rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Descriptive Statistics IT use variables

||||Sampl|e|||||Who|le|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|Mean|p50|sd|p10|p90|N|Mean|p50|sd|p10|p90|N|
|2008|||||||||||||
|Computers-per-worker|0.24|0.19|0.22|0.06|0.50|701|0.34|0.23|0.34|0.05|0.80|4612|
|Share of labor with Internet|0.14|0.11|0.15|0.03|0.28|701|0.23|0.14|0.25|0.02|0.62|4658|
|Share of labor with computer|0.21|0.17|0.19|0.05|0.43|701|0.30|0.20|0.28|0.04|0.81|4658|
|Sales-per-worker|1,011.66|440.079|1,656.63|125.06|2,219.48|701|954.20|483.87|1,385.23|100.30|2,293.84|4566|
|Share of white-collar workers|0.22|0.19|0.18|0.05|0.44|701|0.32|0.22|0.29|0.06|0.90|4658|
|Exports/total sales|0.41|0.16|0.44|0.00|1.00|701|0.19|0.00|0.35|0.00|1.00|4637|
|Share of FDI|46.18|1.00|48.59|0.00|100.00|701|20.14|0.00|39.15|0.00|100.00|4658|
|2012|||||||||||||
|Computers-per-worker|0.29|0.23|0.23|0.07|0.57|701|0.39|0.28|0.36|0.06|0.95|6148|
|Share of labor with Internet|0.20|0.15|0.19|0.04|0.41|701|0.30|0.19|0.29|0.03|0.83|6210|
|Share of labor with computer|0.25|0.18|0.22|0.06|0.50|701|0.34|0.23|0.30|0.04|0.93|6210|
|Sales-per-worker|1,011.33|476.7|1,409.18|141.42|2,486.06|701|1,018.02|446.88|1,608.47|66.00|2,471.17|6148|
|Share of white-collar workers|0.26|0.19|0.23|0.06|0.59|701|0.37|0.24|0.33|0.06|1.00|6210|
|Exports/total sales|0.39|0.15|0.43|0.00|1.00|701|0.16|0.00|0.33|0.00|0.93|6132|
|Share of FDI|44.17|0.00|48.90|0.00|100.00|701|17.16|0.00|36.74|0.00|100.00|6210|

_Source:_ Authors’ calculations with data from ENTIC 2009 and 2013, INEGI
Table A2: Robustness check using the subsample with tenure being 3 years and less—Job search spell and the no. of vacancies applied for

|||Log(searc|h spell)|||Log(no. ap|plications)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.464<sup>_∗∗∗_</sup>|-0.322<sup>_∗∗∗_</sup>|-0.236<sup>_∗_</sup>|-0.318<sup>_∗_</sup>|-0.434<sup>_∗∗∗_</sup>|-0.257<sup>_∗∗∗_</sup>|-0.232<sup>_∗∗∗_</sup>|-0.241<sup>_∗∗_</sup>|
||(0.084)|(0.106)|(0.122)|(0.165)|(0.101)|(0.088)|(0.088)|(0.099)|
|Schooing in years||0.059<sup>_∗∗∗_</sup>|0.047<sup>_∗∗∗_</sup>|0.041<sup>_∗∗_</sup>||0.038<sup>_∗∗∗_</sup>|0.020|0.035<sup>_∗∗_</sup>|
|||(0.012)|(0.010)|(0.019)||(0.014)|(0.015)|(0.018)|
|Age at hire||-0.012|-0.010|-0.010||0.018<sup>_∗∗∗_</sup>|0.025<sup>_∗∗∗_</sup>|0.029<sup>_∗∗∗_</sup>|
|||(0.010)|(0.012)|(0.011)||(0.006)|(0.005)|(0.008)|
|Father’s pri.school||0.091|0.188<sup>_∗∗_</sup>|0.182<sup>_∗∗_</sup>||0.055|0.129|0.100|
|||(0.080)|(0.084)|(0.085)||(0.108)|(0.125)|(0.153)|
|Mother’s pri.school||-0.007|0.049|0.014||-0.004|0.053|0.107|
|||(0.062)|(0.066)|(0.069)||(0.062)|(0.062)|(0.075)|
|Math z-score||-0.001|-0.043|-0.059||0.052|0.025|0.051|
|||(0.051)|(0.063)|(0.093)||(0.046)|(0.048)|(0.061)|
|Language z-score||-0.112<sup>_∗∗_</sup>|-0.105|-0.124||-0.021|-0.046|-0.139|
|||(0.045)|(0.068)|(0.095)||(0.065)|(0.078)|(0.098)|
|Big 5 index z-score||0.039|0.182<sup>_∗_</sup>|0.095||-0.064|-0.140<sup>_∗∗_</sup>|-0.203<sup>_∗∗∗_</sup>|
|||(0.090)|(0.110)|(0.132)||(0.058)|(0.061)|(0.070)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.129|0.176|0.103|0.095|0.093|0.153|0.115|0.108|
|N|1745|1744|1744|1744|1745|1744|1744|1744|

_Note_ . The variable, _Social networks_ , is the dummy indicating that social networks were used to find jobs. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

39
-8-

- the problem of causality current milex could cause changes in the current growth rate or changes in the current growth rate could cause changes in the current level of milex. In this light, the most - important regressors MES, MES2, NMES, etc. - are the average of the three years immediately preceding the six- or seven-year period of the dependent variable.'1

Since the dependent variable is the average growth over six to seven years and since the regressors are typically threeyear lagged averages, the regressions using data for 196949 basically test the cross-section relationship between military expenditures and growth. 7"tare are 71 countries and at most two observations per country. Using multi-year growth rates as the dependent variable, it is difficul **t** o test the time series relationship because the share of milex in GNP changes slowly. To test the effects of changes in MES on the growth rate, we also regressed differences in the growth rates between successive non-overlapping multi-year periods on differences in the regressors.12 The sample was also broken down by geographic regions: Sub-Saharan Africa (SSA), Latin America, and all other - regions (Asia, Middle East, North Africa and Southern Europe ASMENASE). Finer breakdowns would result in too few degrees of freedom in the regressions.

C. D=a The data source for the economic, social, and human capital variables was the World Bank's World Tables. This data series provides annual data for the years 196949 and defines the time period for the study. As discussed earlier, the military expenditure data were from the SIPRI Yarbo, supplemented by USACDA data when necessary. The political condition variables were created by the author, and consist of dummy variables assessed annually for political instability, civil war, and international war. Further details about data sources and variable definitions are provided in the endnote.'4 The countries in the sample are non-Communist developing countries with a population of over 2 million for which there are data in the World Table. It i importan **t** o note that some of the
Table E3: Enrollment Decomposition Using Year 1 Child Data

||(1)<br>Enrollment|(2)<br>% New|
|---|---|---|
|High|0.348|0.025*|
||(0.702)|(0.015)|
|Low Treated|0.776|0.056**|
||(0.740)|(0.024)|
|Low Untreated|-0.382<br>(0.706)|0.024<br>(0.017)|
|Baseline|0.641***<br>(0.048)||
|R-Squared|0.61|0.04|
|Observations|765|711|
|# Schools (Rounds)|765 (1)|711 (1)|
|Mean Depvar|14.69|0.07|
|Test pval (H=0)|0.62|0.10|
|Test pval (_L_<sup>_t_</sup>= 0)|0.30|0.02|
|Test pval (_L_<sup>_t_</sup>=H)|0.56|0.21|

Notes: * p<0.1, ** p<0.05, *** p<0.01 a) This table examines changes in child enrollment status. The dependent variables are from tested children in round 3. Col 1 is the number of children enrolled in grade 4, and col 2 is the fraction of those children who newly enroll in the school after treatment. Enrollment status is determined based on child self-reports; any child who reports joining the school fewer than 18 months before are considered new. b) Regressions are weighted to adjust for sampling and include strata and round fixed effects, with standard errors clustered at village level. The number of observations and schools is the same in this table since we survey children just once. Observations may be lower than the full sample due to missing values for some variables. The mean of the dependent variable is its baseline value or the follow-up control mean.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

40
four reforms.<sup>10</sup> In each case, we compare the effects under the reform to the baseline scenario to assess the effects on household welfare, following a “top down” procedure in which the endogenous outcomes generated by the CGE model are fed into a micro-simulation model (MSM). This approach has been used extensively as a way of connecting the more aggregate outputs of CGE models with the detailed household data available in MSM models. A survey of such models is given in Feltenstein, Lopes, Porras-Mendoza, and Wallace (2014).

# **Incidence and Targeting**

First, we examine the implications of the model in terms of the incidence and targeting implications of the three proposed tax increases. Appendix Table 5, as well as the subsequent graphs, shows the average change in real consumption for each expenditure quintile under the alternative scenarios, in comparison to the baseline. The base case generates highest overall growth rate in real consumption. Of the alternative tax policies, raising the sales tax gives the highest aggregate level of real consumption. Thus, the real consumption is higher for each quintal with the 17 percent sales tax than it is with the 45 percent corporate income tax. Similarly, the sales tax increase generates higher aggregate real consumption than does the tariff increase, although all of the counterfactual scenarios have lower aggregate consumption levels than the base case. It is useful to look at the relative impacts of these alternative tax policies on different income categories. It is interesting to note that the lowest quintal suffers a greater consumption loss under the corporate tax increase than it does under either the sales tax or tariff increases, contradicting standard intuition. Similarly, it is useful to note that the highest quintal is impacted relatively more by the sales and tariff changes than any of the four lower quintals,

> 10 The growth rate we use is the average growth rate of the simulated real income produced by the model in periods one through eight.

22
to schooling and age. In column (2), we exclude the 1970s from the analysis. Our main findings are unaffected by this change in the sample period. Tariffs are shown to have positive effects on average wages and negative and more significant effects on the skill premium. In fact, the impacts on the skill premium are even larger when the 1970s are excluded, strongly suggesting that our results are not driven by the tariffs cuts of this period. In column (3), we exclude all years in the 1974-1982 period, where the tariff cuts were largest (Figure 2). Once again, our findings are robust to the exclusion of these years.

The second concern is the role of non-tariff barriers like quotas or quantitative restrictions. These are usual instruments of the Argentine trade policy, and provided non-tariff barriers are correlated with tariff barriers, their omission in the regressions can cast doubts on the interpretation of our key results. The problem with non-tariff barriers is that we were unable to construct historical series spanning the period under study. Even simple measures of coverage ratios are unavailable (or very hard to construct).<sup>10</sup> In principle, if non-tariff barriers were uncorrelated with tariffs, our estimates would be consistent. However, this correlation is likely to be present, for instance if quotas are high in those industries with low tariffs. Nevertheless, using data on tariffs and non-tariff ad-valorem equivalents compiled by Kee, Nicita, and Olarreaga (2005), we found that the correlation between tariffs and non-tariff barriers in Argentina was positive but very small (around 0.03). This suggests that the omission of non-tariff barriers in the main regressions would not be a severe problem.

We follow two further strategies to account for the role played by non-tariff barriers. One way around the problem of lack of data on NTBs is to exploit the sequencing of trade reforms experienced by Argentina. Berlinski (1994; 2003) has documented that non-tariff barriers were mostly used during the debt crisis of 1982-1989. Before that, non-tariff barriers were not generally used; after that, they were eliminated prior to the tariff cuts of the liberalization of the 1990s. This suggests a way to check the robustness of our results by further breaking down the historical series. In column (4) of Table 4, for instance, we exclude the 1982-1989

> 10The historical trade data that we put together in this paper does not come electronically. Instead, we had to collect hard copies of trade data for thousands of HS items for many years and to input them manually. Non-tariff barriers are usually implemented through legislative decrees specific to the different industries. Building a historical dataset of norms legislated by decrees is practically unfeasible.

14
**Figure 4. Cross-border holdings and extensive margin of portfolio investments**

<!-- Start of picture text --> Panel A: LAC to North Panel B: LAC to South<br>200 80% 12 14%<br>180<br>70% 12%<br>10<br>160<br>60%<br>140 10%<br>8<br>50%<br>120<br>8%<br>100 40% 6<br>6%<br>80<br>30%<br>4<br>60 4%<br>20%<br>40<br>2<br>10% 2%<br>20<br>0 0% 0 0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Panel C: North to LAC Panel D: South to LAC<br>800 60% 16 16%<br>700 14 14%<br>50%<br>600 12 12%<br>40%<br>500 10 10%<br>400 30% 8 8%<br>300 6 6%<br>20%<br>200 4 4%<br>10%<br>100 2 2%<br>0 0% 0 0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br>% of Active Connections % of Active Connections<br>Value (2011 Constant U.S. Dollars Billions) Value (2011 Constant U.S. Dollars Billions)<br><!-- End of picture text --> _Panel E: LAC to LAC_ <!-- Start of picture text --> 14 45%<br>40%<br>12<br>35%<br>10<br>30%<br>8 25%<br>6 20%<br>15%<br>4<br>10%<br>2<br>5%<br>0 0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>% of Active Connections<br>Value (2011 Constant U.S. Dollars Billions)<br><!-- End of picture text --> This figure shows the evolution of the extensive margin for portfolio investments and the evolution of the total value of these connections. The extensive margin is represented by the percentage of active connections, that is, the number of country-pairs that have a positive holding in each year over the number of country-pairs with a positive or zero value in the last year of the sample. North countries are the G7 and Western Europe. South countries are all the others (excluding LAC countries). Offshore centers are excluded from the sample. Holdings are expressed in 2011 constant U.S. dollars.
Waters sector to be found near the bottom.

# 6.3. Plans for Further Work

Clearly there remains huge scope for further development of IPPS. We are now assembling plant-level databases from several developing countries. Our future econometric work will quantify the effects on pollution intensity of national or regional differences in regulatory regimes, factor prices and availability of technology. Using these estimates, we will develop simple procedures which can adjust IPPS parameters for conditions in developing countries.

67
3 Chart 2 Low Income Countries <!-- Start of picture text --> Ratlo<br>0..<br>0.5<br>0.4<br>0.3-<br>0,2<br>0.1<br>0- E<br>1960 1965 1970 1975 1980 1984<br>Year<br>1 Proportion of -4- Proportion of<br>Secondary Level Students Age Eligible Population<br>In Vocational Schools Enrolled In Secondary Schools<br>Chart 3<br>Middle Income Countries<br>(Excluding E.Europe)<br>Ratio<br>0.1<br>0.5<br>0.4-<br>0.3 -<br>0.1<br>100 i065 1970 1975 180 194<br>her<br>1 Proportlon of I4+ Proportion of<br>econdary Lel Students Age Elilible PoputlIon<br>In VooatIonal Sohols Enrollud In 8econdory S8hoold<br><!-- End of picture text -->
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18
11 average of 12.9 percent,<sup>9</sup> and lead to an estimated increase in Sub-Saharan African cotton output and value added of nearly one-third. The real value of cotton exports from SubSaharan Africa would increase by more than 50 percent, while cotton output and exports would fall by one-quarter in the United States and would halve in the EU (Table 7). That would raise Sub-Saharan Africa’s share of global cotton exports from 12 to 17 percent, and the share of all developing countries from 52 to 72 percent.

Also striking is a comparison of the welfare result from cotton reform with that from removing _all_ merchandise tariffs and agricultural subsidies. While the latter gain is nearly 300 times as great as the former globally, for Sub-Saharan Africa cotton reform is crucial: its potential contribution to the region’s welfare of $147 million per year is onefifth of the estimated $733 million gain for the region from the freeing of _all_ goods markets globally.

If those distortions to cotton markets were removed, how different would be the estimated effects of further GM cotton adoption beyond that achieved by 2001? Globally it would be virtually no different, for reasons explained in Alston, Edwards and Freebairn (1988) and Anderson and Nielsen (2004). But the gains to developing countries in the absence of distortionary cotton policies would be slightly greater (12 percent so in the case of Sub-Saharan Africa), while those to high-income countries would be less (middle columns of Table 6).

Were these two reforms (GM catch-up and subsidy removal) to occur simultaneously, they would reinforce each other in Sub-Saharan Africa as each expands the region’s cotton production and exports and so makes the gain from the other change

> 9 This is close to the 10 percent estimated by Sumner (2006, p. 282), which is also the simple average of the studies surveyed by Baffes (2005, p. 122).
public infrastructure investment and private housing construction continued to be the main sources of growth in the non-oil GDP.

<!-- Start of picture text --> Real Sector Development 1994 - 2005<br>80<br>70<br>60<br>50<br>Real GDP<br>40<br>Non Oil GDP<br>30<br>20<br>10<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Years<br>Percentage change<br><!-- End of picture text --> Source: Equatoguinean authorities; and IMF.

The real effective exchange<sup>12</sup> rate of the CFA franc calculated for Equatorial Guinea continued to appreciate during 2005. This pattern largely reflected the appreciation vis-à-vis the U.S. dollar and the persistent large inflation differential with the major trading partner countries: in fact these are evidences of Dutch Disease. The appreciation of the real exchange rate for Equatorial Guinea was the largest in the CEMAC region, reflecting the country’s position as the largest oil exporter and recipient of foreign direct investment relative to GDP.

<!-- Start of picture text --> Equatorial Guinea: Real Efective Exchange Rate 1997 - 2006<br>200<br>100<br>0<br>1997-2001 2002 2003 2004 2005 2006<br>Equatorial Guinea<br>Index 100 (2000)<br><!-- End of picture text --> Sources: Equato-Guinean authorities Bank and Fund staff estimates.

The traditional exports stagnated during the last few years; because producers’ profits were adversely affected by the nominal appreciation of the CFA franc vis-à-vis the U.S. dollar. However, another crucial underlying factor for stagnation in traditional

> 12 Overall movement in the real effective exchange rate appeared to be dominated by the response to favorable terms of trade developments particularly world oil prices and other fundamentals, such as the strong overall fiscal position.

10
# Lemma 2 If w > w<sup>�</sup> , then J < I implies I > I<sup>�</sup> .

This lemma rules out the possibility that I; I<sup>�</sup> and J are ordered such that J < I < I<sup>�</sup> . The ...gure depicts the remaining ...ve possible orderings of the threshold tasks.

Figure 2(a) reproduces the ordering of Figure 1(a). As we have seen, the tasks with the lowest o¤shoring costs have concentrated location and production in the low-wage West. These tasks are indicated in the ...gure by W<sup>c</sup> (where the superscript c denotes that for these tasks capacity and production are concentrated in West). The tasks with intermediate o¤shoring costs have concentrated location and production in the high-wage East and are indicated by E<sup>c</sup> . And the tasks with the highest o¤shoring costs have dispersed location and production, as indicated by D.

Figure 2: Location Equilibrium for All Tasks <!-- Start of picture text --> I * ≤ I<br>(a)<br>0 J I * I 1<br>W c E c D<br>(b)<br>0 I * J I 1<br>W c E o E c D<br>(c)<br>0 I * I J 1<br>W c E o D<br>I < I *<br>(d)<br>0 I J I * 1<br>W c W o D<br>(e)<br>0 I I * J 1<br>W c D<br><!-- End of picture text --> 12