rafmacalaba/data-use-mentions-extended huggingface dataset:
# **V Robustness checks**

Thus far, both the instrumental variable method and the structural model have shown that the demand side of corruption appears to be the driver of firms’ tax evasion, while the bribe supply strategy seems to be geared toward securing government contracts. In what follows, we perform a variety of sensitivity analyses to check the robustness of the findings, examining potential selection bias issues, conducting falsification tests, identifying potential outliers, and cluster fixed effects, and alternative methodology using a propensity score matching method.

## **V.1 Potential selection bias issues**

Assessing illegal activities such as bribery is challenging as the survey responses may suffer from desirability bias. Some firms might decide to reveal their tax evasion and bribery behaviors while others may intentionally decide not to respond to such sensitive questions. The missing bribery and tax evasion data raise questions about potential selection bias issues, particularly if the group of responders and non-responders are statistically different in their observable characteristics. Although we do not have information on why some firms did not respond to corruption and tax evasion questions, we can check whether the groups of responders and non-responders differ in terms of their observable characteristics. Table .8 in appendix reports a set of regressions using individual characteristics of firms as dependent variables. The approach follows Svensson (2003), where the regressor is a dummy variable taking the value of 1 if a firm has missing data on corruption. We extend the analysis by using a dummy variable taking the value of 1 if a firm has missing information on tax evasion and bribing for contracts. Moreover, we consider two additional dummy variables that account for the combination of missing data on (i) corruption and tax evasion, and (ii) corruption and bribing for government contracts. All regressions include country, sector, and year fixed effects given that some missing data on corruption and tax evasion seems to be specific to the country, the sector, and the year of the survey. As seen in panel A, there
less than one percent of the Indian population. Only five cities in the state contain more than 100,000 people. On the surface, Uttarakhandi women may appear to be more empowered than the average. These women led the _Chipko_ movement to prevent deforestation<sup>1</sup> as well as the demand for a separate state. However, while the state has a literacy rate of 72 percent, the Census reports that only 60 percent of all women are literate.<sup>2</sup> A more detailed measure of literacy from a nationally representative household survey finds 43 percent of Uttarakhandi women cannot read at all, while an additional 5 percent can only read parts of a sentence (International Institute for Population Studies and Macro International, 2007). Therefore, the effective literacy rate for females may be closer to 50 percent.

Although 43 percent of all Uttarakhandi women work, nearly two-thirds of these women (64 percent) are not paid for their work, and over 70 percent work in agriculture. These women are likely to work on their family’s farmland, which does little to empower them. In addition, 23 percent of Uttarakhandi women have no say over household financial decisions, and almost 43 percent do not have the final say on their own healthcare. Over half (55 percent) did not have the final say on large purchases made by their household (International Institute for Population Studies and Macro International, 2007). Hence, Uttarakhandi women can have little say in the household or community.

In 1988, _Mahila Samakhya_ was launched in three states of India to empower women through formal, informal, and vocational education. In theory, the community-level program was placed

> 1The Hindi word _Chipko_ means “to stick”. In the _Chipko_ movement of the seventies, Uttarakhandi villagers, and women in particular, literally hugged trees to prevent deforestation.

> 2The national literacy rate is 65 percent, and female literacy of 54 percent. The most literate state is Kerala, with a
Figure 6: Change in sales by informality (a) Change in sales vs. 2019 according to the type of (b) Change in sales vs. informality informality <!-- Start of picture text --> 0 -20<br>-10 -8<br>-25<br>-20 -17 -17<br>- 20<br>-30 -30<br>-40 -36 -38<br>-50 -47 - 49 -45 -46 -44 -46 -35<br>-60 -40<br>Fully formal Necessity De Soto Parasite<br>-.2 0 .2 .4 .6 .8<br>Round 1 Round 2 Round 3 Informality PCA score<br>Change in sales<br><!-- End of picture text --> Notes: Controls for size, sector, age, country, gender of the owner, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

when weighted by size (Figure A8), implying that there is still a role for informality as an additional factor explaining firm performance during crisis.

: _Regression framework_ Results from Table 1, suggest that even after external firm attributes such as size, sector, age, and gender of the owner, fully informal firms (a value of one on the informality index) suffered ten percentage points higher decline in sales relative to formal firms (column 1). If we further condition by initial labor productivity (column 2), the magnitude does not change much. This indicates that even though an important part of the performance of firms can indeed be explained by their characteristics, as the coefficient on informality reduces significantly from the unconditional (-36%) to the conditional specification, there is still a role for informality by itself.<sup>19</sup> Within the types of informal firms,
Overall illicit income earned by drug lords in Colombia from cocaine tra¢ cking can be estimated using the estimated projected production and price of cocaine. The relevant cocaine price however is not clear, as local prices of cocaine are quite low while international prices from the United States during the period of study are too high. Caulkins and Reuter (2010) report cocaine prices from raw production in Colombia up to the retail prices in the United States for the years 1997, 2000 and 2007. They note that the export cocaine price in the ports of Colombia is around one-third of the mid-level wholesale price reported in the United States while the import cocaine price in the ports of the United States corresponds roughly to two-thirds of the mid-level wholesale price reported inside the United States. Assuming that somewhere between the ports of Colombia and the United States, Colombian drug lords deliver their production to others that continue the tra¢ cking in the United States, the cocaine price relevant for estimating the illicit income of Colombian drug lords would be between these two bounds. Accordingly, we use a price of half the mid-level wholesale cocaine price per kilogram reported for the United States by UNODC, as in the following measure Note that we use the consumer price index with base year 2005 to get illicit aggregate income from cocaine tra¢ cking in pesos in real terms. Hence, the aggregate real undetected illicit income from cocaine production per year RUICOCt is given by the following proxy variable Similarly, we do not observe total potential income from common crimes but only income from reported crimes. We use National Police Department statistics for the period under study on the income stolen through common crimes against property which includes 56 o¤enses, such as robbery, theft, burglary, motor vehicle theft, larceny etc., and excludes drug tra¢ cking from aggregate values. This allows us to obtain the following measure of reported income stolen where �tqt measures the probability of detection and con...scation of income from common crimes for some �t > 0. This
|**Ln(Income)**|-3.1922***<br>(0.3255)|-3.676***<br>(0.3778)|-4.2984***<br>(0.4391)|-4.494***<br>(0.4596)|-5.0898***<br>(0.5197)|
|---|---|---|---|---|---|
|**Average loss in job accessibility (%)**|4.846|5.676|6.608|6.996|7.872|
|**Adjusted R**<sup>**2**</sup>|0.436|0.437|0.455|0.457|0.463|
|**Observations**|1126|1126|1126|1126|1126|

# Proximity of Non-Petroleum Based Modes and BRT with income controls

The variables of the previous two regressions were combined to see if the relationship was still true when considered altogether. The results are shown in Table 6, where these relationships do hold true, although the relationship between the losses in accessibility and proximity to each type of mode is not as robust.

_Table 6: Proximity to Non-Petroleum-Based Modes and BRT with Income Controls regression_

|||**Fu**|**el Price Increase=**|||
|---|---|---|---|---|---|
||**10%**|**20%**|**30%**|**40%**|**50%**|
|**Constant**|5.7551***<br>
**Figure S3: Performance by Subgroup**

_Note:_ AUC represents the area under the receiver operating characteristic curve. CDR (Call Detail Record) represents base random forest model, and CDR-W weekly ensemble model. Since logistic regression performed better with bureau data, we present that model here. Bars represent 1 standard deviation. Reproduced from (Björkegren & Grissen, 2018). Source: authors’ analysis from telecom data.

4
The estimation results for the Euro Area show a greater precision of the interest rate pass-through that the one estimated for the entire sample. The precision approaches one—that is, an almost complete pass-through. Interestingly, the Euro Area results reveal a substantial asymmetry of the pass-through. Therefore, for the Euro Area countries the study estimates may suffer from a significant publication selectivity bias. Regarding study-specific, macro-financial, and monetary policy factors, the results show a statistical significance of only three factors: market capitalization, the stock turnover ratio, and the dummy for rates on loans for other purposes. The significance of market capitalization and the dummy appear in all previous estimations regardless of the sample size and the estimation method. As for the stock turnover ratio, the effect seems to be rather small and statistically significant only at the 10% level. Nevertheless, it suggests that more actively-trading (liquid) stock markets can reduce the size of the pass-through.

For the non-EA countries, the estimation sample covers only about one-quarter of our original sample. Therefore, the regression estimation faces the problem of increased collinearity among variables, which could bias the results even after applying the GETS selection procedure. Nevertheless, the model still largely confirms the results of our previous estimations. We confirm significant effects of the dummy for rates on long-term loans, consumer loan rates, average rates, market capitalization, central bank independence (using the Garriga (2016) data series), and the inflation targeting regime. Several additional factors appear significant in this case. Namely, for the non-Euro Area countries, we find that using data in quarterly frequency reduces the size of the estimated pass-through. Further, using the monetary policy rate rather than the money market rate as the reference rate increases the size of the estimated pass-through. Looking at the macro-financial factors, two additional variables appear significant. One, the stock turnover ratio lowers the size of the pass-through—much more so than for the Euro Area subsample. This could suggest that active stock markets may serve as competing source
# **5.5 Mechanisms behind the relation between IT, competition and performance**

As previously mentioned, IT solely cannot increase productivity as it is necessary to incorporate the use of these technologies into the firm’s processes. These requires reorganization and changes in processes which represent complementary investments. Our results support the view of competition providing the right incentives to pursue all of these organizational and processes investments but it is important to deepen into the channels through which this occurs.

Therefore, we analyze variables included in the ENTIC 2013, which are directly related to organizational, product and processes innovation. We use two innovation score variables, one for products and processes innovation, and another one for marketing and organization. The first one includes cost reductions, reduction of training costs, time-saving, labor productivity increases, access to knowledge sources, exchange of ideas, plant and production scaling, improvements in industrial design of products and processes. The organization and marketing innovation score includes access to new markets, better communication within the firm, increasing logistics efficiency, implementation of automatized systems, improving communication with clients, a higher diversity in products delivery and more personalized products or services orders.

As Figure 4 shows, even though the average innovation score is low for firms included in our sample (the score has a range of 0-10), in general firms that face higher competition pressures from China have a higher innovation score, both in terms of product and processes innovation and in marketing and organization. Furthermore, firms that invest in IT in order to cope with competition, hold the highest mean innovation scores.

36
# **1 Introduction**

The recent wave of ‘new’ trade models has rekindled interest in the gains from trade. The results and theorems on the aggregate gains from trade in Dixit and Norman (1980, 1986) have been extended by Arkolakis, Costinot and Rodriguez-Clare (2012), Costinot, Donaldson and Komunjer (2012) and Costinot and Rodriguez-Clare (2014).<sup>1</sup> Concurrently, there has also been a renewed interest in the distribution of those gains. These are the focus of Porto (2006), Fajgelbaum and Khandelwal (2015), Atkin and Donaldson (2015), and Atkin, Faber and Gonzalez-Navarro (2018).<sup>2</sup> In this paper, we combine these two questions and assess the income gains relative to the inequality costs of trade policy. Using survey data for 54 developing countries, we explore the potential trade-off between the gains from trade and the distribution of those gains and we provide a quantification of the inequality-adjusted welfare gains from trade. The evaluation of this trade-off is important, especially because free trade is often opposed on inequality grounds.

We develop a comprehensive model that describes how trade policy affects the real income of different households. Tariffs determine domestic prices which affect households both as consumers and as income earners. As consumers, households are affected through the cost of the entire bundle of traded consumption goods. Similarly, household income is affected through changes in the returns to household production activities, crop growing, family businesses, labor earnings, and government transfers. Our model encompasses all these mechanisms. Following Deaton (1989), we use a first-order approximation to measure how changes in tariffs impact real income.

We then combine tariff data on various goods with household survey data on detailed income and expenditure patterns to estimate these first order welfare effects for 54 low and middle income countries. With estimates of the welfare effects of import tariff liberalization for each household, we study the aggregate gains from trade (as in Arkolakis
of observations, but we observe once again a positive and significant coefficient for most linkages considered.

Taken together, these estimates suggest that firm-to-firm linkages play an important role in the transmission of green technologies across multinational enterprises, especially when the green firm had high emissions to begin with. We also observe some evidence that these linkages matter when one of the firms involved is not an MNE, but in this case the relationships of interest are estimated from a small sample and are less precise.

# **6 Concluding remarks**

While there is a growing recognition that green technologies are key for reducing the carbon footprint of economic activity at the global level, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and on the economic mechanisms underlying this process. In this paper, we have used textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide 12
- 13 -

III. Bergsten and Cline (1985) 21. Following Chenery (1960), Bergsten and Cline (1985), using data for eleven industrial countries plus the EEC as a region for the years 1974-1984, estimated the ratio of imports of goods and nonfactor services to GNP as a function of income, population, natural resource endowments, and transport costs incurred in importing goods from trading partners. In this exercise, all EC member countries were grouped as one region, and only the imports from outside the region were taken as EC imports. Data on imports of goods and nonfactor services and on CNP were both in national currencies. GNP data were in real terms at 1974 dollars as adjusted for cross country differences in purchasing power. Based on the regression analysis using these data, Bergsten and Cline concluded that the ratio of imports to CNP for Japan was explainable by the size of GUP, the natural resource endowments and the transport costs involved in trading and that there was nothing abnormal in Japan's import behavior. 1/ 22. Several issues were raised with the study by Bergsten/Cline (1985). First, the use of per capita income instead of CNP itself is preferable. Second, combining all EC member countries into one "country", 1/ The regression results obtained were as follows: Z = 0.7731 - 0.0628 log Y + 0.0100 log P - 0.0378 log L (28.4) (26.2) (7.57) (7.25) + 0.0414 I - 0.00069 T e 0.0082 Dj, R2 = 0.8950, (3.96) (9.7) (0.47) where Z is the ratio of imports of goods and nonfactor services to CNP, Y is CUP its real terms at 1974 dollars, as adjusted for cross-country purchasing power differences, P is crude oil production per capita, L is arable land per capita, I is a
on those variables.<sup>9</sup> Aftwerwards, a convenience sample was taken to reach 113 localities, accounting for feasibility considerations of the local implementer.

**Table 2. Sampling process and localities remaining in each step.**

|Samplingstep|Oaxaca|Yucatán|Total|
|---|---|---|---|
|Starting point - Official list (INEGI census 2010)|13,936|11,695|25,631|
|Available locality level data (INEGI census 2010)|10,418|2,462|12,880|
|In agrarian unit with forest coverage (INEGI Land use and Vegetation 2017)|636|238|874|
|Without land conflict (INEGI census 2010)|542|178|720|
|With GSM cellphone coverage (private data)|207|115|322|
|10 adult women (18+) or more in a locality (INEGI census 2010 – also excludes<br>those without detailed data on women population)|124|39|163|
|Convenience reduction because of logistical limitations|82|31|113|

Prior to this call for proposals, localities in the sample received on average 1.48 (min = 0, max = 22) forest grants from CONAFOR<sup>10</sup> between 2007 and 2017, with 67% of localities not receiving any grants. Even though we do not have access to this indicator for localities outside the sample, this information highlights that localities included in this RCT typically had little experience with grant or government program application processes.

# _2.5 Randomization and balance_

Randomization was completed in multiple steps. First, a k-mean algorithm was implemented to create 10 groups of similar localities based on four characteristics: i) proportion of females aged three years or older who spoke an indigenous language in 2010; ii) marginalization index by locality in 2010; iii) number of CONAFOR grants
are larger in size and have higher productivity is evident in the BPS data (Figure 1, panels a and b). For example, a firm in the top tercile of labor productivity has a 28% probability of being an exporter, while for firms in the middle and low terciles, the exporting probability is 26% and 23%, respectively. These productivity differences across firms are driven by better management practices and technological capabilities (Syverson, 2011; Bloom et al., 2013; Kogan et al., 2017), a fact that is also borne out in the BPS data (see Appendix Figure C1) and hence a testimony to its quality.

The BPS data also show clear and robust correlations between global engagement and internal firm capabilities. Management practices shape firms’ global engagement: a firm with a low management score has an 8% probability of being an exporter, while that probability for firms with medium and high management scores is 10% and 14%, respectively (Figure 1, panel c).<sup>9</sup> This finding is consistent with existing literature on global engagement and management, which suggests that better-managed firms have superior export performance (Bloom et al., 2021). Since this analysis is not causal, our correlations may also indicate that global engagement encourages firms to become better managed (Caliendo and Rossi-Hansberg, 2012; Chen and Steinwender, 2020; Chakraborty and Raveh, 2018).<sup>10</sup> Digital readiness is also associated with firms’ global engagement: a firm with high digital readiness has a 14% probability of being an exporter, which is 4 percentage-points higher than a firm with medium digital readiness, and double than a firm with low digital readiness (Figure 1, panel d).<sup>11</sup> This is in line with the literature on the link between innovation, technology, and trade (Damijan and Kostevc, 2015; Akcigit et al., 2018) but our results are
.8]|
|**Total Tax Loss =**|1.3|1.0|0.5|1.3|0.9|1.7|1.5|1.6|2.4|2.2|1.5|
|**CIT+Payroll+VAT**|[2.3]|[1.7]|[0.8]|[2.1]|[1.5]|[2.7]|[2.5]|[2.4]|[4.2]|[3.4]|[2.5]|
|**Tax Losses + Firms’ Losses**|2.2|1.8|1.2|5.2|1.3|7.0|3.1|3.7|6.2|11.7|4.4|
||[3.9]|[3.4]|[2.1]|[9.1]|[2.5]|[13.9]|[6.3]|[6.9]|[12.0]|[21.5]|[8.2]|

Note: This table displays predicted tax revenue losses and the increase in firms’ losses, as a percentage deviation from the baseline (last year available in the micro data) in Panel A, and as a percentage change of GDP in the baseline year in Panel B. *Eswatini does not levy payroll taxes.

The third row provides an estimate of lost revenue from the VAT. While we do not work with VAT data directly, we simulate taxable value-added as sales minus material inputs.<sup>12</sup> Since firms adjust their material costs proportionally to the shock, our estimated drop in value-added is exactly proportional to the size and duration
|
|**Bank of Japan**|January-2016|-0.10|3.2|4.6|1.6|2.5|2.9|0.3|1.5|1.4|0.9|2.7|
|**_(Current account deposit rate)_**|||||||||||||
||**Average**||**1.1**|**1.4**|**0.6**|**0.8**|**0.5**|**0.0**|**0.6**|**-0.1**|**0.2**|**0.5**|
||**Median**||**0.9**|**1.3**|**0.3**|**0.4**|**0.3**|**-0.1**|**0.8**|**0.6**|**0.3**|**0.2**|

Sources: JP Morgan, Bloomberg, Haver Analytics, World Bank. A. “Rate” refers to the main deposit policy rate in most cases, and the main refinancing rate for the Riksbank. Exchange rate is the J.P. Morgan EM Foreign Exchange Index, EMBI spread is calculated as the average premium paid over a U.S. government bond with comparable 10 maturity, and equity prices are the MSCI Emerging and Frontier index. The bonds spreads are expressed in basis points, while the exchange rate and equities are in percent.

54