rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8 – Green jobs and labor income in LAC, circa 2019**

||**Log**|**Hourly Labor Inc**|**ome PPP 2011**<br>Omitted category:|
|---|---|---|---|
||Omitted ca<br>green oc|tegory: non-<br>cupation|<br>non-green<br>occupation in non-<br>green sector|
|Green Occupation|0.192***|||
||(0.00638)|||
|New Emerging||2.908***||
|||(0.0509)||
|Enhanced Skills||0.114***||
|||(0.0133)||
|Increased Demand||-0.0305***||
|||(0.00943)||
|Green occ. And Green Sector|||0.227***<br>(0.00876)|
|Green occ. And Green Sector|||0.130***|
||||(0.00447)|
|Green occ. And Green Sector|||0.349***|
||||(0.00887)|
|Constant|-0.162***|-0.114***|-0.239***|
||(0.0160)|(0.0159)|(0.0162)|
|Observations|938,361|938,361|938,361|
|R-squared|0.299|0.310|0.302|
|Robust standard errors in parenthesis||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Note: pooled sample for LAC, circa 2019. Controls include dummy variables for country, educational attainment, gender, rural, as well as age and age squared.

# 4. Robustness Checks

This section discusses some caveats and conducts some robustness checks. First, the classification of occupations used in this study is based on O*NET, which describes the occupations that exist in the US. The main assumption when using O*NET as the source of truth to classify occupations in other countries is that the task content of each occupation in any country is the same as in the United States. Lewandowski et al. (2022) have pointed out that this is almost certain to be problematic for less-developed countries, given significant differences in workers’ skills, technologies, and economic activities, which leads to large 30
|Country Group<br>This table presents estimations of annual eco<br>weighted average of each individual countr<br>i_growth+i_growth*late=0 where i represents<br>presented in italics. *, ** and *** denotes sig|EM<br>nomic growth (q/q data) <br>y belonging to that grou<br>G7 or China. Both panel<br>nificance at the 10, 5 and 1|EM<br>**The R**<br> of EM against G7<br>p. Growth is wei<br>and country by co<br>% level respective|EM<br>**Tab**<br>**eal Decoupling h**<br> and China growth<br>ghted by PPP-meas<br>untry (taken median<br>ly. Source: IMF IFS|EM<br>C b C<br>**le 2**<br>**ypothesis among EM**<br>. CRB Commodity Index G<br>ured GDP in current price<br>values) are presented. Pane<br>Statistics, IMF WEO, Bloo|EM<br>C b C<br>rowth is the log change in <br>s from a year before. The<br>l estimations have country<br>mberg.|EM<br>C b C<br> the CRB general commod<br> last four rows of the tab<br>fixed effects. P-values from|ity index. G7 growth is a<br>le present a joint test of<br>robust standard errors are|
|---|---|---|---|---|---|---|---|
|Estimations|Panel|Panel|Panel|ountry y ountry<br>(median values)|ountry y ountry<br>(median values)|ountry y ountry<br>(median values)|OLS|
|**Variables**|**Real GDP Growth**|**Real GDP**<br>**Growth**|**Real GDP**<br>**Growth**|<br>**Real GDP Growth**|<br>**Real GDP Growth**|<br>**Real GDP Growth**|**CRB Commodity**<br>**Index Growth**|
|G7 Growth|0.509***|1.378***|1.168***|0.578***|1.436***|1.071**|2.507***|
||_0.000_|_0.000_|_0.000_|_0.009_|_0.007_|_0.029_|_0.006_|
|G7 Growth*Late|0.686***|-0.490**|-0.508**|0.635**|-0.700*|-0.693*||
||_0.000_|_0.000_|_0.012_||_0.096_|_0.072_||
|China Growth||0.809***|0.650***||0.793***|0.498**|2.817***|
|||_0.000_|_0.000_||_0.001_|_0.012_|_0.001_|
|China Growth*Late||0.344***|0.252***||0.333*|0.231*||
|||_0.000_|_0.000_||_0.096_|_0.056_||
|CRB Commodity Index Growth|||0.055***|||0.049||
||||_0.000_|||_0.128_||
|Constant|0.026***|-0.075***|-0.053***|0.025***|-0.053**|-0.039||
||_0.000_|_0.000_|_0.000_|_0.000_|_0.046_|_0.152_||
|Observations|1389|1389|1389|71|70|70|70|
|R-squared|0.226|0.322|0.331|0.243|0.474|0.502|0.306|
|**G7 Growth+G7 Growth*Late**|1.195***|0.888***|0.660***|1.032***|0.768***|0.418*||
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.066_||
|**China Growth+China Growth*Late**||1.153***|0.902***||0.877***|0.754**||
|||_0.000_|_0.000_||_0.007_|_0.024_||
gives useful insights on the theoretical underpinnings of those institutions' recommendations.

Consider the case of a small open economy, where output is supply determined and full employment conditions prevail. Output growth is a function of the availability of capital only. However, because of the existence of a repressed financial system, capital accumulation, i.e. investment, is determined residually by credit markets conditions. More specifically, investment is equal to private saving minus e-he desired accumulation of financial assets by private agents plus the change in the stock of banks' credit to the private sector. Consider then the effect of a decline in the rate of change of the public sector's credit (brought about by a fall in government spending). Suppose also that this decline is matched by a (smaller) increase in credit to the private sector. By assumption, income and interest rates are unaffected. Therefore, also saving and assets accumulation do not change. Investment, as a result, increases with a beneficial impact on growth. Also, given that the increase in investment is smaller than the (absolute) value of the decline in government spending, total absorption falls and the current account improves. The orthodox model therefore predicts an unambiguous positive impact of fiscal restraint on both growth and the external account.

The two assumptions that output is supply determined and that there is no Lndependent investment function play a critical role in the orthodox model, as pointed out by Montiel himself. In "Fiscal Issues in Macroeconomic Stabilzation: A Structuralist Perspective" by Lance Taylor, he analyses the impllcations of relaxing these assumptions. First, and foremost, the decline in aggregate demand attendant on a more restrictive fiscal policy stance may have a contractlonary impact on output. The evidence Taylor relies upon comes from a study of stabilization episodes in 18 countries, organlsed by the World Institute for Development Economics Research (WIDER). Those studies show that fiscal restraints were generally followed by a fall in output levels. Second, the contractLonay impact of restrictive fiecal policies may be amplifled by the ensuing fall ln investment. Total investment may fall, rather than 10
**Table 4: The Effect of Policy Delay and Pre-existing Risks**

|||Log(cases/10|00 resident|s)||Pandemic m|ortality rat|e|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1’)|(2’)|(3’)|(4’)|
|Log(GDP per capita)|0.323***|0.231**|0.195**|0.276***|-0.002*|-0.002|-0.001|-0.002|
||(0.071)|(0.091)|(0.092)|(0.087)|(0.001)|(0.002)|(0.002)|(0.002)|
|Log(Population)|0.060|0.039|0.010|0.063*|0.003***|0.003***|0.003***|0.003***|
||(0.042)|(0.039)|(0.039)|(0.036)|(0.001)|(0.001)|(0.001)|(0.001)|
|Average Temperature|0.321|1.385|1.747|0.528|-0.009|-0.016|-0.017|-0.011|
||(1.278)|(1.248)|(1.220)|(1.219)|(0.021)|(0.021)|(0.022)|(0.021)|
|Middle Distance to China|-0.021|0.133|-0.104|0.096|0.010***|0.009***|0.009**|0.009***|
||(0.198)|(0.186)|(0.205)|(0.232)|(0.003)|(0.003)|(0.004)|(0.003)|
|Top Distance to China|0.261|0.165|-0.192|0.197|0.005|0.007|0.007|0.008|
||(0.251)|(0.244)|(0.295)|(0.361)|(0.006)|(0.005)|(0.007)|(0.006)|
|Delay I|0.224|0.189|0.221|0.067|0.007**|0.008***|0.008**|0.008**|
||(0.150)|(0.138)|(0.139)|(0.178)|(0.003)|(0.003)|(0.003)|(0.003)|
|Delay D|-0.319**|-0.273*|-0.312**|0.274|-0.004|-0.003|-0.003|-0.018***|
||(0.136)|(0.163)|(0.150)|(0.380)|(0.003)|(0.003)|(0.003)|(0.006)|
|Elderly||-4.370**|-3.792*|-5.356**||-0.011|-0.014|-0.005|
|||(2.078)|(2.061)|(2.251)||(0.028)|(0.029)|(0.029)|
|Obesity||-1.337|-2.600**|-0.894||0.035|0.046|0.032|
|||(1.496)|(1.300)|(1.296)||(0.030)|(0.035)|(0.030)|
|Urban share||1.127**|1.180**|0.987**||-0.012|-0.013|-0.015*|
|||(0.537)|(0.521)|(0.477)||(0.008)|(0.009)|(0.009)|
|SO2||0.392***|0.405***|0.338***||0.001|0.001|0.001|
|||(0.121)|(0.109)|(0.107)||(0.002)|(0.002)|(0.002)|
|Longitude|||-0.732**<br>||||-0.000<br>||
||||(0.332)||||(0.008)||
|Latitude|||-1.692***||||0.011||
||||(0.598)||||(0.012)||
|Elderly×Delay D||||-2.737||||-0.043|
|||||(6.759)||||(0.110)|
|Obesity×Delay D||||-1.934||||0.074**|
|||||(1.989)||||(0.035)|
|Urban share×Delay D||||-0.331||||0.002|
|||||(1.010)||||(0.018)|
|SO2×Delay D||||0.416<br>||||0.011*<br>|
|||||(0.367)||||(0.006)|
|λ||||0.451*|||||
|||||(0.259)|||||
|ρ||||<br>2.308<br>|||||
|||||(1.572)|||||
|Continental Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs.|144|140|140|140|144|140|140|140|
|Adjusted R<sup>2</sup>|0.685|0.730|0.752||0.278|0.278|0.276|0.301|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors in columns. We have used the Delay I to interact with the four risks variables, but found less significant results, which indicate that the Delay I works on the spread and mortality more independently than the Delay D. For models (2), (4), (2’), and (4’), we have examined whether and are significant, and we report the preferred specification between OLS or the spatial model. ρρ λλ
1:1 Further variables should also be included on the right hand side for a convincing model of savings determination. In White (1992b and 1992c) I report the regression in differences for this data set including also the export rate, inflation and income per capita - in this regression the coefficient on aid is insignificant.

The top graph in Figure 2.1 shows also that there are three points somewha **t** o the right of the rest of the observations. Are these influential points? Figure 2.2 shows the fitted lines from (i) the full sample; (ii) from omitting the point labelled observation 1; and (iii) from omitting observation 2. The slopes of these lines are -0.87, -0.64 and -1.24 respectively. Clearly the results do depend on the sample.

**_Figure_** _2.2_

<!-- Start of picture text --> Aid and savings<br>An unrobust regression C2)<br>Sav I nra rate<br>60<br>P~~<br>-20, :.M.%..-_t-__, Cbs___<br>40: - ------ C.~~~~ - -- --- -<br>.20 - -- 60 __.____.______......... .... .......... . . _______ . . .. _ __. _... _<br>-40<br>Obs.2<br>-80 I I :l I<br>O la 20 30 40 50 s0<br>Aid ratio<br>Without Without<br>Full sample observation 1 observatlon 2<br><!-- End of picture text --> Further illustrations of the importance of data analysis to identify irnluential points may be provided from a recent World Bank report on adjustment in sub-Sahara Africa.'<sup>0</sup> It is usefil that this report contains the scatter plots for many of the relationships discussed. But it is surprising that many of them have evident points of leverage with no discussion of whether or not these points exert undue influence on the results.

18
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Federal Reserve, suggesting that median wealth has declined.<sup>4</sup> Hence, the prospects of current and future retirees are clearly much diminished.

We know that one dollar will buy three times as much in Thailand and twice as much in Costa Rica as it does in the United States.<sup>5</sup> Retirees could maintain their standard of living by relocating to places with a lower cost of living. In 2011, there were about 567,000 beneficiaries living abroad, of whom 350,000 were retired American workers or about 1 percent of American retirees (US Social Security Administration, 2012, Table 5.J2).<sup>6</sup> A quarter of retirees live in developing countries (Table 5.J11). Between 2008 and 2010, 35 percent of new overseas American retirees headed for the developing world –double the flows over the preceding 7 years. According to US Census Bureau projections, by 2020, the number of people above age 60 will increase by about 22 million and by a further 14 million by 2030. If the fraction of those choosing to live overseas increases to say 2 percent by 2020 and 3 percent by 2030, about 2.5-3 million US retirees could be living abroad within two decades.

# _Infirm_

The rising costs of healthcare in the United States imply that if current levels of access to health care are maintained, spending on the major federal health care programs alone would grow from more than 5 percent of GDP today to almost 10 percent in 2037.<sup>7</sup> Since healthcare of comparable quality is much cheaper in other countries, treatment abroad could yield big savings. For example, inpatient knee surgery, roughly 400,000 of which were performed in 2010 in the United States, costs more than $15,000 in a typical community hospital (Centers for Disease Control and Prevention, 2012, Table 132). Even if only one in ten patients who need one of fifteen highly standardized, tradable, low-risk treatments , such as tonsillectomy or cataract extraction, went abroad, the annual savings for the United States would be $1.4 billion (Table 1). An assessment by Steve Brill in Time Magazine with more recent data confirms the large potential savings.<sup>8</sup> Some gains from trade are already being realized. In 2003, the latest year for which data is available, more than 350,000 patients traveled to Cuba, India, Jordan, and Southeast Asia to seek care. A sizable number were patients from industrialized countries, traveling to a growing

> 4 St. Louis Federal Reserve,  Annual Report (2012), p.14. (available at:

> <u>http://www.stlouisfed.org/publications/ar/2012/PDFs/ar12_complete.pdf).</u> 5 Savings arise because rents in San Jose, Costa Rica, are 57 percent lower than in Philadelphia while consumer prices in Chiang Mai, Thailand, are 59% lower than in Boston

> (http://knowledge.wharton.upenn.edu/article.cfm?articleid=3051).

> 6 Others living abroad included disabled workers, widow(er)s and parents, wives and husbands, and children.

> 7 See the CBO’s November 2012 projections (available at:

> http://www.cbo.gov/sites/default/files/cbofiles/attachments/11-30-2012-OECD_Presentation.pdf),

> 8 http://www.time.com/time/magazine/article/0,9171,2136864,00.html

5
first studies to estimate this parameter, Katz and Murphy (1992) find a value of around 3. Card and Lemieux (2001), arguably one of the most influential papers on the extent of substitutability between young and old workers, estimate a, across three countries. Using data on the US, Canada and the UK, they find values between 4 and 6. In more recent studies, Manacorda et al. (2012) estimate a value of around 5 using UK data, Ottaviano and Peri (2012) find a similar value using US data and, Glitz and Wissmann (2016) estimate values greater than 7 using German data. We set 04 = 5 in our main scenario, which is in the mid ~~-r~~ ange of these available estimates. Compared with our central estimate of og, this assumption implies that workers in different age groups within the same skill group are more substitutable than workers in different skill groups, although still imperfectly so. We later allow for different values of 0, in order to explore the sensitivity of our simulated labor market effects to the degree of substitutability between age groups.

3. Elasticity of substitution between natives and migrants (ay,). The elasticity of sub ~~-~~ stitution between migrants and natives, o),, is a crucial parameter in determining the effect of immigration on the labor market outcomes of natives. As such, this parameter has received a fair share of attention in the literature. On one hand, Borjas (2003) suggests perfect substitutability between immigrants and natives, im ~~-~~ plying a value of oj equal to infinity. Card (2009), Ottaviano and Peri (2012) and Manacorda et al. (2012) find values of oy, between 6 and 20. In our empirical analysis, we set oj, to 20, the value DOP use in their intermediate scenario. We again examine the implications of perfect substitutability on labor market outcomes in the extensions section.

4. Elasticity of labor supply (y). y captures how labor supply responds to changes in wages. In their synthesis of the literature, Evers et al. (2008) summarize estimates of y from various studies. Their meta ~~-a~~ nalysis suggests that 0.1 is a reasonable measure of this parameter among men and 0.5 among women. When simulating our labor market effects of interest, we use a conservative value of 0.1 in our baseline scenario.

5. Technological externalities (€,«). In the main analysis, we disregard technological changes and assume € = & = 0. In the long ~~-~~ run version of the model, ¢ measures the extent of productivity spillovers from having a higher share of highly skilled workers in the economy, as defined in equation (19). Acemoglu and Angrist (2000) and Moretti (2004) put € at 0 and 0.75, respectively. We assume an intermediate value of 0.30 in the extension. As for the skil ~~l-~~ biased technical change, Acemoglu (2002) hypothesizes that the skil ~~l-~~ biased technical change fully offsets the substitution effect in the long ~~-~~ run (i.e., « = 1/ag5). We consider both Acemoglu’s (2002) hypothesis as 17
# 7 Conclusions

The impact of immigration on native workers is driven by two countervailing forces: the degree of substitutability between natives and immigrants (which has been the focus of the existing literature), and the increased demand for native workers as immigrants reduce the cost of production and output expands. Using data for Malaysia and a novel instrument we find that the scale effect on average outweighs the substitution effect, such that immigration results in a positive demand shock for native labor. The cost advantages provided by immigration allow industries to expand suffi ciently so as to outweigh the displacement of natives due to substitution. Hence, immigration results in a substantial reallocation of native workers across industries (and regions), but has only very modest effects on average native wages (immigrant wages fall substantially). The impact of immigration is highly heterogeneous for natives with different levels of education and follows an inverted u-shape. The bulk of immigrants have at most primary education and, on net, displace natives in that education category, with a significant negative impact on wages. Those natives with a little more education, lower secondary or completed secondary education, experience the greatest benefits; while the gains for those Malaysians with a vocational diploma or university degree are smaller.

Two lines of inquiry follow naturally from the work in this paper. First, having established the importance of scale effects a next step is to identify their magnitude in different sectors of the economy. This is especially important for immigration policy, since issuing work permits in industries with large scale effects is likely to have a positive effect on native workers, while in those with small scale effects displacement is likely. In 46