rafmacalaba/datause-encoder-data huggingface dataset:
13 influence of trade unions in wage bargaining, the pace of geographic and occupational mobility in the job market, the quality of human capital (labor force education), the nature of active labor market policies, etc. Interestingly, that list of fundamental variables usually does not include the quantity and quality of jobs created by the economy (the supply side of the labor market), which is implicitly assumed to be exogenous to the theoretical analysis of unemployment.

There are many unresolved conceptual issues with that dominant theory of unemployment, including whether they could be applied to developing countries where economies tend to have a distinctly different structure. The ambiguities of defining unemployment and comparing it across countries have also been a source of difficulty. Official definitions of unemployment and underemployment are widely accepted but their operational significance and true meaning is still the subject of debate. The ILO defines an _unemployed_ as a member of the labor force who meets the following criteria: Not employed during the past seven days, even for one hour; looking for work; and available for work. The _underemployed_ are the unemployed plus those who are employed part time (less than 30 hours per week) and want to work full time.

Survey results of percentage of workforce unemployed in 129 countries conducted by Gallup World Poll show that Africa’s performance is generally a bit worse than the world average of 7 percent but many countries appear to perform rather well, or at least in the same bracket as Canada, Australia, Finland, Denmark, Belgium, Israel, Brazil, Argentina, Poland, India, Russia, and many other better performing countries.<sup>10</sup> One realizes that something is wrong with the official numbers when countries such as Nigeria, Cameroon, Kenya, Zambia, or the Central African Republic score much better on that criterion than the United States, France, Italy, Spain or South Africa. The story changes when the coin is flipped and one measures the portion of the labor force employed full time for an employer (those working for an employer at least 30 hours per week): Africa’s general performance is suboptimal.

> 10 Unemployment rates in low-income Africa are about 5-7 percent, with rate of youth unemployment slightly higher. The situation in South Africa and in some other middle-income mineral-exporting countries such as Angola, Botswana and Namibia is almost the inverse of that of low-income countries, with unemployment rates ranging from 25 to 40 percent, with youth unemployment also higher. Source: World Bank data.
19 Argentina and Colombia the TOT shocks have an effect of intermediate significance, while they appear relatively unimportant in Brazil, Chile and Mexico. From the impulseresponse functions (Appendix 4), the following multipliers can be obtained: In Argentina, a one standard deviation shock in the index will raise growth by 1.3% during twelve months. In Brazil a similar shock will increase growth by 0.2% during 18 months. In Chile a one standard deviation shock in the terms of trade variable increases growth 0.3% during 17 months. In Colombia, a shock of similar size raises growth by 0.4% during 21 months, while in Mexico it raises growth by 0.2% during two years.

Table 4 summarizes the sensitivity of output growth to shocks in different variables, as well as the persistence of the shocks in each of the five countries under consideration. This information was calculated on the average behavior of the system for the sample period 1992-1998 and will be used in the next section to examine 1998-1999 in more detail.

# **IV. The Role of External and Domestic Factors on the 1998/1999 Slowdown**

In this section we aim at accounting for the 1998-1999 slowdown in a sub-set of economies of the region (Argentina, Brazil, Chile, Colombia and Mexico). Based on the generalized impulse response functions estimated for the whole period and the shocks that took place during 1997-1999, we calculate approximately the importance of each variable explaining growth rate changes during 1998 and 1999.

|**Sensitivity of Industrial Out**<br>**Average change in output grow**|**Table 4**<br>**put Growth to shoc**<br>**th due**to**a historic**|**ks and persistence of the**<br>**al one standard deviatio**|**effects**<br>**n shock in:**|
|---|---|---|---|
|TOT|Spreads|Real Interest rates|Real Exchange<br>Rates|
|Argentina<br>1.3|3.0|2.0|_1.5_|
|Brazil<br>0.2|1.4|0.9|1.0|
|Chile<br>0.3|1.1|1.3|0.6|
|Colombia<br>0.4|0.4|1.4|0.6|
|Mexico<br>0.2|1.6|0.6|1.2|
|<br> <br>Persistence (in number<br>|<br>of**months) on outp**<br>|<br>**ut growth after a shock**<br>|**to:**<br>|
|TOT|Spreads|Real Interest rates<br>|Real Exchange|
|Argentina<br>12|10|1<br>7|18|
|Brazil<br>18|21|9|12|
|Chile<br>17|15|13|15|
|Colombia<br>21|18|16|18|
|Mexico<br>24|18|17|24|

Table 5 summarizes the evolution of the original variables during this sub-period. The identification of shocks, their timing and measurement is a difficult task due to the endogeneity of all the variables. To account for all the interaction between the variables we used the VAR to forecast the variables and then compared the forecasted value with the observed one. The discrepancy between the forecasted value and the observed one
and (survey data report of) turnover, firms with higher risk scores at baseline (2014) paid lower taxes in 2015 (Table A4, Columns 3 and 4). Second, we find that, controlling for firm characteristics, risk score at baseline is positively correlated with the total amount of audit adjustments and penalties in the following year, conditional on an audit occurring (Table A4, Column 6). These effects are statistically significant for the inverse hyperbolic sine function of total taxes and audit penalties, suggesting that the long tails are of particular importance.

The tax authority only calculates risk scores for legal entities (75 percent of the sample), so it is unavailable for individual entities.<sup>23</sup> Unfortunately, the risk score variable was not available to us at the time of randomization; hence we were unable to stratify upon it. Nevertheless, from Table 1 we find that, within the two subgroups, most variables are balanced, with a few exceptions: Among the low-risk group, firms in group A are smaller and have slightly higher risk scores (both at 10-percent level).<sup>24</sup> As with the full sample, p-values of the joint F-test indicate that the variables are not jointly correlated with the treatment group.

# **6 Empirical Specification**

We use Equation (2) below to examine the relative impact of the two treatments in promoting e-filing adoption as well as the firm characteristics associated with adoption:

> 23 Since randomization was stratified on legal status, this characteristic is perfectly balanced among the different treatment groups.

> 24 We control for these variables in robustness checks and find no significant difference on our main results.

21
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32
on the wage bill from WIOD. The fixed factor share is set to be equal to one minus the labor and intermediate input share. We take the number of workers in each sector from WIOD and assume that the share of workers in the first and second stages is proportional to the relative size of each stage. Since the utility function is assumed to be Cobb-Douglas, the elasticity of substitution between composites of different sectors is equal to one. The Cobb-Douglas shares are calibrated from WIOD. We set the elasticity parameter _θ_ = 5, so that the trade elasticity is equal to _−_ 6. We assume that the elasticity of substitution between robots and workers is high and equal to 10, thus we set _ν_ = 9.<sup>17</sup> Appendix B3 provides details on the data and additional parameters we use to calibrate the model, the solution method, and robustness checks.

# **6.2 of Northern robotization on North-South trade**

We first examine if the calibrated model is able to qualitatively reproduce the key empirical findings we documented above. The upper-left panel of Figure 6 shows how increased robot density in the North in the tradeable sector in which automation is feasible impacts exports to the South. The diagram depicts the percentage change of exports from North to South (relative to the exports from other countries to the South) in the industry that is subject to robotization. Consistent with the regressions discussed in section 5, the quantified model reveals that increased robotization in the North (reflecting a fall in robot prices) leads to a relative expansion of exports to the South. This effect is driven by robot-induced gains in Northern competitiveness in this industry. Perhaps more surprisingly, the upper-right panel of Figure 6 shows that the South also experiences a relative increase in sectoral exports to the North (relative to the exports of other countries to the North). This expansion is driven by the growth of exports of intermediate inputs, reflecting increased demand for these goods by final good producers in the North. The model is therefore able to replicate the main empirical finding reported in the previous section. Importantly, the lower panel of Figure 6 reveals that these relative trade impacts reflect a surge in North-South trade flows not only in relative, but also in absolute terms. Total trade flows increase: the lower-left panel of Figure 6 shows that Northern robotization leads to an increase in the value of exports from North to South; the lower-right panel shows that it also leads to an increase in the value of exports from South to North, which is driven by the expansion of

> 17Although there are no explicit estimates of the elasticity of substitution between robots and workers in the literature, they are perceived to be highly substitutable. Acemoglu and Restrepo (2017b) assume that the elasticity is infinite. As detailed in Appendix B3, we performed sensitivity tests using different values for this elasticity, and found that the results remain qualitatively unchanged.

22
**Figure 3. Net Payers and Net beneficiaries of fiscal policies**

- **a. Taxes and transfers as % of Market Income plus pensions, by components and overall**

<!-- Start of picture text --> 600 500 458.9<br>500<br>400<br>400<br>300<br>300<br>200<br>200<br>169.8<br>59.9<br>100 100<br>15.7<br>-3.1<br>0 -19.4<br>0<br>1.4<br>-100 -10.1 -15.1 -22.2<br>1 2 3 4 5 -100<br>1 2 3 4 5<br>Quintiles of per capita market income plus<br>Quintiles of per capita market income plus<br>pensions<br>pensions<br>Direct Transfers Direct Taxes Indirect Taxes<br>Net Cash Fiscal Position<br>Health Education<br>% of market income plus pensions<br>% of market income plus pensions<br><!-- End of picture text -->

- **b. Taxes and transfers as % of disposable income, by component and overall**

<!-- Start of picture text --> 200<br>200<br>151.8<br>150 160<br>120<br>100<br>80<br>45.8<br>50<br>40 20.6<br>-1.3<br>0<br>0 17.2<br>-20.1<br>0.6 -1.5<br>-12.6<br>-50 -40 -23.1<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of per capita disposable income Quintiles of per capita disposable income<br>Direct Transfers Direct Taxes<br>Indirect Taxes Health Net Cash Fiscal Position<br>Education<br>% of total expenditure/tax % of total expenditure/tax<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

20
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13 Table 6 shows the dynamic products in Egypt's exports over 1980-82 to 1990-92 along with those products where exports declined. (Box 3 provides information on Egypt's largest export products for comparison). For the most part, Egypt's dynamic products differ from those of the region as only cement, leather manufactures, and plumbing equipment also appear on the list of ME fast growing exports. However, a common point is that manufactured goods also are predominant in Egypt's fastest growing exports (15 out of 21 dynamic products are manufactures). Four of Egypt's manufactures exports: iron and steel shapes; glassware; miscellaneous chemicals; and plastic articles maintained a 50 percent compound annual growth rate over the decade.

Five food products (fresh meat and fish, cereal preparations, cheese and miscellaneous food are among Egypt's fastest growing exports and the total trade in these items surpassed $42 million annually in 1990-92. Given the major trade barriers these products face in OECD markets Egypt's exports were directed almost exclusively to other developing countries. For example, over 94 percent of Egypt's 1992 exports of fresh meat (SITC 011) went to Kuwait, Saudi Arabia and Qatar while over 50 percent of the exports of cereal preparations went to Russia and Saudi Arabia. Russia received about one-third of Egypt's exports of miscellaneous food preparation (SITC 099) while about 28 percent of these shipments went to Jordan, Kuwait and Saudi Arabia. Italy was the major destination for Egypt's fresh fish exports (SITC 031) absorbing 65 percent of total shipments.

Eleven of Egypt's "declining" products recorded negative growth rates with crude and refined petroleum accounting for almost half of this groups total exports. The decline is largely the result of weakness in crude petroleum prices which fell by about 50 percent on average over the 1980-82 to 1990-92 period. Cotton exports, which accounted for 6 percent of the declining products exports, largely due to the increased utilization of domestic production the local textile and garment industry and the fact that Egyptian cotton became less competitively priced over the period.

# A. Trends in Intra-Regional Trade

A major problem one faces in trying to analyze trends in intra-regional trade is that some countries have gaps in their import and export statistics reported to the United Nations. Egypt, Turkey, Israel and several other countries are exceptions since they provided the UN with complete trade data from the ea:ly 1960s to 1992. Conversely, Lebanon and Iran have not reported trade data to the United Nations since 1977 and 1988 is the most recent year for which Bahrain's data are available. Major gaps (missing years) exist in Oman, Qatar, Saudi Arabia and the UAE's trade statistics. As such, partner country statistics must be used to derive