rafmacalaba/datause-encoder-data huggingface dataset:
some segment of the labor force that automation will render redundant. To capture this, automation in our model’s production function reduces the marginal product of lesser-skilled labor while increasing the marginal product of high-skill labor, all things being equal. As a result, automation has a different effect on steady state outcomes than technological change in typical skill-biased technological change models.

The important role of education quality is motivated by education often being the prescribed remedy for automation (Brynjolfsson and McAfee 2011, 2014; Golden and Katz 2008). Given rapid adoption of computing technology in the past 40 years, global demand for education has remained strong with rates of return averaging 9-10 percent (Montenegro and Patrinos 2014; Psacharopoulos and Patrinos 2018), despite substantial increases educational attainment over this period (Barro and Lee 2013). Strong returns are attributed to both increased demand for skills but also restricted supply of quality education (Altinok, Angrist and Patrinos 2018).

Our paper offers several new theoretical and empirical contributions to the literature on the effects of automation on labor market outcomes. Theoretically, we show how, in a general equilibrium, overlapping-generations model, education quality can determine whether automation is beneficial or detrimental, helping to reconcile automation’s optimism and pessimism. Second, we provide a definition of automation that differs from skill-biased technological change in an aggregate production function. Third, we show how countries in the midst of development may be most at risk to automation if their education quality is poor. Empirically, we establish the link between skills, educational attainment and the likelihood of an occupation being automated globally. Second, we show that middle income countries tend to have the highest proportion of labor in automation-prone occupations, corroborating our model’s prediction. Coupled with evidence from international student assessments showing that low and middle-income countries generally have poor learning outcomes, our findings suggest that middle-income countries are most at-risk to automation. Finally, we show how vocational education programs that offer direct labor market entry while deemphasizing cognitive and noncognitive skills are especially problematic with automation.

Our paper is structured as follows. We first present an overlapping-generations model in which 5
lower productivity than the average one. One approach to dealing with this concern is to use migration lottery programs, in which a group of applicants apply, and then a random ballot determines which individuals get to migrate. McKenzie et al. (2010) and Gibson et al. (2018) examined such a program for Tongan migrants coming to New Zealand, finding they gain a 263 percent increase in (cost-of-living adjusted) income in the first year, with this gain in income persisting for at least the next 10 years, for a net present value of migrating of at least US$237,000. Mobarak et al. (2021) also use a migration lottery to estimate the income gains from migrating between Bangladesh and Malaysia, finding that migration doubles household incomes.

These estimates suggest that there are indeed large gains in income to be had from international migration. However, there has been more debate about whether there are really gains in income to be had from more internal migration. There are very large productivity gaps between the agricultural and non-agricultural sectors (e.g. Young, 2013; Gollin et al. 2014). However, a large part of this gap appears to reflect differences in the human capital and selection of who works in these different areas. Papers which use panel data to track rural-urban migrants over time have found the income gains to be smaller, but still positive. For example, Hamory et al. (2021) find wage earnings to be 27 percent higher in urban areas than rural areas for movers in Kenya, and 4 percent higher in Indonesia; while Beegle et al. (2011) find in Tanzania that individuals who move away from their rural villages have consumption gains of over 30 percent. Lagakos et al. (2020) use panel data from six countries - China, Ghana, Indonesia, Malawi, South Africa and Tanzania – and find an average gain in (spatially price adjusted) consumption of 23 percent from migrating, ranging from 17 percent in Indonesia to 34 percent in Ghana. Gains in income in South Africa and Indonesia are 29 percent and 15 percent respectively.

However, the challenges with these panel data estimates are both that the gains for the types of people who move might be different from what we might expect for those who don’t migrate, and in dealing with the possible selectivity of when individuals decide to move – e.g. they might move because they experience a negative forecast of what their productivity would be at home, and so their prior earnings might overstate what they would have earned had they stayed.

Experimental estimates from efforts to encourage more rural-urban migration address this issue, and do also suggest that, at least in some settings, gains from internal migration are possible for the people who respond to these incentives. For example, Bryan et al. (2014) find Bangladeshis 7
GFMC, 2011. International Forest Fire News: Fire Situation in Bhutan. Available at: <u>http://www.fire.uni-freiburg.de/iffn/iffn_34/07-IFFN-34-Bhutan.pdf. Accessed 10 June</u> 2011.

- Government of India (GOI), 2012. Status of hydroelectric potential development. Available at: <u>http://www.cea.nic.in/reports/hydro/he_potentialstatus_region.pdf.  Accessed 15 April</u> 2012.

- Gornall, J., R. Betts, E. Burke, R. Clark, J. Camp, K. Willett, and A. Wiltshire, 2010. Implications of Climate Change for Agricultural Productivity in the Early Twenty-first Century. Philosophical Transactions of the Royal Society B: Biological Sciences 365: 2973-2989.

- Gosain AK, S. Rao S, and A. Arora, 2011. Climate change impact assessment of water resources of India. Current Science 101(3):356–71.

Gosain, A.K., A.B. Shrestha, and S. Rao, 2010. Modelling Climate Change Impact on the Hydrology of the Eastern Himalayas: Climate Change Impact and Vulnerability in the Eastern Himalayas - Technical Report 4. ICIMOD. ISBN 978-92-9115-151-6.

- Grace, J., F. Berninger, and L. Nagy, 2002. Impacts of Climate Change on the Tree Line. Annals of Botany  90:537–544.

- Guhathakurta, P. and M. Rajeevan, 2008. Trends in the Rainfall Pattern over India. International Journal of Climatology 28: 1453-1469.

Guisan, A, J.-P. Theurillat, and R. Spichiger. 1995. Effects of climate change on alpine plant diversity and distribution: The modelling and monitoring perspectives. In Potential Ecological Impacts of Climate Change in the Alps and Fennoscandian Mountains, ed. A Guisan, J. I. Holten, R. Spichiger, and L. Tessier, 129-35. Geneva: Conservatoire et Jardin Botaniques.

- Haeberli, W. and M. Hoelzle, 1995. Application of Inventory Data for Estimating Characteristics of and Regional Climate-change Effects on Mountain Glaciers: A Pilot Study with the European Alps. Annals of Glaciology 21: 206-212.

Hasnain, S.I., 2002. Himalayan glaciers meltdown: impact on South Asian Rivers. Proceedings of the Fourth international FIEND Conference. Cape Town, South Africa. International Association of Hydrological Sciences Publication. pp. 417-423.

34
**Figure 1: Histograms of estimated returns to schooling and to potential work experience across developing countries**

<!-- Start of picture text --> 0 5 10 15 20 -5 0 5 10 15<br>1A: Returns per year of schooling in percent 1B: Returns per year of potential experience in percent<br>.2 .2<br>.15 .15<br>.1 .1<br>Density Density<br>.05 .05<br>0 0<br><!-- End of picture text --> Source: Authors’ calculations based on the coefficients from earnings function regressions applied to 122 household survey data sets covering 86 countries 25
Table 4: Probability of success in post-conflict by sector

||Educ.|Energy/Min.|Transport|Private|Urban dvpt|Soc. protec.|
|---|---|---|---|---|---|---|
|**_Projectcharacteristics_**|||||||
|<br>Duration<br>Investment=1|-0.030<br>(0.008)***<br>0.137|-0.029<br>(0.008)***<br>0.145|-0.028<br>(0.008)***<br>0.122|-0.027<br>(0.008)***<br>0.133|-0.028<br>(0.008)***<br>0.134|-0.026<br>(0.008)***<br>0.127|
||(0.043)***|(0.042)***|(0.044)***|(0.042)***|(0.043)***|(0.043)***|
|IDA=1|0.115|0.112|0.129|0.117|0.120|0.114|
||(0.035)***|(0.035)***|(0.036)***|(0.034)***|(0.034)***|(0.035)***|
|Preparation (1-4)|0.384<br>(0.033)***|0.383<br>(0.033)***|0.379<br>(0.032)***|0.384<br>(0.033)***|0.383<br>(0.033)***|0.383<br>(0.032)***|
|Supervision (1-4)|0.473<br>(0.034)***|0.474<br>(0.033)***|0.470<br>(0.033)***|0.473<br>(0.033)***|0.474<br>(0.034)***|0.472<br>(0.033)***|
|**_Country characteristics_**<br>GDP Growth, (t-3;t)|0.008|0.008|0.008|0.008|0.008|0.008|
||(0.003)***|(0.003)***|(0.003)***|(0.003)***|(0.003)***|(0.003)***|
|Ln GDP per cap, (t-3;t)|0.065|0.067|0.069|0.068|0.068|0.071|
||(0.021)***|(0.021)***|(0.021)***|(0.021)***|(0.021)***|(0.021)***|
|Ln population, (t-3;t)|0.003|0.002|0.003|0.002|0.002|0.003|
||(0.010)|(0.010)|(0.010)|(0.010)|(0.010)|(0.010)|
|**_Confict history_**<br>INIT<br>~~P~~C|-0.662|-0.663|-0.665|-0.682|-0.683|-0.668|
|PC Duration|(0.109)***<br>0.216|(0.106)***<br>0.212|(0.104)***<br>0.239|(0.094)***<br>0.229|(0.075)***<br>0.226|(0.101)***<br>0.212|
||(0.099)**|(0.103)**|(0.097)**|(0.097)**|(0.086)***|(0.093)**|
|PC Duration<sup>2</sup>|-0.013|-0.013|-0.015|-0.014|-0.013|-0.013|
||(0.008)*|(0.008)|(0.008)*|(0.008)*|(0.007)*|(0.007)*|
|INIT<br>~~P~~C x Supervision|0.380|0.389|0.381|0.394|0.393|0.384|
||(0.114)***|(0.117)***|(0.114)***|(0.112)***|(0.093)***|(0.109)***|
|PC Duration x Supervision|-0.084|-0.084|-0.094|-0.087|-0.086|-0.084|
||(0.031)***|(0.034)**|(0.032)***|(0.031)***|(0.028)***|(0.030)***|
|PC Duration<sup>2 </sup>x Supervision|0.005|0.005|0.006|0.005|0.005|0.005|
||(0.003)**|(0.003)*|(0.003)**|(0.002)**|(0.002)**|(0.002)**|
|**_Sector characteristics_**|||||||
|Sector|0.089|-0.025|0.098|0.083|0.005|0.149|
||(0.050) *|(0.042)|(0.057)*|(0.077)|(0.069)|(0.077)*|
|INIT<br>~~P~~C x Sector|-0.037|-0.203|-0.038|0.474|0.265|-0.245|
||(0.144)|(0.108)*|(0.196)|(0.014)***|(0.116)**|(0.408)|
|PC Duration x Sector|-0.038|0.012|0.079|-0.657|-0.035|0.121|
||(0.049)|(0.037)|(0.096)|(0.211)***|(0.053)|(0.235)|
|PC Duration<sup>2 </sup>x Sector|0.004|-0.000|-0.002|0.099|0.000|0.001|
||(0.003)|(0.002)|(0.008)|(0.040)**|(0.004)|(0.022)|
|Decade dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Region efects|Yes|Yes|Yes|Yes|Yes|Yes|
|Jointly signifcant test, Chi2|0.061|0.205|0.010|0.000|0.233|0.000|
|(Confict variables*Sector)|||||||
|Pseudo _R_<sup>2</sup>|0.29|0.29|0.29|0.29|0.29|0.29|
|ll|-1213.363|-1213.527|-1205.504|-1212.415|-1214.183|-1211.455|
|No. of frms in sector i|214|385|261|79|132|76|
|Observations|2466|2466|2466|2466|2466|2466|

Note: Robust standard errors in parentheses. Significant at (*) 10%, at (**) 5%, at (***) 1% 17
<!-- Start of picture text --> 100% 4000<br>3500<br>80%<br>3000<br>60% 2500 BHICs<br>Bm LMICs° 2000 BUMICs<br>40% | Ales 1500 mLMICs<br>MERICs 1000 mLiCs<br>20%<br>500<br>0% 0<br>isc6 3000 a004 3008 1996 2000 2004 2008<br><!-- End of picture text -->
Table 9 gives the mean consumption of the poor.<sup>43</sup> For 2005, those living below the $1.25 a day line had a mean consumption of $0.87 (about 3.5% of global GDP per capita). The overall mean consumption of the poor tended to rise over time, from $0.74 per day in 1981 to $0.87 in 2005 by the $1.25 line, and from $0.94 to $1.21 for the $2 line. Poverty has become shallower in the world as a whole.

The mean consumption of Africa’s poor is not only lower than for other regions, it has shown very little increase over time (Table 8). The mean consumption of those living under $1.25 per day in SSA was $0.72 per person per day in 1981 and was almost unchanged at $0.73 in 2005. For the $2 line, the mean consumption of Africa’s poor remained roughly constant. The same persistence in the depth of poverty is evident in MENA and LAC, though the poor have slightly higher average levels of living in both regions. The mean consumption of EECA’s poor has actually fallen since the 1990s, even though the overall poverty rate was falling.

# **6. Sensitivity to methodological choices**

We have already seen how much impact the choice of poverty line makes, though we have also seen that the qualitative comparisons as over time are robust to the choice of line. In this section we consider sensitivity to two further aspects of our methodology: the first is our use of the PPP for aggregate household consumption and the second is our reliance on surveys for measuring average living standards.

**_Alternative PPPs._** The benchmark analysis has relied solely on the individual consumption PPPs (“P3s”) from the ICP. One deficiency of these PPPs is that they are designed for national accounting purposes not poverty measurement. Deaton and Dupriez (DD) (2009) have estimated “PPPs for the poor” (P4s) for a subset of countries with the required data.<sup>44</sup> Constructing P4s requires re-weighting the prices to accord with consumption patterns of those living near the poverty line. Notice that there is a simultaneity in this problem, in that one cannot do the re-weighting until one knows the poverty line, which requires the re-weighted PPPs.

> 43 The mean consumption of the poor is (1- _PG/H_ ) _Z_ where _PG_ is the poverty gap index, _H_ is the headcount index and _Z_ is the poverty line.

> 44 The Asian Development Bank (2008) has taken a further step of implementing special price surveys for Asian countries to collect prices on explicitly lower qualities of selected items than those identified in the standard ICP. Using lower quality goods essentially entails lowering the poverty line. In terms of the impact on the poverty counts for Asia in 2005, the ADB’s method is equivalent to using a poverty line of about $1.20 a day by our methods; this calculation is based on a log linear interpolation between the relevant poverty lines.

28
# **3.1 Sector Breakdown**

The sector breakdown of ASE data is matched to the classification followed by banks in monthly reporting of sector exposures. The advantage of this method is that industry-level results could be directly matched with bank exposure and allow for integration of industry vulnerability analysis into the bank stress-testing exercise. The disadvantage is that some industry brackets end up with very few firms, while others represent fairly heterogeneous groupings. For example, in 2006 there are only 4 firms in the sector labeled “utilities and energy,” while there are 49 firms lumped together under “industry” which is fairly heterogeneous group (i.e. it includes such industries as chemicals, textiles, electronics, machinery, food processing and others).<sup>4</sup> The results for sectors with very few observations should be treated with caution, especially for the utilities and energy sector.

Table 3 reports the results of several key vulnerability ratios by sector in 2006. The last column reports averages across all industries.

Profitability is an important measure of sector performance and potential to meet its debt obligations. Among the sectors considered, the highest profitability is observed in the mining and financial sectors, which report 3.8% and 3.9%, respectively. Industry and trade on average reports ROA of about 1%, which is low by international standards, as shown above.

Transportation is the lowest, with an ROA of 0.3% in 2006. The overall ROA for all listed firms is lower than reported above for the 18 firms available in Worldscope.

In terms of liquidity, tourism and hotels, utilities and real estate companies have the highest quick ratio, i.e. highest ability to meet current liabilities with current assets. Industry, construction and transportation have lower quick ratios, just barely above one on average. These companies are less liquid, and hence more vulnerable to shocks to short-term financing or cash flows.

The overall level of indebtedness is an important factor in evaluating corporate vulnerability. Two measures of total indebtedness are total debt to assets, and total liabilities to

> 4 The category Financials only contains companies classified as real estate and diversified financial services. The focus of this report is on non-financial corporate sector.

7
**<u>Table 2: Schooling Equations: Russia, 2018</u>**

||Females|Males|
|---|---|---|
|Family occupational prestige|0.0204|0.0237|
||(-6.65)|(-6.57)|
|Family education, years|0.111|0.0823|
||(-7.64)|(-5.01)|
|Permskiy Krai|-0.66|-0.891|
||(-2.78)|(-3.72)|
|Tverskaya Oblast|-0.56||
||(-2.31)||
|Krasnoyarskiy Kray|-1.287||
||(-4.32)||
|Rostovskaya Oblast|-0.825||
||(-2.74)||
|Experience|-0.12|-0.153|
||(-8.13)|(-7.71)|
|Experience squared|0.00129|0.00198|
||(-4.34)|(-5.05)|
|Urban|0.52|0.795|
||(-5.43)|(-7.49)|
|Tambovskaya Oblast||<br>-0.923|
|||(-3.92)|
|Kabardino-Balkarskaya Resp||1.382|
|||(-2.4)|
|Constant|13.18|12.74|
||(-55.35)|(-41.77)|
|_N_|<br>2222|<br>1694|
|_adj R_<sup>_2_</sup>|0.2266|0.2359|
|F-value|73.32|66.35|
|Note: t statistics in parentheses<br>Source: RLMS|||

The IV estimation results, using the parental SES and regional dummies, are shown in the upper panel of Table 3. The instrumental variable approach yields the rate of returns to education in Russia of around 14.3 percent for females and 8 percent for males. Females' IV parameters appeared to be tangibly larger compared to the respective OLS estimate of 7.6 percent, while for males the IV and OLS (6 percent) estimates are much closer in magnitude. The female estimates are in line with what other researchers using instruments find in Russia (see, for example, Arabsheibani and Staneva 2012).

15
Thus, the larger the firms, the older they are. This might be related to a number of different factors: the recent establishment of services related to communications and electronic devices, the expansion of small businesses in more recent years due to major economic, political and economic reforms, and more opportunities in general for entrepreneurs. It is also possible that smaller firms are only able to sustain themselves for a few years before closure.

**Businesses present consistent characteristics within sector of operation.** The survey identified a wide range of businesses; indeed, the 513 surveyed firms were classified into 63 different types. Tea shops and printing services were the most common in the sample, but still accounted for no more than 11 percent of the total. The most common business run by women are printing services (11 percent), tea shops (8 percent), non-specialized stores with food and beverage (7 percent), and wholesale and retail of clothing (7 percent). The educational background of the owner is related, to some extent, to the industry the respondents work in. Those in tourism and hospitality have less years of schooling compared to other industries. Another difference across sectors shows that trade and logistics businesses have started operating more recently, possibly related to increased business activity and internal mobility in recent years. The trend indicates political and economic restructuring influencing the development of the latter. In line with this, business related to the more traditional non-farming and light manufacturing tend to be older.

**Small and medium firms in Myanmar offer a number of HRM benefits.** Firms in the sample offer a range of informal benefits, including, maternity/paternity leave, subsidized childcare, annual and sick leave, and subsidized housing. Among all the businesses operating in the different industries, the most commonly offered policy is paid sick leave. Across the difference industries, the most noticeable pattern comes from firms operating in the hospitality sector, which are most likely to provide subsidized housing. On the other hand, maternity leave is low across all firms (42 percent), regardless of the owner’s gender. This represents an extra challenge for mothers wanting to remain in the workforce and could also partly explain why women entrepreneurs are older than men by their entering the workforce once their children have grown. This pattern can be clearly seen in Chart 1.

**Chart 1. Age distribution of owners by gender**

<!-- Start of picture text --> 30% 33%<br>24% 24%<br>21% 20%<br>15%<br>13%<br>10%<br>3% 4%<br>1% 0 1%<br>18 - 20 21 – 30 31 – 40 41 – 50 51 – 60 61 - 70 70 - 75<br>Male Female<br><!-- End of picture text --> The lack of maternity leave also represents a decrease of income for mothers, which in turn is related to autonomy, social equality, and ability to plan for the future.<sup>23</sup> In addition, subsidized childcare helps women who have, or plan

> 23 ILO and Sasakawa, National Assessment of Women’s Entrepreneurship Development Framework Conditions – Myanmar, Draft executive summary.

13
- 35 -

measure, would show that the panel recommendations were appropriate, and, in the circumstances, at least in one case, in accordance with the complainant's request.

In U.S.- Salmon from Norway1<sup>43</sup> , the Panel found U.S. antidumping actions inconsistent with the Tokyo Code as regards: "the calculation of the cost of production in the country of origin"; attribution to one of the Norwegian farms the highest cost of production figure calculated on the basis of BIA without considering how this would affect the representativeness of the sample; and absence of proper consideration of differences in weight categories as a factor "which possibly affected" the comparability between constructed normal value and export prices.<sup>1</sup> 44 The Panel rejected Norwegian contentions on all other points. Would it have been appropriate for the Panel to recommend revocation of U.S. antidumping duty for certain incorrect aspects of methodology followed by U.S. authorities? Here is what the Panel says: ". . . the Panel took into account that the ground upon which it had found that the United States had imposed anti-dumping duties inconsistently with its obligations under the Agreement pertained specifically to certain aspects of the methodology for calculating margins of dumping. It could not be presumed that a methodology of calculating dumping margins consistent with the Panel's findings on these aspects would necessarily result in a determination that no dumping existed rather than in a determination that duties were to be imposed at a different rate. The Panel therefore found that in this situation it could not recommend that the Committee request the United States to revoke the ADD order and reimburse any duties paid or deposited under this order, as requested by Norway."<sup>1 45</sup> The Panel, therefore, recommended to the Antidumping Committee to request that the U.S. bring its measures into conformity with its obligations under the Tokyo Code, and that, to this end, the U.S. reconsider the affirmative final determination of dumping, consistent with the Panel's findings and take such measures with respect to its ADD order, as may be warranted in the light of that reconsideration.<sup>14 6</sup> In Korea-Polyacetil Raisins from the United States,1<sup>47</sup> U.S. itself had requested the Panel to recommend that the Committee on Anti-Dumping Practices request Korea "to bring its law as applied into conformity with its obligations under the Agreement" and no revocation of the duty was expressly sought. Due to the vagueness of U.S. request, the Panel ruled that Korea "bring its measure (the imposition of antidumping duties . .. into conformity with its obligations under the Agreement)" _148_

**143** GATT ADP/87, 30 November 1992.

**144** Id. at217.

45Id. at 218.

> **146** Id

**141** Pescatore, Pierre, William J. Davey and Andreas F. Lowenfeld, Handbook of GATr Dispute Settlement, 693 (Deventer, 1994). **14S** Id. at 73 1.