rafmacalaba/datause-encoder-data huggingface dataset:
_Does parenting show unique components, and associations with family wealth and child development?_ We conducted explanatory factor analysis in order to determine if the parenting scores could be grouped by distinct dimensions of parenting. Sampling adequacy was supported by a Kaiser-Meyer-Olkin statistic of 0.868 (Kaiser, 1974), confirming a compact pattern of correlations between the 25 parenting items. Bartlett’s test for sphericity rejected the null hypothesis of zero covariance, p<.001. Parallel analysis suggested that there are 11 factors, equaling the number of factors with eigenvalue greater than zero. We followed a similar procedure as Jones et al. (2017) to reduce the number of factors retained. We rejected the large number of factors as only 3 of them had more than 1 item with a factor loading greater than 0.3 and all but 3 factors had poor reliability ���.6�. We gradually reduced the number of factors until all factors had reliability of at least ��.6 and at least two items with loadings greater than 0.3. With this procedure we obtained three parenting factors, which are summarized in Table 2.

The three identified parenting factors capture three dimensions of parenting. First, “cognitive parenting” measures how parents actively interact with the child in ways that are likely to develop cognitive competencies: games, reading books and playing with toys or objects. The second dimension, “socio-emotional parenting” captures the emotional support and responsiveness of the child and the parent. Third, “negative parenting”, in which higher values indicate more negative parenting, refers to the degree to which parents use harsh or punitive statements to discipline the child. It is important to note that the constructs of emotional support and responsiveness and the use of harsh or punitive statements are not two extremes of the same dimension; instead, in our sample, they appear to be unique constructs.

To assess the extent to which the three parenting scores capture meaningful differences in parenting behavior, we correlate them with other family characteristics. Table 3 indicates that 16
Liberalization programs and incentive schemes jointly produced significant synergy in export performance. The country saw a steep rise in its export earnings that surged to US$31 billion in 2015 from US$0.75 billion in the 1980s. This was mainly due to the phenomenal performance of the export-oriented RMG industry, which could successfully utilize the facilities under the MFA quotas, GSP, limiting competition and providing ‘protected’ or ‘reserved’ markets for Bangladesh, along with other RMG producing LDCs. Nevertheless, the MFA also provided a sound base for Bangladesh’s RMG industry that helped sustain the magnificent performance of this industry even after the elimination of the MFA in 2005.

To stimulate export-led industrialization, Bangladesh allows the duty-free import of raw materials to manufacture export products. The government imposed a concessionary duty rate of 7.5 percent on imports of capital equipment for several years. Recently, special bonded warehousing licenses have improved the concessionary duty arrangement by allowing the exporters to have their local banks stand as guarantor with one-third liquidated during installation of the equipment and another to be liquidated at a later stage. The industry also received exchange rate benefits before 1992 to bridge the gap between the official and market exchange rates (Younus and Yamagata, 2012).

Despite attractive policy measures, the volume of FDI from India in the T&C sector has failed to increase due to poor infrastructure and weak support services. Bangladesh has so far allocated two SEZs for India for setting up RMG units or supporting industries. Apart from this, the lack of a clear timeline to improve infrastructure, sharing of utilities (especially gas and power), and inadequate port facilities in Bangladesh have also proved to be detrimental in attracting Indian investments into Bangladesh.

# **6. Evidence from a Firm-Level Survey**

In order to support our preliminary findings, we conducted a directed survey and generated primary data from informed parties. These included firms involved in the value chain; government officials from the Ministry of Commerce and Industry, Customs, Directorate of Textiles; researchers; and academia. Information gathered from each source was cross-checked with other sources to ensure verification and validity.

Primary information was generated mainly from T&C firms of both countries. A total of 40 firms (20 in each country) were interviewed in 2016 to generate both quantitative and qualitative information. The firms are spread across India (Coimbatore, Bhilwara and Ludhiana) and Bangladesh (Dhaka, Narayanganj and Gazipur districts). Two sets of comprehensive structured questionnaires were used to collate information. The questions were related to firm size, items traded, quality of infrastructure and institutional services, impact of tariffs and taxes, NTBs, time taken in trade by stages, the role of trade policy and South Asia Free Trade Area (SAFTA), RoO, constraints related to value chains, and others. In addition, interviews were conducted with a 21
Policy Research Working Paper 9981

# **Abstract**

Large arrivals of refugees raise concerns about potential tensions with host communities, particularly if refugees are viewed as an out-group competing for limited material resources and crowding out public services. To address this concern, calls have increased to allocate humanitarian aid in ways that (also) benefit host communities. This study empirically tests whether the presence of refugees in Uganda (one of the largest refugee-hosting countries) has improved public service delivery, and consequently, dampened potential social conflict. The data com- bines geospatial information on refugee settlements with unique longitudinal data on primary and secondary schools, road density, health clinics, and health utilization. This study reports two key findings. First, particularly after the 2014 arrival of over 1 million South Sudanese refugees, host communities with greater levels of refugee presence experienced substantial improvements in local development. Second, using public opinion data, we find no evidence that refugee presence is associated with more negative (or positive) attitudes towards migrants or migration policy.

This paper is a product of the Social Sustainability and Inclusion Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at ggros@upenn.edu.

_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
# Annex 6: Robustness: Training of the RF Model in a Global Model

|Country|Basic<br>House<br>Price<br>_Apartment_<br>_(Current_<br>_USD)_<br>Global<br>Model|Rando<br>Basic<br>House<br>Price<br>_Apartment_<br>_(PPP$)_<br>Global<br>Model|m Forest-bas<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|ed Predictio<br>Local<br>Ratio<br>predicted<br>and<br>observed<br>price<br>_(median)_|n<br>MAPE|Local<br>MAPE|
|---|---|---|---|---|---|---|
|Albania|55,923|148,600|1.07|1.10|0.28|0.24|
|Costa Rica|112,739|193,916|1.03|1.01|0.25|0.20|
|Morocco|56,405|137,148|1.07|1.10|0.29|0.27|
|Pakistan|24,330|101,633|1.09|1.29|0.36|0.45|
|South Africa|60,665|130,856|1.06|1.15|0.30|0.30|

_Note_ : MAPE refers to mean absolute percentage error; local MAPE refers to the mean percentage error based on predictions accuracy of apartments sized between 50 and 60 square meters; local ratio refers to the ratio between observed and predicted values for apartments sized between 50 and 60 square meters.

46
# Table 4.2 – Price-wage Elasticities Dependent Variable – Log of wage **_Control Variables_**

## **_Price - Wage Elasticities_**

|||Region|Skill|Agricultural<br>Products|Food<br>Products|Households<br>Appliances|Other<br>Manufact.|Textiles and<br>Apparel|
|---|---|---|---|---|---|---|---|---|
|Regional CPI|0.327***<br>(0.070)|Border|Skilled|-0.215<br>(0.176)|-0.481**<br>(0.196)|-0.286*<br>(0.251)|0.348**<br>(0.193)|0.409**<br>(0.175)|
|Ailtl Wk|-0.542***|Region|Ukilld|-0.276|-0.629**|-0.016|0.463**|0.534**|
|grcuura orer|(0.023)||nse|(0.256)|(0.204)|(0.291)|(0.235)|(0.228)|
|Permanent|0.290***||Skilld|-0.377*|0.205|-0.577***|0.198|0.343|
|employment|(0.016)|Northern|e|(0.271)|(0.166)|(0.238)|(0.145)|(0.149)|
|Hhld Hd|0.425***|Region|Ukilld|0.713***|-0.551*|-0.216|-0.265|0.174|
|ouseo ea|(0.014)||nse|(0.269)|(0.173)|(0.284)|(0.313)|(0.299)|
|Ae|0.009***||Skilled|-0.301*|-0.262|-0.335**|0.362|0.317|
|g|(0.001)|Central||(0.255)|(0.188)|(0.244)|(0.285)|(0.244)|
|Education Premium|0.186***|Region|Unskilled|0.082|-0.062|-0.591**|-0.412|0.729**|
||(0.005)|||(0.298)|(0.225)|(0.314)|(0.271)|(0.191)|
|Ll Edt|0.001***||Skilld|0.157|0.316|-0.920***|-0.065|0.532**|
|oca nowmen|(0.000)|Mexico|e|(0.344)|(0.251)|(0.426)|(0.311)|(0.283)|
|Gender|0.110***|City|Unskilled|0.223|-0.192|-0.431|-0.716|0.791**|
||(0.013)|||(0.967)|(0.210)|(0.340)|(0.223)|(0.239)|
|D 1989|0.203***||Skilld|0.032|-0.147|-0.440*|0.144|0.292|
|ummy year|(0.066)|Southern|e|(0.436)|(0.221)|(0.474)|(0.460)|(0.416)|
|Dumm ear 1992|0.362***|Region|Unskilled|1.180***|0.474|-1.552*|0.531|-0.192|
|y y|(0.056)|||(0.384)|(0.249)|(0.365)|(0.451)|(0.366)|
|Dummy year 1994|5.383***<br>(0.731)||||||||
|Dummy year 1996|0.390***<br>(0.039)|# of Obs|ervations|44160|||||
|Dummy year 1998|-0.178***<br>(0.028)|R-Sq|uared|0.458|||||
|Dummy year 2000|-0.136***<br>(0.042)||||||||

Note: All variables are in log, with the exception of dummies. White corrected standard errors are shown in brackets. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively.

50
open term contracts or to any other type of open term contracts. It is important to underline that these results are obtained with model specification where one controlled for the type of contract i.e. one may interpret them as the ―pure‖ effect of shadow employment probability on the relative transition probabilities.

The results obtained are in line with one of the main conclusions of the CASE study from 2007 suggesting that shadow employment is more a way of coping with lack of other employment opportunities than an equivalent or even superior alternative to any legal employment contracts. However, it is worth noting that those who enter shadow employment are also the more active part of the group having problems with finding full time/open term employment. They are much more inclined to cope with their situation by entering some form of self-employment than to stay passive and depend on social assistance.

# **References:**

CASE (Center for Social and Economic Research) in co-operation with Millward Brown SMG/KRC (2007) Grabowski, M. (2002), ―Informal Labor Market and Informal Economy during Transition - The Polish Perspective,‖ Paper prepared for the Round Table entitled "The Informal Economy in the EU Accession Countries: Size, Scope, Trends and Challenges to the Process of EU Enlargement", Sofia, April 18-20, 2002.
provide support; assess teacher performance and identify strong performers and problem areas; reward strong performance using social awards, recognition in school assembly and other low-cost methods; and address problems through model lessons, in-service training and other methods. We anticipate that this module would contribute to improvements in the frequency with which head teachers observe teachers, provide feedback, and reward teachers for strong performance, leading to improvements in teacher attendance, effort and teaching practices.

To address problems of school culture, the training included a module on _Creating a positive and inclusive culture towards over-age children and girls_ . This was intended to provide participants with skills to, among others: understand, monitor and prevent inappropriate treatment by teachers and students of over-age children, learners with special needs and girls, and its impact on well-being and learning outcomes; train teachers to identify, encourage and support low-performing students; and monitor and address bullying. We anticipate that this module would lead to increased Head Teacher effort to prevent bullying, leading to reduced incidence of bullying.

Finally, the training included a module on _Maintaining and promoting utilization of financial and management records_ . This supported improved resource utilization, by training participants to maintain full and up-to-date records of school income and expenditures; utilization of textbooks and other non-financial inputs; and teacher attendance and time on task. It also supported improvements in teaching practices and school cultures by training participants to maintain full and up-to-date academic records, including on student attendance and learning, measured through continuous assessment; and to use these records to monitor and address poor teaching practices and chronic student absenteeism and low teacher effort.

We anticipate that, if the intervention were successful, the improvements in resource utilization, teaching practices, and school cultures engendered by the intervention would ultimately lead to improvements in student progression and dropout. See section 2.5.

The intervention was developed by a consortium of The Ministry of Education of Malawi (MoE, particularly the Department of Teacher Education and Development and Directorate of Basic Education; Asian Institute of Developmental Studies, Inc. (AIDSI), a Malaysia-based consulting organization selected by MoE through tender; The School of Education of the University of Malawi (SEUM), acting as local partner to AIDSI; the University of Oxford; and The World Bank, which provided technical support and guidance to the development of content. Following initial development of the materials and protocols, implementation was carried out by the MoE in partnership with AIDSI and SEUM.

The intervention consisted of three main phases: 11
approach for targeting since the survey data would be part of the regular MPCA targeting program. The CLCI adopted a census-like door-to-door house-visit approach in selected neighborhoods chosen based on reported high levels of need. Data collection was non-random, reflecting only households in neighborhoods already identified as vulnerable. The total sample size was 10,818 households. Summary statistics of the variables of interest are shown in Table 1. By design, the data comes from the North – CLCI’s area of intervention at the time – and, thus, is not nationally representative of the displaced population in Iraq. It is, however, representative of the caseload of the CLCI’s assistance and the lessons shared in the paper are highly relevant in the conflict-affected contexts with significant displaced populations and the presence of humanitarian cash transfer programs.

_Table 1: Summary statistics of variables of interest (n = 10,818)._

|Household characteristics|Frequency|%|
|---|---|---|
|Female-headed household|3042|28|
|The household head is disabled|1559|14|
|No household member worked|3745|35|
|The household cannot afford basic need|6680|62|
|The household head document is missing|1636|15|
|Rural households|5472|51|
|Lowest asset quantile|2792|26|
|Status|||
|Displaced_(& refugee)_|1388|13|
|Host_(& remainee)_|639|6|
|Returnee|8791|81|
|Governorate|||
|Anbar|2334|22|
|Diyala|180|2|
|Kirkuk|477|4|
|Ninawa|6113|57|
|Salah_al_din|1714|16|
|Referral|||
|Registered for government social assistance|7078|65|
|Registered for Minha COVID 19 program|3297|30|
|Willingto be referred|7278|67|

# 3.3 Analytical strategy

The analytical strategy adopted in this paper follows a six-step procedure.

**<u>Step 1. PMT score calculation</u>** : We construct 5 PMTs and calculate the PMT scores for each household in the data. These PMTs, as highlighted above, include: 1) MOLSA-PMT score: the full official PMT scoring model which estimates household per capita consumption and determines eligibility for MOLSA social

11
362 point estimates of each effect, respectively,<sup>14</sup> the technique finds that 76 and 66 percent are statistically significant. The mean value for the overall impact is _−_ 0 _._ 31 with a standard error of 1 _._ 12, and the mean value for the internal impact is 0 _._ 79 with a standard error of 1 _._ 30. Thus, while the average values suggest that PTAs tend to be trade-creating among members and net trade-diverting overall, the variance about the means is very large and in fact both effects may not be different from zero. Although these results are not encouraging for the impact of PTAs on world trade, it is important to keep in mind that the primary purpose of meta-analysis is to present average findings from a sample of studies. This approach does not give greater weight to more recent research, which generally tends to rely on tighter theoretical structure and use more sophisticated econometric techniques. Therefore, these findings do not suggest that PTAs have insignificant effects on trade, but rather that the existing literature has not yet provided a cohesive answer to that question.

# **3 PTA and trade variables revisited**

Although substantial progress has been made in enhancing the empirical performance and theoretical foundations of gravity models, a number of areas for improvement remain. This paper focuses on two such areas: the number of PTAs considered by the analysis, and the domain of trade flows explained by the model. Most gravity studies of preferential liberalization take explicit account of only a handful of PTAs in force; while this approach is reasonably well-suited for analyzing the effects of individual agreements, it is not appropriate for making generalizations about the _average_ effect of all PTAs in existence. We are not aware of any empirical works that consider all PTAs in force in a gravity framework, and the fact that existing studies usually limit their attention to larger and more well-known PTAs could easily bias any aggregation of these estimates. Therefore, the first part of this section will discuss a new comprehensive database of PTAs, which improves upon the existing data sources by including a large number of agreements not notified to the WTO. Relying on this database will allow our model to track the average effects of PTAs on trade directly,

> 14The sample was obtained by collecting individual agreement dummy coefficient estimates from each study. The requirements for choosing a study were either a published article or an NBER working paper.

9
Table 5: Impact of tradable service on (non-tradable) consumer services

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Consumer|services|Non-consu|mer services|
||Employment|No. of Firms|Employment|No. of Firms|
|Log tradable services|0.511**|0.386*|0.340|0.103|
||(0.250)|(0.215)|(0.221)|(0.204)|
|Education|-0.003|0.003|0.011|0.005|
||(0.009)|(0.007)|(0.009)|(0.006)|
|Observations|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|

*** ** * Standard errors (in parentheses) are clustered at the district level. _p <_ 0.01, _p <_ 0.05, _p <_ 0.1. Consumer services are (i) retail trade, except of motor vehicles and motorcycles, (ii) personal services, (iii) human health, (iv) residential care, and (v) accommodation and food services. Non-consumer services are all other non-tradable services.

Finally, to further assess the consumer demand channel, we examine the impact of tradable service employment on consumption expenditure by category, at the household level in Table 6. To be consistent with previous literature, following the analysis on district-level household expenditure in India in Fan et al. (2021), we use state fixed effects instead of district fixed effects. Column (1) shows that a 10% increase in tradable service employment leads to a 3.6% increase of household expenditures on education, which is significant at the 1% level. This is consistent with the argument that spillovers from tradable service growth on education are due to increases in local final demand from consumers. We find similar effects for other important non-tradable services, although differences in sector classifications do not allow us to test each of the sectors driving our results separately. Column (2) analyzes medical services, but these are insignificant. Column (3) shows a highly significant coefficient of 0.53 for entertainment. Similarly, the effects on consumer services and the total of these services are also large (at 0.26 and 0.29 respectively) and statistically significant. Lastly, column (6) shows positive and significant effects on the overall monthly per capita expenditure (MPCE) of households.

In sum, we find suggestive evidence that the consumer demand channel rather than the sectoral linkages channel, plays a larger role in explaining the relationship between the district-level growth in non-tradable and tradable services employment.

16