rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table 4: Parameter values and data sources for calculating benefit-to-cost ratio for preprimary interventions in lowand middle-income countries**

||Labor force<br>participation<br>rate|Average<br>nominal<br>monthly<br>wage<br>(country<br>currency)|Annual<br>real wage<br>growth|Lowest<br>treatment<br>effect on<br>cognitive<br>skills (SD)|Lowest<br>treatment<br>effect on<br>social-<br>emotional<br>skills (SD)|Data sources|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(5)|(6)|(7)|
|India (Dillon et al, 2017)|46.3|13143|0|0.09|0.165|Periodic Labour Force Survey, 2019;<br>India Ministry of Statistics and<br>Programme Implementation;<br>BloombergNewswire|
|Malawi (Ozler et al, 2018)|37.2|125000|3.4|0.185|0.252|Integrated Household Survey, 2017 ;<br>National Statistical Office of Malawi|
|Ghana (Wolf et al, 2019a)|57|618|2.16|0.107|0.18|Living Standards Survey, 2017;<br>Ghana Statistical Service; Bloomberg<br>Newswire|
|Peru (Gallego et al, 2019)|77.4|1570|1.7|0.19|---|Encuesta Nacional de Hogares, 2019;<br>ILO SIALC; BloombergNewswire|
|Indonesia (Brinkman et al, 2017)|70.1|2913897|-0.2|---|0.158|National Labour Force Survey, 2020;<br>Statistics Indonesia of the Republic<br>of Indonesia; BloombergNewswire|
|Bangladesh (Spier et al, 2020)<br>Notes: Missing recent data for averag|59.4<br>e real wage growth|12016<br>for Ghana, we|3<br>averaged valu|0.25<br>es across other|0.37<br>countries. Coun|Labour Force Survey, 2017;<br>Bangladesh Bureau of Statistics<br>try currencies were the following:|

Notes: Missing recent data for average real wage growth for Ghana, we averaged values across other countries. Country currencies were the following: India (INR), Malawi (MWK), Mozambique (MZN), Ghana (GHS), Peru (PEN), Indonesia (IDR), and Bangladesh (BDT)
|Gini ‐per capita consumption,nominal|40.3|37.8|40.3|40.2|42.1|
|---|---|---|---|---|---|
|Gini ‐per capita consumption,real|38.5|35.8|38.1|38.4|40.6|
|Palma ‐per capita consumption,nominal|1. 9|1.7|1.9|1.9|2.1|
|Palma ‐per capita consumption,real|1.7|1.5|1.7|1.7|1.9|

# **B. Unconditional quantile decomposition**

This section investigates the basic factors that might explain the discrepancy in inequality (and pro‐ poor growth patterns) between the HBS and NPS surveys by performing the unconditional quantile regression decomposition technique. The method decomposes the changes in consumption over time into two components: one component that is due to improvements in personal characteristics or _endowments_ (better education, increased ownership of land and other assets, access to employment opportunities, local infrastructure, and so forth) and one component attributable to changes in the _returns_ to those characteristics (returns to education, land productivity, returns to business, and so forth). These components are then further decomposed to identify the specific attributes that contribute to the changes of consumption. The decomposition is applied at each decile group of the consumption distribution to understand the patterns of the changes for the different welfare groups.<sup>15</sup> We start by examining the factors contributing to the variation of consumption between 2007 and 2011/12 using HBS data. The results are reported in Figure 10 and indicate that the increase of poor households’ consumption is due mainly to improvements in households’ endowments. Returns also improved but to a lesser extent and only for the 20 percent poorest groups.

One can observe from Figure 10 an improvement of households’ endowments for all the population groups, but the improvements are more marked for the 30 percent poorest segments.

The increase of the endowments is driven by a significant expansion of asset ownership, mainly transportation and communication means, and to a lesser extent agricultural land. Educational attainment of household heads has improved as well but less significantly. The access to local infrastructure has deteriorated in general, but access to local roads seems to have slightly improved

> 15 The decomposition approach is based on the Recentered Influence Function and unconditional quantile regression method proposed by Firpo, Fortin and Lemieux (2009). See Appendix 4 of the Tanzania Mainland Poverty Assessment for more technical details on the approach.

41
# Taxation, Information, and Withholding: Evidence from Costa Rica<sup>*</sup>

Anne Brockmeyer, Marco Hernandez The World Bank September 9, 2016

> * Corresponding author: Anne Brockmeyer, abrockmeyer@worldbank.org. Marco Hernandez: marcohernandez@worldbank.org. This is an update of World Bank Policy Research Working paper 7600 (March 2016). The last version of this paper can found at https://sites.google.com/site/annebrockmeyerworldbank/. We are exceedingly grateful to the Ministry of Finance and the General Directory for Taxation of Costa Rica for outstanding collaboration. In particular, we are indebted to Fernando Rodriguez Garro and Carlos Vargas Duran, as well as to Laura Badilla Castro, Lorena Chacon Sanchez, Jenny Delgado Monge, Graciela Garcia Santamaria, Jorge Richard Muñoz Núñez, Mercedes Padilla Delgado, Manuel Enrique Ramos Campos, Karla Salas Corrales, Ronald Solorzano Vega, Giovanni Tencio Pereira and Marvin Zárate Muñoz. We thank François Gérard, Henrik Kleven, Aart Kraay, David McKenzie, Joana Naritomi, Steven Pennings, Eleanor Wilking and conference/seminar participants at LSE STICERD, NTA, PEUK, PacDev, ABCDE, IIPF, Oxford CBT and the World Bank for helpful comments. Juliana Londoño Vélez, Spencer Smith, Corinne Stephenson and Gabriel Tourek provided excellent research assistance. All errors are our own.
|$422,438<br>Annual Report to the Secretary: Under 500|
|---|
|Total Costs<br>$14,475,600/year|
|2.<br>GLBA Compliance Costs|

Robert Hahn and Anne Layne-Farrar’s 2002 study detailed the industry-wide cost of compliance with the Gramm-Leach-Bliley Act (GLBA). The study found that banking, insurance and securities companies may, altogether, spend around $2- billion to $5 billion on printing costs alone to comply with the regulation’s privacy policy notifications. In 2016, nearly 15 years after Hahn and Farrar’s study in 2002, amendments to the GLBA created exceptions to the annual privacy notice requirements.<sup>135</sup> In response, the Bureau of Consumer Financial Protection calculated the decreased cost of privacy notice procedures yielded from the modifications and found a $3 million reduction in costs incurred per institution.

|**Legislation**|**Estimating**<br>**Entity**|**Affected**<br>**Respondents**|**Cost of**<br>**Compliance**<br>**Components**|**Cost of Compliance**<br>**(US$/year)**|
|---|---|---|---|---|
|GLBA<sup>136</sup>|Fred H.<br>Cate and<br>FleetBoston<br>Fiil|Banking,<br>insurance and<br>securities<br>i|Printing costs for<br>all privacy policy<br>notifications<sup>137</sup>|$2-5 billion in the entire<br>financial industry|
||nanca<br>Corporation|companes<br>(surveyed 40,000<br>financial<br>institutions)|1. Drafting policy<br>2. Consulting<br>lawyers<br>3. Hiring part-time<br>and full-time IT<br>employees<br>4. Hiring a Chief<br>Privacy Officer|Not estimated|
|Amendments<br>to the<br>GLBA<sup>138</sup>|Bureau of<br>Consumer<br>Financial<br>Protection|Banks, credit<br>unions and non-<br>depository<br>financial<br>institutions.|Cost of annual<br>privacy notice|$12 million (pre-<br>amendment) - $3 million<br>(savings from amendment)<br>= $9 million per institution|

> 135 Amendment to the Annual Privacy Notice Requirement Under the Gramm-Leach-Bliley Act, 83 Fed. Reg. 40945 (Aug. 17, 2018) (to be codified at 12 C.F.R pt. 1016).

> 136 Robert W. Hahn & Anne Layne-Farrar, _The Benefits and Costs of Online Privacy Legislation_ , 54 Admin. L. Rev. 85, 145 (2002).

> 137 Fred H. Cate, The Privacy Paradox (Jan. 26, 2001) _(observing that “_ [a]pproximately 40,000 financial institutions will be sending as many as 2.5 billion notices to their various customers by June 12, 2001” to comply with the Gramm-Leach-Bliley Act.)

> 138 _Id._ , 83 Fed. Reg. 40956 (Aug. 17, 2018) (to be codified at 12 C.F.R pt. 1016).

24
Table **3** Correlation Coefikients

||_GRPCGDP_|_Cur/Te_|_Cap/Te_|_Def/Te_|_Edu/Te_|_Hfhfe_|_Tac/Te_|_Other/Te_|
|---|---|---|---|---|---|---|---|---|
|_GRPCGDP_<br>_C/T_||0.043<br>**(0.36)**|**-0.065**<br>**(0.18)**<br>|**0.18**<br>**(0.001)**|_-0.054_<br>**(0.26)**|-0.2<br>**(0.001)**|-0.14<br>**(0.006)**|0.04<br>(0.41)|
|_ure_<br>|||**-0.85**<br>**(0.001)**|**0.08**<br>**(0.01)**|**0.1**<br>(0.002)|<br>**0.13**<br>**(0.001)**|<br>**-0.38**<br>**(0.0c,**|**0.006**<br>**(0.87)**|
|_Cap/Te_<br>||||-0.02<br>(0.54)|**-0.07**<br>**(0.03)**|<br>**-0.09**<br>**(0.005)**|<br>0.47<br>**(0.001)**|-0.12<br>**(0.001)**|
|_Def/Te_<br>_Ed/T_|||||**-0.18**<br>**(0.001)**|**-0.32**<br>**(0.001)**|<br>**-0.08**<br>(0.02)|**_-0.56_**<br>**(0.001)**|
|_ue_<br>_Hh/T_||||||<br>**0.38**<br>**(0.001)**|<br>**0.10**<br>(0.002)|**_-0.52_**<br>**(0.001)**|
|_fe_<br>_T/T_|||||||<br>**0.068**<br>**(0.003)**|**-0.29**<br>**(0.001)**|
|_ace_<br>_Other/Te_||||||||-0.47<br>**(0.001)**|

_Note:_ Parenthesis indicate the level of significance required to reject the hypothesis that the Pearson correlation coefficient is zem.

**27**
insurance, etc. Commercial banks and credit card companies have strongly promoted campaigns to attract more direct debit users as their marketing strategy. Bank customers also widely use noncash payment methods such as credit and debit cards with an average 4.9 credit cards per person at end 2011.

# **Telecommunications environment**

Korea has one of the world’s most active telecommunications and information technology markets backed by strong support from the government since 1990. Vigorous investment in telecommunications infrastructure over past decades helped the country develop solid internet resources to support the growing demand for communications. The number of households subscribing to high-speed internet service was ranked the top among OCED countries already in 2005. Individuals can access bank services through the internet with ease at home and work and internet banking has overpassed face-to-face transaction since 2005.<sup>5</sup> The mobile phone industry in Korea has also grown rapidly based on technological superiority of infrastructure and devices. The world’s first commercial CDMA service was launched in Korea in 1996 and Korea has the world’s leading handset makers, including Samsung and LG.<sup>6</sup> Mobile phone tariffs are discounted for the low-income population and the disabled, and the market neared the 110% penetration rate mark in 2012. The 4G LTE has been rapidly gaining its footage after its first launching in 2011 and replacing 2G and 3G services thanks to the surge of smartphones adoption, which account for about 57 percent of all mobile phone subscriptions.<sup>7</sup> Most mobile phones are post-paid; pre-paid is only used for a tiny number of users or foreigners who use them for temporary communication. There are three operators currently offering mobile phone services in Korea: SK Telecom, KT and LGU plus. The first one accounts for about 50 percent of the total market while the second and third have market shares of about 30 and 20 percent, respectively.

> 5 “ The present and future of Korea's Financial inclusion policy” Press Release from Financial Supervisory Committee, Sep 10, 2010

> 6 Code Division Multiple Access communication standard. It was the dominant standard before GSM (Global System for Mobile Communications) was established.

> 7 Source: Korea Communications Commission & Korea Telecommunications Operators Association.

4
review of the literature to date in Montgomery et al. 2000). More recently Case, Paxon, and Ableidinger (2004) use the simple count approach when analyzing the relationship between orphan status, schooling, and household poverty. They use this approach in preference to principal components because the unit is well defined (it is simply “the number of assets owned”) and, they argue, the resulting indices are therefore comparable across the several countries in their analysis.

Montgomery et al. (2000) analyze the properties of a simple count approach compared to per capita household expenditures in six countries. They find a positive association between the index and expenditures, but report that only a small share of the variation in expenditures is explained by the index. Nevertheless, they find that controlling for the index yields similar results to controlling for expenditures when interpreting the impact of their main variable of interest (women’s education) on outcomes (fertility, child mortality, and children’s schooling). They recommend, however, including the full set of dummy variables capturing ownership of each of the assets separately in these types of regressions.

Bollen et al. (2002) compare a count index to one derived from principal components in their analysis of the impact of economic status on fertility. They find similar estimates of the impact of economic status on fertility in two countries. They also find that the coefficient estimates for the other variables in their multivariate model of the determinants of fertility are virtually identical using either of these approaches. Paxson and Schady (2007), in a study of the relationship between an asset index and child cognitive development in Ecuador, report that an index derived using principal components yields similar results to one using the simple count method. Mukherjee (2006) compares principal components to a simple sum approach using data from rural India and also finds very small differences in rich-poor gaps in child schooling and in adult literacy.

A second simple approach is described by Morris et al. (2000) who aggregate assets into an index using weights for each item equal to the proportion of households who do not own at least one of the item. The justification for this approach is “based on the assumption that households would be progressively less likely to own a particular item the higher its monetary value.” Using data from two household surveys from rural Africa, they compare this to the total value of those assets (based on the respondent-reported current value of each asset). They find that the index and the value of assets are fairly highly correlated: 0.74 and 0.83 (when both 7
tion, net employment change, gross job reallocation, and the Herfindahl–Hirschman index (HHI)<sup>16</sup> ; and the independent variable of interest is the industry-level concentration of BOS, measured as the fraction of employment in BOS relative to total industry employment. To control for industry-level factors affecting firm dynamics, we include the average firm size - measured by employment - and age in year _t −_ 1, employment share, and lastly sector and year fixed effects. Because of the sharp effects of the Covid-19 pandemic on firm dynamics, we exclude 2020 from our analysis, and instead focus on the period from 2016 to 2019.

Table 15 presents the baseline estimation results at the 2-digit industry level for the nine measures of business dynamism. We find that a higher concentration of BOS in industrylevel employment is associated with lower employment share of young firms and increased market concentration as measured by the HHI, consistent with lower levels of business dynamism. On the other hand, we find that the share of employment in BOS is also related with higher job creation and lower job destruction, which result in net positive effect over employment change. However, their similar magnitudes result in an overall effect on job reallocation that is not statistically different from zero. Moreover, we do not find evidence that the concentration of BOS either encourages or discourages firm entry, and exit rates are not statistically different from other industries with lower presence of state businesses. In Table 16, we show that our results are consistent when estimated at a more disaggregated industry classification at the 3-digit level.

Finally, we perform one last robustness check by restricting the sample to competitive industries at the 2-digit level. Table 17 reports that all of the results described previously - considering the entirety of the economy - persist. Additionally, we find a statistically significant negative correlation between exit rates and the concentration of BOS in competitive industries. Our results suggest that industries with a higher presence of BOS are characterized by lower shares of young firms, lower levels of selection and higher market concentration, all of which contribute to restricting business dynamism and firm life growth (Hsieh and Klenow, 2014). However, we do not find a statistically significant relationship between the employment share of BOS and entry rates, which would corroborate the negative relationship reported for entrepreneurship activity, measured by the employment share of young firms. In terms of the labor market, a positive correlation between the presence of BOS and job creation could be reflecting the ability of these firms to grow even in an environment of overall lower business dynamics. As we have shown in section 3, firms with state participation have higher employment growth rates relative to private firms.

> 16Since no information about firm output or sales is contained in RAIS, we compute the HHI using firm-level employment.

41
Policy Research Working Paper 8414

# **Abstract**

The rank-based measures of intergenerational mobility have become increasingly popular in economics literature. Recent evidence shows that rank-based measures are less affected by measurement error and life-cycle bias compared with other standard measures such as intergenerational regression coefficient and intergenerational correlation. However, most of the available household surveys suffer from sample truncation, because coresidency is used to define household membership. There is no evidence on how sample truncation affects the rank-based mobility estimates relative to intergenerational regression coefficient and intergenerational correlation. This paper provides evidence on this in the context of intergenerational schooling persistence, using two exceptional surveys from India and Bangladesh that include all children irrespective of residency status. The analysis shows that the measures of relative mobility (slopes) are biased downward in coresident samples, but the average bias in rank correlation is less than half of that in intergenerational regression coefficient, and comparable to that in intergenerational correlation in magnitude. The intercept estimates are biased upward, with the largest bias found in the intercept of the regression used for intergenerational correlation. Truncation bias in rank-based absolute mobility estimates is the lowest in most cases. The results strengthen - the case for rank-based measures of intergenerational mobil ity when working with the standard household surveys.

This paper is a product of the Development Research Group. 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/research. The authors may be contacted at fshilpi@worldbank.org.

_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
(b) by dismissing lower productivity informal NFE instead of recognizing that they are a key transitional mechanism along the way.

# **3. Is Africa experiencing structural transformation along with growth and poverty reduction? Measurement issues**

There is not much doubt that a number of countries in low income SSA experienced a sustained period of economic growth between 1998 and 2008 which was not confined primarily to one dominant sector, and that growth was accompanied by substantial poverty reduction<sup>6</sup> (World Bank, 2010, 2011a). During the same period, the share of agriculture in value added continued to fall in many of the countries from the pre-independence period high; it now averages around 20-25 percent. However, the share of value added in manufacturing in many of these countries either did not grow or grew very slowly over the same period, leading some to question whether structural transformation in output was taking place (Headey and Dorosh, 2011). Similar numbers for the same period on the employment transition are mostly not available. The share of the population living in urban areas is increasing rapidly, but in most countries the annual growth rate of the population still exceeds 2.5% per annum, owing primarily to high fertility (World Bank, 2010). The question we consider in this section is whether it is possible to reach a conclusion on whether structural transformation is taking place.

The measurement of the four transformations is usually considered fairly straightforward in the empirical literature. _Output_ transition is measured by the

> 6 World Bank ,2011 cites 22 countries with a ten-year growth record of 4% per annum or more (meaning 10 years of per capita economic growth at 1% of more), and these countries made up about 40% of Africa‘s population. Another 30% of the population lives in countries which also experienced strong growth, but this growth was dominated by mineral exports. The rest lived in countries with broad-based, but lower growth.

9
7 Left unchecked, as economies in developing countries grow their governments might be expected to begin protecting agriculture, thereby replacing one costly policy regime with another. That prospect contributed to the resolve of several groups in the 1980s to try to counter national political forces with international influences. For example, agricultural-exporting countries formed the Cairns Group and succeeded in ensuring that an agreement on agricultural reform was included in the Uruguay Round of multilateral trade negotiations. Over the same period, and following two World Development Reports on the topic (World Bank 1982, 1986), international financial institutions made a more concerted effort to encourage developing countries to reduce unilaterally their distortions against agriculture. Accession to preferential trading agreements and to the World Trade Organization (WTO), and the demise of communism, helped in some cases too.

The net effect of these developments on import tariffs is now evident. Simple average ad valorem tariffs have fallen considerably over the past two decades in low-, middle- and high-income countries (table 1). They have fallen less, and still remain higher, for agriculture than non-agriculture (and would even more so if specific tariffs were included), but if there were no other distortions these changes would suggest a decline in the anti-trade bias.

Farmers in developing countries have benefited indirectly from their governments’ cuts not only in manufacturing protection but also in distortions to exchange rates. Overvaluation can discourage production of all tradables relative to nontradable goods and services (except if the government allows a parallel currency market to develop, in which case the outcome depends on which groups of importers are