rafmacalaba/datause-encoder-data huggingface dataset:
# **_<u>________________________________________________________</u>_**

|Report 2004; Overall<br>Trade Restrictiveness<br>Index(OTRI)|||economy|
|---|---|---|---|
|Doha Development<br>Agenda Trade Capacity<br>Building Database<br>(TCBDB)||annual|information on trade-related technical<br>assistance and capacity building projects|
|Technical Barriers to<br>Trade (TBT) Database|690 firms<br>in 17<br>countries|2001-2||
|Global Economic<br>Prospects 2004 database<br>(Chapter 5) : Based on<br>Wilson, Mann, Otsuki<br>(2003)|75|For year 2000|Port efficiency; Customs environment;<br>Regulatory environment; Service sector<br>infrastructure|
|European Union (EU)<br>Market Access database||current|Sectoral and Trade Barriers, Applied<br>Tariffs, Exporters' Guide to Import<br>Formalities|
|Tuck Trade Agreements<br>Database||2003|bilateral and regional free trade<br>agreements and customs union<br>agreements|
|PERINOM Database<br>|18<br>countries,<br>650,000<br>records<br>**CUSTOMS**<br>|**AND BORDER**<br>|Bibliographic database on standards and<br>technical regulations<br>**CROSSINGS**<br>|
|Doing Business: Trading<br>Across Borders|155|Newly<br>implemented<br>from 2005|Documents for import and export,<br>Signatures for import and export, Time<br>for import and export|
|International Exhibition<br>Logistics Associates<br>(IELA)|67|Current|Average days required for customs<br>clearance for sea and air cargo|
|Trade and Transport<br>Facilitation in South<br>East Europe<br>(TTFSE)|8 ECA<br>countries|1999-annual|Performance and efficiency of customs<br>administration, and data on border<br>crossing and clearance times at 23 pilot<br>sites|
|Global Express<br>Association-Country<br>Market and Customs<br>BarriersReports|150|Current|Questionnaires about market access<br>restrictions, foreign investment, and the<br>use of local services.|
|International Road<br>Transport Union|17 ECA<br>countries|weekly data|Waiting Times at Border Crossings|
|Walkenhorst and Yasui<br>OECD Paper|80-100|2003|Border process quality and Border<br>waiting time|
|UNESCAP|Asian<br>countries|2003|Average and Maximum Waiting Times<br>atBorders|

63 ____________________________________________________________________________________________________________
The objectives of this study were to (1) identify the drivers of THE, (2) estimate the prevalence of CHE and IHE, and (3) identify the drivers of and potential financial risk protection (FRP) against CHE and IHE through subsidies in Guinea.

# **Methods**

# **Study design and population**

We conducted a retrospective cohort study based on the 2018-2019 Guinea Living Standards Measurement Study (LSMS).<sup>[11]</sup> This survey is a monitoring and evaluation tool for Guinea's National Economic and Social Development Plan. It was conducted in two phases to take into account variations in consumption related to the country's two main seasons (the rainy season and the dry season). In the first phase, households were interviewed for three months during the rainy season, from July to September 2018, and in the second phase, households were interviewed for three months during the dry season, from April to June 2019. In order to have a nationally representative sample of the population, for both phases, stratified cluster sampling was used and each of the two collection phases involved half of the sample. The strata represented the type of area (urban or rural) in each of Guinea's eight administrative regions. Because the Conakry region was a purely urban area, there were a total of 15 strata. The clusters (enumeration areas) represented specific geographic areas (villages and communities in rural areas and neighborhoods in urban areas). The LSMS was conducted by the National Institute of Statistics of Guinea with technical support from the World Bank's Poverty and Equity Global Practice.

Analyses were conducted at the individual level. All individuals included in the LSMS were considered eligible. However, we excluded those for whom there was no consumption expenditure.

# **Study data**

The LSMS included 20 sub-databases, each focused on a specific theme such as health, employment, social nets, and welfare. We obtained the 20 de-identified sub-databases from the West Africa Unit of the World Bank's Poverty and Equity Global Practice, with permission from the National Institute of Statistics of Guinea. We merged them and extracted relevant individual data on prepayment mechanisms for health 4
The relationship between research-cost intensity and the number of varieties is indeterminate, as _n_ appears in both the numerator and the denominator. Market prices are expected to be positively correlated with research-cost intensity. Moreover, unlike Hausmann and Rodrik (2003), the appropriability problem does not affect prices, but can be thought as affecting the probability of losing a product variety.

This model also predicts that the research-cost intensity of the firm will depend positively on the average price and negatively (and linearly) on the probability of losing one variety to competitors. The function is also inversely proportional to the probability of losing a variety. Since prices are given, innovation expenditures of firms as a share of sales would also depend on the probability of future imitation and on the number of varieties currently produced by the firm. We do observe this variable in firm data. Proxies for market conditions that are likely to be correlated with the average (relative) price of the firm’s varieties, such as the growth of manufacturing value added can also be used to capture the effect of market prices. The probability of entry can be thought to be affected by policies, especially the regulatory environment, as suggested by Aghion et al. (2006).

# 2.b. _Discussion_

The model is quite tractable and intuitive, but it does open the door to many questions of relevance for the empirical work. Although the empirics discussed in the following sections are done with cross-sections of firms, most of the interesting questions are related to dynamics.

First, would the predictions of the model change with the introduction of financing costs or real interest rates? The answer is no as long as all firms face the same opportunity costs or real interest rates.

Second, how would the firm respond when market demand for and the relative price of its product varieties fall? One response would be to reduce research-cost intensity, but another would be to increase research so as to enhance the chances of raising the number of varieties. The latter would be a “retooling” strategy, whereas the former could be called the “costreduction” strategy. Neither strategy is analyzed here, but the point is that either option could be viable, depending on an unspecified production function. One need only assume that firms’ 5
freedom to estimate the relevant coefficients of the variables measure at the national level, which is limited by the number of countries.

Missing data inevitably introduce ambiguity into the inferences that can be drawn from a study, so another caveat is in order. This section, as well as the regression analyses, relies on variables that were taken from firm-survey questions that are straightforward and with no ambiguity. That is, the question on whether a firm introduced a new product in the previous two years is straightforward. Therefore, it is safe to assume that firms that did not answer this question had not in fact introduced a new product. Although this change in the data is marginal, we do obtain more realistic estimates of the share of firms by country that introduced new products. For example, the percentage of firms reporting a New Product in China changes from 25 percent to 15 percent for all firms, which is a more reasonable share. In other countries, such as Turkey, there are no missing values. The data from the countries in Latin America and the Caribbean (LAC) are a mixed bag, but the same procedure was applied to all. R&D/Sales and licensing payments are likely to be measured with error, and this issue is discussed in the context of the econometric methodology in section 4.

# 3.b. _Descriptive statistics_

Table 1 presents some descriptive statistics for a sample of 36 countries, which cover the sample used in the econometric analysis discussed in section 5 below. The sample used for the analyses of section 4 is larger, because that estimations of the partial correlation between R&D and product innovation uses country dummies to control for any country-level characteristic rather than specific aspects of the business environment. The number of firms by country and survey year in the various samples appears in the Data Appendix. Hence the restricted sample was due to the availability of data on the other country-level determinants of product innovation discussed above.<sup>5</sup> The following paragraphs focus on the restricted sample, because it poses some issues about the representativeness of the sample of firms among developing countries.

> 5 Please note, however, that the regressions reported in Table 2 include the average number of years of education of workers employed by each firm. China and Indonesia do not have these data and thus are not in the samples of 59 and 60 countries that are included in the regressions reported in Table 2. But they do appear in the sample of countries used for the estimation of the models reported in Table 3, and thus appear in Table 1.

9
# **Early Education, Preferences, and Decision-Making Abilities**

Joana Cardim (Education Policy Institute) Pedro Carneiro (University College of London, IFS, CEMMAP, FAIR-NHH) Leandro S. Carvalho (University of Southern California) Damien de Walque (Development Research Group, The World Bank)<sup>1</sup> JEL codes: J13, I20, D91, J16, D81 Keywords: economic preferences, decision-making abilities, early education, daycare, children 1 Cardim; Education Policy Institute, 150 Buckingham Palace Road, London SW1W 9TR, UK (joana.almeidacardim@gmail.com) Carneiro: University College London, Department of Economics, Gower Street, London WC1E 6BT, UK (p.carneiro@ucl.ac.uk); Carvalho: University of Southern California, Center for Economic and Social Research, 635 Downey Way, Los Angeles, CA 90089-3332 (leandro.carvalho@usc.edu); de Walque: Development Research Group, The World Bank, Washington, DC 20433 (ddewalque@worldbank.org). Jiewen Luo provided excellent research assistance. This paper benefited from discussions with Isabelle Brocas, Juan Carrillo, Marco Castillo, Gary Charness, Niree Kodaverdian, Ragan Petrie, and Anya Samek, Lise Vesterlund, and from the feedback of participants of the Experiments which Children and Non-Standard Subjects Workshop at the 2019 North American Meetings of the Economic Science Association, the Nordic Conference in Development Economics (Helsinki 2022), the International Conference on Development Economics (Clermont-Ferrand 2022) and the European Economic Association Congress (Milan 2022). We thank Elisabet Meireles, Luiz Eduardo Guedes, Mary Fuhs, and Jorge Alberto dos Santos for help with the survey, and the IADB for letting us use test score data that they collected. Research reported in this publication was supported through the Research Support Budget and the Knowledge for Change Program (KCP) at World Bank, the NIA grant 1K01AG066999-01A1 (Carvalho), the ESRC for CEMMAP (ES/P008909/1) and the European Research Council through grant ERC-2015-CoG- 692349 (Carneiro). Cardim gratefully acknowledges financial support by FCT – Fundação para a Ciência e Tecnologia - SFRH/BD/131707/2017. This study was approved by the Committee of the Secretaria Municipal de Saúde in Rio de Janeiro, Brazil (Plataforma Brasil) on 3/8/2016, (Study ID:1.466.676) and by the IRB at the University of Southern California University Park, on 2/8/2016 (Study ID: UP-15-00641) and registered in the AEA RCT Registry ( <mark>AEARCTR-0004536).</mark> 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 and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or the National Institutes of Health. The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.
# Table S3. Model With Interaction Between Inequality and Initial Income (Instrumenting only Inequalityit)

|Dependent Variable is:|l|n(yit)||
|---|---|---|---|
||(1)|(2)|(3)|
|Inequality Variable is:|Gini<br>Brueckner et al. (2015)|Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|Inequalityit|9.15***<br>(1.13)|10.16***<br>(1.11)|14.71***<br>(1.37)|
|Inequalityit|-1.15***|-1.35***|-2.10***|
|*ln(yit-1)|(0.14)|(0.14)|(0.30)|
|ln(yit-1)|0.71***<br>(0.05)|0.74***<br>(0.04)|0.65***<br>(0.03)|
|Kleibergen Paap F-Statistic|463|402|171|
|||First Stage: Inequalityit||
|Residual Inequalityit|1.49***<br>(0.56)|1.11***<br>(0.46)|1.61***<br>(0.51)|
|Country FE|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|
|Observations|589|768|768|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.
**<u>Table 3: Double difference estimates of impacts from program participation</u>**

|||**DD**||**D**|**Dand PSM **||
|---|---|---|---|---|---|---|
||**PHASE 1**|**DISTRICT**|**S**||||
|**2006 and 2008 panel**|||||||
|Consumption (Rs/year)|711|(288)|**|324|(295)||
|Energy intake (Kcal/day)|76|(47)||10|(49)||
|Protein intake (g/day)|1.11|(0.87)||0.26|(0.89)||
|Nonfinancial assets (Rs/year)|392|(349)||446|(333)||
|Consumption (log)|0.079|(0.022)|***|0.026|(0.024)||
|<br>Energy intake (log)|0.024|(0.020)||-0.002|(0.021)||
|Protein intake (log)|0.019|(0.018)||0.002|(0.020)||
|Nonfinancial assets (log)|0.360|(0.062)|***|0.203|(0.062)|***|
|Number of observations|1,017 + 1|,410 = 2,427||967 +|1,249= 2,216||
|**2004 and 2006 panel**|||||||
|Consumption (Rs/year)|-528|(216)|**|-341|(196)|*|
|Energy intake (Kcal/day)|-184|(43)|***|-86|(40)|**|
|Protein intake (g/day)|-2.34|(0.83)|***|-1.14|(0.93)||
|Nonfinancial assets (Rs/year)|-272|(298)||-211|(227)||
|Consumption (log)|-0.027|(0.023)||-0.019|(0.023)||
|<br>Energy intake (log)|-0.074|<br>(0.021)|***|-0.036|<br>(0.024)||
|Protein intake (log)|-0.044|(0.019)|**|-0.021|(0.020)||
|Nonfinancial assets (log)|-0.053|(0.071)||-0.134|(0.077)|*|
|Number of observations|998 + 1|,344 = 2,342||983 +|1,289= 2,282||
||**PHASE 2 an**|**d 3 DISTRI**|**CTS**||||
|**2006 and 2008 panel**|||||||
|Consumption (Rs/year)|80|(367)||144|(354)||
|<br>Energy intake (Kcal/day)|66|<br>(68)||115|<br>(72)||
|Protein intake (g/day)|1.73|(1.23)||2.50|(1.30)|*|
|Nonfinancial assets (Rs/year)|15|(376)||30|(393)||
|<br>Consumption (log)|0.011|<br>(0.034)||0.016|<br>(0.037)||
|Energy intake (log)|0.025|(0.029)||0.051|(0.032)||
|Protein intake (log)|0.036|(0.027)||0.058|(0.028)|**|
|Nonfinancial assets (log)|0.168|(0.135)||0.056|(0.143)||
|<br>Number of observations|439+|1,435 = 1,87|4|403 +|1,091 = 1,494||
|**2004 and 2006 panel**|||||||
|Consumption (Rs/year)|120|(308)||-274|(300)||
|<br>Energy intake (Kcal/day)|-16|<br>(57)||-75|<br>(60)||
|<br>Protein intake (g/day)|-0.32|(1.12)||-1.76|(1.08)||
|Nonfinancial assets (Rs/year)|713|(332)|**|394|(317)||
|<br>Consumption (log)|0.048|(0.033)||-0.018|(0.032)||
|<br>Energy intake (log)|-0.010|(0.027)||-0.037|(0.028)||
|<br>Protein intake (log)|-0.014|(0.026)||-0.043|(0.027)||
|Nonfinancial assets (log)|0.303|(0.128)|**|0.185|(0.132)||
|Number of observations|430 +|1,297 = 1,72|7|417 +|1,139 = 1,556||

_Source:_ Authors’ computation from household survey and administrative data. _Notes:_ All figures in per capita terms. As explained in the text, the estimates in the lower panel test the parallel trend assumption. DD = double difference estimation, PSM = propensity score matching. Robust standard errors are in parentheses. Significance level: *: 10%, **: 5%, ***: 1%.

18
rather than relying solely on PMM or its variants to compensate for any model specification shortcomings.

Although the methodological approach described in this section applies to both the 2010 and 2016 surveys, for the sake of simplicity, this section will focus on the 2022 to 2016 imputation. However, it is essential to note that the same principles and procedures apply to the 2022 to 2010 imputations.

Data availability consists of at least two cross-sectional consumption surveys, the donor (i.e., HIES 2022) and the recipient (i.e., HIES 2016 or 2010). The official consumption aggregates are different from the two surveys. As mentioned, in 2022, many changes were introduced, like additional items, improvements in fieldwork and data collection, etc. First, to reconstruct trends in distributional indicators, a consumption aggregate is estimated for 2022 based on the commonly collected items in the two surveys. In both surveys, i is the comparable per capita consumption for household , (i.e., ), in survey ( ) with sample size , and i is a vector of characteristics observed yy ii ii= for household in survey . These household characteristics, observed in both survey years, include ii ii 1, ... , NN jj jj= 2016, 2022 NN XX household head characteristics (gender, ethnicity, civil status, education, age, etc.) as well as dwelling ii jj features (access to water, electricity, sanitary facilities, etc.) and other household variables (size, labor conditions, composition, etc.). Second, the linear model where the -th household's comparable log per capita consumption<sup>7</sup> i is explained by household characteristics i for each survey is given by: ii yy i i XX i (7) TT where i is an error term and yy = ααii + ββi is a vector of ii XX + εε _K_ regressors, and jj = 2016, 2022 is the intercept. In this context, + i would represent the portion of log consumption explained by the selected covariates. ii ii εε XX αα αα TT ii The objective is to estimate, for household ββ XX interviewed in the recipient survey (i.e., 2016 or 2010) the additional consumption that would have been obtained if the 2022 survey had been implemented in ii previous rounds. As an alternative to regression-based approaches and to avoid making assumptions about the behavior of the residuals, in this approach, the share of additional consumption is not obtained directly from an econometric prediction but through statistical matching (random hot deck) between households from the two surveys. Thus, the predicted log consumption from the model (7) is one of the variables on which the closeness between observations is measured. More specifically, and are predicted using LASSO regression as follows: ii2016 ii2022 yy yy i L L L L i (8) TT LLjj LLjj 2 where ( L L yy�L L = αα�) = argmin+ ββ̂ ) �∑XX i jj = 1, 2i � �1�, is the penalization TT NNjj TT parameter chosen through cross-validation, and αα� LLjj, ββ̂ LLjj (ααjj,ββjj ii=1�yy �−αα1 is the ii −ββlii1XX norm of the coefficient vector + λλ�ββii λλ . ii ii �ββ ββ

> 7 To simplify notation, in this paper i will be used to refer to the logarithm of household per capita consumption. yy

6
41 146. To simplify these charges and reduce compliance and admli Aation costs, a one-time payment has been proposed by several Indian states. Under this prograi -, private vehicle owners would pay a lifetime annual charge at the time of registration.2w This approach dramaticaly reduces compliance and administrative costs and, when imposed in relation to the value of the vehicles, a higher rate may seem less objectionable. The lifetime charge does have important flaws: (1) data from the annual license tax could no longer be used as a record of the number of vehicles on the road; (2) the annual license tax could no longer be used to force the recording of ownership changes; and (3) withou **t** he annual license tax, it would not be as easy to do an annual check on insurance coverage. Another problem is that the cost of this program would be prohibitive for most commercial vehicles. One-time payment would probably have to be limited to private automobiles and two-wheelers. Thus, some of the potential administrative savings would not be realized.

147. A suggested approach to annual license tax simplification in Tanzania was to reduce the number of vehicle categories in the tax structure and to reduce the tax rate. Unification of several permit, license, and registration certificates was recommended for Nepal to reduce compliance cost and eliminate some duplication of administrative effort. Neither of these proposals was implemented.

148. Some inroads to improved administration have been made in Maharashtra State (India). Records on vehicle registration and licenses have been computerized, billings are automatically generated, and delinquency lists are printed. To reduce compliance cost, payment may be made at banks.

149. Several other administrative practices provide clues that might help improve the annual license tax. In Yugoslavia, tough enforcement appears to generate a lower evasion rate. In India, where enforcement is tougher on commercial vehicles than on private vehicles, the evasion rate on the commercial vehicles is much lower. Ghana's flat rate tax, while easy and cheap to administer, may not be so appealing from a benefits point-of-view (that is, heavier vehicles should pay more, and so on).

# Enforcement and **Penalty**

150. Evasion is discouraged by a high likelihood of detection and by a severe penalty. Of the countries in this sample, Yugoslavia seems to have concentrated most on the penalty approach, - and little evasion is reported even of the annual license tax, when neither a dated number plate nor a displayed registration sticker is required. Success could be due to a no-nonsense approach to dealing with evaders, the fact that there is often little personal gain from evasion in a socialist state, or simply that the government' **s** tatistics are not accurate enough to identify

> _ZQI_ Since **1987, the** Government of Mahamshtra has levied a one time charge on two-wheelers and **tricycles. Ouaat** and U.P. have adopted **the** one time system for personai vehicles, and Tamil Nadu **and** U.T. Delhi will introduce **this** system **oon** (Government of India, 1989, Chapter III).