rafmacalaba/datause-encoder-data huggingface dataset:
# **2.2 Empirics of trade creation and trade diversion**

Ultimately, the welfare consequences of RTAs are an empirical matter. Unfortunately, estimating trade diversion is no easy task. It requires knowledge of the counterfactual: what would have happened to trade if there were no trade agreement? Since this is unknown, assumptions must be made.

Most studies use a gravity equation, which predicts bilateral trade based on income and other characteristics, and focus on variables that capture the extent to which RTA partners trade more or less than would otherwise be expected.<sup>4</sup> The key trade creation variable is a dummy that is one if both countries are members of a common RTA; the key trade diversion variable is a dummy that is one if one country belongs to an RTA and the other does not.<sup>5</sup> A positive coefficient on the former offers evidence of trade creation; a negative coefficient on the latter offers evidence of diversion. Overall, the message from such studies is of a predominance of trade creation. In fact, a concern is that the estimates of the creation effect may be implausibly large, as well as too dependent on the sample of countries and variables included (Haveman and Hummels 1998).

Magee (2008) expands on the traditional approach, with insights from the literature on the proper estimation of gravity models. He uses panel data for 133 countries from 1980-1998, and includes country-pair fixed effects, exporter-year fixed effects and importer-year fixed effects to capture the counterfactual more accurately than standard gravity specifications would. The dyad effects pick up what is natural about the trade partners, the exporter- and importer-year

> 4 An alternative is to use computable general equilibrium models to identify counterfactuals, but results are highly dependent on the parameters assumed; see for example Brown et al. (1992). 5 See Frankel et al. (1995), Carrere (2006), and Lee and Shin (2006).

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additional digital tools. Increasing pre-pandemic technology sophistication by one standard deviation is associated with 3.8 percentage points (pp) higher monthly sales, relative to the same period in 2019. We decompose this estimate into the direct (3.1 pp) and indirect (0.7 pp) effects. The indirect effect results from the fact that increasing pre-pandemic technology readiness by one standard deviation is associated with an increase in the likelihood that firms increase the use of digital technologies during the pandemic by 10 pp. Furthermore, the marginal likelihood of increasing digitization is higher for the firms with higher pre-pandemic technology sophistication. Both the direct and indirect effects on sales are larger among firms in the top quintiles of technology readiness. Our findings are robust to using different measures of digital response as a mediator, as well as to using propensity score or nearest-neighbor matching estimators for the indirect effect.

Previous studies have investigated the effects of work-from-home feasibility based on occupation of workers ( _15_ ), and technology diversification based on the scope of patent applications ( _16_ ) on firm performance during the COVID-19 pandemic or other natural disasters. Our analysis contributes to this literature on the effects of technology on firm resilience in three ways. First, we provide evidence on the positive relationship between pre-pandemic technology and firm resilience by using direct, granular measures of the sophistication of technologies, instead of proxies based on occupations or patents. Second, we decompose the effect of technology readiness on firm performance into a direct effect and an indirect effect through the adoption of digital technologies that allow firms to better cope with the shock. Third, we assess the possibility of a non-linear relationship between pre-pandemic technology and both resilience in sales and digital adoption.

The rest of the paper is structured as follows. Section 2 describes the data. Section 3 explains our hypothesis and empirical strategy. Sections 4 and 5 present the results and robustness checks. Section 6 concludes.

# **2 Description of the data**

Our analysis exploits data for Brazil (State of Cear ́a), Senegal, and Vietnam from the Firm-level Adoption of Technology Survey (FAT) and the COVID-19 Business Pulse Survey (COV-BPS). In 4
Figure 9: GDP growth rate, total labor mobility and moving cost differences, 2004-2015 <!-- Start of picture text --> 300 300<br>200 200<br>100 100<br>0 0<br>-100 -100<br>-200 -200<br>-300 -300<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>(a) Total labor mobility (b) Inverse of implied moving cost differences<br><!-- End of picture text --> _Notes_ : Panel (a) depicts the ratio between the number of workers who change jobs (within the same labor market or to another labor market) and the number of stayers. Panel (b) depicts the inverse of the difference between the moving cost implied by the auxiliary model and the actual moving cost from our estimation. All series are in terms of deviations from the mean and multiplied by the inverse of their standard deviations for comparison.

# **7.1 Data and Simulated Flows**

In Section 4.1, we estimated _m_ �<sup>_kl_</sup> _t_<sup>,and the three parameters of the moving cost function by PPML. We</sup> used the average of the estimated moving cost parameters over the sample period to characterize the initial steady state flows for the counterfactual simulation as explained in detail in Appendix A.5.1.1. The steady state simulation backs out values to match the labor allocations in the data, but does not target flows. In fact, steady state simulations only use labor allocation data, wage data, moving cost, and labor supply elasticity parameters. We show that despite the simple moving cost function, the model matches the labor flows in the data very well. Figure A6 panel (a) shows the average number of workers switching regions in the data versus in the simulation before we plug in a counterfactual shock. Similarly, panels (b)-(d) show comparisons between data and simulations for sector switchers, both sector and region switchers, and workers leaving the residual labor market. The simulated flows and the actual flows from data are highly correlated for all types of mobility.

# **7.2 Alternative of a Position**

The characterization of the job in our model implies that a worker changes jobs when she changes her position which is empirically identified through her detailed occupation. Alternatively, one can attempt to empirically identify changes in positions by adding other criteria, such as changes in worker’s firm (establishment) or detailed industry. We consider the following alternative empirical definitions of a position to check the robustness of our results: (i) a 6-digit detailed occupation category (our baseline definition); (ii) a combination of occupation and firm, therefore if a worker changes either occupation or firm, she effectively changes her position; and (iii) a combination of occupation, firm, and detailed industry (5-digit), therefore if a worker changes occupation, firm, or industry she changes her position.

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this semi-log specification, the coefficient on years of schooling can be interpreted as the average private rate of return to one additional year of schooling because it is the relative increase in wages following an increase in schooling.

Figure 1: Stylized Age-Earnings Profiles

## Earnings

<!-- Start of picture text --> Benefits University<br>graduates<br>Secondary school leavers<br>Costs<br>0 Age<br>18 23 65<br>Time (years)<br>5 42<br>Direct cost<br><!-- End of picture text -->

# **Methods and Data**

We used a simple model to assess the economic impact of school closures due to COVID-19. We — applied the model to three country income groups low-, middle-, and high-income countries, using the World Bank classification.<sup>43</sup> We expressed the loss in terms of lower wages due to lower productivity, and in terms of lost GDP: 6
variables with a dummy indicating whether the flow involves a PowerSeller or not. The only statistically-different coefficient at the 5 percent level is the distance one as shown in Table 4.

To examine whether eBay reduces search costs associated with product information as suggested by Rauch (1999), we use three measures of information asymmetries at the product level. First, we use Broda and Weinstein’s (2006) estimates of elasticity of substitution.<sup>18</sup> The median of their HS-6 digit estimates measures the need for information, or the level of product differentiation, within each category. Indeed as substitution among import sources is smaller there is a stronger need for product information. Next, we construct two measures of trademark intensity that also capture the presence of product information in each sector. Our first measure of trademark intensity uses data from the WIPO Global Brand Database which contains around 660,000 records relating to internationally protected trademarks. We base our trademark intensity measure on the number of registered brands per keyword search, where the keyword is the eBay category. For example, there are 605 registered brands that match the keyword ’music’, and 284 that match ’electronics’. We suggest that the lower this number, the higher the need for information gathering and diligence by importers. If the search costs lowered by eBay are related to product information, we should find eBay to reduce the role of distance most in categories with low trademark intensity for which asymmetric information regarding product characteristics is stronger. Our second indicator of trademark intensity comes from our eBay data and measures the intensity of complaints by trademark owners to eBay about potentially-illegal transactions. We take the share of companies who complain per category as an indicator of trademark intensity. We then suggest that the more complaints there are, the more branded the product category should be. We then interact these product-information indices with distance in our gravity regressions. The results are summarized in Figure 6 (drawn from coefficients found in Table 5). It shows that the distance-effect difference is much larger for products with low elasticities of substitution or low trademark-intensity. As suggested above, it seems eBay is particularly efficient at reducing the distance effect for products that require more information and trust,

> 18The use of Rauch’s (1999) classification into homogenous and differentiated goods is not possible in our sample as all categories fall within the differentiated-good category.

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total workers getting cash at worksites remains high. In some cases this may reflect partial cash payments to workers by the Mukhiya while funds are being transferred to worker accounts.

The Mukhiya or his/her spouse or close family member often act as money lenders as well as contractors, making advance payments to workers.<sup>15</sup> The reasons given include delays in work measurement, delays in obtaining post office/bank accounts and delays in the flow of funds to the worker post office or bank accounts.<sup>16</sup> In principle, the scheme should move towards full reliance on formal accounts rather than cash. But at the time of our survey, practice was still a long way from that ideal. Weaknesses in the flow of funds or administration of accounts, leading to delays in payment, create scope for the Mukhiya or other intermediaries to profit by being able to provide advance cash payments to needy workers.<sup>17</sup> Fieldwork for this study indicated that some local officials also take a cut from the wages due to the worker, possibly in the process of making advance payments.

There have been qualitative reports from the field of partial payments and long delays in receiving wages. Our survey provides corroboration. We asked whether BREGS participants had received wages owed in full. In the surveyed households, 72% of the female participants in R1 and 67% in R2 had been paid in full by the time of our survey, while this was the case for 66% and 72% of participant men. Women who had not been fully paid were still owed 58% and 75% of wages on average in R1 and R2, respectively. Unpaid men were still waiting for 64% and 57% of their earned wages. The amount owed is likely to decline as time since participation increases. Our data confirm that the share of wages received is higher for participants for whom more time has elapsed since they participated in BREGS. This can be seen in Figure 1, plotting the share of wages owed that were actually received by months since the work was completed for those who have not received their full wages. The mean share of wages paid rises with time up to six months, then stabilizes (R1) or falls (R2). However, even at its peak, the share of wages received among those receiving less than they felt they were due is no more than 50%.

There has been hope among advocates that the scheme would reduce the exploitation of rural workers in local labor markets stemming from the labor-market power of large farmers or

> 15 As in other states, one third of elected positions are reserved for women in Bihar. Thus, elected Mukhiyas are sometimes female.  However, once the election is over, a male surrogate, frequently a husband, often plays an active role in the job.  When we refer to the Mukhiya we mean either the elected or surrogate one.

> 16 Also see the discussion in Khera (2011).

> 17 This is not confined to Bihar; Vanaik (2009) reports the same practice in Rajasthan—thought to be amongst the better performing states in implementing MGNREGS.

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31 Table 4 (continued)

|Study|InterDretation<br>of Outcome|Results|
|---|---|---|
|Jung<br>&<br>Marshall<br>(1985)|If<br>and<br>Then<br>i.<br>E<br>>O<br>EP<br>i<br>(export promoted growth)<br>t-<br>2<br>Es>0<br>IGE<br>8a<br>(internally<br>generatedX<sup>8</sup>)|4 countries classified as EP<br>5 countries classified as ERG<br>3 countries classified as IGE<br>2 countries classified as GRE<br>-22 countries couldn't be<br>properly classified.<br>Including countries always<br>characterized<br>as EP examples like<br>South Korea, Taiwan, Brazil.|
||I - 2<br>EG< 0<br>ERG<br>(export reducinp,<br>growth)<br>||
||2- QEa<O<br>GRE<br>(growth reducing exports)<br>ifc<->I<br>or4<br>not<br>significant,<br>no interpreta-<br>tion is given.||
|Hutchinson<br>&<br>Singh<br>(1987)|.Lf=and<br>mhmn<br>X.nf6 >0 Export exter-<br>nalities<br>**A**n-<br>I<br>ESe > 0 Growth exter-<br>nalities<br>X-<br>fi E8< 0 Negative<br>or<br>externalitie<br>**A**n<br>E<br>2<br>a< O|-10countries (mainlyprimary<br>producing) showed evidence of<br>positive export externalities.<br>-3 countries showed evidence of<br>growth externalities.<br>-3 countries showed evidence of<br>negative externalities.<br>s 18 countries couldn't be classi-<br>fied, including Brazil and South<br>Korea.|
nesses observed in our dataset, and they are prominently featured in the subset of actively covered businesses. Figure A.12 provides a breakdown of the agribusiness sectors represented in our sample, showcasing the diverse parts of the agribusiness value chain that are included. Regionally, approximately 30% of the agribusinesses are based in the East Asia and Pacific (EAP) region, with North America Europe, and Central Asia having similar representations (see Figure A.13). In the subset of actively monitored agribusinesses, there is an increased proportion of firms from the EAP region at the expense of European and Central Asian firms, while the representation from other regions remains stable. We use the World Bank’s geographic classifications for this study, which include the following regions: EAP (East Asia and Pacific), ECA (Europe and Central Asia), LAC (Latin America and Caribbean), MNA (Middle East and North Africa), NAC (North America), SAS (South Asia), and SSA (Sub-Saharan Africa).

To assess the representativeness of our agribusiness sample, we compare the sales data from U.S. agribusiness companies in our FactSet sample to the aggregate U.S. industry output. This comparison, while approximate for a subset of firms, offers valuable insights into the portion of agribusiness activity that our data captures. We derive the information on aggregate U.S. agribusiness output from the Bureau of Economic Analysis, which publishes gross output by industry tables. We ensure comparability between datasets by summing output over the BEA industries that correspond to the NAICS codes used to identify agribusinesses in our sample.<sup>11</sup> However, it’s important to note that our method may lead to both overestimating and underestimating the representativeness of

> 11We use the concordances provided by Bureau of Economic Analysis (2023b). We sum over the following BEA industry (groups): Agriculture, forestry, fishing, and hunting; Food and beverage and tobacco products; Fertilizer manufacturing; Pesticide and other agricultural chemical manufacturing; Pharmaceutical and medicine manufacturing; Agricultural implement manufacturing; Grocery and related product wholesalers; Other non-durable goods merchant wholesalers; Building material and garden equipment and supplies dealers; Food and beverage stores; General merchandise stores; Health and personal care stores; Miscellaneous store retailers; Food services and drinking places. Note that the narrowest BEA industry codes “Other nondurable goods merchant wholesalers”, “Building material and garden equipment and supplies dealers”, “Health and personal care stores”, and “Miscellaneous store retailers” include several other NAICS 4-digit codes besides those included in our sample. This leads to an overestimation of U.S. industry output relative to our sample.

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About 25% of the Bachelor-students and 50% of the Master-students reports having a job on the side. Only 10% of the students receive a grant. Ownership of a house by the student’s family is 68% for Bachelorstudents, somewhat higher than for Master-students (61%).

Approximately 35% of the Bachelor-students are enrolled in an Area I subject (Humanities and Social Sciences), 40% in an Area II subject (Economics and Administration), and 24% in an Area III subject (Engineering and Natural Sciences). For Master-students the distribution between the three subject areas is 33%, 47%, and 20%, respectively. Tuition fees are approximately 5,100 pesos per month (US$ 510). The Economic Need Factor (ENF) for Bachelor-students is about 0.15, and for Master-students 0.16. Recall that this ENF is used to calculate the amount of credit for which the student is eligible. On average, a student receives a loan covering 44% of the tuition fee.

# _4.2 SOFES survey_

In the Summer of 2003, SOFES conducted a large-scale survey among students and graduates in Mexico. The survey was designed for this impact study. Four groups were surveyed: students with a SOFES credit, students without a SOFES credit, graduates with a SOFES credit, and graduates without a SOFES credit. Three universities participated in the survey: _Universidad Tecnologica de México_ , _Instituto Tecnológico de Monterrey_ , and _Centro Universitario Grupo Sol_ . These three universities form a small but fairly representative sub-sample of the more than 40 universities offering SOFES loans, and the outcomes must therefore be treated with care.

Appendix Table 2 summarizes the information collected from the survey.<sup>10</sup> The table shows summary statistics of the SOFES survey for students without SOFES credit (i.e. the control group), and SOFES-recipients (the treated group). The survey is organized into seven blocks:

- (1) General information

- (2) University information

- (3) Educational attainment

- (4) Family information

- (5) Student’s employment and income information

- (6) Information on SOFES credit program

- (7) Performance of SOFES credit program

In the first block _general information_ is collected on the students’ age and gender. Students with and without SOFES credit are comparable in terms of average age and gender distribution.

> 10 We only include Bachelor-students in the analysis (too few observations of graduate students are available, so we excluded them). Extreme values of the percentage of repeated courses (>50%) are dropped.

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models can manage. An increase in the use of country-specific modeling is also anticipated as countries will want to evaluate the adaptation policies and the effects of tradable instruments.

In addition, the growing number of project-based investments, together with the emergence of formal markets for tradable permits and derived financial products, will open up new areas for applied research. By way of example, project-based studies are likely to address questions concerning environmental additionality, spillovers and technology transfer that have in the past been addressed using modeling methods. In addition, while models typically suggest that CDM and JI projects will locate where abatement costs are lowest, project-based studies might better be able to examine the role institutions and other determinants of transaction costs play in investment decisions. Observations from formal exchanges can be used to examine the performance of market efficiency and integration. This is especially useful since current policies sometimes limit how tradable instruments can be used. Information across markets and among derivative markets can also be exploited to reveal how market participants price the risks associated with potential policy reversals, guarantees of quality, and performance risk related to specific types of contracts.

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