rafmacalaba/datause-encoder-data huggingface dataset:
**Table 11: Determinants of non‐compliance, within‐firm**

||**All**<br>**categories**|**Disclosure**<br>**categories only**|**Child**<br>**labor**|**Compensation**|**Contracts**|**Discrimination**|**Forced**<br>**Labor**|**Freedom of**<br>**association**|**Occupational safety**<br>**and health**|**Work**<br>**time**|
|---|---|---|---|---|---|---|---|---|---|---|
|Firm size|‐0.027|‐0.051|‐0.484***|‐0.153*|0.010|0.001**|0.001|‐0.020|‐0.064|0.051|
||(0.031)|(0.082)|(0.163)|(0.080)|(0.104)|(0.000)|(0.001)|(0.065)|(0.067)|(0.116)|
|Firm age|‐0.038|0.085|0.049|0.139*|0.010|0.001**|0.001|0.035|0.030|0.051|
||(0.030)|(0.082)|(0.162)|(0.080)|(0.104)|(0.000)|(0.001)|(0.065)|(0.067)|(0.116)|
|Baseline|0.076**|0.182**|‐0.049|0.256***|0.309***|0.001|0.001|0.123*|0.160**|0.235**|
|noncompliance|(0.030)|(0.083)|(0.162)|(0.080)|(0.105)|(0.001)|(0.001)|(0.065)|(0.067)|(0.117)|
|Length of|‐0.014***|‐0.014**|‐0.010|‐0.006|‐0.037***|0.001|0.000|‐0.014***|‐0.029***|‐0.008|
|participation|(0.002)|(0.007)|(0.009)|(0.005)|(0.008)|(0.001)|(0.001)|(0.005)|(0.005)|(0.010)|
|Constant|0.233***|0.174**|0.600***|0.193**|0.124|0.003|0.000|0.140**|0.357***|0.134|
||(0.034)|(0.075)|(0.225)|(0.077)|(0.090)|(0.002)|(0.000)|(0.056)|(0.074)|(0.089)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|240,805|32,934|8,937|35,928|22,455|52,332|13,473|26,946|64,269|16,465|
|R‐squared|0.029|0.041|0.218|0.073|0.081|0.027|0.028|0.047|0.055|0.069|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

25
use of standard survey instruments in majority of innovation data collection in OECD countries has mitigated the likelihood of a different impact of cognitive issues like ordering or wording used on the quality of the data collected. However, methodological issues related to the respondent’s understanding of innovation or to the type of survey instrument used have yet to be solved. The analysis of innovation and core economic indicators for EU countries, for instance, pointed out the existence of what is called the “Norwegian puzzle”, which shows relatively lower innovation rates for Norwegian firms than what would be expected when comparing the Norwegian economy to that of other countries. The results of an experiment conducted by Statistics Norway suggest that this is partially explained by the survey instrument used. The experiment shows that innovation rates are significantly different when captured by a stand‐alone innovation survey versus a combined R&D and innovation survey (Wilhelmsen, 2014). Similarly, an experiment conducted in Flanders region of Belgium shows that innovation rates are considerably and systematically higher when measured with a short questionnaire than when using a long questionnaire (Hoskens, 2015). While results are not clear as to which survey instrument is most accurate with respect to measured innovation rates, they clearly suggest that survey methodology can greatly impact the results.

In relation to context, cultural differences and social desirability there are several factors that can play a relevant role in how respondents understand and respond to innovation questions. Respondents are likely to have different views on what is considered as innovation depending on the context in which they live and operate. People in the USA, for example, are more likely to identify innovation with something new or unique and not to consider improvements as innovation. In Europe, on the contrary, people are more inclined to indicate both novelty and improvement as elements of innovation (Galindo‐Rueda and Van Cruysen, 2015). Furthermore, self‐reported innovation may be biased by social desirability that, in turn, might have a differential effect on respondents from different cultural backgrounds (Johnson & van de Vijver, 2003). As innovation is commonly associated with increased productivity and growth, it may perceived as a desirable outcome. Therefore, respondents to innovation surveys may be inclined to over‐ report innovations when follow‐up questions aiming at ascertain the accuracy of the answer are not asked and, furthermore, the tendency to over‐report innovation may vary across countries.

Finally, methodological issues can also have an impact on the measurement of innovation across firms of different size, as documented in a recent study conducted in Poland. The study, which compares innovation rates of micro and non‐micro firms, shows higher innovation rates for micro firms as compared 8
To construct the counterfactual we use the nearest neighbors matching procedure (Becker and Ichino, 2002).<sup>13</sup> In particular, matching is performed using the _psmatch2_ Stata command (Leuven and Sianesi, 2003). For each of our two models, we compare, using matching techniques, the productivity growth of export starters and matched non-exporters for the periods _t_ to ( _t_ + 1), ( _t_ + 1) to ( _t_ + 2), ( _t_ + 2) to ( _t_ + 3) and ( _t_ + 3) to ( _t_ + 4).

Table 7 reports the results of this comparison. We get that the extra productivity growth (EPG, hereafter) of export starters over non-exporters after one year is 1.6% for Model 1 and 7.1% for Model 2. The former coefficient is statistically significant at a 10% confidence level and the latter at a 1%. Beyond one year the estimates suggest that there is no extra productivity growth of export starters over non-starters.

# [Table 7 arround here]

The fact that the EPG is detected in the first year firms start exporting but lasts only from this year to the next, can be due to various reasons: on the one hand, learning is more important in the initial period of internationalization as firms are exposed to advanced foreign technologies and are faced with foreign competition for the first time; on the other hand, the increase of productivity in the first year of exporting may be the result of a better utilization of firms’ capacity after getting access to foreign demand (Damijan and Kostevc, 2006). Therefore, if nonexporting firms with similar characteristics as the export starters had exported, they would have enjoyed a jump in productivity the first year exporting (measured by the statistically significant estimated EPG from _t_ to ( _t+1_ )). In spite of not detecting statistically significant EPG the following years, the initial jump is enough to ensure a gap in productivity that remains in the future (in comparison to the alternative of continuing as non-exporters). This is the causal effect from

> 13 Abadie and Imbens (2008) show that due to the extreme non-smoothness of nearest neighbors matching, the standard conditions for bootstrapped standard errors are not satisfied, leading the bootstrap variance to diverge from the actual variance. This may be corrected using the Stata _nnmatch_ command (Abadie _et al_ ., 2004).

18
explained variable. However, as the final specification of the model indicates, these variables were not significant even at the 15 percent level. On the basis of the model, short-run deviations from the long-run trend are given as a function of the change in the consumption-to-GDP ratio and as a function of inflation. Based on the estimated coefficients, we can conclude that in the long-run relationship the credit-to-GDP ratio increases with increasing wealth of the economy and with an increasing consumption-to-GDP ratio. This factor then positively affects the explained variable in the short-run relationship as well, while inflation acts in the opposite direction. These conclusions are in accordance with intuition as regards the effects of the variables used on the credit-to-GDP ratio.

**Chart 20. Comparison of credit-to-GDP ratios Chart 21. Credit-to-GDP gap via out-offor the Czech Republic: HP versus OOS (in sample and NPL change in 2009 (gap in p.p.; p.p.) change in NPL ratio in p.p.)**

<!-- Start of picture text --> Credit-to-GDP gap via out-of-sa20 m ple and NPL change in 2009<br>20% (gap in p.p.; change in NPL ratio in p.p.)<br>18<br>15%<br>LT 16<br>10%<br>14 LV<br>5%<br>12<br>0%<br>RO 10<br>-5%<br>8<br>-10%<br>6<br>-15% BG<br>HU 4<br>-20% SK EE<br>PL 2<br>CZ<br>-25% SI<br>0<br>2000q1 2001q3 2003q1 2004q3 2006q1 2007q3 2009q1<br>-40 -30 -20 -10 0 10 20 30 40<br>HPgap OOSgap<br>Source: CNB, IMF, authors' calculations Source: IMF, authors' calculations<br><!-- End of picture text --> Source: CNB, IMF, authors' calculations The estimated parameters of the model were applied to data for the Czech Republic to obtain values of the ―equilibrium‖ credit ratio.<sup>19</sup> The OOS calculations imply significantly different conclusions regarding excessive credit compared to the calculations using the HP filter (Chart 20). According to the HP filter, the credit-to-GDP gap indicates for the Czech Republic excessive credit in the recent period, whereas the econometric estimate does not confirm this excessive credit level (values in the positive part of the chart indicate excessive private credit-toGDP ratios). Although statistical filtering techniques such as the HP filter do have a role to play in the analysis as a first step in the interpretation of the available data, a broader set of indicators and methods should be employed to determine a country’s position in the credit cycle.

An analysis of relationship between an increase in nonperforming loans during the crisis year 2009 and credit-to-GDP gaps estimated by OOS before the crisis (in the second half of 2008) for all CEE countries of the EU indicates that the most indebted countries experienced the largest increase in credit risk (Chart 21). After developing further the methods based on economic fundamentals such as the OOS, we are considering using the results in calibrating countercyclical capital buffers once Basel III is implemented.

> 19 We applied the parameters also to other CEE countries. The results and the accompanying discussion of the consequences for the countercyclical capital buffer are discussed in Geršl and Seidler (2011).

18
guaranteed for repayment by a public entity. For instance, public and publicly-guaranteed debt was about 75 percent of the outstanding external debt in the region by 2017. However, it is important to point out that the share of private non-guaranteed debt in total external debt stocks has increased from about 4 percent in the early 1990s to about 25 percent in 2017 ( **Figure 9** ).

This section highlights two recent developments in the evolution of external public debt in SubSaharan Africa over the past decade.

# **Fact I. There is a shift in the composition of public external debt: Countries have started to seek more financing from non-Paris Club governments and private creditors.**

For the region as a whole, there is heightened concern about public debt sustainability due to changes in the composition of the debt portfolio: there is an increasing portion of the public external debt that is owed to private creditors and non-Paris Club governments. Sub-Saharan African countries have traditionally sought financing from multilateral organizations. These organizations typically charge low interest rates and are often willing to renegotiate debts in the event of economic problems. However, a large and growing share of the debt of SSA governments is now owed to commercial creditors, who charge higher yields and are typically less open to restructuring. For instance, the share of the public external debt in the region owed to multilateral organization and bilateral creditors has decreased from about 80 percent of the public external debt in the 1990s to 59 percent over the period 2013-17 ( **Figure 10** ).

**Figure 10. PPG External Debt in Sub-Saharan Africa, 1990-2017** _(% GNI)_

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>PPG Multilateral PPG Bilateral PPG Bonds PPG Commercial Banks PPG Other Private Creditors<br><!-- End of picture text --> _Note: PPG = Public and publicly-guaranteed. GNI= Gross national income. Source: The World Bank – World Development Indicators_ 11
# **Figure S2: Sample Selection Panel I: Selection by Bureau Summary Score**

<!-- Start of picture text --> Has Score Missing Score<br>3000<br>2000<br>1000<br>0<br>0 1 2 3 4 5 −0.3 0.0 0.3<br>Summary Bureau Score (lower is better)<br>count<br><!-- End of picture text -->

# **Panel II: Selection by Phone Spending (a) Distribution of Spending**

<!-- Start of picture text --> All Banked All Unbanked Sample<br>1.0<br>0.5<br>0.0<br>$1 $3 $10 $30 $100<br>Density<br><!-- End of picture text --> <!-- Start of picture text --> (b)<br><!-- End of picture text -->

## **Performance by Usage Quartile**

<!-- Start of picture text --> ● Bureau ● CDR ● CDR−W<br>●<br>0.70 ● ● ●<br>0.65 ●<br>● ●<br>0.60 ● ● ●<br>0.55 ● ●<br>0.50<br>$1 $3 $10 $30 $100<br>(c)<br>Sparsified data<br>0.70 ● ● ● ● ●<br>0.65 4% 10% 20% 33% 100%<br>0.60 ● ● ● ● ●<br>0.55<br>$1 $3 $10 $30 $100<br>Monthly Phone Spending (log)<br>AUC<br>AUC<br><!-- End of picture text -->

**:**

_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. Sparse CDR results derived from estimating and testing on synthetic datasets that subsample every _k_ th transaction transactions. Bars represent 1 standard deviation. In our sample, spending is measured per phone account. National spending levels obtained from a household survey; that survey reports total spending per household; plot assumes one phone account per household. (Bottom two figures reproduced from (Björkegren & Grissen, 2018).) Source: authors’ analysis from telecom data.

3
15 information, or indirectly through contact with their aides. Most foreign lobbying appears to be informational, since foreigners are prohibited from contributing to election campaigns. The amounts expended by foreign principals are still substantial. Total foreign lobbying spending in the U.S. rivals political spending by domestic PACs.

This paper empirically explores the specific subject of tourism-related foreign lobbying by Caribbean principals. These principals include Caribbean governments and individuals in the private sector. We match U.S. tourist arrivals in Caribbean destinations annually between 19912000 with the total amount of lobbying spending by Caribbean principals. Estimates from fixed effects models and random effects gravity model indicate a strong positive association between foreign lobbying spending and the number of U.S. tourists. The relationship is quantitatively significant as well. A number of sensitivity analyses affirm this association. Further, lobbying appears to decrease the price elasticity of tourism demand, that is, destinations that lobby are able to differentiate their product. This finding suggests that foreign lobbying may be strategically used to increase tourism.

The answer to the question posed in the title of the paper is thus, in the affirmative. The implications for developing economies are obvious. There exists a legal channel for lobbying in the U.S. that might be used by governments and individuals alike to promote trade and tourism with the U.S. Rauch and Trindade (2002) emphasize the role that informational barriers play in restricting trade, and how networks overcome those barriers. Lobbying by foreign principals may also be viewed as a mechanism of overcoming informational barriers. While advertising may accomplish the same task, it is expensive to do. Further, advertising is far less credible on destination characteristics about which U.S. tourists really care – safety from diseases, security, protection against natural calamities – than an assenting nod from the U.S. government in the form of such information posted on the State Department website.

Our results are among the first in the literature and therefore deserve scrutiny. For example, we have used the general price level in computing the real exchange rate to infer whether lobbying makes tourism demand inelastic. An improvement would be to use more hardto-find sector-specific price data for this purpose. It is also relevant to question whether lobbying participation by all islands would have the same impact as reported in this paper, or whether lobbying competition would result in rent transfers to the government being lobbied.
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33
are homogeneous of degree one.<sup>40</sup> Most of the literature whose primarily goal is to measure the welfare gains from transit infrastructure within cities has focused on the first term and direct effects, by assuming that there are no wedges in the economy and that it operates under perfect competition. I contribute to this literature by analyzing the effect of transit improvements on the second and third margins.<sup>41</sup>

# **5 Empirical Strategy and Estimation**

In this section, I describe the main empirical strategy and estimation of the main parameters. This section is divided into four parts: parametrization of commuting and trade costs; estimation of trade and commuting elasticities; estimation of the labor supply elasticity across sectors - _κ_ -, and model inversion to recover the fundamentals of the economy such as technological and amenity parameters.

## **5.1 Trade and Commuting Costs**

For the counterfactual analysis, I parametrize commuting costs as in the urban economics literature (Ahlfeldt et al., 2015; Heblich et al., 2018; Tsivanidis, 2019). I assume that both iceberg commuting and trade costs are parametrized using the following expressions: where time _ni_ is the average travel time in minutes across different transportation modes of moving from location _n_ to location _i_ .<sup>42</sup> The main objects of interest are the parameters _δd_ , and _δτ_ that transform travel times to iceberg costs. I estimate these parameters from a nested logit specification using the 2017 Origin-Destination Survey. I use trips to from home to work and vice-versa to estimate _δd_ , and trips to restaurants, outlets, and retail shops to obtain the parameter _δτ_ .

The estimation is based on the following choice model. A worker _ω_ is choosing between different transportation modes to travel from _n_ to _i_ . These transportation modes are grouped into different nests, for example public or private nests denoted by _G_ . Denote the set of transportation modes in _g_ , by Υ _g_ . The indirect utility of choosing transportation model _m ∈_ Υ _g ⊂G_ is:

> 40Holmes et al. (2014), Świ ̧ecki (2017), and Asturias et al. (2016) use a similar formula using hat algebra.

> 41Since this formula applies to the case in which the change in commuting/trade costs is infinitesimal, for the counterfactual analysis, I estimate and decompose the change in welfare using percentage changes and exact hat algebra.

> 42I calculated a weighted average of travel times across the different transportation modes using each transportation mode’s aggregate share for commuting and consumption from the travel survey data. Hence, in terms of workers’ utility, the assumption is that transportation modes’ preferences take a Cobb-Douglas form. This is a conservative assumption. For example, in the case of CES or random idiosyncratic shocks, workers will substitute more other modes of transportation for the subway after the transit shock.

25
combination with the detailed expenditure shares available from the CILSS made it possible to construct such disaggregated price indices. In this section we show some examples.

Perhaps the most useful breakdown of the consumption basket is between food and non-food. This is relevant for poverty analysis, in view of Engel's law. Also, in developing countries many food items are subsidized - and those subsidies often need to be eliminated under structurd adjustment programmes. To address nutritional concerns a further breakdown according to types of food is also useful. Table 4 shows iegional price indices for 3 food categories and 5 non-food categories. It needs to be pointed out that conceptually such disaggregation requires the assumption that consumer preferences are separable, i.e. that prices of items outside the sub-index do not affect the consumption of items included in the sub-index (see Deaton and Muellbauer, 1980). Clearly, this assumption becomes less tenable the finer the disaggregation.

Table 4 shows that in general food items display a wider regional price variation than non-food items. Food is significantly cheaper in rural areas than in Abidjan, and more so than is the case for non-food. Several non-food categories are in fact more expensive in some rural areas than in Abidjan. This directly confirms our hypothesis stated in section 3 that the omission of non-food prices in a price index (as was the case for the McKay index) wil result in over-estimation of regional price differences.

In the context of structural adjustment, the distinction between tradeable and nontradeable goods and services is important since adjustment programs aim to change relative prices in favor of tradeables (World Bank, 1990). Table 4 also shows the regional price index for food and non-food items, broken down according to their tradeability. For food items, there is no systematic pattern: in some regions the price differential of tradeable food exceeds that of non-tradeable food, but in other regions it is the reverse. Tradeable non-food items however are systematically priced lower in rural areas than in Abidjan, with the biggest differential in East Forest and Savannah. Non-tradeable non-food items are priced 11