br>Reliable Services ..................................<br>17|
|IV.|CONCLUSIONS AND POLICY IMPLICATIONS ........<br>.....<br>21|
||TEXTTABLES .......................................22|
||LIST OF_PREYOUSSTuDIS............................._<br>35|
||APPENDIX TABLES ..................................<br>36|
when lending to VFs, which averaged 4.13% annually. This may seem low, if the riskiness of lending to some VFs is taken into account; on the other hand, no adjustment is made here for interest paid to VFs on deposits held at the BAAC or GSB, which would reduce net capital costs.

The most difficult capital cost to measure is the proportion of loans that are not repaid and are not used productively. In the 2009 SES, 2.1% of VF borrowers said they had “not yet repaid” their VF loan, and a further 2.4% had repaid “ in part”. If they were truly lost, the unreimbursed funds cannot be deployed elsewhere, and so represent an economic cost. On the other hand, if the funds remain in the hands of the borrower, and continue to be put to productive use, then the fact that they were not repaid to the VF does not represent an economic cost per se; the VF loss is offset by an equivalent gain to the borrower. The relatively high repayment rate suggests, however, that there is a strong “repayment ethic”, and so our working assumption is that non-reimbursed loans were essentially lost, and therefore do represent an economic cost in their entirety.

In this paper we do not measure the impact of VF lending, but for the purposes of our cost-benefit analysis we draw on the findings of Boonperm et al. (2001), who estimate that VF lending in 2004 raised expenditure per household by a statistically significant 3.5%, and income per household by 1.4% (although this number is not statistically significant). These are equivalent to returns, per baht borrowed, of 27-34% (using the consumption estimates for median and mean incomes), or 13-18% (using the income estimates); details are shown in Table 29. Given their standard errors, these estimates are not mutually inconsistent.

When borrowing leads to higher income, the economic benefit is easy to interpret, and so we begin here. Using mean costs (from the VF survey) and mean income (from the SES survey), Village Fund lending generates a net benefit of 8%; benefits are 18% of the loan, and costs are 10%. If instead we use the median values, which are less influenced by outliers, the net economic benefit is about 4%. An implicit assumption here is that the extra income did not require any additional effort on the part of the borrower; this is not entirely plausible, which would mean that the net benefits shown here may be overstated.

Although consumption is a more attractive measure of wellbeing than income, it is more difficult to interpret as a measure of benefit. If borrowing allows me to buy a sofa, my consumer spending may rise sharply, but the net contribution to economic wellbeing is likely to be more moderate – just some fraction of the total borrowing. Thus the net benefits based on the consumer spending impact – between 18% and 23% - are surely on the high side.

42
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<!-- Start of picture text --> Mexico<br>120<br>Output<br>Efficiency Wedge<br>115 102<br>Labor Wedge<br>Investment Wedge<br>Risk−Prem. Wedge (right axis)<br>110<br>101<br>105<br>100 100<br>95<br>99<br>90<br>85 98<br>1995 1996 1997 1998<br>Canada<br>105<br>100 100<br>95<br>99<br>90<br>98<br>1982 1983 1984 1985<br>Risk−Premium Wedge<br>Risk−Premium Wedge<br><!-- End of picture text --> Figure 1: Output and four wedges in two recessions. (Data normalized to equal 100 before the recession; risk premium wedge is on left axis.) 25
<!-- Start of picture text --> Rice<br>450,000 450<br>e 400,000 400<br>< 350,000 350 @<br>r=<br>=Cal 250,000 250 4<br>- 150,000 150 ;<br>§ 100,000 100<br>50,000 50<br>00<br>05 10 15 20 25 30 35 40<br>Time period -1971/72 to 2007/2008<br><!-- End of picture text --> <!-- Start of picture text --> 250,000 Soybean 350<br>= 200,000 300 =<br>< 20 8<br>§ 150,000 200 £R<br>= 2<br>3 400,000 150 é<br>3 100 3<br>50,000 a<br>50<br>00<br>05 10 15 20 25 30 35 40<br>Time period -197 1/72 to 2007/2008<br><!-- End of picture text -->
**Development Economics as Taught in Developing Countries**<sup>**#**</sup>

David McKenzie, _Development Research Group, The World Bank_ Anna Luisa Paffhausen, _University of Passau_ _Keywords:_ development economics; undergraduate education; knowledge transfer _JEL codes:_ A22, O10, I23.

> # We thank commentators on a blog post on the Development Impact blog for helpful discussion, and the instructors who participated in our data collection for their contributions.
losing considerable amounts of money before<sup>reform.</sup> Consequently, the government was concerned that privatization of telecommunications<sup>wouldleaveitthe unprofitablepostal</sup> corporation, which could prove to be a significant<sup>drain on governmentfinances.Finally,there</sup> was only modest support for reform from the<sup>privatesector.Because the Malawianeconomyis</sup> predominantly rural, support for telecommunications reform was relatively muted.<sup>5</sup> ' Furthermore, support from the non-rural private<sup>sector was alsomodest -theGovernment,state-</sup> owned enterprises, and the socially owned Press<sup>CorporationLimited(PCL)dominatedmuch of</sup> the non-rural economy. Consequently, according to most observers,<sup>donors(especially</sup> DANIDA and the World Bank) were probably<sup>the most importantgrouplobbying forsector</sup> reform. Although donor pressure alone is unlikely<sup>todefineareformagenda,pressurefrom</sup> donors can provide reformers with needed support.

The modest reform resulted in modest gains. Although<sup>theintroductionof competitionin</sup> cellular telephony resulted in growth, it was<sup>notasrapidasitwasincountriesthatreformed</sup> more aggressively. Furthermore, cellular prices increased dramatically<sup>assoonastheywere</sup> freed. Finally, although the fixed-line system expanded,<sup>penetrationgrew moreslowly than was</sup> originally hoped, despite aid from donors that helped<sup>finance systemexpansion.</sup> There have also been some problems associated<sup>withthecredibilityof theregulator.A</sup> first problem is that the 1998 Communications<sup>ActgivestheMinisterofInformation</sup> considerable power over the regulator. Given<sup>thattheregulatorhaswide-ranging powersthat</sup> allow it to modify licenses, void interconnection<sup>agreements,and interferein the sectorin several</sup> other ways, the lack of independence appears<sup>to</sup> be<sup>aconcern.Inaddition to concernaboutthe</sup> effect that the 1998 Act might have on regulatory<sup>independence,theseisalsoevidencethat the</sup> Goveniment is able to apply pressure in other ways.<sup>Mostimportantly,theregulator'sboardhas</sup> been replaced twice since MACRA was fonmed<sup>-somethingthat doesnotappearconsistentwith</sup> provisions of the 1998 Communications Act.

> 5 Clarke and Wallsten (2002) provide data on rural telecommunications<sup>throughoutAfrica.Evenby thelate-1990s,</sup> fewer than I percent of rural households had a telephone in<sup>14of15countriesin Africaforwhich datawere</sup> available.

49
provincial bank generated 1.41 pesos in income per peso of cost. There was, however, a difference between privatized and private banks in the composition of income. About two-thirds of the income of private banks was financial income, while only a third was from services. In contrast, over half of the income of the privatized banks was generated through services, perhaps due to their unique relationship with the provincial governments. As described below, however, that relationship is far less pronounced in credit allocation decisions -- neither private nor privatized provincial banks lend much to the public sector. Other than this, Table 7 indicates that the privatized provincial banks generate income as efficiently as large private banks, and far more efficiently that the remaining public provincial banks.

Table 7: Ratios of Income to Cost

||Financial|Income from|Total|
|---|---|---|---|
||Income/Administrative<br>Costs|Services/Administrative<br>Costs|Income/Administrative<br>Costs|
||Mean|Mean|Mean|
|**_Overall:X_**||||
|Private|.915|.529|1.44|
|Provincial|.375|.402|.778|
|Privatized<br>**_1994:_**|.669|.736|1.41|
|Private|.764|.543|1 .307|
|Provincial|.524|.386|.911|
|Privatized|.413|.798|1.211|
|**_1995:_**||||
|Private|.883|.494|1 .377|
|Provincial|.245|.339|.584|
|Privatized|.633|.751|1.385|
|**_1996:_**||||
|Private|1.088|.506|1 .595|
|Provincial|-.026|.425|.399|
|Privatized|.758|.742|1.501|

Data Source: B.C.R.A.

Notes: The privatized sample includes only those banks that had completed their privatization as of March, 1996. Only these cases provided at least six months of post-privatization data on income and costs. The cases include Chaco, Corrientes, Entre Rios, Formosa, La Rioja, Misiones, Rio Negro, and Salta. The private sample includes the ten largest private banks in Argentina as of 1996. All public provincial banks are included in the provincial sample, including pre-privatization observations for the eight banks in the privatized sample. In those years where data were available for only a sub-sample of months for a given bank, the data were annualized. For example. for Salta in 1996, costs and income data were totaled over the six months for which data were available, and then multiplied by two.

17
Burkina Faso 2), targeting errors are lower and the CGH indices higher, ranging from 1.58 to 1.69. On the other hand, where geographical targeting was narrower (in Niger 1, Niger 2, Mali, and Cameroon), targeting errors are higher and the CGH indices lower, ranging from 1.21 to 1.33.

Previous multi-country studies on targeting have found large variations in targeting performance across programs employing the same method, concluding that implementation plays an important role (Coady et al., 2004; Devereux et al., 2017). While these studies have included a larger and more diverse set of countries, the different programs considered also employ a wide range of selection thresholds. Our results may contrast with these two studies because we study programs in a similar environment (the Sahel region) and because we account for selection thresholds.

# _Reaching the poor (and not the poorest)_

Up to now, we have assessed the performance based on how well we reach the _poorest_ or _most foodinsecure_ households, defined by the selection threshold. That is, if a method selects 35 percent of households, we measure its performance in reaching the worst-off 35 percent of households (the poorest or most food-insecure). Nonetheless, governments may also have the objective to reach _any_ poor or foodinsecure household, especially in low-income settings presenting homogeneous populations and imprecise or costly information. If this is the case, the relevant eligibility thresholds may be the national poverty or food insecurity line. While this adjustment may seem trivial, it changes the levels of inclusion and exclusion errors drastically: in our sample of poor households, it will mechanically decrease levels of inclusion errors (there are few non-poor) and increase exclusion errors (programs do not have the resources to include all the poor).

Results show that when it comes to exclusion errors under a fixed poverty line, what matters the most by far are budgets (Figure A2 in the Addendum, available upon request). Indeed, applying a PMT performs nearly as a random selection in excluding households, and exclusion errors decrease at a constant rate as budgets increase. For inclusion errors, initial levels of poverty are a more important determinant of performance. Overall, given the high prevalence of poverty in our samples, inclusion errors remain very low, and PMT plays a greater role at low selection thresholds in avoiding these inclusion errors.

# _Insights from studying targeting methods in practice_

Program managers often simulate the performance of different PMT models to make decisions on how to select program beneficiaries. The academic literature is also largely based on the simulation of PMT methods, which themselves are drawn from nationally representative surveys, to assess their performance. Nonetheless, there are several reasons why simulation results may differ from those obtained in practice 19