rafmacalaba/datause-encoder-data huggingface dataset:
we study here are changes that were undertaken during major policy reform episodes (both in the late 1980s and under NAFTA), and many observers have argued that the lowering of trade barriers was mainly used by the Mexican government to signal its commitment to overall policy reform (Tornell and Esquivel, 1995). Second, and somewhat related to the first point, in our data virtually no industry experienced a rollback of the liberalization effort once tariff rates had been reduced. Finally, we note that such pattern of endogeneity would only cause a bias against our reported findings. That is, if such bias exists, the true short-run effect of trade policy changes on income risk is even larger than what we report in this paper. However, it also means that our finding that trade liberalization has no long-run “level effect” could be the result of two opposing effects canceling each other out.<sup>27</sup> Estimation bias could, of course, also arise if systematic changes in non-tariff barriers reversed the effects of tariff reductions, but these changes in non-tariff barriers were not taken into account by us. To ensure that this is not the case, we studied the patterns in the use of non-tariff barriers (NTBs) in Mexico in the years included in our sample. NTB use in Mexico primarily took the form of anti-dumping duties in these years and the anti-dumping duties were concentrated entirely in the ‘Basic Metal Products’, ‘Chemicals’ and ‘Textiles’ industries.<sup>28</sup> Studying the link between trade policy and income risk using data from the remaining industries did not alter qualitatively or quantitatively any of the reported estimates (see Table VIII).

Our estimation results could also be biased if there is unobserved heterogeneity among work-

> 27Notice also that despite the work by Alesina and Drazen (1994) and others, major trade policy reforms are in general rather difficult to understand theoretically once policy is treated as being endogenous. The dominant theory of endogenous trade policy determination - the interest group theory - simply does not predict such dramatic changes in policy. Since the competing strengths of various interest groups are not expected to (and do not) change dramatically over the medium term, the theory predicts stickiness in trade policy over these horizons (consistent with what is observed most of the time). Lacking theoretical guidance, the choice of suitable “exogenous variables” to help with identification is even more difficult than usual.

> 28See the recent UNCTAD study, “Mexico’s Experience with the use of Anti-dumping Measures,” 2002.

23
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**Table 12: Trade Openness and Industry Labor Demand for White Collar Workers in Mexico (1987-90)**

||||**(s**|**tandard errer**|**s in parenthesi**|**s)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Ln L t-I|Ln WBt|Ln WBt-l|Ln WWt|Ln WWt-l|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|Trade Policy<br>Specification(b)|0.372<br>(0.295)|-1.450(***)<br>(0.860)|0.214<br>(0.594)|-0.297<br>(0.462)|1.294<br>(0.661)|0.053(**)<br>(0.027)|-0.059<br>(0.044)|--|0.303|0.203|8196<br>(2049)|
|Variable* Tariff Rate|0.003|-0.044|-0.001|-0.0002|0.040||-0.056|0.127||||
||_(0.005)_|(0.030)|(0.018)|(0.016)|(0.028)||(0.060)|||||
|Variable* Inputs Tariff|-0.005|<br>0.028|<br>0.003|<br>-0.004|<br>-0.023||0.049|0.167||||
||(0.004)|(0.021)|(0.011)|(0.011)|(0.018)||(0.052)|||||
|Variable* Non-Tariff-|0.0003|0.0005|-0.0003|-0.001|0.002(**)||0.001|0.344||||
|Barriers|(0.0003)|(0.001)|(0.001)|(0.001)|(0.001)||(0.003)|||||
|Variable* Real|0.0001|0.019(***)|-0.002|0.002|-0.017(***)|||0.304||||
|Exchange Rate|(0.002)|(0.012)|(0.008)|(0.006)|(0.009)|||||||
|Wald Test: p-value(c)|0.688|0.341|0.904|0.521|0.031||0.786|||||
|Trade Flows<br>Specification(d)|1.051(*)<br>(0.385)|-0.648(***)<br>(0.358)|-0.311<br>(0.448)|-0.278<br>(0.333)|0.297<br>(0.426)|0.033<br>(0.028)|-0.088(*)<br>(0.034)|--|0.610|0.665|9508<br>(2377)|
|Variable* Import Ratio|-0.002|0.008|-0.011|-0.001|0.017||0.054|0.744||||
||(0.004)|(0.014)|(0.019)|(0.012)|(0.024)||(0.041)|||||
|Variable* Export Ratio|0.001|0.011|0.014|-0.011|-0.028||-0.049|0.955||||
||(0.004)|(0.023)|(0.025)|(0.019)|(0.035)||(0.054)|||||
|Variable* Real|-|0.004|-0.002|0.002|0.004|||0.058||||
|Exchange Rate|0.004(***)<br>(0.002)|(0.003)|(0.004)|(0.003)|(0.004)|||||||
|Wald Test: p-value(c)|0.326|0.174|0.951|0.502|0.869||0.229|||||
|Price-Cost-Margin<br>Specification(e)|0.185<br>(0.176)|-0.653(*)<br>(0.251)|0.606(*)<br>(0.233)|0.419(**)<br>(0.190)|-0.503(*)<br>(0.192)|0.144(*)<br>(0.040)|-0.014(**)<br>(0.007)|--|0.440|0.229|9524<br>(2381)|
|Variable* Price Cost|0.0001|0.021(*)|-0.018(*)|-0.019(*)|0.017(*)||-0.001|0.008||||
|Margin|(0.001)|(0.008)|(0,007)|(0.006)|(0.006)||(0.012|||||

Notes: GMM estimates with first-differenced data. Time dummmies were included but are here omitted. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and further lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Wald Test of Joint Significance of the variables interacted with a given openness variable. (b) Additional Instrument: the second lag of the tariff rate. (c) Wald Test of Joint Significance of openness variables interacted with a given variable. (d) Additional Instruments: third and further lags of import and export ratios. (e) Additional Instruments: second and further lags of price-cost-margins.
reduction targets of 2010 and 2015. This study considers three scenarios for carbon mitigation based on how effectively the PNMV is achieved: 1) PNMC is fully achieved, i.e., 70% reduction in deforestation (and associated CO2 emissions) relative to BAU will be achieved from 2017 onward; 2) PNMC is achieved by 75%, i.e., 52.5% reduction in deforestation relative to BAU will be achieved from 2017 onward; and 3) PNMC is achieved 50%, i.e., only 35% reduction in deforestation relative to BAU could be achieved by 2017 and later. Under each scenario, this study considers a 30-year time horizon, from 2010 to 2040. Since the national emissions reduction target is set only for 2020, this study assumes that beyond 2020, there will be the same emissions reduction target for all years as in 2020, i.e., the emissions reduction rate is assumed to stay at 37.5% relative to the projected BAU level for later years. Similarly, after 2017, the effectiveness of PNMC in reducing deforestation relative to BAU is assumed to stay the same as that of 2017.

Following de Gouvello et al., this study assumes that the BAU annual deforestation level would remain the same as its 2007 level, which represents 1,259 million ton-CO2 emissions per year (Ministry of Science and Technology, 2010). Based on this assumption, the BAU simulation suggests that while Brazil‟s national emissions may increase by around 50% in 2040 compared to the 2010 level (from 1,742 million ton-CO2/year to 2,610 million ton-CO2/year), CO2 emissions from energy consumption and industrial processes may increase by almost threefold in 2040 compared to the 2010 level (from 483 million ton-CO2/year to 1,351 million-ton CO2/year), as shown in **Figure 3** . Equivalently, the results show that in 2010, while CO2 emissions from energy consumption and industrial processes only account for around a quarter of total CO2 emissions, they could be responsible for more than half of the total CO2 emissions in 2040 in the BAU scenario, which implies in the long run, cutting CO2 emissions from energy consumption and industrial processes could also be crucial in mitigating CO2 emissions.

Figure 3 provides an evidence that if the target of PNMC, a 70% reduction in deforestation by 2017, is fully achieved, the national CO2 emissions, including emissions from energy use and industrial processes and those from deforestation, would be in line with and even somewhat lower than the national reduction target set by the Law 12.187. If the 70% reduction rate in deforestation (relative to the BAU level) is maintained up to 2040, there will be no need to reduce the emissions from energy use and industrial processes before 2035, and even from 2035 onward, the burden of reducing emissions from energy use and industrial processes is still quite 17
contemporary data points but estimates based on posterior data.<sup>3</sup> Although their approach was in several respects less than ideal, it was, at that time, perhaps the only possible since historical income distributions are so scarce.

More recently, Baten, Foldvari, van Leeuwen, and van Zanden (2009) [in the rest of the text BFLZ] have expanded and improved on the Bourguignon and Morrisson approach by using for the countries for which actual income distribution data were lacking either (i) an estimate of inequality based on the evolution of the unskilled wage/GDP ratio,<sup>4</sup> or (ii) by substituting for the missing income distributions, the data on the distribution of individual heights. For (i), if unskilled wage-to-GDP ratio increases, the assumption is that income inequality declines; for (ii), if there is a strong correlation between distribution of individual heights and distribution of income, then we can “enrich” the dataset on countries’ income distributions by adding the data on countries for which we possess the distributions of heights.<sup>5</sup> In that way, the Bourguignon-Morrisson “backward projections” are not used at all. The other building block of the exercise, the reliance on Maddison’s GDP per capita data, remained.

In this paper, I proceed to do three things. First, I use social tables from thirteen 18<sup>th</sup> and 19<sup>th</sup> century countries to estimate global inequality for the early 19<sup>th</sup> century. Social tables have not been used for such a purpose before. Second, I present a story of global inequality between the beginning of the 19<sup>th</sup> and the beginning of the 21<sup>st</sup> century that at its two end-points relies on my own estimates, and uses Bourguignon-Morrisson or BFLZ estimates for the years in-between. Third, I apply the concept of the inequality extraction ratio (used earlier by Milanovic (2006), and Milanovic, Lindert and Williamson (2009) within country-wide framework) to the global scale. In other words, I ask how close was global inequality to its maximum feasible amount during the last two centuries. I conclude the paper with some speculative notes.

> datum, but just an overall statistic  like the Gini, available. It is also possible, that, basing themselves on published quintiles, Bourguignon and Morrisson estimated  deciles.

> 3 The point was made by Baten, Foldvari, van Leeuwen, and van Zanden (2009)

> 4 The approach was pioneered by Williamson (1998). It was used most recently by Prados de la Escosura (2008).

> 5 The approach was introduced by Baten (2000).

3
7 intensive-adjustment-lending (EIAL), other adjustment lending (OAL), and non-adjustment-lending (HAL) (Table 1).' The observed performance of an adjusting<sup>country results from (a) the</sup> policies that would have been in place in<sup>the absence of adjustment lending</sup> from the Bank, (b) world economic conditions,<sup>(c) the effects of a</sup> Bank-supported program, and (d) other shocks to the economy<sup>(droughts,</sup> earthquakes, etc.). To isolate the net<sup>cos.tribution</sup> of the Bank-supported program, a c,unter-factual scenario was created<sup>by estimating the effects on</sup> perfox tnce of:

- External shocks (interest rate, terms of trade,<sup>non-official</sup> lending).

- The economic policies in the pro-program period<sup>(indicated by the</sup> real exchange rate, ratio of the fiscal<sup>deficit to GDP, and annual</sup> rate of inflation).

3 The groups of countries and periods used here<sup>aredifferent from those</sup> of RAL - 1. Here the EIAL countries include all<sup>the intensive-adjustment-</sup> lending cc rntries of RAL - 1 plus 13 more. Because another year of performance - can be looked at, the RAL 2 intensive adjusters<sup>include additional countries</sup> whose second or third adjuatment loans came<sup>after 1985. Within each group a</sup> breakdown is made between low- and middle-income countries. Low-income countries are defined as all the IDA cour~ies (including<sup>those receiving a blend of IDA</sup> and IBRD loans) and the middle-income countries as all<sup>the rest.</sup> The Bank's first Report on Adiustment<sup>Lending (RAL-1) concluded that by</sup> the end of 1987 the 30 countries receiiving structural adjustment loans (SALs) before 1985 performed better on average than did<sup>the developing countries not</sup> receiving such loans. This conclusion was based<sup>on two comparisons: the</sup> performance of countries before and after receiving<sup>adjustme : loansl the average</sup> performance of countries receiving adjustment loans<sup>before 1985 and of countries</sup> not receiving such loans. The 30 countries receiving loans had modest improvements in performance, despite a more<sup>unfavorable external environment,</sup> as compared with the other group of countries. The<sup>12 countries that received</sup> 3 or more adjustment loans before 1987 had more<sup>pronounced improvements.</sup>
TABLE 7. Five Simulations Testing the Reliability of Generated Cells with Missing Data

||**1**<br>**Propensit**<br>**remov**|**y (2000**<br>**ed)**|**2**<br>**Interpolati**<br>**remov**|**on (1960**<br>**ed)**|**3**<br>**Interpolati**<br>**remov**|**on (2000**<br>**ed)**|**4**<br>**Missing**<br>**remov**|**(1960**<br>**ed)**|**5**<br>**Missing**<br>**remov**|<br>**(2000**<br>**ed)**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|Correlation||||||||||
||coefficient|Log<br>ratio|Correlation<br>coefficient|Log<br>ratio|Correlation<br>coefficient.|Log<br>ratio|Correlation<br>coefficient|Log<br>ratio|Correlation<br>coefficient|Log<br>ratio|
|**Australia**|0.945|–<br>0.575|0.998|–0.067|0.990|–<br>0.126|0.954|–<br>0.242|0.946|–<br>1.302|
|**U**nited<br>States|0.893|–<br>0.243|0.961|0.169|0.972|–<br>0.262|0.596|0.284|0.250|0.124|
|**Switzerland**|0.771|0.393|0.971|0.041|0.900|–<br>0.312|0.899|–<br>0.006|0.818|0.010|
|**Chile**|0.688|0.059|0.997|0.183|0.897|0.038|0.498|–<br>0.080|0.376|0.009|

_Note:_ A cutoff of 250 migrants is implemented for calculating the log ratios since they can be highly skewed by the predictions of very small corridors.

_Source:_ Authors‘ calculations based on data described in text.

42
<!-- Start of picture text --> Workplaces Grocery and Pharmacy<br>oOr oON<br>OoSo Oo L—> SS:Za<br>~ \ ° \<br>Oo N<br>$'°y<br>2020m1 2020m4 2020m7 2020m10 2021m1 2020m1 2020m4 2020m7 2020m10 2021m1<br>Date Date<br>Residential Retail and Recreation<br>w°<br>9N \N<br>2<br>\<br>eo)<br>3<br>o ©<br>2020m1 2020m4 2020m7 2020m10 2021m1 2020m1 2020m4 2020m7 2020m10 2021m1<br>Date Date<br>———_ Low income ———— Lower middle income<br>—————_ Upper middle income | ——— High income<br><!-- End of picture text -->
file) to all matches and validate each match only if names, activity and neighborhood were consistent in both databases.

The “reclink” command allows inputting different weights to match on each of the three variables used (surname, first name and gender). In order not to rely on the weights used, we reiterate the process with different weights until no additional matches were found.

Since it is possible that the first name in the program data corresponds to the second name in the GUFE data, we also reiterate the whole process for all combinations of first to fifth names. Surname and first name were inverted in one of the two databases. So we also reiterate the process with other combination of surname and first names.

# **Checking that the matching method is working:**

To assess whether our matching method is working efficiently, we used the following methods:

1. First we looked at whether we could find additional matches using a more usual method of matching. That is looking at the two lists (sorted by surnames) and trying to find each business of the GUFE data in the program database. So it means looking mainly at businesses with surnames starting with the same letters. We were not able to find any additional matches.

2. Secondly, we looked at the proportion of business which formalized with the _entreprenant_ status during the first 3 months after program launch. Indeed, most businesses which formalized with the _entreprenant_ status should be also in the program data (in theory they are the only businesses aware of this new status). We matched 78% (119/153) of the newly registered _entreprenants_ .

3. We then took the 34 businesses which formalized with the _entreprenant_ status and were not matched with the program data and we tried harder to match these businesses. We took the program data and looked at all the businesses in the same neighborhood as the unmatched businesses. We were able to find 6 new matches. These 6 matches are very imperfect matches with surnames somewhat different and first name sometimes different. For two cases, the match was not done with our main method because the surname is missing in the program data.

29
**Figure 3. Skilled workers emigration rates by country group**

<!-- Start of picture text --> A. Average rate by region<br>0.100<br>0.080<br>0.060<br>0.040<br>0.020<br>0.000<br>America<br>Europe<br>Africa<br>Asia<br>Oceania<br>1990 2000<br>2000<br>1990<br><!-- End of picture text --> <!-- Start of picture text --> B. Average rate by income group<br>0.120<br>0.100<br>0.080<br>0 .060<br>0.040<br>0.020<br>0.000<br>High<br>Upper<br>L owe r<br>M iddle L ow<br>Middle<br>1990 2000<br>2000<br>199 0<br><!-- End of picture text --> <!-- Start of picture text --> C. Average rate by country size<br>0.250<br>0.200<br>0.150<br>0.10 0<br>0.050<br>0.000<br>Large<br>Upp er<br>Lower<br>M idd le Low<br>M iddle<br>1 990 2000<br>2000<br>1990<br><!-- End of picture text --> 11
Table 7: Robustness checks: alternative tariff measures

|Dependent variable<br>Lognumber of exportedproducts|Logexport sales|
|---|---|
|(1)|(2)|
|Tariff 99_fj,t−_1<br>-0.172***|-0.552***|
|(0.061)|(0.126)|
|Tariff 99_fj,t−_1_×_China_j_<br>-0.301**<br>(0.150)|-0.806***<br>(0.301)|
|Extensive margin competition_fjt_(N<sup>_◦_</sup>countries)<br>0.388***<br>(0.038)|0.513***<br>(0.078)|
|Extensive margin competition_fjt_(N<sup>_◦_</sup>frms)<br>0.445***<br>(0.023)|0.970***<br>(0.050)|
|Intensive margin competition_fjt_<br>-0.054***<br>(0.003)|-0.019***<br>(0.006)|
|Country level controls<br>Yes|Yes|
|Firm fxed effects<br>Yes|Yes|
|Country fxed effects<br>Yes|Yes|
|Year fxed effects<br>Yes|Yes|
|Observations<br>24,394|24,394|
|_R_<sup>2</sup><br>0.596|0.613|

_Notes_ : The regressions are OLS estimations of equation (3) for the period 1999-2005. In columns (1) to (4) the dependent variable is the logarithm of the number of exported products to country _j_ by firm _f_ in year _t_ , and in columns (5) to (8) the dependent variable is the logarithm of the export sales in country _j_ by firm _f_ in year _t_ . Fixed effects by firm, country and year and a constant are included in all specifications. Tariff _fjt_ is the tariff measure faced by firm _f_ when exporting to country _j_ in year _t_ . Using HS6 product level tariff data from TRAINS, we construct the firm level tariff by taking the average of tariff over the basket of all the HS6 products exported by firm _f_ in the initial year (1999) to country _j_ . This basket is kept fixed over the period. Country level control variables definitions are reported in table 4. _∗∗∗, ∗∗,_ and _∗_ indicate significance at the 1, 5 and 10 percent levels respectively.

# _Alternative country samples_

In the previous regressions, the sample of Asian countries selected as a group of comparison included countries that are homogeneous in terms of trade integration and geographical proximity, but they differ in terms of economic development. Since the export patterns of French firms might differ according to the income level of the destination country, in this section we carry out two additional sensitivity tests.

We first restrict the sample to LDC Asian economies and exclude the high-income Asian countries like Korea, Japan and Singapore. Table 8 reports these results. The previous findings are robust to the countries selected as a group of comparison. When we compare the results in columns (2) and (3), on the one hand, and columns (5) and (6), on the other, the effect of Chinese tariff liberalization relative to 29
|Occupational income|Number of people in|Average annual income|
|---|---|---|
|(in milreisper annum)|occupation|relative to mean|
|3600|66|11.55|
|3906|13|12.53|
|3967|78|12.72|
|4000|7703|12.83|
|4320|394|13.86|
|4461|180|14.31|
|4675|161|15.00|
|4748|78|15.23|
|4799|448|15.39|
|4800|464|15.40|
|5000|1520|16.04|
|5312|694|17.04|
|5339|90|17.13|
|5459|181|17.51|
|5856|90|18.78|
|5859|13|18.79|
|5936|13|19.04|
|5948|540|19.08|
|6000|3774|19.25|
|7119|540|22.83|
|7123|39|22.85|
|8000|934|25.66|
|8784|90|28.18|
|8899|90|28.54|
|9598|138|30.79|
|10000|244|32.08|
|10679|270|34.25|
|12000|403|38.49|
|14000|75|44.91|
|14396|64|46.18|
|19195|34|61.57|
|20000|132|64.15|
|23994|22|76.96|
|28793|3|92.36|
|30000|66|96.23|
|33592|35|107.75|
|_Total_|_312_|_1_|

**Income distribution data.** The occupational data come from the Brazilian 1872 Census. The annual incomes by occupation were estimated by the team of economic historians Bértola, Castelnovo, Reis and Willebald (2006). The original data include 813 professional groups. For simplicity they are consolidated in the table shown above: different professions with the same estimated income are summed up.

**Population and area.** Current land area of Brazil. Population from Maddison (2004).

43