rafmacalaba/data-use-mentions-extended huggingface dataset:
that varied systematically by firm size.

Consistent with the synthetic control results, the panel data analysis indicates that the IDA amendment did not affect total employment, output, capital and output per worker in treated firms.<sup>9</sup> But, there was a significant increase in their employment of contract workers. There is

> 6The synthetic control method has been used in comparative case studies across a wide range of contexts, such as tobacco taxation in California (Abadie, Diamond and Hainmueller 2010), German reunification (Abadie, Diamond and Hainmueller 2015), economic liberalization episodes (Billmeier and Nannicini 2013), natural disasters (Coffman and Noy 2012), affirmative action policies (Hinrichs 2012) and political connections (Acemoglu, Johnson, Kermani, Kwak and Mitton 2016).

> 7Having only three years of post-treatment data, we focus on outcomes such as employment and output that are likely to respond to the policy change within this relatively short time frame, as opposed to more long-term outcomes such as firm entry and investment.

> 8We are not the first paper to examine the impacts of Rajasthan’s amendments using firm level data. The amendments were first examined in an annual report of the government (G.o.India 2019). Goswami and Paul (2021) employ firm level data and panel analysis methods similar to that used in this paper to examine the amendments, with similar findings.

> 9We use the terms factory, manufacturing establishment and firm interchangeably; the primary unit of analysis is a factory.

2
17. X. Cirera, D. A. Comin, M. Cruz, K. M. Lee, Anatomy of technology in the firm, _National Bureau of Economic Research, Working Paper, n. 28080_ .

18. N. Bloom, J. Van Reenen, Why do management practices differ across firms and countries?, _Journal of economic perspectives_ **24** , 203 (2010).

19. Google, Google covid-19 community mobility reports, _https://www.google.com/covid19/mobility/_ (2020). (accessed 2020-Aug-07).

20. C. Buffington, _et al._ , Measuring the effect of covid-19 on us small businesses: The small business pulse survey, _Working Paper CES-20-16, U.S. Census Bureau, Center for Economic Studies_ , _Tech. rep._ (2020).

21. M. Caliendo, S. Kopeinig, Some practical guidance for the implementation of propensity score matching, _Journal of economic surveys_ **22** , 31 (2008).

22. A. Abadie, G. W. Imbens, Bias-corrected matching estimators for average treatment effects, _Journal of Business & Economic Statistics_ **29** , 1 (2011).

22
, there is little applied or even theoretical research on this topic—maybe because there is even less data available than for all the other topics covered in this survey.

With the exception of a database compiled by Clarke and Xu (2003) for Eastern Europe and some sense of the ranking of utilities among corrupt institutions from the _Global Corruption Report_ (2004), to my knowledge the annual _Global Competitiveness Report_ provides the only comparable, quantitative, multicountry (59 developing countries) overview of corruption in infrastructure sectors, ranking countries according to the perceived degree of corruption (based on interviews with private firms), among many other criteria.<sup>28</sup> Aggregating the country-specific results by country income group and focusing on the two questions relevant to infrastructure yields sobering results in the report’s 2002–03 edition:

> 27 There is an extensive literature on how to define corruption and on the semantic practices of different institutions; a helpful recent survey is Lanyi (2004). For a recent survey on economic analysis of corruption, see Aidt (2003); on levels of corruption, see Kaufmann and others (2003).

> 28 There are also country-specific databases dealing with publics services, but these are the exception rather than the rule (see Reinikka and Svensson 2002, Svensson 2003, and many of the country reports from Transparency International available on its Website).
and also those who did not take the survey. A person may not use the app because s/he may prefer not to use the app or s/he does not have access to the app for reasons such as lack of internet access, cannot afford a device to use the app, less educated, etc. Although the usual solutions to the selection problem consist of correcting for the bias through models like Heckman regression, this was not feasible in the paper because the survey does not include the non-users of the app. Furthermore, dating apps also tend to attract a particular segment of the population, such as those likely to be young, single, wealthy, and open-minded, introducing biases in online surveys (Castro et al. 2020; World Bank 2018).

Additionally, we expect bias in estimation presented in this paper without including controls for baseline conditions of the respondents before the pandemic. For instance, although the survey asks respondents about their status during the pandemic, it is not the same to find an increase in stress during the pandemic from a seriously anxious pre-pandemic state versus a stress-free pre-pandemic state. Similarly, expecting to lose a job during the pandemic for someone who never lost a job versus someone whose precarious job was frequently lost before the pandemic is also not the same.

Nevertheless, with the restricted analytical ability due to the small sample and the lack of data on non-users of the app and the non-participants of the survey, this paper attempts to understand the effect of the pandemic on transgender and non-binary people – who generally remain understudied due to a lack of existing data. Hence, the paper is primarily exploratory. Results presented in this paper must therefore be interpreted keeping in mind the limitations imposed by the survey design and the sample size.

10
Firms|Firms with a Standard Industrial Classification(SIC)code between 60 and 67. Source: Worldscope.|
|Foreign Owership|Total value of shares held by investors whose main residence address is outside mainland China over the total value of shares.<br>Source: Refinitiv.|
|Leverage|Total debt over total assets (ratio).|
|Marging Trading Stocks|Dummy variable that equals one for firms with stocks that became available for margin trading during 2010-2017. Source: Hong<br>KongStock Exchange webpage.|
|Market Capitalization|Product of equity market price (fiscal period end) x common shares outstanding. For companies with more than one type of<br>common/ordinary share, market capitalization represents the total market value of the company. Unit: Constant 2011 U.S.<br>dollars. Source: Wordscope.|
||Dummy variable that equals one for firms with stocks purchased by the Chinese national team during 2015-19. According to|
|Government Stock<br>Purchases|Wind database, the national team is represented by five groups: (i) CSF (China Securities Finance Corporation Limited), (ii) CCH<br>(China Central Huijin Investment Limited), (iii) affiliates of the State Administration of Foreign Exchange, (iv) CSF customized<br>asset management plans, and (v) CSF customized funds. Source: Wind.|
|Research and<br>Development|Direct and indirect costs related to the creation and development of new processes, techniques, applications, and products with<br>commercial possibilities. It includes software design and development expenses. Unit: Constant 2011 U.S. dollars. Source:<br>Wordscope.|
|State Owned|Firms whose main(top1)shareholder is agovernment-connected entity. Source: Wind.|
|Total Assets|The sum of total current assets, long-term receivables, investment in unconsolidated subsidiaries, other investments, net property<br>plant and
.00102)||(0.00137)||(0.00148)||||
|Distance to nearest|||||||||
|population center|-0.00232***||-0.000171||-0.000992||||
||(0.000755)||(0.00115)||(0.000678)||||
|Constant|0.754***|0.531***|0.613***|0.586***|0.547***|0.508***|0.349***|0.00946|
||(0.0463)|(0.0480)|(0.0397)|(0.0332)|(0.0394)|(0.0335)|(0.0361)|(0.0154)|
|Observations|2,786|2,786|4,507|4,511|3,064|3,064|3,757|4,027|
|R-squared|0.105|0.017|0.081|0.080|0.069|0.062|0.042|0.019|
|Survey Wave FE|YES|YES|YES|YES|YES|YES|YES|YES|

Note: Results from OLS regression of extensive margin fertilizer use on household characteristics. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

28
. This rapid growth demonstrates that the transition from physical to digital commerce, often perceived as a phenomenon of high-income countries in urban areas, can happen at a lower level of development and in rural areas as well.

# **_2.1. Taobao Village Survey data and summary statistics_**

This analysis uses the Taobao Village Survey, a collaboration between the World Bank, Alibaba Group, and Peking University, to examine the characteristics of the Taobao Villages and households. The survey, conducted between August and September 2018, is representative of the universe of 2,118 Taobao Villages in 2017. The survey uses a stratified random sampling design with villages as the primary sampling units, which are stratified by the gross merchandise value (GMV) of the villages’ online sales in the Alibaba platforms, including Taobao.com and Tmall.com. The secondary sampling units are the cell grids over the satellite image of the village maps, where a specific number of grids are randomly chosen according to the estimated number of households

> 5 Under the Rural Taobao Program ( 农村淘宝 ), Alibaba Group establishes thousands of e-commerce outposts across China in collaboration with regional governments and recruits local talent to serve the demand of the local communities. Alibaba Group’s rural strategy is supported by its various initiatives involved in rural e-commerce, such as the Specialties of China ( 特色中国 ) and agricultural resources ( 淘宝农资 ) channels on Taobao Marketplace, Taobao University, Tmall.com’s fresh produce channel ( 喵鲜生 ), and the Blue Stars program ( 满天星计划 ).

6
effect, but this trend reversed in 2021, suggesting that the steeper drop in 2020 was strongly influenced by the enforcement of mobility restrictions. Moreover, consumer surveys by CACE indicate that among online shoppers there was an _increase_ in visits to physical stores.<sup>6</sup> In other words, e-commerce currently generates more traffic than it avoids, as a consequence of an increased number of freight vehicle trips and a very limited substitution effect on the demand for private vehicle trips. This means that for short- and medium-term sustainable mobility strategies (e.g., mobility master plans), e-commerce is an additional challenge to manage and not a significant opportunity that can help solve mobility problems.

Despite the limitations of e-commerce as an effective tool in reducing congestion, it opens up another set of opportunities for mobility planners. If urban distribution fleets are electrified faster than the private passenger vehicle fleet, e-commerce can be effective at reducing emissions from the transport sector in a future scenario of mass adoption. In this scenario, any substitution effect would have a positive impact on emissions reduction by replacing some private vehicle trips with electric freight vehicle trips. The impact of e-commerce on road accidents and the cost of road maintenance (elements highlighted by the Bogota Mobility Master Plan as impacts of urban freight distribution) will be more complex to address, as it depends solely on the optimization of distribution chains and routes to avoid a disproportionate increase in freight vehicle kilometers traveled as a result of the growth of e-commerce. This optimization can also contribute to reducing emissions without waiting for a complete electrification of distribution fleets.

The associated recommendations for policy are as follows:

- **_Establish close relationships with e-commerce stakeholders_** . In a context of limited legal capacity to prescribe data sharing by the private sector, transport authorities should invest more resources in establishing close relationships with local players in the e-commerce sector. This may lead to a greater willingness to share information and data on e-commerce demand and its implications for mobility (RojasHuerfano et al. 2018
# <u>Oversight Tools and Income Level</u>

In the 2002 World Development Indicators published by the World Bank countries are divided in three groups : high-income economies, in which the gross national income (GNI) per capita was $ 9,266 or more; middle-income economies, which have a GNI per capita of between $ 755 to 9,265; and low-income economies, in which the GNI per capita is below $ 755. We transformed this information into a quantitative variable, by assigning value 1 to countries in the low-income group, value 2 to countries in the middle-income group and value 3 to countries that belong to the high-income group. Having created this variable, we then investigate whether there is a relationship (and if so, what type of relationship) between income levels and oversight. By crosstabulating our Income variable with the number of oversight tools that are available to a country’s parliament, we find that there is a clear, and strong linear relationship between the income level and the number of oversight tools. The number of oversight tools in the countries that have provided information in this respect varies from a minimum of 4 to a maximum of 7. Parliaments in low-income countries have an average of 5.5 oversight tools at their disposal, parliaments in middle-income countries have an average of 6.25 oversight tools while parliaments in high-income countries have an average of 6.27 oversight tools. Further details are presented in Table 6.

13
of late payments as a share of the volume of outstanding loans rose from fiscal 1990/91 to 1993/94 and then declined. This rate was 5.7 percent in fiscal 1995/96. Data do not allow for a calculation of the program's annual default rate. However, program organizers report the cumulative default rate as being below 2.8 percent.<sup>43</sup> Further. their figures show this rate declining slightly over the last two years. The program's arrears rate and default rate would likely be judged sound by international microcredit standards.<sup>4 4</sup> 41 Where credits overdue is defined as credits whose payments are one day late or more.

- n Actual annual arrears rates may be below the figures presented here because many participating banks do not write off loans. Thus, current annual arrears rates reflect some bad debts incurred in previous years.

- 43 In the absence of data on default rates, the maximum possible cumulative default rate is assumed to be the inverse of the cumulative repayment rate. In fact, the cumulative default rate will be below this figure since at least some of the loans for which repayments are currently late vill eventually be repaid. The cumulative repayment rate is defined as: total cumulative repayments / (cumulative repayments +cumulative arrears).

- **44** For example, the Committee of Donor Agencies for Small Enterprise Development (1995) set as an acceptable standard for microenterprise lending, that 10 percent or less of total loans should have late payments of 30 days or more. and lenders should have annual losses from defaults of 4 percent or less of outstanding loan volume. PHBK's arrears rate is slightly above the 10 percent level but includes loans in arrears by one day or more. The program's default rate is likely below 4 percent.

57
**_<u>________________________________________________________</u>_**

Though the audit is tailored mainly for World Bank operations, it can be applied in several projects by other agencies. For example, it can be implemented within the context of a transport or export competitiveness project, or as a stand alone exercise. Additional information is available at: www.worldbank.org/trade.

# _<u>Transit Corridors Europe Caucuses Asia (TRACECA)</u>_

This program was initiated by the European Union in 1993 to develop a transport corridor on west-east axis from Europe, across the Black Sea, through the Caucasus and the Caspian Sea to Central Asia. <mark>As part of TRACECA’s Harmonization of Border Crossing Procedures Project, a ‘BordAudit’ Database from 70 border crossing points in several countries</mark><sup>5</sup> <mark>has been established providing detailed information on the procedures observed (including a breakdown of the time</mark> spent on each procedure) and main documents required when crossing the borders by road, rail, and sea. Subsequent recommendations concerning harmonized border procedures have been based on this database.

The online country overview includes the list of the Technical Assistance Studies and the Investment Projects for the Rehabilitation of Infrastructure in each country and general information on the country.

# **_2.3.1  Tariff and Customs_**

The International Chamber of Commerce (ICC) produced _<u>Customs Guidelines</u>_ with the WCO, a comprehensive set of practices that the ICC consider should characterize all modern Customs administrations. The World Bank produced _<u>Customs Modernization Handbook,</u>_ which provides practical guidelines that draw lessons from successful customs modernization examples and reform initiatives. These reports are useful guidance for the recurring issues of current customs modernization initiatives.

_<u>Asia Pacific Economic Cooperation Tariff Database</u>_ Tariff rates for all 21 members of APEC
_B2_ ), but replace loan demand with the lending rate. The coefficient on the interest rate is positive (as expected _a priori_ ), but statistically indistinguishable from zero; this is also the case for the IV specifications that follow. The coefficients on the three channels are also the same as before (negative), but again are statistically insignificant. Due to the endogeneity problems inherent in the estimate, however, we regard these results are merely illustrative, and proceed to a discussion of the IV estimates.

Columns ( _I2_ )–( _I4_ ) report IV estimates that correspond to the benchmark specifications ( _B2_ )–( _B4_ ). The instruments satisfy the relevance condition—Kleibergen-Papp LM tests are statistically significant—and the (insignificant) Hansen _J_ statistic indicates that the instruments are valid. While _F_ statistics (not reported) imply that the first-stage fit is reasonable, the corresponding Kleibergen-Papp F statistics are relatively low ( _F ∈_ [4 _._ 41 _,_ 4 _._ 94], equivalent StockYogo critical values place this range at around 25 percent relative bias), which suggests that the specifications could be weakly identified. We recognize this possible weak instrument problem, and, consequently, our interpretation of the results here proceed with greater caution.

While the results are largely similar to those in Table 1, the asset volatility channel does not appear to be operative any longer in any of our specifications. We conjecture that the inclusion of the interest rate could dilute the impact of the asset volatility channel, since interest rates

> 19The question on changes in demand conditions posed in Senior Loan Officer Survey does not distinguish between borrower nationality. While cross-border exposures of U.S. banks are mainly to high-income countries, the response may nevertheless incorporate some consideration of demand conditions in developing countries.

19
Shapira, Gil, Ina Kalisa, Jeanine Condo, James Humuza, Cathy Mugeni, Denis Nkunda, Jeanette Walldorf. (2018). "Going beyond incentivizing formal health providers: Evidence from the Rwanda Community Performance‐Based Financing program." _Health Economics_ 1-20.

Shengelia, B., A. Tandon, O. B. Adams and C. J. L. Murray (2005). "Access, utilization, quality, and effective coverage: An integrated conceptual framework and measurement strategy." Social Science & Medicine 61(1): 97-109.

- Sun, X., X. Liu, Q. Sun, W. Yip, A. Wagstaff and Q. Meng (2016). "The Impact of a Pay-forPerformance Scheme on Prescription Quality in Rural China." Health Economics: 25(6):706-22

- Van de Poel, E., G. Flores, P. Ir and O. O'Donnell (2016). "Impact of Performance-Based Financing in a Low-Resource Setting: A Decade of Experience in Cambodia." Health Economics: 25(6): 688-705

Wagstaff, Adam, Caryn Bredenkamp, and Leander R. Buisman. 2014. Progress on Global Health Goals: Are the Poor Being Left Behind? _The World Bank Research Observer_ ; 29:137-162.

<mark>Witter, S., Fretheim, A., Kessy, F. L., & Lindahl, A. K. (2012). Paying for performance to improve the delivery of health interventions in low-and middle-income countries.</mark> _<mark>Cochrane Database Systematic Reviews</mark>_ <mark>,</mark> _<mark>2</mark>_ <mark>(2), CD007899.</mark> Yip, W., T. Powell-Jackson, W. Chen, M. Hu, E. Fe, M. Hu, W. Jian, M. Lu, W. Han and W. C
pre-crisis levels by December 2010.<sup>11</sup> Moreover, during the rebound, emerging economies witnessed growth rates in IP sometimes even larger than pre-crisis rates, a phenomenon not observed in advanced economies. For example, using the data in Figure 3, we observe that IP in advanced economies was over sixteen percentage points below its geometric trend by December 2010, but in emerging economies it was about nine percentage points below trend by that date.<sup>12</sup>

# _2.3. Volatility or resilience?_

One important difference between emerging and developed economies is their degree of volatility in economic activity. To the extent that typical business-cycle fluctuations are more volatile in emerging economies, we expect larger declines and recoveries in these economies. To address this issue, we report in Table 1 (middle panel) statistics on yearly changes in GDP growth during crisis and non-crisis years over 1980–2010.<sup>13</sup> We first observe that GDP growth in emerging economies is twice as volatile as that of advanced economies during tranquil periods: the average positive (negative) change in GDP growth is 3.2 (-3.1) percentage points in the former and 1.5 (-1.5) in the latter. This difference becomes more acute during crisis years (excluding the 2008–2009 crisis) as the average decline in GDP growth is -5.9 and -1.7 percentage points in emerging and advanced economies, respectively. In contrast, in 2009, the average change in GDP growth was -5.9 and -4.4 percentage points, respectively. These numbers show that the 2008–2009 crisis was not a typical business-cycle fluctuation: the average crosscountry decline in GDP growth during 2009 was almost twice as large as that during tranquil

> 11 Alternative measures capturing the length of the recession, displayed in the bottom panel of Table 1, confirm that emerging economies started to recover earlier than advanced economies.

>