rafmacalaba/data-use-mentions-extended huggingface dataset:
nouncement date, which usually happens around mid-month, and the date when the rebalancing is made effective, which is the last trading day of the month, and thus occurs 11 to 17 days after the announcement. Also, the MSCI quarterly reports provide information on the benchmark weight (the relative weight) of each stock in each index.

To compute elasticity estimates with the conventional methods used in the literature, we collect from the MSCI website information about the AUM tracking the MSCI Emerging Markets Index (MSCI EM) and the MSCI All Country World Index (MSCI ACWI). Since these data are provided only for June 2015 and November 2016, we complement them with data from the EPFR Global Fund Flows database. This database covers a subsample of the population of mutual funds and ETFs and provides information on the benchmark indexes they track. For both indexes, we compute the ratio of the AUM reported by MSCI relative to those resulting from EPFR in each of the two periods for which we have data from both sources. Then, we compute the median coverage ratio for each index and multiply it by the AUM in EPFR to infer the total AUM benchmarked against the indexes.<sup>8</sup> Finally, to measure the AUM of passive funds and ETFs benchmarked against the indexes, we rely on EPFR data only, which has an extremely good coverage of the population of such funds. At the end of 2016, the Investment Company Institute (ICI) reported total assets for ETFs worldwide of 3.5 trillion dollars, which is also the total assets under management for ETFs in EPFR database.

# **2.2 The Colombian Stock Exchange**

The BVC runs like a standard electronic central limit order book. Trades are submitted via authorized broker-dealers registered at the exchange and buy and sell orders meet via an automated trading system. In contrast to fragmented markets in the United States and Europe, the BVC is the sole authorized trading venue for Colombian stocks, which means that we can observe the universe
**Figure 12:** Intergenerational Mobility and Literacy of the Old Generation

<!-- Start of picture text --> Bolivia Uruguay UruguayTrinidad and TobagoJamaicaJamaica Brazil IGM Down = .242 -.255 lit_par (.0127) (.0215)<br>Costa Rica MexicoPeruPeru Costa RicaTrinidad and TJamaicaChileArgentinaTrinidad and TobagoMexic Uruguay Trinidad and Tobago UruguayUruguay Ecuador o Trinidad and TobagobagoArgentinaChileArgentinaSurinameCuba Cuba BrazilHaiti Haiti<br>Saint LuciaBrazilGuatemalaHondurasHondurasParaguaySaint LuciaHondurasBoliviaMexicoBrazilBoliviaParaguayDominican RepublicHaiti Guatemala HaitiGuatemalaCosta RicaVenezuelaPanamaColombiaEl Salvador Ecuador PanamaUruguayEcuadorHondurasBNicaraguaParaguayazil Nicaragua Costa RicaVenezuelaNicaraguaMexicoPeruArgentinaEl SalvadorEl SalvadChileBolivia Colombia PanamaEcuadorCChile o Paraguaylombiar BoliviaVenezuelaArgentinaBrazilDominican RepublicEcuadorPanamaColombiaColombiaDominican RepublicVenezuelaPanamaCosta RicaPanama Saint Lucia Guatemala GuatemalaParaguayBrazilGuatemalaNicaraguaHondurasHondurasParaguaySaint LuciaHondurasBoliviaMexicoBrazilBoliviaParaguayDominican Republic Guatemala HaitiGuatemalaCosta RicaVenezuelaPanamaColombiaEl Salvador Ecuador PanamaUruguayHondurasBrazilNicaraguaParaguay Nicaragua Costa RicaCosta RicaVenezuelaNicaraguaMexicoPeruArgentinaEl SalvadorEl SalvadorChileBolivia Colombia PanamaEcuadorColombiaChileParaguayBoliviaVenezuelaArgentinaBrazilDominican RepublicMexicoEcuadorPanamaPeruPeruColombiaColombiaBoliviaCosta RicaTrinidad and TobagoDominican RepublicJamaicaChileArgentinaTrinidad nd TobagoVenezuelaMexico Uruguay PanamaTrinidCosta Rica UruguayUruguay Ecuador a d and TobagoTrinidad and TobagoPanamaArgentinaChileTrinidad and TobagoArgentinaJamaicaSurinameCubaJamaicaCuba<br>GuatemalaParaguayNicaragua Ecuador<br>Brazil Guatemala Uruguay<br>BrazilHaiti IGM UP = .165 + .7996 lit_par (.0211) (.0358) Uruguay<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Share literate old Share literate old<br> 1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>1<br>.4<br>.8<br>.3<br>.6<br>.2<br>Upward IGM.4 Downward IGM .1<br>.2 0<br>0 -.1<br><!-- End of picture text -->

**(a)** Upward mobility **(b)** Downward mobility

Notes: These estimates are computed by birth decade cohort of the children.

mobility at regional level (see Equation 3) run country by country: where _eicroyt_ is a dummy variable equal to 1 if the completed educational level of the old generation observed for individual _i_ from country _c_ region r is at least primary. Similar to before, _γo_<sup>_b_and</sup><sup>_γ_</
work on violence against firms in Russia has tended to be either qualitative in nature or use perceptions based measures from surveys of firms. As noted in the introduction, our paper provides one of the first systematic tests using objective data, which we discuss in detail below.

The government has recognized the extent of pressure on businesses and its negative consequences for economic development. One example of attempts to react to this phenomenon is the changes made to the criminal code in 2009 to prevent arrests of business owners whose firms are being investigated. Another project, the NGO Center for Public Procedures “Business Against Corruption” (BAC-Center) was founded in 2011 by the association ‘Business Russia’ with direct support by top-level governmental officials and given a mandate to track violent pressure on firms and raider attacks. State support for the center at the highest levels was clearly signaled when the office of the ombudsman for entrepreneurs was formed within the presidential administration in 2012 and the center was formally adjoined to it (Yakovlev, Sobolev, and Kazun 2014).

However, even after these high-level attempts to limit pressure on businesses, the situation did not change substantially. The arrest of billionaire Vladimir Evtushenkov, who was the principal owner of the “Sistema” holding company, and the subsequent confiscation of his company’s shares in the oil company “Bashneft” has shown that even completely loyal oligarchs are not necessarily protected from unpredictable pressure from the state. In Evtushenkov’s case, his firm’s shares in “Bashneft” were coveted by state-owned firms.<sup>8</sup> In February of 2016, President Vladimir Putin personally took the initiative to create a special working group to resolve conflicts between the coercive apparatus and businesses.<sup>9</sup> On the one hand, the President’s initiative reflect an awareness at the highest levels that the continued threat of coercive structures to property rights is a major issue for future economic development. On the other hand, the creation of this group can also be
2013) *|Household|Tanzania|Migration|Road quality improvements decrease the probability of migration by 7%, and<br>even more so in well-connected communities.|
|Jedwab and<br>Storeygard<br>(2022) *+|Location|Sub-Saharan<br>Africa|Migration|Greater market access increases city population on average; particularly for<br>smaller and more isolated cities and for domestic markets.|
|Storeygard<br>(2016) *|Pixel|Sub-Saharan<br>Africa|Income|An oil price hike induces the income of cities near that port to increase by 7%<br>relative to otherwise identical cities 500 km farther away.|

Source: Compiled by the authors.

Note: Findings column is based on the main finding of the paper. An asterisk identifies peer-reviewed papers. Plus identifies papers following the instrumental variable approach.

Sub-Saharan Africa is the region with the highest degree of isolation, scoring just 30 percent on the Rural Access Index, for the percentage of the population living within two kilometers of an all-season road. Nevertheless, South Asia and Latin America, with Rural Access Index scores of 58 29
., Van Huffel C., 2001, « Intégration économique, investissements directs étrangers et concentration spatiale dans les pays en développement », _Région et développement,_ 13, 11-46.

Catin M., Ghio S., Van Huffel C., 2002, « Investissements directs étrangers, diffusion technologique et concentration spatiale dans les pays en développement», _Région et Développement,_ 16, 55-84.

Catin M., Van Huffel C., 2003, « Inégalités spatiales et développement économique : le cas français (1850-2000) », _Revue d’Economie Régionale et Urbaine,_ 5, 799-814.

Catin M., Van Huffel C., 2003, « Concentration urbaine et industrialisation », _Mondes en Développement,_ 31, 121, 85-107.

Catin M., Van Huffel C., 2004a, « L’impact de l’ouverture économique sur la concentration spatiale dans les pays en développement », _Région et Développement,_ 20, 123-158.

Catin M., Van Huffel C., 2004b, « Institutions, ouverture et inégalités régionales de développement en Chine », _European Journal of Development Research,_ 16, 4, 809-826.

Chai J.C.H., 1996, « Divergent development and regional income gap in China », _Journal of Contemporary Asia,_ 26, 1, 46-58.

Chen J., Fleisher B.M., 1996, « Regional income inequality and economic growth in China », _Journal of Comparative Economics,_ 22, 141-164.

Cheng L. K., Kwan Y. K., 2000, « What are the determinants of the location of foreign direct investments? The Chinese experience », _Journal of International Economics,_ 51, 379-400.

Cheung K., Lin P., 2004, « Spillover effects of FDI on innovation in China : evidence from the provincial data », _China Economic Review,_ 15, 25-44.

Démurger S., 2001, « Infrastructure development and economic growth : an explanation for regional disparities in
<sup>6</sup> The findings indicate that the index witnessed a modest increase of approximately 0.7 percent over the decade, which is lower than anticipated given the commodity boom period. Consistent with other research, the study highlights the presence of a gender gap in employment quality, as well as a widening disparity in job quality between urban and rural areas.<sup>7</sup> The most recent contribution to the literature, Hovhannisyan et al. (2022 _a_ ), aims at measuring job quality with a global view, using a harmonized dataset for 40 developing countries and across four dimensions: sufficient income, access to employment benefits, job stability, and adequate working conditions. Employing the Alkire and Foster framework, it

> 5The Fuzzy Sets theory had been traditionally used to construct a multidimensional poverty index. Lelli (2001) provides a solid review and comparison between factor analysis and the use of fuzzy sets.

> 6The social security benefits and workplace variables are also included as components of the index.

> 7We replicate the model presented by Ya ̃nez (2018) using the Bolivian household surveys. Similarly, we also find a steadiness of the job quality index based on the fuzzy sets theory.

5
countries have accepted targets for limiting or reducing emissions. These targets are expressed as levels of allowed emissions, or “assigned amounts,” over the 2008-2012 commitment period. The allowed emissions are divided into “assigned amount units” (AAUs).

> 2 The JI and CDM are also intended to attract the private sector to contribute to mitigation efforts. According to the JI and CDM pipeline database, most of the projects are private initiatives (UNEP Risoe CDM/JI Pipeline Analysis and Database, 01 November 2008).

> 3 As set out in Article 17 of the Kyoto Protocol, Annex B countries with fewer emissions than permitted are allowed to sell the excess AAUs to the countries with more emissions than permitted.

2
<mark>Table 3: Transition Matrices - Synthetic vs. Actual Panel Data Using Different Panel Length</mark>

||<br>L|<br>Panel SurveyI<br>Th|<br> <br>U|<br> <br>L|<br>Panel SurveyII<br>Th|<br>U|
|---|---|---|---|---|---|---|
|Status in Year[1],<br>Year[2]|ower<br>Bound<br>Estimates<br>[1]|rut<br>Confidence<br>Interval<br>[2]|pper<br>Bound<br>Estimates<br>[3]|ower<br>Bound<br>Estimates<br>[4]|rut<br>Confidence<br>Interval<br>[5]|pper<br>Bound<br>Estimates<br>[6]|
|_Panel A: Chile; Years i_|_n Panel Surv_|_ey I: 1996-2001_<br>|_; Years in_<br>|_Panel Surv_|_ey II: 2001-2006_<br>||
|Poor,Poor|10.43|(5.44,8.17)|5.19|8.48|(3.18,6.00)|3.49|
|Poor,Non-poor|7.37|(9.59,13.02)|21.74|13.94|(8.21,12.29)|25.46|
|Non-poor,Poor|2.95|(5.23,7.91)|8.19|0.48|(2.99,5.73)|5.47|
|Non-poor,Non-poor|79.24
The _relative_ rankings between States are broadly similar to those in the Prayas Report, although some jurisdictions to which the Ministry of Power gave good marks for regulation were heavily criticized in the Prayas Report – notably Haryana and Delhi State. However, the _absolute_ scores in the Ministry of Power’s report suggest a much more optimistic picture than in the Prayas Report. For most of the major reformers in the Ministry of Power report, SERC factors were given marks of 15 or more out of 20 (ie the glass was three-quarters full or more). The states so categorized were: Andhra Pradesh, Karnataka, Haryana, Rajasthan, Delhi State and Uttar Pradesh. This list – and the judgments on the adequacy of the SRC’s in some of these States - is not only very different from that in the Prayas Report but also from that of other observers.

# **3.4 Conclusions from Surveys of Regulatory Governance Practice**

The key conclusions from this section are:

- 1) _It is possible to operationalize the measurement of informal as well as formal regulatory governance by systematic, structured case studies using a common framework._

Note that this typically requires probing under a framework of headings by informed observers. It cannot, in practice, be carried out by fully specified questionnaire-driven interviews and particularly not by web-based/e-mailed or postal questionnaire. The latter are only suitable for collecting basic information on formal governance characteristics, as in the EBRD/WB survey of transition country regulators and the Domah survey used in Domah, Pollitt and Stern.

Establishing a strong, common coding framework is crucial for the success of such studies.

- **2)** _For DTEs, studies that concentrate on formal governance characteristics alone typically over-state the quality of regulatory governance, usually by substantial amounts_ .

In addition, studies of regulatory practice can only reliably be made after the regulatory agency has been in place for a reasonable period (eg 2-3 years or more).

- **_3)_** _As a consequence
including single-worker to the largest establishments, formal and informal, from both manufacturing and services sectors. For our analysis, we include all non-agricultural economic sectors, excluding public administration and defense. The 2013 Economic Census uses the three-digit National Industrial Classification 2008 (NIC 2008) to classify sectors. We follow this nomenclature in the following analysis.

Several studies have confirmed the Economic Census as a good source to study the spatial characteristics of establishments in India over longer periods. However, it has a coverage issue related to micro enterprises at the left-most end of the firm-size distribution (Amirapu et. al 2019; Manna 2010; and Raveendran 2006). Following the literature, we exclude establishments with fewer than five workers from the baseline analysis. And we use these observations in robustness checks.

In terms of places, we restrict to the towns and villages within 100-kilometer driving distance from the border in both Uttar Pradesh and Uttarakhand. Overall, there are 19,088 towns and villages in this area. Among them, over 4,100 were not canvassed in the 2013 round of the Economic Census, because of the

> 7 We use the Socioeconomic High-resolution Rural-Urban Geographic Dataset for India (SHRUG) to match the 1990 and 1998 Economic Census to the 2011 Population Census (Asher et al. 2019).

> 8 - Open Source Routing Machine can be accessed at http://project <u>osrm.org/.</u>

15
Macours, K. (2011). “Increasing Inequality and Civil Conflict in Nepal.” _Oxford Economic Papers,_ vol. 63, n. 1, pp. 1-26.

Muwanga-Zake, E. S. K. (1985). “Sources of Possible Errors and Biases in Agricultural Statistics in Uganda: A Review.” Kampala, Uganda: Institute of Statistics and Applied Economics, Makerere University.

Palmegiani, G. (2009). Measuring cultivation parcels with GPS: a statistical evidence. Mimeo, LUISS University: Rome.

Schoning, P., Apuuli, J. B. M., Menyha, E., Zake-Muwanga, E. S. K. (2005). “Handheld GPS equipment for agricultural statistics surveys: Experiments on area-measurement and geo-referencing of holdings done during fieldwork for the Uganda Pilot Census of Agriculture.” Statistics Norway Report 2005/29.

Sud, U.C., Ahmad, T., Gupta, V.K., Chandra, H., Sahoo, P.M., Aditya, K., Singh, M., Biswas, A., and Indian Agricultural Statistics Research Institute (2015). Gap Analysis and Proposed Methodologies for Estimation of Crop Area and Crop Yield under Mixed and Continuous Cropping. _Global Strategy Working Paper No. 4_ . FAO: Rome.

Swetz, F. (1992). “The Sea Island Mathematical Manual: Surveying and Mathematics in Ancient China.” The Pennsylvania State University Press; University Park, Pennsylvania.

- UNDP (2014). Support to the Ministry of Lands and Rural Resettlement Action Plan 2014-2016 Action Plan, Harare, Zimbabwe.

Van Diggelen, F. (2007). “GNSS Accuracy – Lies, Damn Lies and Statistics”, _GPS World,_ 18(1): 1-4.

World Bank, FAO, United Nations (2010). Global Strategy to Improve Agricultural and Rural Statistics. Report No. 56719-GLB. Washington DC: The World Bank.

31
# **V Robustness and additional results**

In this section I run three additional exercises. First, with the aim of addressing concerns of endogeneity, I investigate whether the baseline results are robust to the use of a subset of moves that are plausibly more exogenous, to instrumenting destinations with historical migration, and to the blending of both approaches. Second, I explore potential heterogeneity by estimating regional childhood exposure effects for sub-populations. Third, I explore whether the evidence of regional childhood exposure effects obtained in the baseline specification holds if I focus on intergenerational upward mobility computed with secondary education as an alternative outcome. The motivation is that primary education as the threshold of interest may seem less policy relevant for current cohorts, although as I have shown, the shares of parents with less than primary school, as well as the children with less than primary school, in the sample under analysis are significant.

## **V.1 Addressing concerns of endogeneity**

### **V.1.a Anomalous migration outflows**

To alleviate concerns that time-varying factors may jointly drive household moves and children’s educational investments in proportion to exposure to the region with higher mobility, I re-estimate the model using a subset of moves that are more likely to reflect plausibly exogenous moves.

For this purpose, I construct a panel data set of outflows by region-of-origin-and-year-ofmove by counting the number of movers by region of origin and year of move (i.e., census year minus age at move) regardless of their destinations. Next, I use this panel data set to regress outflows on a constant and a linear trend by region of origin, compute the residuals from these regressions, and use them to sort (in ascending order) observations within each region of origin (assigning them percentile ranks). In this way, years that are assigned large percentile ranks (i.e., years with large residuals) can be considered as periods with anomalously large 19
people unemployed, outside the school system and not undergoing any training—the so-called NEETs—form about 30 percent of the Moroccan population between ages 15 and 24. That number looks even grimmer when compared to NEET rates in the MENA region, already characterized by particularly high rates of NEETs (ILO, 2019).

Subsequent rounds of Moroccan labor force surveys have already presented the opportunity to develop a clearer profile of NEETs in the country. What is missing, however, are the data to examine how the circumstances of people in the NEET condition have evolved over a reasonably long period of time. Our adaptation of the synthetic panel methodology to this specific issue enables us to overcome the data limitations and to provide helpful insights into NEETs dynamics in the last decade.

The first part of our analysis showed the key determinants of the NEET condition. As expected, individual characteristics play an important role. The probability of becoming NEET is higher for

> 11 For space considerations, we just summarize the findings here; the full set of results is provided in the Appendix.

24
Recharge cards|85.13%|85.07%|-0.06|
|Air Conditioner|2.57%|1.68%|-0.89|
|Washing Machine|2.16%|1.96%|-0.20|
|Cars and other vehicles|8.47%|9.99%|1.52|
|Generator|24.96%|25.49%|0.52|
|Microwave|2.47%|1.74%|-0.73|
|TV Set|48.03%|47.73%|-0.29|
|Computer|4.71%|4.17%|-0.55|
|Note: the table shows the average value of non-monetary in<br>to-survey imputations. Data are from the 2018/19 NLSS and2<br>between two visits (post-planting and post-harvesting). For 2<br>NLSS zone population shares and ensure comparability.|dicators used in the con<br>018/19 GHS. For GHS d<br>018/19 GHS zone weigh|sumption model for th<br>ata the average reflect<br>ts are adjusted to mat|e purpose of survey-<br>s the average value<br>ch 2018/19 official|

Table 3 Sectoral GDP growth rates 2009-2019

||GDP, trillions<br>(Constant LCU)|Sectoral GDP|, trillions (Cons|tant LCU)|Sectoral|GDP growth|rates|
|---|---|---|---|---|---|---|---|
||Total|Agriculture|Industry|Services|Agriculture|Industry|Services|
|2009|54.6|13.0|13.8|27.7|5.9|2.5|12
|3.1.4 Spatial Implications<br>In this subsection, we assess the model by comparing global maps for its predictions and IUCN<br>occurrence region [0-100] to facilitate comparison.<br>Figure 2 displays the global distribution of<br>comparison with Figure 2, each cell value in Figure 3 is the sum of total predicted probabilities for<br>all overlayed species (normalized to [0 — 100]).<br>Spatial patterns in the two figures are strikingly<br>Figure 3 displays total model-predicted cell probabilities for GBIF plants and vertebrates that are<br>rated in the IUCN database. We extend the exercise by using the estimated ordered logit model<br>vertebrates) that are not rated by IUCN. Addition of these species more than triples the size of<br>the application database (from 77,000 to 281,000). Figure 4 replicates the methodology of Figure<br>displaying the cellwise changes from Figure 5 to Figure 6 (in normalized values).<br>Incorporating<br>coastal North America, the Northern part ofSouth America and Atlantic Brazil, coastal West Africa,<br>emphasize that while normalization to the species occurrence region<br>[0 — 100]<br>is useful for<br>comparisons, it should not mask the fact that tripling the species population has the effect of<br>raising absolute values across the board.<br>A balanced interpretation of Figures<br>2 — 5 should<br>therefore note that many more potentially-threatened species have been identified across the<br>globebythisexercise,whilethespatialdistributionofthreatenedspecieshasalsobeenaltered.|
|---|