rafmacalaba/datause-encoder-data huggingface dataset:
a payment. We estimate that the baseline email increased the probability of ling by 21.3 p.p., which is consistent with Figure 2. The eect of the information email, at 23.2 p.p., is signicantly greater than the baseline email. The two emails also increased the probability that a rm reported a positive net liability (by about 5 p.p.) and made a positive payment (by about 3.4 p.p.). Note that the payment rate is lower than the share of rms reporting a positive liability, as some rms make quarterly advance payments or are subject to withholding at amounts that fully cover their liability, and others under-pay or pay with delay. The fourth and fth columns show that the emails also increased the average payment amount, although this is driven by a greater number payers, not larger payments conditional on making a payment.<sup>27</sup> Using PPML, we estimate that the baseline email increased income tax payments by CRC 8,168 (US$ 15) per taxpayer, on average, while the information email increased payments by CRC 10,192 (US$ 19).<sup>28</sup> The information email had a larger impact on all outcomes than the baseline email, but the dierence is statistically signicant at conventional levels only for payment, where we observe the highest average and presumably the least noise among the dummy variable indicators.

Table 5 shows the analogous results for rms not covered by third-party information. Recall that, for these rms, the baseline treatment omitted any mention of third-party information, and the information treatment mentioned the general use of third-party information by the tax authority, without making specic reference to the email recipient (see Section 3). The emails increased the probability of income tax ling by 15 p.p.; they also increased the probability of reporting a positive net liability and of making a payment, but the magnitude of the eect is small0.6 p.p.compared with the eect on ling. The eect on the payment amount is statistically signicant but not economically large. The treatment increased payment by CRC 215 on average (US$ 0.39).

In sum, we nd that the emails signicantly improved income tax compliance by non-lers. For rms covered by third-party information, the eects of emails that listed specic examples of information known to the tax authority were generally higher. All treatment eects estimated here equal or exceed those from other communication experiments (Castro and Scartascini 2015; Del Carpio 2014; Fellner, Sausgruber, and Traxler 2013; Kettle et al. 2016), which suggests that combining dierent messages contents that have proved to be impactful individually (deterrence,

> 27Note that we consider payments made by the taxpayer with her annual declaration, and ignore the quarterly advance payments during the year. If a taxpayer is a non-ler and also has outstanding advance payments, those would have to be made with the annual declaration, and are thus considered in our estimation.

> 28We winsorize payment amounts at the top 0.1% of the unconditional payment distribution to reduce the inuence of outliers.

19
Table 4

# Deficit Ceiling

|||||||||(Mill|inna<br>|ofBath)|
|---|---|---|---|---|---|---|---|---|---|---|
|Fiscal|<br>Ce|Ca|Re|Ra|(Ce-Re)|(Ca-Ra)|<br>PR|Cm|Ce/Cm|Ca/Cm|
|Year|_1/|2/|_1/|_2/|------|------|(Principa|l (Maximum|<br>(%)|(S)|
||(Expe|nditure)|(Reve|nue)|Ce|Ca|Repayment)|Expend.)|||
|1970|27,300.0|23,617.0|19,020.0|17,909.0|0.303|0.242|-|-|-|-|
|1971|28,645.0|26,978.0|21,800.0|19,088.0|0.239|0.292|-|-|-|-|
|1972|29,000.0|28,905.0|21,700.0|21,165.0|0.252|0.268|-|-|-|-|
|1973|<br>31,600.0|<br>30,937.0|<br>23,300.0|<br>25,344.0|<br>0.263|<br>0.181|<br>716.5|<br>29,698.2|106.40|104.17|
|1974|<br>36,000.0|<br>34,629.0|<br>26,520.0|<br>37,921.0|<br>0.263|(0.095|) 1,511.6|<br>34,359.3|104.78|100.78|
|1975|<br>48,000.0|<br>43,541.0|<br>38,500.0|<br>38,229.0|<br>0.198|<br>0.122|<br>2,357.2|<br>50,010.8|<br>95.98|<br>87.06|
|1976|<br>62,650.0|<br>53,686.0|<br>48,675.0|<br>42,731.0|<br>0.223|<br>0.204|<br>3,244.1|<br>63,439.0|<br>98.76|<br>84.63|
|1977|<br>68,790.0|<br>63,470.0|<br>50,470.0|<br>51,710.0|<br>0.266|<br>0.185|<br>1,414.3|<br>64,218.9|107.12|<br>98.83|
|1978|<br>81,000.0|<br>74,716.0|<br>62,000.0|<br>62,022.0|<br>0.235|<br>0.170|<br>4,124.8|<br>80,799.8|100.25|<br>92.47|
|1979|<br>92,000.0|<br>86,157.0|<br>72,000.0|<br>75,109.0|<br>0.217|<br>0.128|<br>2,125.6|<br>91.700.5|100.3a|<br>93.95|
|1980|114,556.5|114,287.0|<br>92,680.0|<br>92,147.0|<br>0.191|<br>0.194|<br>3,168.5|118,384.8|<br>96.77|<br>96.54|
|1981|140,000.0|129,941.0|120,000.0|110,329.0|<br>0.143|<br>0.151|<br>3,403.1|152,722.5|<br>91.67|<br>85.08|
|1982|161,000.0|152,168.0|140,000.0|113,810.0|<br>0,130|<br>0.252|<br>4,578.1|178,662.5|<br>90.11|<br>85.17|
|1983|177,000.0|165,100.0|151,000.0|136,448.0|<br>0.147|<br>0.174|<br>6,196.7|193,707.4|<br>91.37|<br>85.23|
|1984|192,000.0|177,402.0|160,000.0|147,847.0|<br>0.107|<br>0.167|<br>8,412.7|206,730.2|<br>92,87|<br>85.81|
|1985|213,000.0|197,468.0|178,000.0|159,196.0|<br>0.164|<br>0.194|13,216.6|233,073.3|<br>91.39|<br>84.72|
|1986|21,000.0|204,016.0|185,000.0|166,254.0|<br>0.151|<br>0.185|13,833.2|242,316.6|<br>89.96|<br>84.19|
|1987|22-,500.0|207,817.0|185,500.0|192,484.0|<br>0.185|<br>0.074|15,500.0|244,275.0|<br>93.13|<br>85.08|
|1988|243,500.0|220,655.0|199,500.0|245,646.0|<br>0.181|(0.113|) 17,357.6|263,261.1|<br>92.49|<br>83.82|

-----------------------------------------------------------------

__----------__------------------------Source _l/ Estimated level (Thailand's Budget in Brief) 2/ Actual level (Monthiv Dulletin, BoT) Note 1* According to the hudgetary law, government deficit must not exceed 20X of the estimated expenditure plus 80X of the principal repayments of that vear.

C aovernment expenditure R Rovernment revenue a actual e estimated or planned _Cm_ r maximum or ceiling expenditure (1.25*Re * 0.8PR) 4 3
Similarly, the finding of a significant positive effect for potential enterprise owners does not appear to be caused by better record-keeping making them realize profits are higher than they thought.

The second approach is to check whether training affects the difference between self-reported profits (elicited via a direct question), and business revenue minus expenses. De Mel et al. (2009b) discuss several reasons why these two measures of profits may differ, such as mismatches in the timing of input purchases and sales based on those purchases. Nevertheless, if the training causes individuals to keep better track of their finances and start calculating profits differently with greater reference to revenue and expense records, we would expect training to reduce the absolute difference between these two measures. Column 3 of appendix Table 2 shows that this is not the case – training leads to an insignificant increase in the absolute difference between the two measures.

Taken together, these two approaches suggest that our results are not being driven by changes in reporting of profits.

|**Appendix Table 2: Are Impacts Being Driv**|**en by Better Rec**|**ord-Keepi**|**ng?**<br>Absolute difference|
|---|---|---|---|
||Truncate<br>in round|d Profits<br>s 4 and 5|between revenue-expenses<br>and reportedprofits(R4 and R5)|
|_Panel A: Current Enterprises_||||
|Assigned to Cash if finish Training|274.6|-230.1|1,892|
||(809.0)|(797.5)|(1,371)|
|Assigned to Training only|-543.9|-769.2|1,432|
||(868.8)|(872.4)|(1,409)|
|Record-keeping Score||493.5***<br>(169.1)||
|Observations|1017|1017|1016|
|_Panel B: Potential Enterprises_||||
|Assigned to Cash if finish Training|804.7|743.1|839.9|
||(830.2)|(827.3)|(858.5)|
|Assigned to Training only|2,244**|2,020**|1.067|
||(975.9)|(940.8)|(785.9)|
|Record-keeping Score||718.4***||
|||(171.4)||
|Observations<br>Notes: Regressions in columns 1 and 2 al<br>record-keeping score for the current ent<br>at the firm level. *, **, and *** indicate s<br>Absolute difference between profits and|675<br>so control for bas<br>erprises. Standar<br>ignificance at th<br>revenue minus|675<br>eline profi<br>d errors in<br>e 10, 5 and<br>expenses t|675<br>ts and baseline<br>parentheses, clustered<br>1 percent levels respectively.<br>runcated at the 99th percentile.|

29
����SPTime �ε��������SPTime, ε� ≡�������SPTime �ε|���0��

The condition ���SPTime �ε|���0� plausibly holds since most major household shocks usually precede the disbursements of social assistance (see the implementation review in footnote 3). Further, the fixed effects help to control for potential channels through which a correlation might exist between household or zonal shocks and the potentially endogenous variables. Estimation and interpretation of our results thus hinge on the plausibility of the above conditions and controls. Similar arguments can be made for the other interaction terms.

# **Other source of endogeneity**

Section II above discusses the identification of effects, primarily for the social transfers; showing that the effects on social transfers are identified as most major household shocks usually precede the disbursements of social assistance.

We turn next to potential endogeneity of “self-reported” shocks. Particularly, it is possible that the probability of reporting a shock as significant in the surveys could increase with economic distress, and thereby introduce measurement errors via mis-reporting. We follow the tradition that, by definition, the occurrence of shocks is exogenous (not the magnitude of shocks, which could likely be over/under-estimated). With our focus on the occurrence of shocks, any potential measurement error from mis-reporting is likely non-classical and therefore captured in the error terms, conditional on our controls for unobserved heterogeneity.<sup>21</sup> First, it is useful to note that the problem of mis-reporting shocks is not unique to our “grouping” approach. Second, as a sanity check for potential mis-reporting and its importance for our analysis: we correlate the households’ reported shocks (i.e., yes/no for drought, flood) with publicly non-manipulable weather data (i.e., rainfall, drought) from MERRA-.v2 (a global reanalysis database that assimilates space-based observations of aerosols and represent their interactions with other physical processes in the climate system).<sup>22</sup> Reassuringly, the share of households reporting

> 21 In addition, the fact that we estimate models that directly control for households’ coping strategies help to overcome additional concerns that shock reporting may be endogenous to coping strategies.

> 22 See <u>https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.</u>

43
2 Figure **1:** Per capita exports, 1989 and 1993 (US$) <!-- Start of picture text --> ToaEU ToTbhe World<br>1000<br>900<br>800 "I<br>700<br>600 H Lry<br>-500 CR R<br>400<br>300 l Lria ri d<br>200 ~<br>100 -<br>0<br>89 93 89 93 93 93 89 93 89 93 89 93 899 89es93<br><!-- End of picture text --> constramined by restricted market access for 'sensitive' products. Rapid growth in exports occurred in _both_ sensitive and non-sensitive items. Indeed, export growth in certain sensitive products-agriculture foremost-has been far below potential (market access granted), suggesting that the CEECs may not have as strong a comparative advantage in agriculture as is often believed.

# **2.** Measures of **Change:** Aggregate Trade Indicators

What follows presents and discusses a number of sunmnary measures that have been compiled on the basis of import data reported by the European Communities. Data on the imports from the CEECs are used rather than reported exports by each of the CEECs.<sup>4</sup> We also mostly abstract from exports to the rest of the world. Although this reflects data availability, an analysis that is limited to the EU is of interest given the fact that the CEECs are clearly .specializing' in the EU market. The EU now accounts for over 50 percent of the CEECs' exports (Table 1).

The various measures that are discussed in what follows are indirect indicators of the restructuring underway. What matters is not the absolute value of any indicator, but how thz Eastem European countries compare to each other. Such comparisons provide some information on the impact of the different strategies with regard to 'restructuring' policies: privatization, trade liberalization, etc. Macroeconomic factors will of course be important as well, but mainly affect aggregate performance, not changes in the structure and composition of exports. It is the latter that is the focus of attention here.

Table 1: Share of EU in total **exports.**

|CEECs|29.3|52.3|
|---|---|---|
||<br>.|_32A_|

It is useful to start with a brief discussion of total exports of the CEECs and the FSU in the last 5 years. Figure 1 reports per capita exports of the CEECs. to the world and to the EU. Two points stand out. First, the decline in the value of total external trade after
||||||||Root-|mean|-squar|e erro|r|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.08|<br>1.90|0.39|2.55|0.30|2.33|0.19|0.22|0.09|0.13|0.05|0.10|
|0.1|0.3|0.15|1.17|0.39|3.58|0.33|5.51|0.24|0.33|0.15|0.18|0.09|0.10|
|0.1|0.5|0.16|0.75|0.38|4.92|0.34|4.74|0.28|0.37|0.19|0.20|0.12|0.12|
|0.1|0.7|0.12|0.44|0.38|3.78|0.42|3.10|0.34|0.35|0.23|0.19|0.15|0.12|
|0.1|0.9|0.05|0.18|0.18|2.82|0.24|3.12|0.35|0.23|0.41|0.13|0.31|0.08|
|0.3|0.1|0.07|1.31|0.26|1.82|0.19|0.21|0.10|0.11|0.05|0.06|0.03|0.04|
|0.3|0.3|0.14|1.25|0.31|2.01|0.22|0.29|0.14|0.16|0.08|0.09|0.05|0.06|
|0.3|0.5|0.16|1.06|0.31|2.12|0.24|0.33|0.16|0.17|0.09|0.09|0.05|0.06|
|0.3|0.7|0.13|0.82|0.31|2.30|0.24|0.32|0.16|0.16|0.09|0.09|0.05|0.06|
|0.3|0.9|0.06|0.47|0.30|1.78|0.32|0.21|0.23|0.11|0.11|0.07|0.06|0.04|
|0.5|0.1|0.06|0.84|0.26|0.50|0.18|0.17|0.10|0.09|0.05|0.05|0.03|0.03|
|0.5|0.3|0.13|1.09|0.29|0.98|0.21|0.26|0.13|0.14|0.07|0.07|0.04|0.04|
|0.5|0.5|0.16|1.15|0.29|1.11|0.22|0.27|0.14|0.15|0.08|0.08|0.05|0.05|
|0.5|0.7|0.13|1.09|0.28|0.81|0.21|0.25|0.12|0.13|0.07|0.08|0.04|0.04|
|0.5|0.9|0.06|0.84|0.26|0.49|0.18|0.17|0.11|0.09|0.05|0.05|0.03|0.03|
|0.7|0.1|0.06|0.47|0.31|1.03|0.31|0.21|0.23|0.11|0.12|0.07|0.06|0.04|
|0.7|0.3|0.13|0.82|0.31|2.61|0.25|0.31|0.16|0.16|0.09|0.09|0.05|0.05|
|0.7|0.5|0.16|1.06|0.32|1.32|0.25|0.33|0.16|0.17|0.09|0.09|0.05|0.05|
|0.7|0.7|0.14|1.25|0.31|0.87|0.23|0.29|0.14|0.16|0.08|0.09|0.05|0.06|
|0.7|0.9|0.07|1.31|0.26|0.50|0.17|0.20|0.09|0.11|0.05|0.06|0.03|0.04|
|0.9|0.1|0.05|0.18|0.19|2.37|0.25|0.96|0.36|0.23|0.44|0.13|0.31|0.08|
|0.9|0.3|0.12|0.44|0.39|4.19|0.41|1.20|0.34|0.35|0.24|0.19|0.15|0.12|
|0.9|0.5|0.16|0.75|0.40|4.66|0.35|1.25|0.28|0.37|0.19|0.20|0.12|0.12|
|0.9|0.7|0.15|1.17|0.39|3.94|0.34|1.35|0.24|0.34|0.15|0.18|0.09|0.11|
|0.9|0.9|0.08|1.90|0.37|2.76|0.30|0.58|0.19|0.22|0.09|0.13|0.05|0.09|

Table 4: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with no covariates and ρ = 0.5.

27
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Using _κ_ (0) = 0, Differentiating (31), and using _κ_ (0) = 0, Substituting (32) for the substitution of _db_ , We consider two cases for the cost function _κ_ ( _·_ ). First, assume that the cost of default _κ_ is an increasing and convex function such that _κ_<sup>_′_</sup> (0) = _κ_ (0) = 0. In this case, _C_<sup>_′_</sup> (0) _<_ 0 in (33). It is then obvious that some debt reduction in period 1 is efficient. The property is equivalent to the “smoothing” of the cost of default between periods (like the tax smoothing property in efficient inter-temporal taxation).

Second, assume that the marginal cost is constant and equal to _α_ . The previous expression becomes An increase of the debt forgiveness, _x_ , in period 1 generates a smaller total cost of debt reduction if and only if Note that the default takes place for _y <_ ( _z − x_ ) _R_ , with _R_ determined in (30). Therefore, the probability of default _π_ is a decreasing function of the debt reduction, _π_ ( _x_ ). No debt reduction in period 1 is efficient if _π_ (0) _<_ 1 _/_ (2 + _α_ ). The previous discussion is summarized by the following proposition.

23
We use annual, country-level data for the period 2000-08 which cover about 90 countries. The insurance variables were mostly taken from the insurance data provider AXCO, although asset data were also taken from national sources. The independent variables were obtained from a variety of sources including AXCO, the _International Financial Statistics_ (IMF), the _World Development Indicators_ (World Bank), and national sources. Table 2 shows the definitions, sources and descriptive statistics of the variables. Table 3 shows the correlations between the dependent and explanatory variables.

As shown in Table 2, there is substantial variation in the insurance variables. NONLIFE, the most ubiquitous form of insurance, ranges from virtually 0 percent (e.g. Angola, Bangladesh, Myanmar) to over 3.5 percent of GDP (e.g. USA, UK). LIFE, a form of insurance which emerges at more advanced stages of development, ranges from virtually 0 percent (e.g. Kazakhstan, Syria, Vietnam) to over 10 percent of GDP (e.g. South Africa, Portugal). ASSETS range from virtually 0 percent (e.g. Tanzania, Kenya, Saudi Arabia) to over 90 percent of GDP (e.g. Sweden, UK, France).

In line with the previous section, we group our explanatory variables in four categories. First, we consider _economic variables._ Our main income level variable is GDP per capita (GDPPC). Our measure of income inequality is the fraction of income held by the richest 20 percent of the population (INCOMETOP20) and we use annual inflation (INFLATION) as a proxy for inflationary expectations. As mentioned before, we add two additional economic variables to examine the non-life sector. We include the number of passenger vehicles per 1,000 inhabitants (CARS) because most countries require at least third party liability insurance (comprehensive car insurance is usually voluntary but also common in many countries). We also consider trade activity, the sum of import and export activities as a fraction to GDP (TRADE), since trade often relies on the availability of marine, cargo, and liability insurance.

Second, we explore the impact of _demographic variables_ such as population size (POP), population density (POP_DENSITY), and average life expectancy (LIFE_EXP). The first two variables have been neglected in empirical research, but could prove relevant for both the life and non-life sector, especially the former. We also include the overall age dependency ratio 10
results seem not driven by the bias. If anything, the devaluation should be biasing the estimates against us finding positive coefficients. Finally, as already mentioned, the sign of the bias is hard to predict for fully integrated plants. However we would expect this to be negligible after 1997-1998 and inexistent for 1994. Once more Figure 7 seems to suggest that our results do not follow the dramatic trend of the exchange rate.

Figure 7: Real exchange rate and treatment effects (Source: Penn World Tables 6.1 and authors’ calculations) <!-- Start of picture text --> 1994 1996 1998 2000 2002<br>year<br>Exporter and Importer Just Exporting<br>Just Importing 1990 1992 1994 1996 1998 2000 2002<br>Source: INEGI, BANXICO and Authors’ Calculations year<br>(a) Treatments - All Firms (b) Exchange Rate<br>.3 2<br>.2<br>1.8<br>.1<br>Treatment Effect<br>0 RealXrate<br>1.6<br>−.1<br>1.4<br><!-- End of picture text --> We conclude that on the basis of the economic reasoning and the inspection of our results, we do not find evidence that these are driven by the exchange rate devaluation. If anything, the bias does not appear to be the main driver of our findings. We cannot exclude that our results are influenced by the devaluation but we have no basis to believe that our results are driven by the exchange rate fluctuations. In fact, because of their timing, and the similarity in some of their effects, it is very difficult to be able to disentangle the effects of NAFTA from those of the devaluation. Overall, if we adopt a more general perspective we can, however, say that our results do not apply narrowly to NAFTA but more generally to a move towards higher integration and openess of the overall Mexican economy. In this sense, the devaluation was part of this move and we are capturing its effects jointly with those of the NAFTA reforms.

# 5 Conclusions

This paper answers two questions: (1) Did NAFTA reforms make Mexican plants more productive? (2) If so, through which channels? As opposed to previous studies, we have been able to identify the trade integration status at the firm level and not at the sectoral level (Pavcnik 2002). Also, improving on previous studies that analyze the 25
_Figure 6: FEI rankings for water infrastructure projects in Sri Lanka_ <!-- Start of picture text --> Financial and Economic Indicator (FEI)<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>‐‐‐‐ Budget constraint of US$ 0.35b<br>Source: Authors' calculations<br>P17 P18 P13 P19 P14 P26 P1 P8 P28 P20 P15 P3 P16 P10 P7 P22 P23 P2 P24 P4 P6 P5 P27 P11 P12 P21 P25<br><!-- End of picture text --> Project 14, the Dankotuwa Water Supply Project, ranks the lowest on the SEI scale. This is because the project serves an area that has low levels of poverty and a good existing water supply system in terms of both quantity and quality.<sup>22</sup> According to the FEI, on the other hand, projects 17 and 18 (i.e. Divulapitiya Water Supply; Mirigama, Kandalama and Kaleliya and Ganegoda Group Towns Water Supply, respectively) score the lowest. These low FEI scores are driven by their low benefit‐cost ratios, since both projects have high costs per beneficiary (Project 17‐ US$ 1299 per beneficiary and Project 18 ‐ US$ 1518 per beneficiary).

Distinctively, two projects rank extremely high on both the SEI and FEI. Project 25 (Construction of Treatment Plant at Kethhena) scores the highest on both indicators. This is largely because it would have a high number of both beneficiaries (90,824 new beneficiaries per million dollars invested) and jobs created (67 jobs per million dollars invested). These numbers are significantly higher than any other project in the list. However, it must be repeated that this project is distinctly different in nature to the others in the project list: it is the only water treatment project, while all others are water supply projects.

The other project that scores highly on both indicators is Project 21, Yan Oya Water Supply Project. This project has the lowest safe water coverage (38.59%) and demonstrates a high level of need with respect to water service quality (water supply of between 1‐6 hours per day and in an area with a prevalence of chronic kidney disorder). The third project in the high

> 22 Both project areas have a supply of 18‐24 hours of water, low incidence of water quality issues and a high safe water coverage rate.

26