rafmacalaba/datause-encoder-data huggingface dataset:
# Policing Unfair Imports: The U.S. Example

by J. M. Finger and Tracy Murray

# Table of Contents

|1.|Sequence<br>and Time Limrits of U.S. Procedures|2|
|---|---|---|
|I.|Cases and Outcomes in the 1980s|7|
|Ill.|What Drives the Unfair Trade Law--?|15|
|IV.|Concluision|20|
|Refer|ences|21|
|Anne|x|22|
# **Appendix Table I. 5: Organized Sector Log (Employment) and Log (Output) - Differential Effect of Labor-Intensity and Skill-Intensity**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Labor-Intensive<br>Industries<br>|Skill-Intensive<br>Industries<br>|Labor-Intensive<br>Industries<br>|Skill-Intensive<br>Industries<br>|
||Employmen|Spillovers|Output Sp|illovers|
|Hitl Sill f||_Other-se_|_ctor Spillovers_||
|orzona povers rom|||||
|Unorganized sector|0.0941***|0.1081***|0.0652***|0.1108***|
||(0.0160)|(0.0185)|(0.0199)|(0.0249)|
|Above Median * Horizontal|||||
|Spillovers from Unorganized Sector|0.1616***|0.1454***|0.1914***|0.1371***|
||(0.0200)|(0.0187)|(0.0242)|(0.0208)|
|Forward Spillovers from|||||
|Unorganized sector|0.2308|0.2089|0.8852***|0.3576|
||(0.2014)|(0.1837)|(0.3123)|(0.3127)|
|Above Median * Forward Spillovers|||||
|from Unorganized Sector|(0.0274)|0.0441|0.2201|0.7574**|
||(0.1876)|(0.1870)|(0.3179)|(0.3500)|
|Backward Spillovers from|||||
|Unorganized sector|0.6971***|0.4723***|(0.0781)|0.4795+|
||(0.1273)|(0.1174)|(0.2433)|(0.3202)|
|Above Median * Backward|||||
|Spillovers from Unorganized Sector|0.5883***|0.2941**|(0.0849)|-0.3364++|
||(0.1138)|(0.1304)|(0.2449)|(0.2522)|
|||_Own-sec_|_tor Spillovers_||
|Forward Spillovers from Organized|||||
|Sector|1.3044***|1.3650***|3.0121***|3.1780***|
||(0.2835)|(0.2707)|(0.8611)|(0.7947)|
|Above Median * Forward Spillovers|||||
|<br>from Organized Sector|1.3523***|1.4351***|3.1535***|2.6335***|
||(0.2833)|(0.2825)|(0.7992)|(0.8949)|
|Backward Spillovers from Organized|||||
|Sector|1.0748***|1.1959***|0.9981**|1.1708**|
||(0.1528)|(0.1590)|(0.4541)|(0.4907)|
|Above Median * Backward|||||
|Spillovers from Organized Sector|1.4036***<br>|1.4991***<br>|1.4282***<br>|2.1296***<br>|
||(0.1641)|(0.2131)|(0.4640)|(0.5250)|
|||_Other-sector Pr_|_oductivity Spillovers_||
|Average TFP of Horizontal-linked|||||
|Unorganized Plants|0.0187|0.0173|0.1155|0.1179|
||(0.0435)|(0.0434)|(0.1170)|(0.1190)|
|Average TFP of Forward-linked|||||
|Unorganized Plants|0.0720|(0.0085)|(0.0249)|(0.2333)|
||(0.1315)|(0.1325)|(0.4143)|(0.4188)|
|Average TFP of Backward-linked|||||
|Unorganized Plants|0.1960**|0.1524*|0.9494***|0.8735***|
||(0.0929)|(0.0914)|(0.2764)|(0.2776)|
|Constant|-32.5822***|-31.2996***|-101.3892***|-110.1288***|
||(4.4249)|(4.4093)|(19.5937)|(20.2183)|
|Observations|5888|5888|5888|5888|
|R-squared|0.1891|0.1854|0.1475|0.1414|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 58
# **CHAPTER 2:  CONSTRAINTS ON THE SUPPLY OF FINANCIAL SERVICES**

1. This chapter examines supply side aspects of access to financial services in Colombia from the perspective of constraints on financial institutions in Colombia that prevent low income clients from gaining access to formal banking services.  Three groups of supply side issues have been identified: (i) regulatory issues, (ii) cost constraints and (iii) physical difficulties.  The following will look at each one of these in more detail.

## **2.1 REGULATORY CONSIDERATIONS**

2. In Colombia, existing regulation has a mixed impact on the use of financial services. On the one hand, norms such as strict account opening requirements and taxation on financial services might discourage the use of formal financial services. On the other, regulation on income tax returns and sales tax refunds on credit and debit card transactions could encourage the use of formal banking services.

3. **Existing regulation constraints entry to formal financial services for low income people.** Anti-money laundering regulation introduced in 2003<sup>20</sup> created a number of complex account opening requirements. In order to open an account with a Colombian bank, individuals have to provide multiple documents, including proof of identity, employer information and monthly income and expense statements, such as proof of salary and rental payments (see Table 2.1). These information requirements apply to all clients regardless of their socio-economic characteristics. By implicitly assuming that clients are active in the formal economy, this regulation creates significant barriers to entry for low income clients. As the demand survey shows (see Chapter 3.1), a significant share of the unbanked is not active in the formal sector, and hence, cannot fulfill the formal eligibility criteria, such as proof of income or employment. Consequently, low income people are severely disadvantaged under existing regulation. In addition, multiple documentation requirements are needed to open a savings account in Colombia<sup>21</sup> . Banks require proof of employment, personal references or, in some cases, even proof of tax income for opening an account that entails no credit risk (see Figure 2.1). Finally, many banks also require a minimum deposit or a minimum salary. _Banco Caja Social_ is one of the few financial institutions that do not require minimum opening amounts (see Box 2 and Table 2.5).

- 20 _Superintendencia Bancaria, Prevención y Control del Lavado de Activos, Circular Externa_ 025 de 2003, Annex 1.

> 21 The only exception is savings accounts opened by an employer in the name of their employees for salary payments which do not require additional documentation. ( _Prevención y Control del Lavado de Activos, Circular Externa 025 de 2003_ ).

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store.<sup>17</sup> Following the methodology described above, in order to capture the real effect for each sector, I apply score matching for each case and verified that the assumptions for identifying the matching (common support assumption) were satisfied.<sup>18</sup> On matched merchants, I perform the same difference-in-differences regression estimation as before. Table 4 shows the estimation results. Not surprisingly, the resilience of merchants who accept contactless payments is most important for bakeries, which are typically small stores and sell low-value items. The average effect increases by 28.7 percent<sup>19</sup> for bakeries that accept contactless payments compared with bakeries that do not. This area is indeed where consumers used to pay cash before, especially for small-size payments. The effect is greater during the second containment, probably due to the Christmas season when people are used to buying cakes. The estimation results also confirm a significant benefit for supermarkets and food stores offering contactless payments compared to the control group, respectively about 19.2 percent and 14.6 percent increase in the amount of card sales. Gas stations with contactless payment technology have the same card sales before and during the COVID-19 crisis as the gas station without contactless payment technology. However, pharmacies and health services such as dentists or general practitioners with contactless technology are more resilient than their counterparties.

> 17I use the ”Nomenclature des Activit ́es Franc ̧aises” of the National Institute of Statistics (INSEE) to classify business sector. I particularly use the following NAF codes: Supermarket (4711D and 4711F), Food (4721X, 4722X, 4723X, 4725X, 4729X, 4781X, 4631X, 4632X, and 4638X), Grocery (4711B and 4711C), Pharmacy (4773X), Health (861XX and 862XX), Bakery (1071X and 4724X), Tobacco store (4726X and 4635X), Fuel (4730X).

> 18Due to space limitations, the results of the tests are not reproduced in the paper. They are available upon request.

> 19The estimated coefficient for log of card-sales amount on Table 4 is 0.252, which is equivalent to a percentage increase of 28.7 percent (= exp(0.252) – 1).

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when exchange controls limit international diversification. Legal, regulatory and tax policies can strongly affect the cost and availability to lower income households of such products as insurance and mortgage finance.

Equity is the prime risk product, and availability of traded equity has been greatly increased in many developing countries through privatization. This process has been enormously influential in increasing participation in equity markets (Boutchkova and Megginson, 2000), even though the voucher-based experiments towards popular capitalism in Eastern Europe were quite problematical (Ellerman, 2001). In turn the political commitment, which privatization to domestic households can represent, lowers political risk and increases stock market valuations which in turn can favorably affect growth (Oijen and Perotti, 2001).

Moving beyond the risky/safe dichotomy, available data is not only limited but hard to bring to a common base, given the different types of financial asset and financial product and the different ways in which information about holdings has been measured in different countries. One large cross-country database that avoids these difficulties is that assembled in recent years for the 25 member states of the European Union, which now includes some middle-income countries.<sup>32</sup> The pattern of usage of different financial products found in the EU data (Table 3) suggests a fairly clear pecking order for some products, in that there is a fairly regular progression in the use of these products as overall financial access increases. (Here overall financial access is measured by the percentage of respondents who report use any of the products). By regressing for each product the percentage using that product on (the log of) overall financial access, we can estimate how sensitive each product is to overall

> 32 The European Commission have conducted financial surveys in member states almost every year since the late 1990s.  The objective of the survey includes analysis of satisfaction with products, confidence in intermediaries and the effectiveness of cross-border competition.  The data discussed here were collected in 2005 and reported in European Commission (2005).  Thanks to Francesco Gaetano for the special tabulation in the final column of Table 3 showing usage of _either_ checking or deposit account – also plotted in Figure 9.  The European Commission stresses however, that the Eurobarometer survey was designed to elicit attitudes about the importance of various financial issues rather than measuring usage, therefore the statistics on usage should therefore be treated with caution.  In particular, they caution that “the given response rate should not be misinterpreted as implying that the remaining proportion of respondents do not have the corresponding item.”

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Table 12: Correlates of Politicians’ Integrity - Selection Traits

||(1)<br>Integri|ty|
|---|---|---|
|_Public Sector Motivation_|||
|Attraction to Policy Making|0.009|(0.067)|
|Commitment to Public Interest|0.339***|(0.127)|
|Social Justice|0.016|(0.096)|
|Civic Duty|−0.209*|(0.108)|
|Compassion|0.244***|(0.082)|
|Self-Sacrifce|0.149|(0.092)|
|_Non-Cognitive Measures_|||
|Extraversion|0.213***|(0.082)|
|Conscientiousness|0.241**|(0.097)|
|Openness|−0.138|(0.090)|
|Emotional Stability|−0.050|(0.091)|
|Agreeableness|−0.037|(0.079)|
|Grit|−0.028|(0.077)|
|Decision making|0.135|(0.092)|
|Hostile Bias|0.010|(0.069)|
|_Other Measures_|||
|Altruism|0.100|(0.175)|
|Choose dominated risk option|−0.028|(0.104)|
|Cognitive|−0.014|(0.034)|
|Asset index - total|0.002|(0.039)|
|Female|−0.083|(0.081)|
|Age in years|0.008**|(0.004)|
|Held political ofce before|−0.078|(0.078)|
|Complete College|−0.062|(0.085)|
|Masters/PhD|0.157|(0.130)|
|Constant|−4.964***|(0.748)|
|Observations|768||
|R-Squared|0.329||

_Note:_ This table reports regressions using politicians’ integrity as dependent variables. Non-reported controls also include District dummies.

* p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses.

63
<!-- Start of picture text --> Figure 3. MIC Share in Capital Flows to<br>Developing Countries<br>r . Share in private flows * Share in official flows _<br>too 89 90 96<br>660-<br>40 71 Or. - ;; .as#!fi55g§|aaaaaaaf<br>20<br>0 - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~<br>1970s 1980s 1990s<br>Source World Bank staff estimates. Source DEC Development Group data.<br><!-- End of picture text -->

**Figure 4. Net Private Flows to MICs, 1970-98**

_5._ In 1970-1999, MICs consistently registered higher GDP growth rates than both high- and low-income countries (Figure 5), and this trend is likely to continue.<sup>2</sup> Many of these countries, especially the fast-growing economies in East Asia, embraced trade liberalization and export promotion as a means to raise GDP growth rates. This is reflected in the rising ratio of exports to GDP in these countries (Figure 6). The share of MICs in world population remained constant at 70 percent over the past two decades and is expected to stay at much the same value up to 2015. Population growth rates vary considerably, however, between regions.

**Figure 5. GDP Growth Rate**

**Figure 6. Exports as a Share of GDP**

<!-- Start of picture text --> l MICc. aflg-iwonm ELIC, *NOCN *U C<br>0 61 40<br>6 30 2a82<br>4 3 4<br>19709 1990 1990, 1970, 1980, 1999,<br>Note The MIC 1990s total excludes transition economies.<br>Source World Bank staffestimates. Source World Bank staffestimates.<br><!-- End of picture text -->

# **Importance of MICs for Global Public Goods**

6. With 70 percent of the world's population and a 20 percent and rising share of world GDP, growth, stability and associated reforms in the MICs are public goods with considerable potential benefit for the rest of the international community. The MICs are of systemic importance in key areas with the characteristics of global public goods, including poverty reduction, financial stability, protection of regional and global environmental commons, public health, orderly flows of trade, movement of capital and 2 High-income countries are OECD countries excluding the Czech Republic, Mexico, Poland, South Korea, and Turkey. Low-income countries are as defined in WDI with the exception of nine IBRD-eligible countries. For the purposes of this paper, the low-income IBRD-eligible countries are included in the MIC category.

3
|e x Gini<br>cient<br>(10)<br>-0.565<br>(0.073)**<br>0.691<br>(0.052)**<br>1.566<br>(0.184)**<br>0.528<br>(0.033)**<br>-0.288<br>(0.104)**<br>-1.000<br>(0.343)**<br>1.404<br>(0.458)**<br>59388<br>4840<br>231.69<br>cifcations<br>per capita<br>e mean of<br>2, and the|
|---|
|Revenu<br>coef<br>(9)<br>-0.503<br>(0.071)**<br>0.187<br>(0.018)**<br>1.503<br>(0.183)**<br>0.533<br>(0.033)**<br>-0.302<br>(0.105)**<br>-1.020<br>(0.345)**<br>1.425<br>(0.456)**<br>59388<br>4840<br>198.23<br>vel. All spe<br>ta revenue, <br>s p_<_.10. Th<br>ncome is 0.2|
|Revenue<br>x med inc<br>(8)<br>-0.349<br>(0.053)**<br>1.561<br>(0.072)**<br>0.032<br>(0.139)<br>0.448<br>(0.027)**<br>-0.088<br>(0.048)+<br>-0.501<br>(0.318)<br>0.487<br>(0.208)*<br>59388<br>4840<br>235.26<br>unicipality le<br>s of per capi<br>; + indicate<br> per capita i|
|Revenue<br>x avg inc<br>(7)<br>-0.832<br>(0.199)**<br>1.288<br>(0.103)**<br>0.203<br>(0.516)<br>12.261<br>(0.179)**<br>0.465<br>(0.278)+<br>1.166<br>(1.236)<br>-1.391<br>(1.173)<br>59388<br>4840<br>92.60<br>ed at the m<br>, 1997 level<br>icates p_<_.05<br>000 median|
|(6)<br>0.260<br>(0.190)<br>2.053<br>(0.147)**<br>-0.582<br>(0.476)<br>1.489<br>(0.091)**<br>-1.041<br>(0.354)**<br>-3.157<br>(0.889)**<br>4.563<br>(1.418)**<br>59388<br>4840<br>199.82<br>and cluster<br> pre-reform<br>_<_.01; * ind<br> mean of 2|
|an = 9.61)<br>(5)<br>0.462<br>(0.185)*<br>0.526<br>(0.048)**<br>-0.779<br>(0.470)+<br>1.506<br>(0.090)**<br>-1.081<br>(0.356)**<br>-3.227<br>(0.901)**<br>4.629<br>(1.419)**<br>59388<br>4840<br>157.22<br>arentheses, <br>ted with the<br>* indicates p<br> is 0.49, the<br>ional.|
|revenue (me<br>(4)<br>0.791<br>(0.193)**<br>-0.938<br>(0.498)+<br>1.536<br>(0.093)**<br>-1.034<br>(0.336)**<br>-3.440<br>(0.947)**<br>4.701<br>(1.456)**<br>59388<br>4840<br>183.24<br>rors are in p<br>rend interac<br> squared. *<br>apita income<br> Tesouro Nac|
|per capita <br>(3)<br>0.029<br>(0.036)<br>2.070<br>(0.146)**<br>1.490<br>(0.091)**<br>-1.052<br>(0.357)**<br>-3.162<br>(0.888)**<br>4.602<br>(1.424)**<br>59388<br>4843<br>290.94<br>tandard er<br>near time t<br> enrollment<br>erage per c<br>0,000s.<br>INEP, and|
|Municipal <br>(2)<br>0.155<br>(0.031)**<br>0.529<br>(0.047)**<br>1.507<br>(0.090)**<br>-1.096<br>(0.360)**<br>-3.234<br>(0.900)**<br>4.680<br>(1.428)**<br>59388<br>4843<br>235.77<br>r. Robust s<br> well as a li<br> pre-primary <br>n of 1997 av<br>asured in 10<br>rom IBGE,|
|(1)<br>0.423<br>(0.023)**<br>1.537<br>(0.093)**<br>-1.051<br>(0.340)**<br>-3.450<br>(0.944)**<br>4.759<br>(1.467)**<br>59388<br>4843<br>346.57<br>ipality - yea<br>d efects, as <br>llment, and <br>45, the mea<br>ulation is me<br>ed on data f|
|Dependent Variable:<br>Simulated revenue<br>Simulated revenue x avg income<br>Simulated revenue x med income<br>Simulated revenue x gini<br>Per capita income<br>Population<br>0-6 population<br>7-15 population<br>Observations<br>Municipalities<br>F Stat, Excluded Instruments<br>_Notes:_ An observation is a munic<br>include municipality and year fxe<br>transfer revenue, pre-primary enro<br>simulated revenue per capita is 6.<br>mean Gini coefcient is 0.39. Pop<br>_Sources:_ Author’s calculations bas|

44
# <u>Table 4. Cross and Serial Correlation of Disaster</u>

# <u>Measures</u>

Panel A reports the results of regressions of each disaster incidence measure (at the top of the column) on the other two broad measures and a set of country fixed effects. Panel B runs similar regressions of each disaster incidence measure on two of its lags and a set of country fixed effects. *, **, and *** denote significance at the 10, 5, and 1 percent, respectively.

||(1)<br>Geological|(2)<br>Climatic|(3)<br>Other|
|---|---|---|---|
|A. Cross correlation <br>|of the incidence of d<br>|isasters within co|untries<br>|
|Climatic|0.00456||0.00428|
||(0.00506)||(0.00459)|
|Other|-0.00583|0.0532||
||(0.0142)|(0.0575)||
|Geological||0.0501|-0.00516|
|||(0.0553)|(0.0125)|
|Country FE|Yes|Yes|Yes|
|N|3584|3584|3584|
|B. Serial correlation|of the incidence of|disasters within co|untries|
|L.Geological|-0.0166<br>(0.0313)|||
|L2.Geological|-0.0424<br>(0.0298)|||
|L.Climatic||0.149***<br>(0.0295)||
|L2.Climatic||0.0792**<br>(0.0280)||
|L.Other|||0.0342<br>(0.0347)|
|L2.Other|||0.00892<br>(0.0354)|
|Country FE|Yes|Yes|Yes|
|N|1260|3360|1320|

35
provides an estimate of the UQME, which we argue is a key parameter for policy decisionmaking.

The UQR estimator is based on influence functions, which were introduced by Hampel (1988) as a tool in robust estimation techniques.<sup>17</sup> Using notation (largely) from FFL, consider some distributional statistic, (Fy), such as the median, inter-quantile range, or any quantile. The influence function, IF(Y; , Fy), represents the influence of an individual observation on the distributional statistic, (Fy). A key innovation of FFL is that the authors add (Fy) to the influence function to center it; this new function is called a recentered influence function (RIF). By design then, the expectation of the RIF at the<sup>th</sup> quantile is the value at the<sup>th</sup> quantile (e.g., the median if =50); or, more formally, E(RIF(Y; , Fy))= (Fy). FFL define (X) = E(RIF(Y; , Fy)|X) as the unconditional quantile regression model. The parameter estimates from the RIF regression provide estimates of UQME, or in our example, the marginal effect of a change in wheat flour prices on food expenditure evaluated at the food poverty line of the unconditional distribution, while controlling for the covariates in our model specification.<sup>18</sup>

With the quantile estimator, marginal effects are typically compared at fixed points on the conditional distribution. For example, Chamberlain (1994), Nguyen _et al._ (2007), Patrinos and Sakellariou (2006), and Stifel and Averett (2009) all examine the CQME at fixed intervals (such as the 10<sup>th</sup> , 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> , or 90<sup>th</sup> quantiles) on the conditional distribution. For our analysis, we similarly compare the UQME of the food security-related dependent variables at the 25<sup>th</sup> , 50<sup>th</sup> , 75<sup>th</sup> quantiles of their respective distributions to examine whether the effects differ in informative ways. Table 5 displays the values of the food security measures at various points of the unconditional distributions. The observed variation in these indicators suggests that the UQME may differ for households at the bottom and top of the distributions.

We also take a slightly different approach and examine the UQME for each of the

> 17 Robust statistics are statistics and estimators that are not heavily influenced by deviations from model assumptions, nor alternatively, heavily influenced by single observations. Influence functions provide a formal way of measuring the extent to which a particular estimator is affected by a single observation in the sample. 18 FFL provide an estimation method based on transforming the dependent variable into the re-centered influence function and then using OLS estimation. FFL show that this approach yields a consistent estimator of the average marginal effect, E[d Pr[Y > | X]/dX], if Pr[Y > | X=x] is linear in x. In order to estimate the standard errors, we follow FFL (2009b) and use a bootstrap estimator of the sampling variance. For the interested reader, FFL (2009a) derive the asymptotic properties of the estimator and provide the analytical standard errors.

14
**The issues associated with these demographic developments are aggravated because EU11 does a poor job of attracting migrants.** The difference between EU11 and EU15 in net migration rates is remarkable (Figure 20).  The rate at which highly skilled workers leave the EU11 is the highest of the EU regional aggregates (Figure 21). To the extent that EU11 economies evolve toward innovation and technologically advanced products, a shortage of skills could constrain economic growth in the region. Creating incentives to bring skilled individuals back may soon become a priority for EU11 economies.

**Figure 20. Net Migration Rates by Region**

**Figure 21. Emigration Rates of People with Tertiary Education**

<!-- Start of picture text --> Net migration, per 1,000 population<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>EU15 North EU15 EU15 South EU11 SEE Eastern<br>Continental Partnership<br>Source: World Bank<br><!-- End of picture text --> <!-- Start of picture text --> Emigration rate of tertiary educated (% of total tertiary<br>educated population)<br>25<br>20<br>15<br>10<br>5<br>0<br>EU15 North EU15 EU15 South EU11 SEE Eastern<br>Continental Partnership<br>Source: World Bank<br><!-- End of picture text -->

**Apart from increasing the retirement age, policies to increase EU11 labor participation rates will necessarily have to incentivize and retain highly skilled individuals.** A wage premium as high as 25–30 percent was found recently for workers in Eastern Europe who had experience in Western Europe (Iara, 2008). Standard estimates of the returns to education in the region may thus not be very informative for domestic policy if decisions to migrate, and eventually to return, are taken into account. Such earning differentials have potent signaling effects for education decisions. They also may turn the EU11 “brain drain” into a “brain rental” by enabling the return of skilled emigrants to the region.<sup>19</sup> Such beneficial effect, however, requires impulses in terms of demand for skills in EU11 and thus the fostering of innovation activities and entrepreneurship to attract talented emigrants back to the region.

> 19 See also the theoretical underpinnings in Mayr and Peri (2009).

21