rafmacalaba/datause-encoder-data huggingface dataset:
technological work to cheaper locations. Blinder and Krueger (2009) estimate that 34% to 58% of jobs in the professional, scientific and technical services industry can be offshored from the United States, two or three times higher than the national average. This outsourcing has become especially common among cash-strapped start-up companies for website development and mobile apps (e.g., Kerr and Brownell 2013). We provide new insights about how diaspora connections shape these contract flows and the biases that managers may have in their choices. Our work also provides insights on the overall effectiveness of outsourcing contracts to India.

# **2. oDesk Outsourcing Platform and Ethnicity Assignments**

oDesk is an online platform that connects workers who supply services with buyers who pay for and receive these services from afar. Examples include data-entry and programming tasks. The platform began operating in 2005. oDesk is now the world’s largest platform for online outsourcing.<sup>6</sup> The oDesk market is a unique setting to study the diaspora’s impact on economic exchanges due to its recent emergence and exceptionally detailed records. One important feature is that any worker can contract with any firm directly, and all work takes place and is monitored via a proprietary online system. In exchange for a 10% transaction fee, oDesk provides a comprehensive management and billing system that records worker time on the job, allows easy communication between workers and employers about scheduled tasks, and takes random screenshots of workers’ computer terminals to allow monitoring electronically. These features facilitate easy, standardized contracting, and any company and any worker can form electronic employment relationships with very little effort.

A worker who wants to provide services on oDesk fills out an online profile describing his/her skills, education, and experience. A worker’s entire history of oDesk employment, including wages and hours, is publicly observable. For jobs that have ended, a feedback measure from previous work is publicly displayed. Figure 1 provides an example of a worker profile.

> 6 oDesk’s expansion mainly reflects increasing demand for online labor services over time. Statistics from compete.com, a company that tracks Internet traffic, show that unique visits to oDesk and its four largest competitors (some of which pre-date oDesk) increased simultaneously in recent years. Overall growth of online outsourcing slowed with the financial crisis, but oDesk has continued to grow rapidly.

7
|**Table 5.2: 26-year**<br>**Income**|**Transition M**<br>**(1970–96)**<br>**End Clas**|**atrix,**<br>**s**|**RN**|
|---|---|---|---|
|**P**<br>**ss**<br>**Class**|**L**<br>**M**|**U**|**R**|
|**P**<br>0.27<br>**Cla**|0.15<br>0.31|0.23|0.04|
|**L**<br>0.38<br>**al**|0.21<br>0.09|0.15|0.18|
|**M**<br>0.32<br>**iti**|0.15<br>0.26|0.12|0.15|
|**U**<br>0.30<br>**In**|0.10<br>0.27|0.13|0.20|
|R<br>0.23<br>P = poor; L = low income; M<br>and R = rich.<br>_Source:_Estimates based on IP|0.12<br>0.04<br>= middle income; U =<br>EA’s municipal datas|0.12<br>upper inc<br>et.|0.50<br>ome;|

Must poor municipalities remain poor or can they become rich? Over a 26-year horizon, there is a large amount of transition.<sup>9</sup> The probability that a municipality will shift from a low- to a high-income class, from group P (poor) to R (rich), is low (4 percent), and vice versa (Table 5.2).

Municipalities in middle-income classes L, M, and U are highly mobile, and they have a higher likelihood of becoming poor than rich. For example, for a medium-income municipality the likelihood is 32 percent that it will become poor and 15 percent that it will become rich.

# **5.5 Ergodic Distribution**

Do convergence clubs exist in RN? This is address here using an ergodic distribution. The vector of ergodic probabilities can be viewed as indicating the unconditional probabilities of each of the k different states, that is, independently of the initial state. According to Hamilton (1994), it can be showed that if M is the transition matrix<sup>10</sup> , then:

> 9 Recent literature on economic growth has used the transition matrix to deal with transitional dynamic (see Quah 1993).  According to Kremer, Onatshi, and Sotch (2001), this methodology allows a more flexible relationship between the level of income and growth rate of income than the standard convergence approach in which countries’ or regions’ growth rates are assumed to be a linear function of their income levels. For more details on RN see Tebaldi and Verner (2001)

> 10  It is assumed that M is an ergodic Markov Chain.

18
purposes. Third, even though these polygons were originally designed for the species with exclusive concessions, in practice they provide clear boundaries for other species as well (CotaNieto 2010). Cooperatives with adjacent polygons may fish for species without exclusive concessions in the neighbor’s polygon, but this typically involves negotiated agreements between the leaders of the two cooperatives involved (J. J. Cota-Nieto, pers. com., May 2015). Fourth, FEDECOOP has created a system in which fishers in each of the member cooperatives are expected to spend a fraction of their time in monitoring and vigilance efforts to enforce spatial restrictions (McCay et al. 2014). Fifth, FEDECOOP members can be removed from their cooperatives if they fail to sell exclusively to their cooperative or fail to comply with other rules (McCay et al. 2014). The lost benefits from eviction could be much more substantial for Abreojos than for the La Paz cooperatives because of the consistently high value of FEDECOOP fisheries (ensured by productive waters, local monitoring, FEDECOOP’s employment of fisheries scientists, and FEDECOOP’s engagement with the state) (McCay et al. 2014). While exclusive sale to the cooperative may be a nominal requirement for La Paz area cooperative fishers, the cooperative leadership in La Paz do not have the same degree of control of their members to ensure that sales of product outside the cooperative do not occur (J. J. Cota Nieto, pers. com., May 2014). To be sure, illegal fishing still occurs in the areas fished by the FEDECOOP cooperatives, but for these five reasons the scale of illegal fishing is likely to be less on the Pacific side of the Gulf. We therefore view Abreojos as an “exclusive” cooperative and Pichilingue and Sargento as “nonexclusive” cooperatives for the purposes of testing our hypotheses below.

Finally, it is important to understand whether any given fisher or cooperative can influence the market price of fish through their catch decisions. There are a large number of fishers in the area. As of 2010, based on data compiled by the National Commission of Aquaculture and Fishing (CONAPESCA), the number of fishers in La Paz alone was estimated at 974 people (748 cooperative members plus 226 unregistered fishers) (Leslie et al. 2015). According to data collected from La Paz fish markets by researchers from CBMC and the Scripps Institution of Oceanography, the three cooperatives we studied (Punta Abreojos, Pichilingue, and Sargento) were estimated to each provide approximately 12%, 5%, and 8%, respectively, of the fisheries product sold in the La Paz market (Sanchez, Nieto, Osorio, Erisman, Moreno-Baez, and Aburto-Oropeza 2015). Abreojos is more focused on the export market, however. These numbers come from an effort to enumerate the major players in La Paz markets and may be an over-estimate as smaller sellers are not easy to find. The size of the market shares suggest that these cooperatives will have 9
## _Measuring skills_

To measure firm-level skills, entrepreneurs were asked questions on their own and their staff’s education and training. These questions were based on the World Bank Enterprise Skills Survey, which has been conducted in Tanzania (Tan, Bashir & Tanaka 2016) and in Zambia (Cao and Saeki 2018). Table 2 presents the list of questions used for measuring skills in the main analysis. In addition, the survey complemented these with questions about the skills profiles and skills deficits of managers and workers.

**Table 2. Survey questions on worker and management skills**

|Area|Question|Answers|
|---|---|---|
|Education and<br>training|What is the highest level of education completed by this<br>establishment’s top manager?|•<br>No school<br>•<br>Primary School – First level<br>•<br>Primary School – Second level<br>•<br>High School- First level|
|||•<br>High School- Second level<br>•<br>Professional School|
|||•<br>Undergraduate Degree|
|||•<br>Post-graduate degree|
||What percentage or how many of the full-time permanent<br>workers completed secondary school?|... %|
||What percent of this establishment’s permanent full-time<br>employees employed at the end of the last complete fiscal<br>year had a university degree?|... %|
||Over the last complete fiscal year, did this establishment<br>have formal training programs for its permanent, full-time<br>employees?|•<br>Yes<br>•<br>No|
||Referring to the training programs run over the last<br>complete fiscal year, what percentage of permanent, full-<br>time employees of the following categories received<br>formaltraining?|... % (Separate answers for production and<br>non-production employees)|

# **4 Empirical strategy**

To explore the relative importance of firm capabilities, we follow Bloom, Schweiger and Van Reenen (2012) estimating the following equation:<sup>6</sup> 0 1 2 3 4 5 6 (Eq. 1) ii ii ii ii ii ii ii ii YY = αα + αα ll + αα kk + αα nn + αα MM + αα SS + αα ZZ + μμ where is a measure of firm performance, is the logarithm of labor, is the logarithm of capital (i.e. the logarithm of the total value of all assets - machinery, vehicles, and equipment - that are ii ii ii YY ll kk currently used by the business in their current condition regardless of whether the establishment

> 6 See Bloom, Schweiger and Van Reenen (2012) for a description of a similar approach.

13
agriculture is shrinking. Labor demand is affected by the following three factors: labor productivity in agriculture is exogenous and slightly higher than in the rest of the economy; income elasticity of private consumption is below one for agricultural commodities and above one for other commodities; and, finally, international prices for traded agricultural products are decreasing through time. These three factors concur in reducing demand for labor in agricultural sectors and are the key drivers for the migration towards the non-agricultural segment.

These trends in the supply and demand for labor are equilibrated by movements in relative wages. In the time horizon considered here, real wages of the skilled increase at 1.3% annually. In non-agricultural sectors, wages for unskilled workers increase by the yearly rate of 0.9%, however their upward trend is dumped by migration. Conversely, the implied reduction of supply due to out-migration boosts agricultural wages which are growing at a 1.7% annual rate.

_<u>Table 2: Medium term labor market structural adjustments, 2001-2015</u>_

||Producti-<br>vity of L|Income Elast<br>of Demand|Skilled Unskilled<br>Employment|Skilled Unskilled<br>Wages|Sending<br>Pop<br>Unsk<br>Migration|Receiv-<br>ing Pop<br>illed Lab<br> as % of:|Cumulative<br>Migration<br>2001-2015<br>|
|---|---|---|---|---|---|---|---|
||Yearly gr|constant|Yearly gro|wth rates|Yearl|y%|Millions|
|Agri|2.9|0.54|0.0|1.7|1.7||-4.0|
|Non-Agri|1.0|1.05|2.2|0.9||0.5|4.0|
|Economywide|||2.0<br>1.7|1.3||||

Source: Authors’ calculations As shown in Table 3 these structural trends result in a significant 5 percent points shrinking in the agricultural employment for unskilled combined with a reduction of the unskilled wage gap between agriculture and non-agriculture. Notice that the employment percent structure of this table is one of the key variables linking the macro and micro models.

_<u>Table 3: Employment shares and wage ratios in 2001 and 2015</u>_

||2001<br>Skil|2015<br>Employm<br>led|2001<br>ent %<br>Unski|2015<br>lled|2001<br>Unskilled|2015<br>Wages|
|---|---|---|---|---|---|---|
|Agri|4|3|27|22|||
|Non-Agri|96|97|73|78|||
|Ratio N-Agri/Agri|||||1.8|1.6|

Source: Authors’ calculations These 2001-2015 structural trends should not be taken as forecasts of what is going to happen to the Brazilian economy; these are just assumed and correspond broadly to what has happened to Brazil in the last decade. They serve as a benchmark against which 28
# **5** **<u>Changes over time: Who are the winners and losers?</u>**

In this section we return to the issue of which country-deciles have contributed to the overall changes in the global distribution. The evidence on the changing regional composition (Figures 3 and 4) was a first step into this direction, although it did not say anything about the movement of individual country-deciles. This section tries to identify the particular country-deciles which have gained or lost most over this period.

The sample used in the main text includes all countries which are observed in 1988 and 2008, even if there are gaps in the intervening benchmark years. In the Appendix, we have replicated the results using all countries observed in 1993 and 2008, which improves the coverage particularly in Sub-Saharan Africa and Russia/Central Asia/South-East Europe.

## **5.1** **<u>Quasi non-anonymous growth incidence curves</u>**

In order to identify the winners and losers, we first consider “income mobility profiles” (van Kerm, 2009), or “non-anonymous growth incidence curves” (Bourguignon, 2011a and Grimm, 2007). When applied to individual-record data, the distinction into anonymous and nonanonymous GICs is straightforward: The (standard) anonymous GIC compares the incomes of say the 20<sup>th</sup> percentile in the initial and final period distributions. As long as there is some mobility in the distribution, the individuals at this percentile might be different. By contrast, the non-anonymous growth incidence curve is the (non-parametric) regression of income growth against the rank (percentiles) in the initial distribution.<sup>73</sup> Because this growth rate is obtained for each individual, it is non-anonymous, taking into account the joint distribution of initial and final incomes. However, in our case the unit of analysis are income deciles of a particular country, so while we preserve the identity of a particular country-decile, these deciles are defined over different people. Hence we refer to our figures as “quasi-non-anonymous” growth incidence curves.

Figure 6 shows the quasi-non-anonymous GICs for 1988-2008 and 1993-2008. It plots the growth over the next twenty (fifteen) years against the normalised fractional rank in the 1988

> 73 A special type of non-anonymous GIC would consist in showing the suitably weighted average growth rate of all individuals who have belonged to a given percentile in the initial year (and doing it, of course, for all 100 percentiles).  Such a special non-anonymous GIC can consist of a comparison of weighted mean income of the same people (belonging to a given percentile) in the final and initial year.

41
**<u>Table 4 Determinants of assets of oil-exporting countries</u>**

||_FDI_|_Portfolio_|_Debt_|_Total_|
|---|---|---|---|---|
|Sovereign wealth fund|1.487***|0.198|1.227***|1.081***|
||(0.432)|(0.532)|(0.195)|(0.201)|
|Ln(Credit)|1.872***|0.677***|0.610***|0.582***|
||(0.191)|(0.236)|(0.0862)|(0.0891)|
|Intermediate exchange rate|0.752*|–5.742***|–1.080***|–0.918***|
||(0.421)|(0.518)|(0.190)|(0.196)|
|Constant|–5.013***|2.092|4.617***|5.208***|
||(1.694)|(2.087)|(0.763)|(0.789)|
|Year dummies|Yes|Yes|Yes|Yes|
|Observations|334|334|334|334|
|Number of countries|11|11|11|11|

_Note_ : Standard errors in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

As per liabilities, the existence of an SWF is associated with less debt and more portfolio liabilities (table 5). Financial development is also associated with greater portfolio liabilities and less debt. The elasticity of SWF with respect to debt liabilities is higher than the elasticity of portfolio liabilities, suggesting that it can lead to a larger decrease in debt, thanks to a better allocation of resources. As oil-exporting countries are mainly creditors not debtors, the oil dummy is insignificant. Exchange rate policy also turns to be insignificant.

**<u>Table 5 Determinants of liabilities – all countries</u>**

||_FDI_|_Portfolio_|_Debt_|_Total_|
|---|---|---|---|---|
|Sovereign wealth fund|–0.0443|0.0622***|–0.933***|–0.911***|
||(0.0351)|(0.0109)|(0.158)|(0.165)|
|Ln(Credit)|0.00554|0.0103***|–0.0824*|–0.0754|
||(0.0110)|(0.00348)|(0.0491)|(0.0515)|
|Intermediate exchange rate|0.00322|–0.00355|–0.00984|–0.00775|
||(0.0200)|(0.00635)|(0.0879)|(0.0922)|
|Oil|–0.0577|–0.0296*|0.866|0.778|
||(0.0759)|(0.0171)|(1.158)|(1.233)|
|Constant|0.194**|–0.0308|0.527|0.708|
||(0.0763)|(0.0212)|(0.869)|(0.924)|
|Year dummies|Yes|Yes|Yes|Yes|
|Observations|626|626|626|626|
|Number of countries|21|21|21|21|

_Note_ : Standard errors in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

In oil-importing countries, the SWF dummy drops out from the liabilities’ regression, as only exporting countries have SWFs (table 6). For FDI-receiving countries, financial development matters more for FDI attraction than portfolio or debt liabilities.

15
|2013|2.24|2.11|2.52|-0.14|0.28***|0.42***|
|---|---|---|---|---|---|---|
|2014|2.29|2.33|2.56|0.04|0.27***|0.23***|
|2015|2.30|2.45|2.55|0.15|0.25***|0.10|
|2016|2.33|2.55|2.55|0.22|0.22***|0.00|
|2017|2.31||2.53||0.22***||
|Whole period|2.28|2.31|2.53|0.02|0.25***|0.22***|

Note: Difference in means tests are reported with significance at *10%, **5%, ***1%. We tested differences in means also controlling for sector-fixed effects and the results are qualitatively similar.

# **5.3 Trade policy, investment integration, and productivity**

# **_Baseline results_**

The results of estimating equation 1 are reported in Table 7. We find a significant negative overall effect of input tariffs on within-firm productivity (column 3). Our estimates indicate that a onestandard deviation increase in input tariffs (0.2 percentage points in our sample) induces a decrease in productivity of 1.3 percent.

Table 7 – Productivity and input tariffs– manufacturing firms only

|||Whole period||2014-2017|
|---|---|---|---|---|
|Dep. Var.: TFP (log)|(1)|(2)|(3)|(4)|
|Output tariffs (lagged)|0.002**|0.009|0.007|-0.002|
||(0.001)|(0.012)|(0.009)|(0.010)|
|Input tariffs (lagged)|-0.088**|-0.021|-0.073**|-0.067*|
||(0.036)|(0.042)|(0.025)|(0.035)|
|Export share (lagged)|0.145***|0.315***|0.297***|0.278***|
||(0.027)|(0.030)|(0.033)|(0.039)|
|Sector-level FDI (lagged)|0.000|-0.000|0.000|0.000|
||0.002**|0.009|0.007|-0.002|
|Firm FE|No|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Sector-by-year trend|No|No|Yes|Yes|
|Observations|1477|1477|1477|1170|
|Firms|322|322|322|316|

Note: The table reports the estimates of linear regressions. In column 1 we also control for foreign ownership, province and firm size based on quartiles of the distribution of sales. Standard errors clustered at the sector level are reported in parenthesis. We obtain similar results when using an alternative measure of TFP in table A2 of the Appendix. Significant at *10%, **5%, ***1%.

Over the period, all sectors experienced ups and downs in input tariffs, although economy-wide there has been a tariff decrease. Because our weighted measure of upstream tariffs uses fixed weights (inverse Leontief coefficients), the change in the indicator is only driven by changes in import tariffs. Back-of-the-envelope calculations suggest that the largest positive effects were experienced by the chemicals sector, where the average decrease in input tariffs over the period is associated with a 0.8 percent increase in productivity. On the other hand, the highest average increase in input tariffs was experienced by the metal sector and is associated with a 0.7 percent decrease in productivity. The positive and significant effect of output tariffs shown in column 1 becomes insignificant once we control for firm fixed effects, possibly suggesting that sectors with higher productivity display higher 16
corresponding labor market effects from each of these scenarios endogenously, with the reference scenario being that of a low-price path of international energy prices.

Another widely used global sectoral macro-econometric model, E3ME (the Energy-Environment-Economy Model at the global level), analyzes long-term energy and environment interactions within the global economy and evaluates the short- and longterm impacts of climate change policy. The model covers 59 global regions that include all major developing (e.g., Brazil, Mexico, India, and China) and developed (e.g., EU27 member states, the United States, and so forth) economies and 42 economic sectors, including a disaggregation of the energy sectors and 16 service sectors. It provides projections forward annually to 2050.<sup>4</sup> Pollitt et al. (2015) expand on the modeling that was carried out for the official impact assessment of the European Union’s proposed energy and climate targets for 2030. They provide a macroeconomic assessment of the potential effects on employment of the 2030 climate and energy package compared with a reference case based on current and planned policies to 2020. Using E3ME and drawing on results from the PRIMES energy systems model, in the first scenario, they conclude that a 40 percent reduction in GHG emissions (compared with 1990 levels) could lead to an increase in employment of up to 0.7 million jobs in Europe. In the second scenario, they combine the same GHG reduction target with a 30 percent target for renewables and stricter energy efficiency standards. The results show that the net increase in jobs is as high as 1.2 million. They also emphasize that the large investment stimulus needed to meet the combined targets leads to higher levels of GDP and employment, and suggests medium-term economic and social benefits from including all the targets combined in the future in an energy and climate package. Their additional sensitivity testing highlights that the ways in which the energy efficiency and renewable measures are funded are important factors in determining overall economic impact.

> 4 Detailed information about the E3ME model is available in Cambridge Econometrics (2011) and at https://www.camecon.com/how/e3me-model/.

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