rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.2 Data**

## **4.2.1 Bilateral trade data**

The dependent variable captures the aggregate exports between country of origin _o_ and destination country _d_ . The trade data to construct the dependent variable are taken from the _IMF Directions of Trade Statistics._ They cover the period from the first quarter of 1990 to the last quarter of 2009. We work with a set of 32 developed and developing countries. The sample selection is largely dictated by data availability, but we also aim at having a representative sample.

## **4.2.2 Uncertainty**

Similarly to Bloom (2009) we use a measure of stock market volatility as a our main proxy for uncertainty. Implied volatility such as VIX would be the preferred measure, because it better reflects the sentiments about near future. Such measure is not readily available for a large set of countries and therefore we will be using the actual volatility of the stock market in the importing country. However, Bloom (2009) demonstrated in the example of the US, that for the subsample in which both the actual and implied volatility are available the correlation exceeds 0.8. This very high correlation should make the actual volatility an acceptable proxy. An additional argument in favor of using stock market volatility is also that this measure is correlated with many alternative measures of uncertainty used in the empirical literature. Alexopoulos and Cohen (2009) show that the variance of the stock market is highly correlated with more pragmatic measures, such as the number of times a major newspaper mentions the word uncertainty within a given period. Moreover, Bloom (2009) reports statistical evidence showing that stock market volatility is strongly linked to other measures of productivity and demand uncertainty, including the variance of firm profit growth and TFP growth and the disagreement among professional forecasters in expectations about macroeconomic variables.

In our estimations, instead of using the stock market volatility explicitly, we employ dummy variables identifying periods of exceptional uncertainty. We adopt the
provided childcare, however, would increase the rate of women‟s labor force participation by as much as 12-15 percent. The elasticity of mothers‟ labor supply with respect to childcare cost are found to be -0.17, which is in line with estimated elasticities reported in the related literature based on data from the U.S. and Canada.<sup>21</sup> Fong and Lokshin (2000) conclude that government subsidies for childcare are an effective means of increasing the number of mothers who work, increasing the incomes of poor households and lifting some families out of poverty, but that the effects of such policies are less significant for the poorest households.

A similar study by Lockshin (1999) studies mothers‟ participation in the labor force, working hours and demand for childcare in Russia. In the 1980s, most women in Russia worked and the government heavily subsidized childcare programs that were widely available. A decline in GDP in the 1990s led to a sharp decrease in the availability of state-run child care facilities and an increase in the cost of sending children to these facilities. As described in Lockshin (1999), Russia moved from a country in which childcare was provided by the government and almost all households with children had access to affordable or free childcare to one in which few households have access and the cost of day care significantly affects labor force participation decisions. Lockshin (1999) builds a static utility maximizing model of households‟ decisions about labor force participation, working hours, and choice of childcare mode to motivate an econometric model that he uses to assess the effects of three different kinds of policy interventions: family allowances, childcare cost subsidies and wage subsidies. The model is estimated using panel data from the Russian Longitudinal Monitoring Survey (RLMS).

The simulations show that childcare subsidies increase maternal employment by almost twice as much as comparable wage subsidies. Also, childcare subsidies are more effective than wage subsidies or family allowance transfers (transfers to families with children) in increasing family income.
Wong (2020) documented pronounced backhaul effects in the spot freight rates reported by Drewry, especially for the US-China routes, where it costs three times more to ship a container from China to the US than from the US to China.

**Stylized Fact 2.** _Between 2002 and 2020, maritime trade was highly imbalanced. The containerized imports exceeded exports for 7 of 8 regions except for the Far East, for which the net exports increased from 10 to 30 million TEU. The largest relative imbalances are observed in the Middle East and Africa._

# **2.2 Industry Growth, Technological Changes, and Economies of Scale**

The shipping industry has responded to the growing demand for transportation by building larger vessels that presumably improve efficiency through economies of scale. For example, containerships have increased in size from 700-1,000 TEU in the 1960s to more than 23,000 TEU (Fan et al., 2018). Using the Clarksons SIN data, we document that the growth of the containerized maritime fleet has been remarkable in the last 25 years.<sup>6</sup> We plotted the trends in Figures 3 and 4 and summarize them as following.

**Stylized Fact 3.** _The total containerized fleet capacity has increased eight times between 1996 and 2021, with the average vessel size increasing 2.7 times. The total capacity share of the largest ships, of over 8,000 TEU, has increased from 0% in 1996 to 55% in 2021._

Indeed, while the containerized fleet capacity, measured in twenty-foot equivalent units (TEU), has increased eight times, the number of vessels has increased only three times. From Figure 4, we learn that smaller ships, with the capacity of under 3,000 TEU, were rapidly replaced with the larger ones. Specifically, the capacity share of the smaller vessels has decreased from 65% in 1996 to 18% in 2021. Instead, as pointed out above, the capacity of
data collection, supervision, cleaning and analysis have been affected in several countries. Adjustments in survey instruments, data collection methods and weighting schemes have become necessary to address issues related to low response rates and non-random patterns in nonresponses. Even with these adjustments, response rates have dropped in several cases. The United States Bureau of Labor Statistics documents that the response rate for the 2020 May Establishment Survey was 69%, compared to a 75% average between March 2019 and February 2020. The corresponding figure for the household survey in May 2020 was 67%, compared to the 82% average over the 12 months ending in February 2020 (BLS, 2020). Furthermore, focusing on the nature on non-responses in the March and April 2020 rounds of the CPS, Montenovo et al. (2020) show that the drops in responses in these two months were not random.

The specific disruptions emerging from the crisis have also implied that standard labor market definitions may not be sufficient to fully capture labor market dynamics under the pandemic. For example, given the different forms of mobility restrictions and social distancing measures currently in place, variations in unemployment may be misleading. In fact, in the COVID-19 era, slow increases in unemployment may coexist with significant job losses. This is because non-employed people, despite being interested in working, might not be actively looking for a job as a result of restrictions on economic activities or the perceived risk of contracting the disease at work. As such, going beyond the analysis of employment, unemployment and labor force

> 4 Official estimates on UI claims are released 12 days after the end of the week they refer to.

4
(Mungiu-Pippidi, 2015). In practical terms, limited access can be achieved if public officials steer contracts to a favored bidder by, for example, unjustified sole sourcing or direct contract awards; favoring a certain bidder by tailoring specifications or sharing inside information; or paying connected suppliers on time while delaying due payments to others (World Bank, 2009). Moreover, access might also be limited by colluding firms barring non-cartel member firms from access (e.g. through physically preventing them from submitting paper-based bids).

23. “Administrative efficiency” refers to the total transaction costs for completing a procurement process to society. It is approximated by the total administrative cost incurred by the government for achieving the predetermined outcome of public procurement, that is, the successful completion of the contract. We largely refrain from the full analysis of administrative costs for bidders due to a lack of sufficiently detailed time-series data (i.e. not considered in the micro-estimates but taken into account in the macro modeling).

24. The total direct savings achieved through an e-procurement intervention could hence be approximated by adding monetized values of the three outcome dimensions.

## FORMULA 1

Total savings= ∆Access+ ∆Economy+ ∆Efficiency (Govt. +Firms)

# 3.2 MECHANISMS

25. The main conceptual challenge of linking e-procurement to the above three outcomes is that e- procurement is a bundled intervention. Bangladesh’s e-procurement system, like most e-procurement interventions, bundles together a variety of changes which have a range of impact channels (Blum et al., 2018). We consider three main impact mechanisms as the most important for explaining the three outcomes of interest. While there is no simple mapping between mechanisms and outcomes, their relationship can be summarized as follows: 26. E-procurement can (i) make it more costly for public officials to erect intentional barriers to entry, by reducing their de facto discretion and by increasing the risk of detection, among others. It can (ii) reduce nonintentional barriers to entry
_Fareed M. A. Hassan, ECSPE_ _6_ What is the potential revenue that this income tax structure could generate? To calculate the potential revenue, it is important to know not only the aggregate tax base -- individual incomes -- but also its distribution.

**Table 2: Bulgaria's Personal Income** Tax **Schedule, 1992**

|Annual incorne (Lev)|Annual tax schedule|
|---|---|
|0- 9,000|tax free|
|9,000 - 12,000|20 % on excess over 9,000|
|12000.01- 36,000|600+ 24 % on excess over 12,000|
|36,000.01 - 72,000|6,360+ 28 % on excess over<br>36,000|
|72,000.01-120,000|16,440 + 32 % on excess over<br>72,000|
|120,000.01-240,000|31,800 + 36 % on excess over<br>120,000|
|over 240,000|75,000 + 40 % on excess over<br>240,000|

_Source: Tax Notes International Vol. 6, No. 19, 1993, p. 1140._ Bulgarian households' income level and distribution for 1992 was derived from the 1992 Individual Budgets of Households Survey compiled by the National Statistical Institute (NSI) of Bulgaria.<sup>2</sup> The sample covered 2202 households. It was constructed as a two-tier random sample based on a sample frame developed from the 1985 Population Census. The data allow a preliminary examination of the questions I am interested in: (i) how well the tax brackets and rates capture the revenue potential of their respective income groups, and (ii) how well they serve any distributional objectives implicit in the existing tax structure? To this end, the section briefly discusses the relationship
Table D2: ANCOVA Regressions by Head-of-Household Gender [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 1 HHI 2<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -
|1,476,213|6.442|6.518|

_Note_ : The table reports the average log real wage and total number of workers, working in the private sector, in Brazil, Costa Rica, and Ecuador, using household surveys and administrative data. Wages are defined as real monthly wage in the main occupation. Values are deflated using consumer price indexes and purchasing power parities between each local currency and dollars. All totals from the household surveys are computed applying sampling weights/expansion factors provided by the Institutes of Statistics. Formal workers are defined in PNAD, ENAHO and ENEMDU as those who report having access to a pension through their job. RAIS refers to Brazil’s Social Security Records (Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais), PNAD refers to Brazil’s National Household Survey (Pesquisa Nacional por Amostra de Domic ́ılios), CCSS refers to Costa Rica’s Social Security Records (Caja Costarricense de Seguro Social), ENAHO refers to Costa Rica’s National Household Survey (Encuesta Nacional de Hogares), IESS refers to Ecuador’s Social Security Records (Instituto Ecuatoriano de Seguridad Social), and ENEMDU refers to Ecuador’s National Household Survey (Encuesta Nacional de Empleo, Desempleo y Subempleo). PPP = purchasing power parity.

51
attitudes and mental health outcomes were changed from a 3-point Likert scale at baseline to agree/disagree options in the follow-up surveys to make it easier for adolescents to respond accurately. In the Appendix we detail the individual statements in the attitudes index and present the Cronbach’s alpha to test for scale reliability (Cronbach’s alpha of the gender attitudes index is 0.798).

> 32Parents were also asked to provide basic information on the household including demographic information, plus food security, household assets and consumption modules.

10
in 2016<sup>11</sup> ), the vocational education sector is relatively small as well with only one vocational high schools and 9 academic high schools. In the broader Baoshan Municipality where Tengchong County belongs, only 28 percent of high school enrollment is in vocational schools.

The stakeholder analysis includes both quantitative and qualitative measures and has a broad-based representation. The study team visited an academic school with Grades 6–12, a vocational high school, and a high school targeting minority students. The student surveys collect information on student demographics, academic achievement, career expectations, their attitudes and their parents’ attitudes toward vocational education. The parent survey includes questions on family income, parents’ educational level, career expectations for their children, and their attitudes toward vocational education. The interviews with the educational administrators, principals, and teachers aim to identify the current status and the existing problems of vocational education, while the interviews with the students and parents focus on further exploring their attitudes toward vocation education. Our analysis of the responses from the surveys and the interviews shows the following results.

Most parents and students in Tengchong County preferred pursuing higher education to receiving vocational education, either at the secondary or tertiary level. As shown in Figure 4.6, only 17 percent of the middle school students and 23 percent of the parents surveyed are willing to choose vocational high schools, while only 34 percent of the high school students are inclined to attend vocational colleges. Meanwhile, more than 80 percent of the surveyed middle school students and high school students believe that middle school or high school diplomas alone would be insufficient to prepare them for the labor market (Figure 4.7).

Figure 4.6 Survey results of the inclination for educational choices: middle school students, parents of middle school students, and high school students <!-- Start of picture text --> Middle school students<br>2%<br>Attend college after attending<br>academic high school<br>17
# Inequality of opportunity, income inequality and economic mobility: Some international comparisons

Paolo Brunori Francisco H. G. Ferreira Vito Peragine<sup>1</sup>

**Keywords:** Equality of opportunity, income inequality, social mobility

**JEL classification:** D71, D91, I32

**Sector Board:** Poverty Reduction

1 Brunori and Peragine are at the University of Bari. Ferreira is with the Development Research Group at the World Bank and IZA, Bonn. This paper was prepared for a volume on “The Triple Challenge of Development: Changing the rules in a global world”, which draws on a conference held at Mount Holyoke College in March 2012. We are grateful to Michael Grimm, Peter Lanjouw, Branko Milanovic and Eva Paus (the editor) for various helpful comments and suggestions. We are also grateful to Ambar Narayan and Alejandro Hoyos Suarez for help with the data on human opportunity indices and to Tor Eriksson and Yingqiang Zhang for providing us additional estimates for China. Ferreira would like to acknowledge support from the _Knowledge for Change Program_ , under project grant P132865. All errors are exclusively our own. The views expressed in this paper are those of the authors, and they should not be attributed to the World Bank, its Executive Directors, or the countries they represent. Correspondence: fferreira@worldbank.org, peragine.vito@gmail.com and paolo.brunori@gmail.c3om.
# **Figure 6:** Distortions Index

<!-- Start of picture text --> ARG BRA CHL COL CRI MEX PER<br>3<br>2<br>1<br>0<br>−1<br>−2<br>2005 2008 2010 2012 2015<br>Distortion Index<br><!-- End of picture text --> Source: ITU data, author calculations.

Note:This figure is based on data from ITU. The index is generated using standard principal components on data standardized across countries. The variables included are connection fees, broadband wireless charges, broadband tax rates, business telephone installation fees, broadband caps, monthly subscription charges, and broadband speeds.

33
.960]<br>[1.062]<br>[1.131]<br>156,133<br>156,133<br>156,133<br>156,133<br>No<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>No<br>No<br>No<br>No<br>Yes<br>ed on NSS 2004 – 2008 (Rounds 61 and 64) and program expenditure data for SG<br>ural Development, Government of India.<br>e order10_−_2. Each column presents results from a separate regression. All regre<br>es are in logs. The sample is composed of all men aged 18 to 60 interviewed from<br>variable in panel A is the percentage of days in the past seven days that an indivi<br>entage of days in the past seven days that an individual spends in private works. A<br>s are computed using sampling weights. The standard errors in square brackets ar<br>na. NREGA: National Rural Employment Guarantee Act.<br>_<_0_._01|
|_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_NREGAd,t × Dry_<br>_NREGAd,t × Rainy_<br>Observations<br>Worker Controls<br>District Controls FEs<br>SGRY Controls<br>_Source_: Author’s analysis bas<br>data is from the Ministry of R<br>_Notes_: All estimates are of th<br>effects. All dependent variabl<br>to June 2008. The dependent<br>variable in panel B is the perc<br>the main article. All estimate<br>poorna Grameen Rozgar Yoja
<!-- Start of picture text --> T1A T1A<br>T1B T1B<br>T1C T1C<br>T2A T2A<br>T2B T2B<br>T2C T2C<br>−2.5 0.0 2.5 5.0 7.5 −2.5 0.0 2.5 5.0 7.5<br>(a) Balanced panel (b) Unbalanced panel<br><!-- End of picture text --> Notes: the figures present the posterior distribution of treatment effects after wave 2. The vertical bar marks the median of each posterior distribution. The shaded areas indicate the 95% credible intervals. A total of 8,000 posterior draws sampled from 4 independent Markov chains were used.

Figure 4: Posterior distributions of treatment effects for ORF scores.

to the short exposure and the one-off measurement of ORF immediately after treatment. That said, the estimates suggest that an IVR intervention for parental engagement in their children’s reading will have a positive impact on children’s reading skills. This is an encouraging finding given the relatively “light touch” of this intervention. Based on the estimates from the unbalanced panel, it is more than 95% likely that implementing the arm with the highest engagement, T1B, which asks parents to carry out a few simple reading exercises sequenced the same for all children, will lead to positive reading fluency gains. While the effects of the 4.5-week intervention tested here were moderate, it stands to reason that exposure for the full term or even the full school year will generate larger effects. The program may also lead to continued joint reading between parents and children after the calls end.

A remaining question is how the treatment effects on fluency compare between the different arms and whether call exposure and efficacy vary significantly strongly so that one of the arms with lower call engagement could be more effective for reading outcomes. Unfortunately, the answer is hampered by the quality of
> Index (1-21, higher better)<br>14 Interquartile range mean<br>13<br>12<br>11<br>10<br>9<br>8<br>7<br>6<br>2007 2018<br>F. Fiscal rules<br>Number of countries<br>80<br>60<br>40<br>20<br>0<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Source: Huidrom et al. (2019), International Monetary Fund, Kose et al. (2017), World Bank. Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A.-D. Unweighted averages.

A. Gross government debt.

C. The sustainability gap indicates the difference between the current primary fiscal balance and the debt-stabilizing primary fiscal balance at current growth rates and interest rates. A negative value indicates that government is on a rising trajectory. See Kose et al. (2017) for details.

D. Based on data for 97 EMDEs.
<!-- Start of picture text --> (c) Share of Increased exports from Brazil (2000-2010)<br>(d) LAC Imports from China & Emerging<br>Asia ($ billions)<br>140<br>100%<br>Other Countries<br>120<br>90%<br>China From Emerging<br>80% 35.8% 100 Asia<br>EU27 From China<br>70%<br>80<br>Argentina<br>60%<br>21.0% United States 60<br>50%<br>40<br>40% Japan<br>19.0%<br>30% Sub-Saharan Africa 20<br>20% 8.7% Russia 0<br>4.4%<br>10% 3.3%<br>2.9% India<br>0% 2.3% 2.6%<br>(f) Brazil's Merchandise Imports, by origin<br>(e) Share of Increased Imports into Brazil<br>($ billion)<br>2000-2010<br>100% Other 45<br>90% 31.1% 40 EU27<br>EU27<br>80% 35 United States<br>70% China 30 China<br>60% 19.6% 25<br>United States Argentina<br>50% 20<br>40% 19.5% Argentina 15 Japan<br>Sub-Saharan<br>30% Sub-Saharan 10 Africa<br>20% 11.4% Africa 5 India<br>India<br>10% 6.1% 0 Russia<br>4.9%<br>Japan<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>2000 2001 2002 2003 2004 2005 2006 2007