rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Descriptive Statistics - Final Sample

||Mean|SD|p50|N|
|---|---|---|---|---|
|_Panel A: Taxpayers’ characteristics_|||||
|Corporations|0.33|0.47||31,396|
|Individual Business|0.41|0.49||31,396|
|Self-employed service providers|0.06|0.23||31,396|
|Corporations, IB or self-employed|0.79|0.41||31,396|
|Distrito Central|0.27|0.45||31,396|
|San Pedro Sula|0.22|0.41||31,396|
|_Panel B: Past fling behavior_|||||
|Reported revenue (Sales) (2019) (L1,000s)|617.79|2,876.15|39.60|31,396|
|Declared revenue (Sales) (2019) (L1,000s)|1,432.67|9,745.31|190.36|31,396|
|_Income Tax 2018_|||||
|Declared income tax in 2018|0.86|0.34|1.00|31,396|
|Reported revenue (Income) (2018) (L1,000s)|544.36|2,477.44|46.78|31,396|
|Declared revenue (Income) (2018) (L1,000s)|1,305.15|2,670.35|274.22|31,396|
|Declared revenue 2018 | declaring (L1,000s)|1,509.82|2,817.80|381.22|27,140|
|Not liable for taxes|0.47|0.50|0.00|27,140|
|Liable for income taxes|0.50|0.50|1.00|27,140|
|Liable for asset taxes|0.03|0.16|0.00|27,140|
|Taxable base 2018 | declaring (L1,000s)|115.47|265.81|41.47|27,140|
|Tax liability 2018 | declaring (L1,000s)|15.34|75.74|0.14|27,140|
|Efective tax rate|0.12|0.11|0.06|14,374|
|_Income Tax 2017_|||||
|Declared income tax in 2017|0.81|0.40|1.00|31,396|
|Reported revenue (Income) (2017) (L1,000s)<br>|483.26|2,538.67|5.40|31,396|
|Declared revenue (Income) (2017) (L1,000s)|1,233.77|2,499.54|256.28|31,396|
|Declared revenue 2017 | declaring (L1,000s)|1,532.62|2,702.42|421.76|25,274|
|Taxable base 2017 | declaring (L1,000s)|121.47|260.80|62.22|25,274|
|Tax liability 2017 | declaring (L1,000s)|16.57|71.99|0.29|25,274|
|_Panel C: Third-party information_|||||
|Third-party information available (2019)|0.71|0.45||31,396|
|Revenue reported by other taxpayers|0.88|0.33||22,423|
|Revenue reported by POS operators|0.22|0.42||22,423|
|Revenue reported by government|0.03|0.17||22,423|
|Revenue reported by customs|0.01|0.09||22,423|
|_Anomalies_|||||
|Declared losses for fve years|0.08|0.27||31,396|
|Atypical fnancial transactions|0.07|0.26||31,396|
|Atypical declared revenue|0.49|0.50||31,396|
|_Risk assessment_|||||
|Low risk|0.21|0.40||31,396|
|Medium-low risk|0.28|0.45||31,396|
|Medium risk|0.23|0.42||31,396|
|Medium-high risk|0.19|0.39||31,396|
|High risk|0.10|0.30||31,396|

_Note_ : This table presents descriptive statistics for the entire experimental sample. The first panel presents taxpayers characteristics such as type (corporation, individual business or self-employed service providers) and location. The second panel presents descriptive statistics on past tax paying behavior, for both income and sales taxes, in FY2017 and FY2018. Finally, the last panel describes the sources of third-party information and behavioral anomalies used in the tax authority’s risk model, and also the distribution of taxpayers across the five broad risk-levels used by the TA.

24
CCC 2100, Large Farms <!-- Start of picture text --> N<br>+<br><!-- End of picture text -->

# %Change of Ag Income

IMP_farm.D100_ccc_b HM - 1.4129 - -0.3000 HR -0.2999 - -0.2000 MN -0.1999 - -0.1000 I -0.0999 - -0.0500 I -0.0499 - 0.0000 [YP 0.0001 -0.0500 (0.0501 -0.1000 HE 0.1001 -0.2000 HE 0.2001 -0.3000 BEE 0.3001 -0.9900 TLSLSLSI —__s—*iT Decimal Degrees 0 4 8 16 24 32 NIGGOL SEO, YALE UNIV.
Özden and Reinhardt [2002] demonstrate that there are a number of good predictors of US preference eligibility. These include (a) distance from country i to the United States; (b) a dummy indicating country i maintains a formal alliance with the United States<sup>19</sup> ; (c) average annual US total aid per capita to the country during the previous decade; (d) the geographic location of the country - dummy varaibles for the continental location. We should note that the US is likely to grant preference eligibility when it also has political alliances with a target country and likely to remove preferences along with other political sanctions, a criterion explicitly written into the preference-authorizing statutes.

Table 4 presents the IV estimates of the main equation. Our results are similar to the ones in table 2 which provides the appropriate comparison. Column 1 is for 2001 and the CBI/Andean/AGOA coefficient is positive and significant. The results indicate that being a CBI/Andean/AGOA beneficiary increases your exports to the US by more than 3 (exp(1.115)) times in 2001. Furthermore, the FTA’s impact is even larger than CBI/Andean/AGOA. The coefficients of the gravity variables have the predicted signs and values closer to previous estimations. All of the variables in the first stage probit estimation have the predicted and highly significant coefficients as can be seen at the bottom of the table<sup>20</sup> .

In the next stage, we assume that FTA eligibility is also determined by geostrategic interests, thus we treat it together with CBI, Andean, and AGOA. Our new variable takes the value 1 if a country is eligible for any of these four programs<sup>21</sup> . Column 2 presents the results for 2001 and these are very similar to the previous column. Eligibility in these programs increases the exports of the member countries by more than 3 times in 2001.

> 19Distance and alliance data are from the Correlates of War Project as distributed by Bennett and Stam [2000], updated by the authors to 2000.

> 20We did not report the coefficients of the continent dummies from the first stage regression. These are also all significant.

> 21Estimates from auxiliary Ordered Probit regressions for eligibility for these programs indicate that one cannot reject the hypothesis that the probability of receiving FTA preference is determined by the same threshold values of the explanatory variables used in the selection equation (and in the Probit equation of the treatment regressions) as those that determine the probability of being elegible for the other programs (CBI/Andean/AGOA).

17
> where _wg_ , _h_ is the wage of member _g_ in household _h_ and _Yho_ represents income from other sources of household _h_ . Hence<sup>_y_</sup> _h_ measures per capita household disposable income, the welfare measure used here. The _maquila_ sector is linked to household welfare—and hence poverty—by changes in wages and employment attributable to the sector’s performance. Following human capital theory, the log of wages is defined as a function of personal characteristics and a random component. In order to identify the conditional gender wage gaps and the wage effects accounted

> by the _maquila_ sector, we introduce a dummy variable for women and for workers in _maquila_ :

According to equation (3), the wage of worker _i_ is a function of _j_ personal characteristics, _xi,j_ ; a dummy variable, _Dw_ , which takes the value 1 when the worker is a women; a dummy variable for workers in the _maquila_ sector, _Dm_ ; a set of parameters; and a random component, ε _i_ .<sup>8</sup> To test the hypothesis that part of the relation between the _maquila_ expansion and poverty operates through gender, let us decompose the impact from _maquila_ to wages and express this as a linear function of gender: Substituting equation (4) into equation (3) yields Parameters δ 1 and δ 2 in equation (5) measure the gender and _maquila_ premium, respectively; δ 3 captures the wage effects of the _maquila_ sector (over and above the gender impacts) that operate through gender. An alternative interpretation for the interactive effect, δ 3 , can be found in the economics of discrimination literature, which posits that the gender wage gap tends to be smaller in export-oriented sectors (Becker 1971). The gender wage gap is linked to the exportoriented _maquila_ sector by the increased competition brought about by trade integration (Artecona and Cunningham (2002), Arbache and Santos (2005)). The interactive effect, δ 3 , is equal to the difference in the gender wage gap in and out of the _maquila_ sector (table 5) **.** If the _maquila_ sector is more competitive (and hence employers care more about workers’ productivity rather than the gender) and there is some degree of discrimination in the labor market, the wage gap between male and female workers in the _maquila_ sector should be smaller than in other sectors; δ 3 should thus be greater than zero.

> 8 Given data restrictions, the textile and apparel industry is used as a proxy for _maquila_ sector. In 2006, the textile and apparel industry concentrates 77 percent and 79 percent of the total labor force and value added of the _maquila_ sector, respectively (Banco Central de Honduras 2007). For more details see Annex 2.

9
a question about the objective of EU migration policy, 56% of respondents said migration should benefit source countries, 38% worried about negative effects of brain drain, and 27% thought that temporary movement possibilities needed to be enhanced.<sup>7</sup> While the views expressed by respondents to the survey may not reflect the beliefs of the EU public at large, they do suggest it may be possible to find support for effective temporary labor movement schemes.

A temporary migration program has many advantages. First, temporary labor inflows will have little impact on the demographic profile of the EU, thus easing political resistance while partially addressing labor market needs. Second, it would be consistent with the temporary nature of the excess supply of labor in Mediterranean countries. As noted previously, the demographic profiles in these countries are such that, in the next 20 years or so they will experience a reduction in the supply of workers. Third, temporary migration flows are likely to reduce brain drain losses for source countries, while still generating significant remittance flows.<sup>8</sup> Fourth, returnees help raise productivity in home countries since they bring back newly acquired human and physical capital. Fifth the incentives for undocumented migration by facilitating legal and recurrent crossborder movement of workers are reduced.

The aggregate potential gains associated with liberalization of temporary movement can be significant. Walmsley and Winters (2005) analyze the potential global welfare gains associated with a relaxation of barriers to temporary movement of workers and conclude that aggregate gains to the world can easily exceed the complete elimination of remaining barriers to trade in goods. Tani (2005) analyzes the potential growth effects of temporary movement of people, focusing specifically on business travelers, and finds that such movement is positively correlated with economic growth.<sup>9</sup>

> 7 EU Public Consultation Report, 2005:

> <u>http://ec.europa.eu/comm/development/body/theme/consultation/doc/Stat_IPM_EN.pdf.</u> 8 In principle remittances associated with temporary movement may be higher than that associated with longer-term movement, as links with source country household members are likely to be much stronger. More generally, however, Philippe Fargues has argued that as migration comes to involve younger workers who are not encumbered by families and have less need to take care of extended families that remain in source countries, the relative importance of remittances will decline. Instead, income earned will be devoted more to consumption and investment in the human capital of the migrants themselves.

> 9 Note that migration – temporary or permanent – is not a solution to the fiscal pressures created by an aging population. This will require changes in the level of social benefits and the way they are financed. An example is shifting from defined benefit pension programs to defined contribution based schemes.

12
# **REGULATION AND MACROECONOMIC PERFORMANCE**<sup>*****</sup>

Norman V. Loayza Ana María Oviedo Luis Servén _The World Bank University of Maryland The World Bank_

## September 2004

### **Abstract**

Regulation is purportedly enacted to serve specific social purposes. In reality, however, it follows a more complex political economy process, where legitimate social goals are mixed with the objectives of particular interest groups. Whatever its justification and objectives, regulation can have potentially significant macroeconomic consequences, by helping or hampering the dynamics of economic restructuring and resource reallocation that underlie the growth process. This paper provides an empirical analysis of the macroeconomic impact of regulation. It first characterizes the stylized facts on regulation across the world, using a set of newly constructed, comprehensive indicators of regulation in a large number of countries in the 1990s. Using these indicators, the paper studies the effects of regulation on economic growth and macroeconomic volatility employing cross-country regression analysis. In particular, the paper considers whether the effects of regulation are affected by the country's level of institutional development. Finally, the analysis controls for the likely endogeneity of regulation with respect to macroeconomic performance. The paper concludes that a heavier regulatory burden reduces growth and increases volatility, although these effects are smaller the higher the quality of the overall institutional framework.

**_JEL_** _classification:_ H11, K20, K30, E32, O40.

_Keywords:_ Regulation, government structure, volatility, economic growth.

> * This research has been supported by the World Bank’s Latin America Regional Studies Program and the Poverty Reduction and Economic Management Network.  We are very grateful to Raphael Bergoeing, Caroline Freund, John Haltiwanger, Patricia Macchi, Andrea Repetto, Roberto Zagha, and participants in the EGDI-WIDER Conference on the Informal Sector (Helsinki, 2004) for useful comments and data.  The views expressed herein are those of the authors and do not necessarily represent those of the institutions to which they are affiliated.

1
53 Economic Parameters of Dcforcstation <!-- Start of picture text --> Open Access Case<br>Timber Price Path<br>Prim<br>.. ~~~~~~...... .....<br>Tirm<br>OMCM GPM Ah C~<br>LIand Use<br>Land Clb<br>rime<br>Agriculture E Managed Forest U Unanaged Forest<br>Blsckn dupictft base as lbnd u. for cadson.<br><!-- End of picture text --> Figure 12
# **5.5 Regulations and Job Flows**

The next step in our analysis is to look at the possible impact that labor regulations have on observed job flows (Table 13). We focus on the restricted sample for the transition economies as discussed above. The first specification (column (1)) is a simple cross-country estimate in which we include the U.S. job flow benchmark and the labor regulation indicator, but we do not interact the latter with the U.S. benchmark. These results are only preliminary, not least given the possible omitted variable bias due to the exclusion of country fixed effects. The estimated coefficient of the synthetic indicator of the stringency of hiring and firing regulations is negative and statistically significant at the 1 percent level. This result is largely unchanged if we allow the coefficient on the U.S. job flow benchmark to vary across the three regions (column (2)).

The next step is moving to the difference-in-difference analysis by focusing on the variation of job flows across industry/size classes within each country. Column (3) presents the basic model with the U.S. job flow benchmark and its interaction with the hiring and firing labor regulation variable, plus country fixed effects (as in equation (4) above). We find that the interaction term is negatively signed but not statistically significant at the conventional level. This result holds even if we differentiate the effect of labor regulations by region.

Previous research (see, e.g., Caballero et al. [2004], Heckman and Pages [2004]) suggests that the degree of enforcement of labor regulations - as well as other regulations - varies across our sample of countries that include the OECD countries, Latin American and transition economies. Not only are some firms and jobs not registered in Latin America and increasingly in the transition economies and some Southern European countries, registered firms may also not fully comply with the existing rules and regulations. As an indication of the different degree of enforcement of laws and regulations, we consider the law and order indicator from the Fraser Institute (based on the Political Risk Component I (Law and Order) from the International Country Risk Guide, ranging from 0 to 10, 10 being the worst).<sup>13</sup> The indicator shows the highest compliance with laws and regulations in the OECD sample of countries (average of 0.56), followed with the transition economies (average of 1.76), and by the Latin American countries (average of 4.96).

To control for possibly differing degrees of enforcement of laws and regulations we

> 13Micco and Pages [2004] also make an attempt at controlling for different degrees of enforcement of regulations by using an indicator of rules of laws and government effectiveness (see Kaufmann et al. [2004]). We used the Fraser index of law and order because it is available for the time period for which our job data are available for the countries.

33
17

# Figure 2

# Relative performance of privatized and state firms

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# 4. OWiNERSIH1P EFYECTS

The privatization effect estimated from equation (1) is the average effects across all owners of privatized firms. Equation (2) allows its estimation for particular categories of private owners.39 However, because of a relatively small number of observations within some categories (see Table B.2, Appendix B), the estimates of some ownership effects -- even though of considerable magnitude -- are not precise enough to allow statistically defensible inferences. We therefore augrnent these results by estimating the ownership effects for all outsider-owned firms and all insider-owned firms taken together. Full regression statistics for all estimates are given in Tables A. and A.2, Appendix A. Table 2 below provides the estimated privatization effects and **1** indicates their significance levels.

39 **Recall that** in **the context used here, "owner" means** the **largest hareholder, taeequivalency made possible** by the **high concentration of holdings in the sample.**
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per 1000 in Sikkim to 43 in Jharkhand)<sup>8</sup> . Four states –Jharkhand, Madhya Pradesh, Rajasthan and Orissa – register child mortality greater than 39 per 1000 live births. However, these are also states that are inhabited by a large number of STs<sup>9</sup> .

**<u>Table 4. Child mortality rates among Scheduled Tribes by state, 2005</u>**

||Neonatal|Post-<br>neonatal|Infant|**Childhood**|Under-five|
|---|---|---|---|---|---|
|**Rajasthan**|24|19.3|43.3|**40**|83.3|
|<br>Sikkim|11.7|25.3|37|7.7|44.7|
|Arunachal Pradesh|32.4|23.9|56.3|26|82.3|
|Nagaland|12.7|18.8|31.5|17.3|48.8|
|Manipur|33.6|22|55.6|9.3|64.9|
|Mizoram|15.6|16.7|32.3|23.8|56.1|
|Tripura|23.8|40.1|63.9|11.6|75.5|
|Meghalaya|24.9|21.5|46.4|14.6|61|
|Assam|35.6|13|48.6|34.5|83.1|
|**Jharkhand**|52.1|25.3|77.4|**43**|120.4|
|**Orissa**|52.2|28|80.2|**39.2**|119.4|
|Chhattisgarh|44|22.8|66.8|30.8|97.6|
|<br>**Madhya Pradesh**|50.2|25.2|75.4|**41.6**|117|
|Gujarat|29.5|38.8|68.3|28.5|96.8|
|Maharashtra|47|8.8|55.8|30.7|86.5|

**_Notes_** :  Only states with 9 <u>percent ST population; calculations based on NFHS-3 for the cohort 0-4 years old</u>

<!-- Start of picture text --> Figure 2<br>Orissa: District Level Relationship Between Infant<br>Mortality and Concentration of Scheduled Tribes<br>Source: World Bank Staff Calculations fromRCH II Survey<br>80<br>Malkangiri<br>60<br>40<br>20<br>0 Bhadrak<br>0 20 40 60 80 100 120<br>Infant Mortality Rate<br>% STs in the District<br><!-- End of picture text --> These high levels of child mortality in states with larger tribal populations also bear out at the district level. Analyzing the health status of different social groups in Orissa, the World Bank (2006) for instance found that infant mortality is directly proportional to the proportion of Scheduled Tribes in the district (Figure 2).

# **2.  WHAT EXPLAINS THESE PATTERNS?**<sup>**10**</sup>

8 While the authors have made every attempt to match the mortality numbers in Table 4 with published numbers, it has not always been possible because DHS implemented a proprietary code to compute their mortality rates. For their calculations, the authors of this paper relied on the ltable command in Stata, using matching interval int(0,1,3,6,12,24,36,48,60), as suggested by the DHS methodology guide (Rutstein and Rojas, 2006). This approach is also recommended in O’Donnel et al (2008). Beyond deriving matching statistics, relying on DHS’s methodology does not offer any additional benefits since there are a number of other reliable techniques for calculating mortality rates. In fact, moving beyond descriptive analysis of mortality requires shifting away from DHS’s methodology altogether in favour of survival estimation based on the Kaplan-Meier method used in stcox regressions in Stata.

9 The states of Jharkhand, Madhya Pradesh, Rajasthan, Orissa and Maharashtra together account for 58 percent of the ST population in India.

10 The sections on access to helath for Adivasis draws heavily from Das et al (2010) 9