rafmacalaba/datause-encoder-data huggingface dataset:
As shown in Teti (2020), the standard sources for tariff data, i.e., the World Integrated Trade Solution (WITS) that draws on data from the UN and the WTO, suffer from massive measurement error. Existing data include many missing observations because countries do not report tariffs every year. The issue is particularly bad for preferential tariffs and for low-income countries. WITS deals with the missing tariffs by imputing them with MFN tariffs of the respective importing country. For time series data this procedure leads to large irregularities: In years in which the country does not report preferential tariffs, the tariffs jump up from the relatively low preferential tariffs of the previous year to the MFN tariff which is often substantially higher. This type of time variation creates substantial measurement error, in particular in models which identify on time variation as specified in Equation (4).

Figure 1 from Teti (2020) illustrates this point for the tariff that can be found through WITS for exports from the United States to Mexico. Panel (a) shows the simple average of the raw tariffs, MFN and preferential, which can be downloaded through WITS. The average comprises over 5,000 products. The blue dots equal Mexico’s MFN tariff, the red dots are the preferential tariffs under NAFTA that entered into force in 1994. The figure shows that neither MFN nor preferential tariffs are available each year. However, the preferential tariffs (red dots) are less likely to be available indicating that Mexico is not reporting the preferential tariffs on imports from the United States every year. The solid line displayed in Panel (b) equals the tariff that WITS reports when one downloads the so-called “effectively applied tariff”. Recall, WITS imputes the missing preferential tariffs with MFN tariffs, resulting in the spikes as the effectively applied tariff shoots up to the level of the MFN tariff every time when the data on preferential tariffs are missing but MFN tariffs are available.

**Figure 1:** Missing Data and Resulting Measurement Error in WITS

<!-- Start of picture text --> (a) Missing Data (b) Measurement Error in WITS<br>MFN Pref MFN Pref Trains - Effectively Applied Tariff<br>20 20<br>1994: NAFTA 1994: NAFTA<br>15 15<br>10 10<br>5 5<br>0 0<br>1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text -->

**Note:** This figure is from Teti (2020) and shows the simple average of the raw MFN and preferential tariffs, as well as the effectively applied tariffs imputed by WITS for the pair Mexico/United States. Details can be found in the corresponding paper.

8
# Table 5 Values<sup>of Parameters</sup> Used to Measure<sup>tJrer</sup> Costs of Capital and Effective Tax Ratea for Companies<sup>ODerstint</sup> in Thailand. Domestic and Foreign<sup>OwnershiT</sup>

|Country|CorDOrate<br>Domestic<br>Income (2'|Tax Rate<br>Foreign<br>Income (2) (D|Capital<br>Cost<br>Allowance Rate<br>Balance Equivalent<sup>(2)</sup>|Accrual-Equivalent<br>Capital<br>Gains Tax<br><sup>Rate on Parent (2)</sup>|Dividend<br>Payout<br>Ratio (2)|Subsidiary<br>Debt-Asset<br>Ratio (2)|Interest<br>Rate (2)|Inflation<br>3<br>Rate (2)|
|---|---|---|---|---|---|---|---|---|
|Thailand<br>|30, 35<br>|35<br>|10<br>10|15<br>8|53.9|34.0|12.5<br>10.0|3.9<br>7.4|
|Hong Kong<br>|18<br>|0<br>|<br>10|<br>8|47.8|57.4|10.5|4.5|
|Taivan<br>|25<br>|25<br>|<br>136|<br>13|48.0|42.5|9.3|4.0|
|United States<br>|38<br>|38<br>|.<br>155|<br>12|43.5|43.1|10.3|4.9|
|United Kingdom<br>|35<br>|25<br>5 561|.<br>10|<br>6|70.5|95.5|8.3|1.2|
|West Germany<br>|56<br>|0, 0,<br>|<br>10|<br>19|39.6|66.1|4.9|0.7|
|Japan<br>|58<br>|532<br>|<br>126|<br>4|43.5|28.2|6.0|1.5|
|Singapore<br>|33<br>|33<br>|.<br>10|<br>4|36.8|38.8|7.8|0.7|
|Netherlands<br>|42<br>|0<br>|<br>186|<br>4|4R.0|50.7|5.2|1.9|
|Switzerlands<br>|30<br>|30<br>|.<br>10|<br>4|48.0|50.7|9.5|2.7|
|France|42|0|||||||

Sources Socmhai<sup>Richupan andInternational</sup> Bureau of Fiscal Documentation.

1O2 **applies** to dividends<sup>for qualifying ownership in subsidiary,</sup> 502 for branches, 562 otherwise. 2 Corporate lncome plus enterprise tax.

> 3 1ased on 1988 conuoer price Index.
Third, they must have at least 30 employees in every sampled year. Fourth, they must be located in continental Portugal and cannot operate in the primary sector<sup>15</sup> . The fi nal sample is an unbalanced panel of 3,410 domestic fi rms, 194 foreign fi rms and 103 foreign acquisitions of domestic fi rms<sup>16</sup> . I have also identi fi ed a group of 37 domestic acquisitions of foreign fi rms (24 in manufacturing and 13 in non-manufacturing<sup>17</sup> ). Due to the small number of these observations I focus the analysis on the group of foreign acquisitions<sup>18</sup> .

Table A1 in the Appendix characterizes the fi nal sample in several dimensions. Panel 1.6 shows that sector composition of the foreign investment explains part of the wage differentials between foreign and domestic fi rms. The proportion of fi rms located in the top highest paid sectors are at least two times larger in foreign owned fi rms than in domestic fi rms. Therefore, a better measure of the differences in skill composition and wages between foreign and domestic fi rms must control for the fact that foreign fi rms tend to be concentrated in different sectors of activity. Let p<sup>k</sup> js<sup>be theshare ofeducation group kin thetotalworkforce</sup> of foreign fi rm j operating in sector s, p<sup>k</sup> s<sup>be the share of education group kof domesticfirms</sup> operating in sector s, wjs<sup>kbetheaveargehourlywageofeducationgroup kinforeignfirm j</sup> and ws<sup>ktheaveragehourlywageofeducationgroupkindomesticfirmsoperatinginsector</sup> s. Column (1) of Table 1 reports the average difference across all sectors for p<sup>k</sup> js<sup>−pk</sup> s<sup>and</sup> lnwjs<sup>k−lnw</sup> s<sup>k.The differences in employment are reported as the average across sectors of the</sup> empjs<sup>whereempjsistheemploymentoftheforeignfirmjoperatinginsectorsandemps</sup> emps<sup>,</sup> is the average employment in the domestic fi rms of the sector. I compute the differences between fi rms operating in the same two-digit sector of activity. Panel A reports the results for manufacturing sectors and panel B for non-manufacturing sectors.

Column (1) compares foreign and domestic fi rms. In manufacturing, the share of low ed-

> 15The primary sector includes agriculture and mining. Firms operating in 3 digit sectors with exclusively domestic fi rms that fi ll in these requirements were excluded from the sample.

> 1673% of the domestic fi rms in the sample and 58% of the foreign and acquired fi rms operate in manufacturing sectors. More than 90% of the fi rms in the sample are observed for more than six years. 1% of domestic fi rms are observed for less than fi ve years because they have less than 30 workers in the worker’s fi les during that year.

> 17There are only two fi rms that fi ll these requirements and that experienced more than one ownership change, i.e., a foreign and a domestic acquisition).

> 18However, at the end of each section I discuss the fi ndings when this sample is also included.

8
**Table 13 Firm Size Growth and Investment Climate Variables with Interactions (All Data)**

|IC Variable|Small|Medium|Large|IC|IC*Small|IC*Medium|IC*Large|Const|Obs|R<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|---|---|
|External Fin|‐0.031|‐0.223|‐0.149|0.004|‐0.005|0.003|0.002|0.158|668|0.23|
||0.078|(0.089)**|(0.082)*|(0.002)*|0.004|0.004|0.004|(0.074)**|||
|Collateral|‐0.086|‐0.244|‐0.044|‐0.001|0.000|0.001|‐0.001|0.086|219|0.35|
||0.196|0.168|0.208|0.001|0.001|0.001|0.001|0.219|||
|Sales on Credit|‐0.251|0.015|0.32|0.000|0.002|‐0.002|‐0.006|0.319|660|0.21|
||0.196|0.232|0.234|0.003|0.003|0.003|(0.004)*|(0.175)*|||
|Overdraft (y/n)|‐0.112|‐0.426|‐0.32|‐0.161|‐0.038|0.382|0.286|0.352|656|0.21|
||0.132|0.28|0.265|0.168|0.196|0.387|0.412|(0.103)***|||
|Line of credit|0.261|0.019|0.378|0.783|‐0.629|‐0.294|‐0.79|‐0.105|665|0.24|
||0.185|0.165|0.236|(0.252)***|(0.320)*|0.262|(0.413)*|0.163|||
|Loss PowOut|‐0.263|‐0.362|‐0.255|‐0.001|0.01|0.023|0.007|0.165|223|0.33|
||(0.119)**|(0.109)***|(0.117)**|0.009|0.015|0.019|0.019|0.11|||
|Infor Pay|‐0.054|‐0.133|0.006|0.14|‐0.171|‐0.08|‐0.196|0.226|551|0.28|
||0.036|(0.054)**|0.051|(0.051)***|(0.059)***|0.048|(0.052)***|(0.085)***|||
|Tax Inspect|‐0.193|‐0.2|‐0.148|0.000|0.000|0.000|‐0.002|0.524|324|0.31|
||(0.088)**|(0.058)***|(0.087)*|0.001|0.001|0.001|(0.001)**|(0.135)***|||
|Time Tax|‐0.094|‐0.109|0.338|0.003|‐0.001|‐0.001|‐0.015|0.174|617|0.19|
||0.095|0.136|0.216|0.003|0.003|0.005|(0.007)**|0.132|||
|Days to Export|‐0.095|‐0.237|‐0.162|0.016|‐0.013|‐0.001|‐0.012|0.296|336|0.39|
||0.171|0.151|0.181|0.012|0.012|0.011|0.013|(0.121)**|||
|Days to Import|‐0.606|‐0.796|‐0.903|‐0.025|0.025|0.032|0.045|0.885|208|0.55|
||(0.218)***|(0.198)***|(0.212)***|(0.013)*|(0.014)*|(0.015)**|(0.017)**|(0.194)***|||
|Inspections|‐0.111|‐0.162|‐0.09|0.003|‐0.008|0.001|‐0.013|0.236|636|0.19|
||(0.054)**|(0.056)***|0.095|0.016|0.015|0.017|0.024|(0.070)***|||

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Control for foreign and government ownership, export status, 2‐digit industry, and region fixed effects. *** p<0.01, ** p<0.05 p<0.1 21
# **Table 5. Global financial and trade flows**

|**Dependent Variable**|||**Log(V**|**alue+1)**|||
|---|---|---|---|---|---|---|
|**Database**|**Syndica**|**ted Loans**|**M**|**&A**|**Gree**|**nfield**|
|**Sample of Source Countries**|**North**<br>**Countries**|**South and LAC**<br>**Countries**|**North**<br>**Countries**|**South and LAC**<br>**Countries**|**North**<br>**Countries**|**South and LAC**<br>**Countries**|
|Log(Total Trade+1)|0.0657 ***|0.0064 ***|0.0244 ***|0.0033 ***|0.0652 ***|0.0096 ***|
|RCA Source Country|-0.0031|0.0005 *|0.0050 ***|0.0001|0.0180 ***|0.0012 ***|
|RCA Receiver Country|0.0113 ***|-0.0010 **|0.0050 ***|0.0000|-0.0013|-0.0006 **|
|RCA Source Country * LAC Receiver Dummy|-0.0004|0.0000|-0.0034 ***|-0.0001|-0.0089 ***|0.0001|
|RCA Receiver Country * LAC Receiver Dummy|-0.0013|0.0014 **|-0.0012|0.0005 *|0.0035|0.0004|
|**Gravity Controls**|||||||
|Geographical Distance between Countries|-0.1540 ***|0.0007|-0.0620 ***|-0.0005|-0.0460 **|0.0003|
|Differences in Latitude|0.0027 ***|0.0001 *|0.0009 *|0.0000|0.0002|-0.0001 **|
|Differences in Longitude|0.0004|0.0000|0.0001|0.0000|-0.0001|0.0000|
|Differences in Time Zones|0.0182 **|-0.0014 *|0.0056|-0.0011 ***|0.0113 *|-0.0006|
|Colony Dummy|0.0234|0.0485|0.0404|0.0149|0.0695 *|0.0559 ***|
|Common Legal Origin Dummy|0.2370 ***|0.0500 ***|0.1210 ***|0.0390 ***|0.0993 ***|0.0339 ***|
|Common Language Dummy|-0.0421|0.0137 *|0.0172|0.0055|-0.0179|0.0001|
|Source Country Dummies * Time Trend|Yes|Yes|Yes|Yes|Yes|Yes|
|Receiver Country Dummies * Time Trend|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|**Number of Observations**|540,707|1,743,205|498,248|2,127,160|408,610|1,994,079|
|**R-squared**|0.208|0.043|0.089|0.019|0.172|0.031|
|**Sum of LAC coefficients**|||||||
|RCA Source Country + RCA Source Country * LAC Receiver Dummy|-0.0035|0.0005 ***|0.0017|0.0000|0.0091 ***|0.0013 ***|
|RCA Receiver Country+ RCA Receiver Country* LAC Receiver Dummy|0.0100 **|0.0004|0.0038 **|0.0005 *|0.0022|-0.0002|

This table shows the relation between financial and trade flows using sector-level data. Total trade is measured as the sum of exports and imports. The relative comparative advantage (RCA) is based on Vollrath (1991). The LAC Receiver Dummy equals 1 if the receiver is a LAC country and 0 otherwise. The regressions include gravity control variables which help explain different levels of financial flows between each country pair based on the geographic distance between the two countries (in logs), differences in latitude and longitude, differences in times zones, whether the receiver (sender) country is (or has been) a colony of the sender (receiver), whether they have a common legal origin, and whether they share a common language. The regressions also control for source and receiver country dummies and sector dummies. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Standard errors are cluster by country-pairs. *, **, and *** represent statistical significance at 10%, 5%, and 1%, respectively.
# Figure 10. Sovereign vulnerabilities in EMDEs

# A. Government debt

<!-- Start of picture text --> Percent of GDP<br>60 2018 2007<br>55<br>50<br>45<br>40<br>35<br>30<br>EMDEs Commodity- Commodity-<br>exporting EMDEsimporting EMDEs<br>C. Sovereign credit ratings<br>Index (1-21, higher better)<br>2007 2018<br>14<br>13<br>12<br>11<br>10<br>9<br>8<br>7<br>6<br>EMDEs Commodity- Commodity-<br>exporting EMDEs importing EMDEs<br><!-- End of picture text -->

# E. Cyclically-adjusted primary fiscal balance

<!-- Start of picture text --> Percent of potential GDP<br>1.5 2018 2007<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>EMDEs Commodity- Commodity-<br>exporting importing<br>EMDEs EMDEs<br><!-- End of picture text -->

# B. Government debt

<!-- Start of picture text --> Number of economies<br>60 2018<br>2007<br>50<br>40<br>30<br>20<br>10<br>0<br>Percent of GDP<br>0-25 26-50 51-75 76-100 100+<br><!-- End of picture text -->

# D. Maturity of government debt

<!-- Start of picture text --> Years<br>17 Interquartile range Mean<br>15<br>13<br>11<br>9<br>7<br>5<br>2007 2018<br><!-- End of picture text -->

# F. Cyclically-adjusted primary fiscal balance

<!-- Start of picture text --> Number of economies<br>100<br>2018 2007<br>80<br>60<br>40<br>20<br>0<br> < -5 -2.5 to -5 0 to -2.5 > 0<br>Percent of potential GDP<br><!-- End of picture text --> Source: International Monetary Fund, Kose et al. (2017), World Bank.

A. Unweighted average of government debt ratios for 85 EMDE commodity exporters and 60 EMDE commodity importers.

B. Based on data for 146 EMDEs.

C. Unweighted averages of foreign currency sovereign credit ratings for 54 EMDE commodity exporters and 40 EMDE commodity importers. Whiskers denote interquartile ranges.

D. Unweighted averages of the average maturity of government debt based on 38 EMDEs.

E. Unweighted average of cyclically adjusted primary balance-to potential GDP ratios for 91 EMDE commodity exporters and 64 EMDE commodity importers. F. Based on data for 149 EMDEs.
- test.) This particular estimator which minimizes the sum of squared deviations to find the line of best fit - is known to be suspect when there are outliers; in effect, it tries "too hard" to accommodate the extremes. From the plotted data points in HPA, I would be particularly worried about the effects on these estimates of some of the large deviations above the trend, which have under-five mortality rates as much as five times higher than other countries at a similar average income (Oman is an example). The issue here is not that these extreme figures are suspect, but rather that they will have undue influence on the fitted regression equation. With this rather crude estimation method, the quantitative assessments in HPA of performance relative to income are queetionable. A better procedure would probably have been to look at the residuals around a line of best fit which minimized the absolute deviations (the so-called MAD estimator).

The Sen (1981b) methodology of looking for outliers in a regression of social indicators against mean income has been criticized in recent literature for another reason. Bhalla and Glewwe (1986) have argued that this approach fails to control adequately for "initial conditions". They re-examine the case of Sri Lanka, which Isenman (1981), Sen (1981b), and HPA have claimed to be an above average performer in terms of its social indicators, given the country's income level. Bhalla and Glewwe estimate regressions across countries of the changes in various social indicators from 1960 to 1978 against changes in mean income over the same period (rather than having both variables in levels for a single date, as in Sen, 1981b, and HPA). In the process, their results also suggest that income growth is nogatively correlated with changes in the social indicators (though they comment little on this particular result). Using these regressions, they find that Sri Lanka 25
DISCRIMINATION AGAINST SEXUAL MINORITIES IN EDUCATION AND HOUSING: EVIDENCE FROM TWO FIELD EXPERIMENTS IN SERBIA

# **Gay couples face more discrimination than lesbian couples**

Gay couples face a much higher likelihood of being refused a rental apartment (29 percent) compared to their lesbian counterparts (8 percent; figure 5).<sup>xxvii</sup> <!-- Start of picture text --> Figure 5: Acceptance and refusal of gay and lesbian couples when seeking rental<br>apartments<br>LESBIAN COUPLE<br>GAY COUPLE<br>8%<br>29%<br>71%<br>92%<br>Accepted Refused Accepted Refused<br>Database: treatment groups only, 80 observations per group, 160 observations in total.<br><!-- End of picture text --> Gay couples also faced more hesitation than heterosexual or lesbian couples. Of the gay couples accepted, 12 percent were accepted with hesitation, while only 1 percent of the corresponding heterosexual couples experienced such hesitation (figure 6).<sup>xxviii</sup> The difference in the case of lesbian couples was smaller, and not statistically significant: 7 percent of lesbian couples were accepted with hesitation and 1 percent of the corresponding heterosexual couples.<sup>xxix</sup> Figure 6: Acceptance with hesitation—gay couples and heterosexual couples <!-- Start of picture text --> HETEROSEXUAL COUPLES<br>GAY COUPLE<br>(CONTACT MALE)<br>1%<br>12%<br>88%<br>99%<br>Accepted without hesitation<br>Accepted without hesitation<br>Accepted with hesitation<br>Accepted with hesitation<br><!-- End of picture text --> <mark>Database: accepted couples only; 100% of heterosexual couples, 71% of gay couples</mark> 12
The individual's market wage rate is no greater than her marginal value product in onfarm production, which is equal to her marginal value product in household production, with pi the household's valuation of her marginal unit of domestic output. This equilibrium is decentralized by the household's setting her implicit wage equal to wi* = qgi = piki.

**Case 4** : Individual i is at a corner solution with respect to market and on-farm work. In that case hi = 0 = fi, li > 0. Then we have the inequalities

Thus the marginal value of i’s domestic output, pi, is at least as great as its marginal cost, and the marginal value product of her time in on-farm production, at zero level of input, is less than or equal to the marginal value product of her time in domestic production. The household decentralizes this allocation by setting her wage at wi* = piki.

Time and space constraints preclude further discussion of this model. Clearly empirical applications will require data on time use and outputs from within the household, since many of the relevant prices are endogenous and household specific. The model does however provide a reasonably general and comprehensive framework within which gender issues can be discussed, and also demonstrates the importance of time use data for modeling household decisions.

# **5 Time use survey design**

The central aim of the preceding section has been to set out an approach to modeling household behavior that is appropriate for analyzing the welfare effects of changes in policy. The particular model that is relevant in a given context, as I pointed out earlier, will depend on the set of questions being asked. Here, the questions of interest concern the effects of reforms on the intra-household allocation of resources, on living standards and on household behavior. I have emphasized that finding answers to these questions 41
15 Several important points emerge from Table 4. First, much of the dynamism in the intra-trade of these goods is associated with a shift in the regional orientation of exports toward Mercosur. For example, in 1988 the trade weighted share of these products within Mercosur was approximately 49 percent higher than that for other destinations (as reflected in an index value of 1.49), yet by 1994 this index value rose more than two-fold to 4.55. Road motor vehicles (SITC 732) played a major role in this overall shift as intra-Mercosur trade in these goods increased by a factor of more than 10, i.e., in value terms from $207 million to over $2.1 billion in 1994. Second, it should be noted that 24 out of these 30 "dynamic" products recorded a shift in the regional orientation index toward Mercosur, with half of the 30 products' indices more than doubling.'12 Although the results are somewhat mixed, a second important point is that the dynamic products generally consist of goods that are relatively capital intensive in fabrication. Overall, the 30 items listed in - Table 4 have a factor intensity index ratio of 118 which indicates they are 18 percent more capital intensive than average for all manufacturing activity. In contrast, an investigation by Yeats (1989) that employed these same data determined that the recent capital intensity of Hong Kong, Republic of Korea, and Taiwan (China) exports were 20 to 25 percent below average, while Singapore's ratio was about 2 percent higher. This point is troubling since economic theory holds that countries like those in Mercosur should not have a comparative advantage in the production of relatively capital intensive goods such as those that are growing most rapidly in intra-trade.

'<sup>2</sup> The possibility exists that exports to third markets (particularly of agricultural products) may have been greatly constrained by high tariffs and nontariff measures. In these cases, RO index values could rise because of restrictions in third markets and not the more favorable tariff treatment of member countries in inter-block trade. There is reason to believe, however, that this situation occurs infrequently. World Bank-UNCTAD records show that most of the products listed in Table 4 do not encounter major OECD restrictions (the processed foodstuffs are an exception). In addition, this study shows (see Table 7) that Mercosur provides sizeable trade preferences on intra-trade. It is also possible that idiosyncracies in demand patterns and in the ability to produce certain varieties of goods made trade between Mercosur countries increase disproportionately fast as a result of the MFN liberalization. This is sufficiently unlikely, however, that the burden of proof must lie with advocates of this view.