rafmacalaba/data-use-mentions-extended huggingface dataset:
of inequality. However, if top incomes increase faster than the rest, the surveys would also underestimate the _trend_ in inequality. There is some suggestive evidence pointing in that direction. First, while the evidence from administrative data remains very limited, in some of the available countries top income shares are rising contrary to household-survey-based inequality measures (e.g. Colombia, Malaysia) (Alvaredo and Gasparini, 2015).<sup>3</sup> Second, much has been written about the declining labor shares in many countries around the world (Karabarbounis and Neiman, 2014). For example, in China at the same time as the labor share was falling, household savings recorded in national accounts were growing faster than GDP.<sup>4</sup> This suggests that the inequality in incomes grew faster than the inequality in consumption expenditures, which we use to estimate inequality in China. Third, a number of alternative data sources also point toward rising top incomes. In many developing regions of the world, billionaire wealth (according to the “rich list” published by Forbes magazine) increased faster than national income (Lakner and Ruggeri Laderchi, 2016). For example, between 2012 and 2015, a period of only three years, the number of billionaires in China doubled from 251

> 1 As a result, our results would tend to underestimate within-country and thus global inequality. However, Anand and Segal (2015) argue that the differences are very small. We find that moving from percentile to decile groups in a slightly different global distribution reduces the global Gini index by approximately 0.5 percentage points.

> 2 The difference between income and expenditure are net savings.

> 3 I am abstracting here from the fact that the Gini index is most sensitive to changes around the middle, so an increase in top incomes need not show up as a significant increase in the Gini index. See Alvaredo (2011) on the relationship between top income shares and the Gini index.

> 4 Household
2000 constant US dollars using exchange rates from the IMF’s International Financial Statistics.

While we have data available for a broader array of institutions, we focus on three types. First, low-end financial institutions which include credit unions, building societies, community banks, cooperatives, microfinance institutions, cash lenders, mutual banks, postal banks, rural banks, savings and loans institutions, and thrift banks. This category is supposed to capture nonbank institutions that serve the low-end of the market, including small and medium-sized enterprises. Second, specialized non-bank financial institutions which comprise – among others – finance companies, factoring companies, banks specialized in housing, merchant banks, and

> 5 See Appendix Table 1 below for data availability across countries and categories.

6
# 6 Networks and trade at the ...rm level

While their simplicity is attractive, the aggregated measures we employed in the previous section su¤er from important limitations for examining the e¤ect of migrant networks on export participation and intensity. In fact, a key insight of the model presented in section 2 is that the e¤ects on export participation of trade costs and demand factors depend on ...rm attributes. We are unable to examine the role of ...rm heterogeneity in determining export participation if we measure the extensive margin as the number of exporters. Another concern about this measure stems from the existence of a minimum statistical threshold for the collection of data on internal trade, which implies that the number of exporters is measured with error.

On the other hand, the use of average shipments per exporter for measuring the e¤ects of migrant networks on export intensity is also problematic. In the context of a Melitztype framework, Lawless (2010) shows that the e¤ect of variable trade costs on average sales per exporter is ambiguous. This is because a fall in variable costs increases the sales of every ...rm serving a country, but may also lead to entry of lower-productivity ...rms (and thus lower-sales ...rms) in that market. A similar reasoning applies to the e¤ects of ...rm-destination networks, ac(j). In this section, we therefore exploit the ...rm-destination detail of the data to estimate the e¤ect of migrant networks on export participation and intensity at the ...rm-level.

## 6.1 Networks and export participation

We begin by focusing on the e¤ects of migrant networks on export participation. Based on (7), we estimate a speci...cation of the form: where: E jc = 1 if ...rm j exports to country c and P is a probability distribution function; 'j is a proxy for .
Formally, region _r_ ’s optimal demand for the _ntc_ and _fbn_ aggregates by sector and labor type takes the form: where the WO are the wage indices dual to the LO and the _σl_ are the elasticities of substitution between labor of _ntc_ and _fbn_ origin.

Region _r_ ’s economy-wide demand for these labor bundles is then given by: The LOT bundles are in turn CES composites over labor from the origin regions classified as _ntc_ or _fbn_ via a set mapping from origin regions _rt_ to _s_ for each host region _r_ denoted by maps( _r,s,rt_ ).

The optimal bottom-level demand in region _r_ for _l_ -type labor of origin _rt_ thus takes the form where WRTr,l,rt is the corresponding wage rate, _σlz_ r,l,s denotes the elasticity of substitution between type- _l_ labor from different regions of origin within nest _s_ , and the _lameff_ are labor efficiency parameters that serve to explain the ‘observed’ initial wage differentials across workers of different origin in the benchmark data set.

The model adjusts the total populations in host and origin regions arising from exogenous changes in migrant flows by assuming that the ratio of migrating people including accompanying dependents to migrating workers is the same as the baseline ratio of total migrants to migrant workers of the same origin in the host region. The model also considers remittances from households in the host region to the region of origin. Household remittances from the host region to the region of origin are fixed fractions of migrant workers’ labor income.

# **2.2 Data**

The model is updated and calibrated for use with the GTAP 8.1 and GMig2 databases of Narayanan et al. (2012) and Walmsley et al. (2013). The GTAP database provides a globally consistent database of input-output tables, allowing for the modeling of production, consumption, and trade for 134 countries, 57 sectors, and five factors of production. The GMig2 database contains the latest available model-consistent estimates of bilateral migration stocks, labor earnings, and
39,425.6<br>----|117,746.5|
|Total in our sample|297,828.9|202,002.0|376,968.4|
|**as**%of Total Manufacturing Output|38.05|42.08|27.75|

**a)** indirect taxes and subsidies are deducted (see table 2.3).

b) includes 25,004.7 million pesos (excl. indirect taxes **and** subsidies) for petroleum ref ining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico. Source: Figures for Brazil from Censo Industrial, figures for Mexico frcim Resumen General (except for figures mentioned under footnotes a) and b)), and figures for USA from the 1977 Census of Manufactures.
Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2014). “The New Empirical Economics of Management”, _Journal of the European Economic Association,_ 12(4):835–876 Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2016). “International Data on Measuring Management Practices”, _American Economic Review: Papers & Proceedings,_ 106(5): 152–156 Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten. and J. Van Reenen. (2019a) “What Drives Differences in Management Practices?”, _American Economic Review_ , 109(5): 1648-1683 Bloom, N., L. Iacovone, M. Pereira-López and J. Van Reenen. (2019b) “Spillovers, Market Size and Misallocation: Management in Mexico”, mimeo Chacko, T. I. and J. G. Wacker. (2001) "An Examination of Strategic Goals and Management Practices of Russian Enterprises," _International Business Review_ , 10(4): 475-490, Cusolito, A.P. and H. Winkler. (2015). “Productivity Growth and Convergence in Russia: A Dynamic FirmLevel Diagnostic”, Mimeo.

Danilin, I. (2015) “State-Owned Enterprises as innovation agents in Russia: new developments or innovation dead end?” Institute of World Economy and International Relations Working Paper, Russian Academy of Sciences, Moscow, Russian Federation.

Estrin, S.; S. Poukliakova and D. Shapiro (2009) “The Performance Effects of Business Groups in Russia”, _Journal of Management Studies_ , 46(3): 393-420 Friebel, G. and H. Schweiger. (2013) “Management Quality, Ownership, Firm Performance and Market Pressure in Russia”, _Open Economies Review_ , 24(4): 763-788
# **References**

Commitment to Equity Institute (2019). Data Center on Fiscal Redistribution, country studies. https://commitmentoequity.org/ Inchauste, G. and Rubil, I. (2017). “The Distributional Impact of Taxes and Social Spending in Croatia”. World Bank Policy Research Paper #8203.

Krstić, G., <mark>Žarković Rakić</mark> , J. (2017). Please, mind the gap: income inequality in Serbia. In: Stojković, D., Božović, M. and Ranđelović, S. (ed.), _Economic Policy for Smart, Inclusive and Sustainable Growth_ , 15-17 Jun 2017, Belgrade, p. 213-227 Lustig, Nora (editor). 2018. _Commitment to Equity Handbook. A Guide to Estimating the Impact of Fiscal Policy on Inequality and Poverty_ . Brookings Institution Press.

Randjelovic, S and Zarkovic, J. (2013) "Improving Working Incentives: Evaluation of Tax Policy Reform Using SRMOD". _International Journal of Microsimulation,_ Vol. 6, Issue 1, <mark>157-176</mark> World Bank (2020). Serbia Systematic Country Diagnostic Update.

23
The potential impacts of e-commerce on the mobility of people are usually classified into four types of effects: _substitution_ , or a reduction in travel demand due to the virtualization of activities that motivated travel; _complementarity_ , or an increase in travel demand because the virtualization of the activity allows or promotes other activities that do require travel; _modification_ , or changes in demand patterns not directly related to an increase or decrease in the number of trips; and _neutrality_ , or the absence of impact on travel demand. Studies to date have found both substitution and complementarity effects, with the former being slightly more common (Le et al. 2022). Studies that find complementarity effects suggest that the possibility of doing more information search online (one of the activities of e-commerce) not only promotes more online shopping but also more physical shopping (Xi et al. 2020). The studies that find substitution effects have indicated some factors that may enhance this effect, such as shoppers not having their own vehicle (Shi et al. 2019) and sameday deliveries (Xi et al. 2020). Ferrell (2005) quantified the substitution relationship with data from California, finding that for every 100 minutes of time spent on e-commerce, consumers reduced 5 minutes of time spent on shopping trips. There is no consensus among studies that incorporate other dimensions of travel demand such as trip distance, trip chaining, mode choice, or trip frequency for other purposes. Modification effects have been detected in the use cases addressed in each study, but often with contradictory results from each other (Le at al. 2022).

# **3. Methodology**

The current paper analyzed how the growth of e-commerce is impacting mobility in Bogotá and Buenos Aires and how the patterns in e-commerce changed during the pandemic. A distinction can be made between two types of impacts of e-commerce on mobility: (i) its effects on the mobility of people, insofar as they allow the virtual development of the activity of buying products, which required physical interaction before the arrival
|
|Sector (Manufacturing)|0.044|0.050|-0.068|-0.026|
||(0.029)|(0.024)*|(0.030)*|(0.031)|
|_Country income level (Low income)_|||||
|Lower Middle income|0.084|-0.036|0.054|-0.101|
||(0.059)|(0.033)|(0.046)|(0.059)+|
|Upper Middle income or high income|0.263<br>(0.072)**|-0.086<br>(0.023)**|0.054<br>(0.048)|-0.231<br>(0.045)**|
|Observations (number of countries)|9676 (38)||||
|Predicted probability at the mean|0.259|0.165|0.309|0.267|
|Wald test for rho = 0|Chi2(1)= 6.612;P|rob > chi2 = 0.010|||

Standard errors in parentheses (and adjusted for clustering on country). Where relevant, base categories of dummies are indicated in parentheses in italics. + = significant at 10%; * = significant at 5%; ** = significant at 1%. Data source: ICS.

30
early.<sup>12</sup> To be conservative in our measure of absence, we exclude all school closures due to bad weather, school construction/repairs, school functions and alternative uses of school premises (for instance, elections). We also exclude all part-time teachers, teachers who were transferred or deputed elsewhere, or teachers reportedly on a different shift.

We construct a school infrastructure index by adding binary indicators for the presence of four indicators of school facilities - drinking water, toilets, electricity and a library. We construct a remoteness index by taking the average of nine normalized indicators of distance to various amenities including a paved road, bus station, train station, public health facility, private health clinic, university, bank, post-office and Ministry of Education office. A lower score on the remoteness index represents a better connected school.

During each survey visit, we record the date of the most recent school inspection. We measure the extent of monitoring and supervision as the mean probability of being inspected in the past three months across all three visits. We used a similar procedure for constructing the mean probability of a parent-teacher association (PTA) meeting. Average parental education of children in a school is computed from the basic demographic data collected for the sample of fourth-grade students chosen for assessments of learning outcomes.

For most of the analysis in this paper, we use the village as our unit of analysis and examine mean village-level indicators of both inputs and outcomes because a large number of new schools had been constructed between 2003 and 2010, including in villages that already had schools. This school construction resulted from a policy designed to improve school access by ensuring that every habitation with over 30 school-age children had a school within a distance of one kilometer. Thus, to ensure that our sample was representative in 2010, and at the same time amenable to panel data analysis relative to 2003, we constructed the panel at the village level, with a new representative sample of schools drawn in the sampled villages.<
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variables such that the total seasonal effect zero over a full day-of-week dummy equals cycle of 7 days.

For estimating the causal effect of the Covid-19 lockdowns in our sample of cities, the regression component is crucial. It allows us to construct counterfactual predictions of the daily traffic congestion index series from a synthetic control using traffic conditions from a city or a combination of cities that did not face Covid-19 lockdown measures ( _untreated cities_ ). Unlike the local trend and seasonal sub-models, the observed traffic congestion index series from the pool of _untreated_ cities allows us to capture the variance in traffic conditions in the treated cities. In this application, we primarily use Hong Kong SAR as a relevant untreated candidate city for two reasons other than data availability. First, Hong Kong SAR did not implement a Covid-19 lockdown over the entire period of the analysis. Second, and more importantly, it is implausible that traffic conditions in Hong Kong SAR are affected by the effects of lockdown-related changes in our sample of treated cities (Bogotá, New Delhi, New York City, and Paris), an important assumption for causal inference in the BSTS setting.

Using the natural logarithm of the traffic congestion index data from Paris before the lockdown—i.e. between March 20, 2019 and March 17, 2020—we show in figure 2 the contribution to the BSTS model of the trend, seasonal, and regression components, respectively. There is clearly a daily pattern in the congestion data characterized by significantly more variation in the seasonal component relative to both the trend and regression components. With substantially fewer variation than in the regression component, the local trend exhibits a few instances of major reduction in congestion, especially around summer and other national holidays (e.g., Armistice day), followed by relatively short-lived episodes of increased traffic congestion. Note that in addition to the traffic congestion index for Hong Kong SAR, the covariates in the regression component include precipitation and the natural logarithm of temperature. These two control time series are also unlikely to be
rounds with respect to age, education and civil status (2,502 respondents).

The final sample contains 6109 households, some consisting of a single person and some of a couple, for a total of 5482 women and 4843 men.

Tables 1 and 2 present summary statistics for the estimation sample and for all EPS respondents, imposing the age restriction only. Table 1 shows demographic and labor force participation information whereas table 2 describes the distribution of earnings, non-pension 36We construct a wealth measure that also includes the reported value of equity in major household assets, such as the home and car.

> 37We obtained a dataset on the recognition bond values from the Superintendence of Pensions, which we linked to the survey data.

21
disallowed by the registrar's office, for example, in the event that one of the parties is a minor, stamp duties and registration fees have not been paid, or the transaction falls outside the jurisdiction of the Indian Stamp Act, 1899.

_Issue of encumbrance certificates:_ Persons keen to know encumbrances on a property over a given period of time provide details of the property and the period for which information is required, and deposit the fee for the search and preparation of the certificate. On receiving the request, the clerk searches the Index II register of the village in which the property is located for the required period.

_Issue of certified copies:_ Persons requiring a certified copy of a document have to deposit search, inspection and copying fees. Search is done on the Index register from which the preserved photocopy of the document is located and copied manually onto stamp paper.

_Back office activities:_ These include preparation of: (i) Indexes I and II to help search for the information for issuing encumbrance certificates and certified copies, (ii) valuation register which records information, for every village, on date of registration, area and type of land, and its value, (iii) fee book which maintains records of different types of fees collected such as registration fee, search fee, inspection fee, copying fee, and miscellaneous fee like marriage fee and petition fee, and (iv) the monthly receipt and expenditure statement.

Stamp duty and registration fees are important sources of revenue for the state government. In 1988-89 (the last year for which comparative data are available), income from stamp duty and registration fees together constituted 6 percent of the state's revenue (Table 3). While its relative contribution to total state income has declined from 10 percent in 1958-59, in absolute terms stamp duty and registration fees contributed nearly Rs.600 million to state income from various sources in 1997-98 (Table 5). This figure, however, does not

- 20 It is customary for
a proxy of participating in the training) and the list of students at risk, respectively. This information was collected in the last follow-up questionnaire, which was the only paper-based follow-up questionnaire and the one with the highest response rate. The proportion of schools with data on the reception of the guide was about 85 %, with no significant differences across control and treatment arms. For reception of the list of students at risk, the response rate was lower, with significant differences between the treatment and the control groups (the proportion of schools with data on this question was 77% and 55%, respectively). The difference may be driven by school principals in the control group not understanding the question (as they likely never heard about the list of students at-risk). The data on the take-up of treatment 3 comes directly from SIRE administrative records and is proxied by a dummy capturing whether the school principals logged into SIRE at least once in the month that followed the activation of the reminders on the platform.

# **Outcome data**

The main outcome indicator is a dummy variable capturing whether individual students who were enrolled in primary school in April 2018 had dropped out by April 2019. To construct this variable, we exploit the panel structure of our data—using the students’ identification numbers to track their academic trajectories through different years—with a variable reporting the enrollment status of the students during any given academic year (i.e., currently enrolled or dropped out). If a student is not found in the next year’s panel, we consider that the student has dropped out. In addition, potential errors in the linkage of students’ administrative records across years would also be interpreted as dropouts in our dataset. We do not expect these challenges would affect our identification strategy for two main reasons. First, even with these limitations, the SIRE data replicates well the dropout patterns observed in Guatemalan household surveys (Adelman et al, 2018). Second, there is no reason to believe