rafmacalaba/data-use-mentions-extended huggingface dataset:
Productivity,” _Journal of the European Economic Association_ ,

- 2020, _18_ (3), 1400–1443.

- Duranton, Gilles, “Growing through cities in developing countries,” _The World Bank Research Observer_ , 2015, _30_ (1), 39–73.

- FAO, “FAOSTAT,” 2020.

- Fay, Marianne and Charlotte Opal, _Urbanization without growth: A not so uncommon phenomenon_ , Vol. 2412, World Bank Publications, 2000.

- Gai, Qingen, Naijia Guo, Bingjing Li, Qinghua Shi, and Xiaodong Zhu, “Migration Costs, Sorting, and the Agricultural Productivity Gap,” Working Papers tecipa-693, University of Toronto, Department of Economics April 2021.

- Gjerlow, Haakon and Carl Henrik Knutsen, “Leaders, Private Interests, and Socially Wasteful Projects: Skyscrapers in Democracies and Autocracies,” _Political Research Quarterly_ , 2019, _72_ (2), 504–520.

- Glaeser, Edward L, “A world of cities: The causes and consequences of urbanization in poorer countries,” _Journal of the European Economic Association_ , 2014, _12_ (5), 1154–1199.

- Glaeser, Edward L. and Joseph Gyourko, “Urban Decline and Durable Housing,” _Journal of Political_
purchaser takes delivery. If they arrive late, the purchaser may refuse to take delivery, which means markdowns or outright returns.

# **Global Logistics Indicators**

An initial screening of all items in the questionnaire produced an extensive set of possible logistics indicators (table 1), including selected additional indicators for landlocked economies (table 2).<sup>8</sup> The research initially focused on a small number of global logistics indicators (for both exporter and importer) that theoretically would be important for supply chain management, based on time, cost, variability, complexity, and risk factors (see Hausman 2004 and Lee and Whang 2005):

- Total time for trade-related procedures (average and maximum number of days).

- Total cost for trade-related procedures.<sup>9</sup>

- Total time for document processing (days).

- Total number of documents per trade transaction.

- Number of signatures per trade transaction.

- Time to resolve customs appeals (average and maximum number of days).

- Shutdown of port due to natural disaster and labor dispute (days per year).

- Vessel turnaround time (days).

- Percentage of containers inspected.

> 8 Many of these indicators are of independent interest. Selected indicators are published in World Bank (2005) as well as on the Web at http://rru.worldbank.org. 9 Costs are in international dollars (U.S. dollars adjusted for purchasing power parity) . Only the costs from the imports section of the questionnaire are used.

12
to India using data from oDesk. oDesk is the world’s largest online labor market, processing $30 million per month in contracts as of May 2012. It provides a platform for companies to post job opportunities, interview workers, monitor performance, and pay compensation. Workers worldwide bid on these jobs, complete tasks, and receive public feedback on their performance.

India is the largest country destination for outsourced contracts on oDesk, with more than a third of the worldwide contract volume. We investigate the role of the Indian diaspora using 2
stopping actors in the developing world from leveraging these digital platforms to their own benefit. The recent founding of the Shanghai Silicon IP Exchange (SSIPEX) in China and the TAEUS patent book project – albeit slightly different from IPXI - suggest that this model is gaining popularity and could be a valuable means for patent owners to monetize their rights through a secondary market.

SSIPEX offers a range of IP consulting services and analytics software but its main offering is to act as a center for the transfer of IP related to semiconductors. It serves as a distribution channel for semiconductor technology owners and a demonstration center for local firms in order to help them assess which, if any, technology is suitable for their next product (Chesbrough 2011). It works with owners of semiconductor technologies in order to aggregate databases of manufacturing design tools, reference designs and other information. Foreign companies own 70% of the IP made available through SSIPEX and domestic firms own the remaining 30% (Chesbrough 2011). Chinese companies are invited to assess the technology and SSIPEX brokers a license agreement if the Chinese firm wishes to integrate the technologies into new products. Crucially, SSIPEX is open to all members and does not discriminate according to which foundry is eventually used in the manufacturing process. Revenue comes from membership fees charged for access to the IP databases, fees charged for displaying IP on the exchange, and fees for transaction brokerage between members and IP owners. A laboratory was established in 2006 inside SSIPEX to manufacture prototypes based on design brought by members. Functioning as a type of innovation black box, it prevents members from seeing exactly how the output was manufactured and it prevents them from reverse-engineering it. This enables prospective buyers to get more information relating to the IP while still protecting owners’ rights (Chesbrough 2011).

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In the South South region—that is, in the Niger Delta area—several militant groups, targeting primarily the oil industry, have caused disruptions to the oil-led economy. The conflict has long historical roots; some form of violent conflict has been ongoing since independence. Most recently, the conflict is related to demands for a more equitable redistribution of oil resources as well as concerns related to environmental degradation. In 2009, amnesty was declared to militants (Nwankpa 2014; Ajodo-Adebanjoko 2017). However, new militant groups have emerged since, and fatalities have increased during the last few years (figure B.1).

In the Niger Delta region, 22 percent of households in our data reported at least one conflict event between 2010 and 2017, a similar figure to the conflict intensity in the North Central region. Bandits and criminals were the most common type of perpetrator (42 percent of cases).

# 3.   Data

## 3.1.   Data sources

We combined the GHS panel data with data from a telephone survey on household victimization that were conducted with a subset of the GHS panel households during 2017. The GHS is an LSMS data set that has been collected in three waves between 2010 and 2016. The waves are 2010–11, 2012–13, and 2015–16, and they include two visits each: a postplanting visit during the autumn months and a postharvest visit during the spring.

A separate telephone-based conflict survey was administered to 717 of the GHS panel households selected from the most recent visit (wave 3, visit 2).<sup>7</sup> The purpose of the survey was to understand the extent to which households experienced conflict: whether they had become victims of violence or property-related crime or if they had experienced other events related to conflict and criminal activity since 2010. These events are thus based on participants’ recall of the period between January 2010 and May 2017. Participants were asked to recall events that occurred each year during this span. The survey covered
Policy Research Working Paper 10834

# **Abstract**

This study uses individual-level census data from Argentina to examine the socioeconomic disparities between Native and non-Native people. Native people fare worse across a variety of indicators, including housing, education, employment, and health. On average, the observed disparities amount to 12 percent of the standard deviation and persist even after controlling for factors such as geographic location. Furthermore, there are differences in the intergenerational transmission of education between Natives and non-Natives: for each level of education of the parents, the children of Natives have, on average, fewer years of education than the children of non-Natives. Finally, the study also reveals large differences between Native groups: while some achieve average outcomes that surpass those of the non-Native population, others significantly lag behind. Notably, these differences are correlated with a characteristic of their pre-Columbian economy: the practice of agriculture.

This paper is a product of the Development Research Group, Development Economics. 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 carolina_lopez@worldbank.org and pedro_dal_bo@brown.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
with more detail in Appendix 1. The dataset contained a sequence of events and their timestamps for each consignment. On average, there were 9.8 events associated with each consignment. Additional static information on each consignment includes UN/LOCODEs for origin and for destination.

Compared to aviation and postal tracking data, the container data is more complex. Events do not happen in a strict sequential order, as for UPU or Cargo iQ. Shipping transshipments or land corridors at origin and destination increase the number of possible combinations.

The sequence of a container is as follows (possible but not compulsory legs are in italics):

1. An empty container is sent from a depot to the exporter’s premise where it is stuffed (sending from the depot is a registered event, not the stuffing)

_2. The full container arrives at an inland facility_ _3. The container leaves the inland facility (by rail, barge, or by default truck)_ _4. Processes 2-3 are repeated at intermediate facility_ 5. The full container arrives at the port of export 6. The container is loaded on vessel 7. The vessel departs

> 14 <u>https://unece.org/trade/cefact/unlocode-code-list-country-and-territory</u>

9
mobility widely used in the current literature (see, for example, Hertz et al. (2008), Azam and Bhatt (2015), and Narayan et al. (2018)). But, under the more plausible assumption of risk aversion, the measures of mobility developed in this paper incorporate the eects of family background operating through conditional variance.

For our empirical analysis, we use household survey data from China Family Panel Studies (CFPS) 2010, India Human Development Survey (IHDS) 2012, and Indonesia Family Life

> 3We are not aware of any studies on intergenerational mobility that estimates the eects of parent's economic status on the conditional variance of children's economic outcomes.

2
binary interacting variables, all possible splits are tried out and the split with the greatest rejection of equality of the passthrough rates is chosen.

Our source for growth in per capita HFCE and per capita GDP is the World Development Indicators (WDI). We use surveys reported in PovcalNet to calculate growth in survey means. Growth between any two consecutive surveys for a country is referred to as a survey spell. Our main sample consists of a total of 1,671 survey spells if the per capita GDP aggregate is used, and 1,511 spells if per capita HFCE is used. After accounting for survey comparability, 1,429 of 1,671 spells remain when per capita GDP is used and 1,323 of 1,511 spells for per capita HFCE.

# **4.2. Results**

Figure A.1 shows the results of the MOB algorithm. There is significant evidence in favor of the welfare measure in the survey (income or consumption) being relevant for passthrough rates. Using the per capita HFCE growth rates, the MOB algorithm suggests that no other variable besides this yields a significantly different pass-through rate.<sup>23</sup> Table 4 reports the pass-through estimates for each sub-sample in Figure A.1. Observations using income-based surveys have a pass-through rate of 1.00 with a 95 percent confidence interval between 0.89 and 1.12, while observations using consumption-based surveys have a passthrough rate of 0.66 with a 95 percent confidence interval between 0.58 and 0.75.<sup>24</sup> With a p-value of 0.024, we can reject that the coefficients are identical for these two subgroups at a 5 percent significance level.

Given the results from MOB and the fact that the CES are consumption-based surveys, we use the consumption-based partition as the sample to calculate a pass-through rate for India. Our preferred estimate of the pass-through rate thus uses the global sample of comparable consumption survey spells in combination with per capita HFCE
In Table A1, we check if the identified sample shows similar short-run effects of the intervention to the ones in the original sample. The effect of treatment on financial proficiency scores is positive and statistically significant in both samples. The point estimates are slightly higher in the longterm impact evaluation sample, but just like in the short-term sample they decrease moderately between follow-up 1 and 2 and as more controls are included in the regressions.

# **_3.d. Data Sources_**

To capture the effects of the financial education program on students’ financial life and employment outcomes, we combine four individually identified<sup>6</sup> administrative data sets housed at the BCB. This section briefly describes the information available in each of these data sets. A caveat is that most of the administrative data cover only formal financial and employment

> 5 For students with more than one age-compatible homonym, we could further try to identify the correct CPF in the SRF data using geographic information. However, the SFR contains only the most recent location and not location of birth, so that using this information could lead to false matches if students have moved. The location in the SRF registry is updated when individuals file their tax reports or when the individual requests an update. Imposing that the student location from the short-term impact evaluation matches the one in the SFR gives 2,130 additional unique matches. Out of these, 440 had reported a CPF in the short-term impact evaluation and 6.14 percent of these CPFs are not the same as those identified by the algorithm. Given that this mismatch rate is higher than in the sample that does not use location to identify matches, we use only the 15,940 students uniquely identified through name and age matching in the analysis.

> 6 All individually identified information was handled exclusively by the BCB staff.

10
of women at the top of the income distribution and discuss why female presence in the top 1% highest earners in Honduras seems to be larger than in many high-income countries (Neef & Robilliard, 2021).

A second key contribution is the construction and use of a shareholder dataset to assign undistributed corporate profits to firm owners in proportion to their ownership shares. Distributing corporate income to individuals is a challenging task – Alvaredo _et al._ (2020) note how being able to ”match the individual profit of firms to the tax record of the owners (...) is very rare” and Piketty _et al._ (2022), in reference to Bruil _et al._ (2022), mention their study is ”the most sophisticated, as it links individual business owners to their businesses and hence can assign business-level taxes to individual owners with great precision”. Other than a handful of exercises in high-income countries such as Norway (Alstadsæter _et al._ , 2016), Chile (Fairfield & Jorratt De Luis, 2016) and Uruguay (De Rosa & Vil ́a, 2023), to our knowledge we provide the most detailed account of the importance of business income at the top of the distribution for a middle-income country.

Finally, this paper builds on the broader DINA initiative, providing a new data point on top income shares using high-quality tax records combined with survey and national accounts data.<sup>1</sup> Recent studies have provided valuable insights on income inequalities across the Middle-East (Alvaredo _et al._ , 2019, Assouad _et al._ , 2018), Asia, Africa (Czajka, 2020) and Europe (Bruil _et al._ , 2022). In Latin America, only 11 countries are considered to have ”high-quality” measures of income inequality by the World Inequality Database project, due to the lack of available tax records.<sup>2</sup> Recent research on top income inequality in the region includes Alvaredo & Londo
to each other. This paper focuses on the economic impacts of infrastructure projects at the district level.

**Export potential for Central Asian countries** In Soviet times, the Central Asian republics were interlinked within the centrally managed economic system of the USSR. Each of these republics specialized in producing a specific agricultural commodity according to its endowments. For example, Uzbekistan specialized in producing cotton, while Kazakhstan was the breadbasket of the region.<sup>6</sup> Central Asian agriculture is sharply differentiated by subregion. Geography

> 4More details on the BRI database and cross border times are provided in Annex B.

> 5Kazakhstan was producing and exporting commodities and wheat. During the American Civil War, it became difficult for Russia to import cotton from the United States. Uzbekistan, which had recently been absorbed into the Russian empire, was then chosen to become the main producer and supplier of cotton. The whole transport network set up in the 20th century was designed to connect periphery countries to Russia, with railway lines in Central Asia mostly connecting the capitals with Russia.

> 6After their independence in 1991, Central Asian countries were faced with a disruption of the earlier trade arrangements and economic linkages for production and distribution of farm products. In addition the quantity and quality of the production as well as the natural resources have been degrading as a result of the heavy emphasis put by the Soviet system on production instead of production efficiency. The transition in agriculture from a central command system to market-driven mechanisms has been slow.

6
that can be considered before the COVID-19 pandemic. The Google mobility data contains information on various categories of places such as: i) retail and recreation, ii) grocery and pharmacy, iii) parks, iv) workplaces, v) transit stations, and vi) residential areas. In the school closure analysis, we include control variables for mobility around retail and recreation, and workplaces since they are the most relevant for business outcomes.<sup>19</sup> The final main piece of data we use provides us with a measurement of social norms around gender equality. In December 2019, prior to the start of the COVID-19 pandemic, the standard bi-annual Future of Business survey collected data on individuals’ personal beliefs and norms towards gender equality across 109 countries (based on measures from Bicchieri (2016)). The survey elicited individual beliefs on gender equality by asking how much the survey respondent agreed with the following statement: do you think “men and women should have equal opportunities” using a four-point scale: strongly disagree, disagree, agree and strongly agree. The individual belief variable was coded as 0/1 where 1 is the respondent agrees or strongly agrees with equal opportunities; and 0 if respondent does not agree with equal opportunities. These beliefs are then aggregated at the country level to give a measure of the share (percentage) of the country who agree with gender equality in opportunities among the study sample.<sup>20</sup> A low share of agreement indicates more conservative views towards gender equality within a country and a higher share of agreement indicates more liberal or progressive views towards gender equality within a country. See Appendix D for a full description of the norms constructs used in this paper.

> 19Retail and recreation index covers visits to restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. For example, during wave I of the Future of Business survey, on May 23rd 2020, globally, retail and recreation has a mean of
make use of retrospective questions included in the TWMS. The TWMS recorded households‘ health care utilization at the beginning (October 2008) of the crisis and at the time of data collection (June 2009). With such a quasi-panel, we can isolate household fixed effects by estimating the probability of change in health care utilization for each household during the crisis. Comparing the estimated reduction in the probabilities of health care utilization of Green Card holding households versus households with no insurance coverage, then allows us to assess the protective impact of the program.

Table 5A provides summary statistics for household head characteristics with respect to insurance access, distinguishing Green card holders from those with access to the _SGK_ 11
(0.187)|(0.141)|(0.140)|(0.161)|(0.170)|
|Tourism<br>0.724***|-0.090|0.210|0.839***|0.624**|
|(0.221)|(0.195)|(0.184)|(0.274)|(0.251)|
|Trade<br>0.224|-0.330*|-0.098|0.285|-0.280|
|(0.220)|(0.168)|(0.155)|(0.259)|(0.220)|
|_cons<br>-0.266|0.402|-1.352***|1.225***|0.566|
|(0.312)|(0.315)|(0.494)|(0.388)|(0.394)|
|N<br>441|441|441|441|441|
|note: .01 - ***; .05 - **; .1 - *;<br>Note: Clustered standard errors by township for each specifi<br>considered for this analysis. The variables: female owner, hig<br>has documents to request loan and light manufacturing, tour<br>% employees high education - % of employees who have co<br>Hours dependents and hh chores - number of hours spent l<br>of values missing, we recoded missing to 0 and included a m<br>Gender equality index: gender equality, maternity and patern<br>Social policies index: all the gender equality policies plus sick<br>Subsidy policies index: house, food and transport subsidy.<br>
the reversal of the decline in corruption control in Bulgaria in the last few years. Single bidding is one of the strongest determinants of high prices and belowoptimum quality in public procurement. While a high number of bidders should not be pursued at all costs, lowering the incidence of tenders with no or little competition is a safeguard for value for money in public procurement (Fazekas and Blum 2021).

**Recommendation** : Foster competition to further reduce the number of single bidder contracts and increase the average number of bidders. Competition can be promoted by breaking down market entry barriers and by reducing the transaction costs of competing.

**Implementation** : Borges (2019) shows that more open (less restrictive) technical and financial conditions in tenders can lead to stronger competition, hence greater savings. Furthermore, matching tender sizes to the market capacity and median bidder size can also successfully increase the number of bidders. For example, breaking up large tenders into smaller lots can help smaller enterprises compete. It is also crucial to make sure that open tender procedures apply and this practice is not used to engage in noncompetitive procedures such as direct negotiations. Moreover, Fazekas and Kocsis (2020) show that bid evaluation criteria, which are more concrete and objective, encourage a higher number of bidders and lower corruption risks in the EU. The implementation of such interventions could be closely monitored using real-time public procurement data and the dashboard advocated earlier.

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