rafmacalaba/datause-encoder-data huggingface dataset:
United Kingdom (92%), France (94%), Switzerland (93%), China (92%), and India (90%), among others.<sup>9</sup> Patent data do not provide unique identifiers for inventors appearing in more than one application. Although this is a drawback, I follow Kerr (2008) and treat each record as if it were a different individual, and compute the migration variables aggregating by country pairs and moving time-windows of five years.<sup>10</sup> Out of all records with complete information, about 5 million, around 9–10% have a migratory background, that is, residence different from their nationality. Figure 1 depicts the evolution of the share of inventors with migratory background (solid line), alongside the same figures broken down by a number of selected receiving countries/areas. As can be observed, the share of worldwide migrant inventors has steadily increased over time.<sup>11</sup> Among the most receiving countries of the world, 9. Compared to other highly patenting countries, the United States, Canada, and the Netherlands present relatively low coverage rates. This is because numerous applications with inventors from these three countries were applied to the USPTO before being extended internationally through the PCT system. In consequence, the United States was not mentioned in the applications as designated state and the inventors were only inventors and not applicants at the same time, which exonerates them from providing nationality information. In principle, there is no reason to believe that this less than complete coverage in these countries may bias the analysis one way or another. However, in order to address this inconsistent coverage of migration information, I repeated the analysis splitting the sample into shorter time windows and excluding some countries. No important differences arise regarding the main conclusions of the study. 10. The priority date of applications is used to allocate individuals in time. By “priority date” I mean the first year the patent was applied worldwide.

11. In order to make these figures comparable, it is worth looking at differences with other migration datasets. While 8.62% of inventors of PCT patents have a migratory background in 2000, data compiled by Beine et al. (2007) show that general migration rates in 2000 for populations aged 25 years and over 14
results for these two linkage economies; the 1988 PPP exercise of the CMEA, which included the PSU; plus relative growth and inflation measures to move PPP results forward to 1990.

Annex S describes the preliminary details available from the 1990 ICP exercise, for the FSU and Austria. It explains how the matched item prices were averaged below the level for which expenditure weights are available; and the effects of using 1985 weights (and sometimes to rely on Austrian weights, where detailed FSU weights were not yet available).

Annex 6 provides basic data on the "two Austrias" that emerge for 1985 when the items priced for its OECD comparison are compared with those priced for its G2 comparisons with Eastern Europe and the FSU. Even after discounting for the far greater diversity of items priced for OECD purposes (by discarding information on VCRs, microwave ovens, etc., that were not even considered for the Group II comparison), there is a clear difference in the sample frame which systematically steers the OECD comparison towards higher, and the Group II comparison towards lower, quality goods.

Annex 7 explores the extent of overlap and deviation, at the item level, between the preliminary ICP results for 1990 and the CIA's 1976 exercise. While further documentation from Austria will be required to complete the matching process, the comparison lends credence to the hypothesis that goods in the FSU have changed far less than their US comparators; and that imputation of US price trends to constant ruble value series inserts a spurious upgrading of quality/diversity into FSU series. And while that upgrading is modest from year to year, its cumulativ **e** ffect could well explain much of the difference between 1990 estimates from CIA and ICP PPP exercises.

27
proportions are adjusted by the ratio of each sector’s profit rate to the average profit rate for the whole economy. This is similar to the method used by Thurlow (2004), the details are discussed in the Appendix A. Also, Autonomous Energy Efficiency Improvement (AEEI) in the CGE model is considered in this study, and is assumed to be 1% per year following the common assumptions in CGE. Since the available social accounting matrix (SAM) is for 2012, our base year is 2012. If the model has to adopt a projected growth rate of GDP (e.g., projected by the government), it is done through adjustments in total factor productivities (TFPs).

The BAU scenario is constructed using assumptions on labor supply growth and projected real GDP growth, as shown in Table 2.

**Table 2** The Labor supply growth rate and GDP growth rate from 2012 to 2030

|Year|Labor supply growth rate|Average annual GDP growth rate|
|---|---|---|
|2012-2015|2.70%|8.51%|
|2015-2020|4.60%|6.56%|
|2020-2025|0|5.49%|
|2025-2030|0|5.47%|

_Note: Values for 2012-2015 are actual or observed values. The projected GDP is set at a moderate level based on a review of relevant literature_<sup>_[6-8]_</sup> _. Labor supply growth rate are taken from China Statistic Year book 2017 and UNDESA_<sup>_[5]_</sup> _with very small modification based on our understanding about labor quality development tendency of China in the future._

# **2.3 Bottom-Up TIMES Model**

The bottom-up energy sector TIMES model is an optimization model. It is based on a reference energy system (RES) principle. An RES refers to an optimal system where useful energy demands by end-uses (e.g., light, heat, electric traction, motive power, etc.) in each sector (e.g., industrial, households) are met through various channels or networks, which transport energy commodities (coal, oil, gas, electricity) from domestic primary energy sources or imported primary or final energy sources. Figure 3 illustrates the RES on which the TIMES model used for this study is based. Various energy consumption technologies that produce final energy to useful energy (e.g., a boiler converts natural gas to heat, a light bulb converts electricity to light, an electrical motor converts electrical energy to mechanical energy) in the demand side whereas energy production or transformation technologies (e.g., electricity power 10
_g_ = _k_<sup>2</sup> are the most used in this literature. With the hyper- _g_ we can only assign large prior mass to regions with high _g_ by taking _a_ very close to 2, which gives us a fat tail for _g_ , but does not really change the shape of the prior. In order to propose an alternative class of priors in the next subsection, we initially focus on the shrinkage factor, _δ_ .

# _3.3. A Benchmark Beta Prior_

If we start from a Beta( _b, c_ ) prior on the shrinkage factor _δ_ = _g/_ (1 + _g_ ), we can base our proposal on ensuring that prior moments are reasonable (they always exist, as we are dealing with a finite support). In particular, let us set the mean shrinkage factor equal to the one that corresponds to the Fern ́andez _et al._ (2001a) recommendation _g_ = max _{n, k_<sup>2</sup> _}_ . This fixes one parameter as a function of the other, as then _b/c_ = max _{n, k_<sup>2</sup> _}_ . The second parameter can then be chosen by considering the spread around this mean: where is the chosen prior mean of the shrinkage factor, and thus larger _c_ corresponds to a tighter prior around this mean.

For example, for the cross-country growth data used in Fern ́andez _et al._ (2001b) with _k_ = 41 and _n_ = 72, the value _c_ = 1 would correspond to a prior standard deviation of the shrinkage factor of 0.0006 (approximately equal to 1 _− d_ ) and the corresponding _b_ = 1681. For _c_ = 1 _/_ 1000 we obtain a prior standard deviation of 0.015 (approximately 25 times 1 _− d_ ) and this corresponds to _b_ = 1 _._ 681. For likely choices of _c_ (say, _c_ = 1 _,_ 0 _._ 1 _,_ 0 _._ 01) and max _{n, k_<sup>2</sup> _} >_ 100 (as for typical datasets in econometrics) we have no prior moments for _g_ , but we do have an interior mode equal to which is approximately max _{n, k_<sup>2</sup> _}/_ 2 for _c_ = 1 and is a lot smaller for the smaller values of _c_ .

Figure 1 shows three prior densities (expressed in logs to make matters easier to visualize) of _g_ and _δ_ from this class of benchmark priors. The figure illustrates the effect of _c_ : For _c_ = 1 the density is most concentrated while for the smallest _c_ the prior assigns a lot of the mass to the far right tail of _g_ (for example, _P_ ( _g >_ 5 _,_ 000) = 0 _._ 95 for _c_ = 0 _._ 01). Notably, all priors for _g_ have an interior mode. As _c →∞_ the benchmark Beta prior tends in the limit to the case with fixed _g_ = max _{n, k_<sup>2</sup> _}_ .

As a special case of the benchmark Beta class, we can get a single hyper- _g_ prior by choosing _c_ = 1 _/_ max _{n, k_<sup>2</sup> _}_ . This would lead to a hyper- _g_ prior with 7
2009q1) coefficients of the latter model. Finally, due to concerns about collinearity, we also present results for the pure “time-to-assemble” specification, which includes imports and related imports together with only one lead of the export and related exports explanatory variables.

# **5. Results**

Before proceeding to the main results, we present some basic statistics for both the trade and labor market variables in Table 2. It is interesting to note that, on average, the quarterly changes in exports and imports were close to zero during this period. These figures, however, mask considerable heterogeneity across industries over time. Also, imports and exports are much more volatile than their associated relatedness indices, which will be important later when we interpret the coefficients from regressions using these variables.

The presentation of the results follows the same structure of Section 4. The estimation results are discussed sequentially with the employment equations followed by the wage equations. We include the results for relatedness within the employment and wage sections. We then discuss change in composition and conclude with the continuous-treatment models of employment and wages that allow us to examine the robustness of our results.

# **_5.1. Imports, Exports, and Employment_**

Table 3 presents the results from the WLS estimations in which initial employment in 2007 in each industry is the weight.<sup>8</sup> The first column contains the basic results, and only imports from the United States appear significant and positive. The second specification is the assembly-time model that includes the one-period-ahead trade variables. Both export variables

> 8 Similar results were obtained when using the inverse of the standard errors from the first-stage wage equation. These are available upon request.

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# **1. Introduction**

Triggered by the so-called commodity super cycle, Latin America experienced vigorous growth during the 2000s coupled with falling household income inequality (Alvaredo and Gasparini 2015; Lakner and Milanovic 2013). More than redistributive policies, the main force behind this inequality reduction was falling wage dispersion (L ́opez-Calva and Lustig 2010; Azevedo, Inchauste, and Sanfelice 2011; Rodr ́ıguez-Castel ́an et al. 2016). This is in stark contrast with increasing wage inequality in developed (Acemoglu and Autor 2011; Atkinson 2008) and other developing countries, including China (Ge and Yang 2014), India (Lee and Wie 2017), and Indonesia (Lee and Wie 2015). These differences raise two important questions. First, what are the main patterns behind the reduction in wage inequality in Latin America? Second, what are the forces behind these patterns? This paper breaks new ground on these questions using household surveys and matched employer-employee data.

This paper provides systematic evidence of the evolution of wage inequality in Latin America between 1995 and 2015, emphasizing the main stylized facts with which any potential story about inequality reduction in the region should be consistent.<sup>1</sup> It first documents the main wage inequality trends, highlighting differences across countries. To this end, it uses harmonized household surveys for 16 countries during 1995–2015 (covering the formal and informal workforce). Using these data, this paper disentangles the evolution of wages at the bottom and top of the wage distribution in each country and analyzes the changes in relative wages across skill groups. It then decomposes the evolution of wage inequality into forces operating between demographic and skill groups and within them. In this vein, it examines whether changes in wage inequality occurred within sectors-occupations or were associated with compositional changes, and assesses the potential contribution of labor formalization to changes in inequality. In line with recent studies, it emphasizes the role of firms (Card, Heining, and Kline 2013; Alvarez et al. 2018; Song et al. 2018). In particular, it uses detailed longitudinal matched employer-employee administrative data covering the formal sector in Brazil, Costa Rica, and Ecuador to identify the contribution to changes in inequality of changes in wage dispersion among workers, changes in pay across firms within sectors, and their interactions.

The paper builds on Messina and Silva (2018), which summarizes the main findings of a large research project on wage inequality in Latin America. It reports on these analyses and complement them in four important ways. First, the paper assesses trends in (overall,

> 1Unless otherwise noted, the analyses focus on the evolution of hourly earnings from labor of employees and self-employed workers. Hence, employers and unpaid family workers are excluded. This paper uses earnings and wages interchangeably to refer to this measure of remuneration.

2
Table 15: Program Impact on Child Participation in Tending for Younger Siblings

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old|||6-12 years old||1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School||||||||||
|Meals|-0.041|0.004|-0.084|-0.055|-0.015|-0.081|-0.001|0.070|-0.110|
||[0.023]*|[0.025]|[0.037]**|[0.026]**|[0.030]|[0.042]*|[0.046]|[0.044]|[0.085]|
|Dry Rations|-0.006|n.a.|0.009|-0.015|n.a.|-0.006|0.022|n.a.|0.051|
||[0.030]|n.a.|[0.036]|[0.035]|n.a.|[0.041]|[0.061]|n.a.|[0.077]|
|Follow-up|0.001|0.011|-0.012|0.023|0.024|0.018|-0.078|-0.029|-0.121|
||[0.015]|[0.018]|[0.025]|[0.018]|[0.022]|[0.028]|[0.030]**|[0.031]|[0.054]**|
|Girl|0.181|||0.172|||0.214|||
||[0.011]***|||[0.013]***|||[0.023]***|||
|Constant|0.165|0.182|0.335|0.169|0.175|0.336|0.114|0.136|0.302|
||[0.019]***|[0.024]***|[0.027]***|[0.020]***|[0.025]***|[0.028]***|[0.030]***|[0.031]***|[0.046]***|
|Observations|6047|2462|3585|4757|1931|2826|1290|531|759|
|R-squared|0.13|0.12|0.11|0.13|0.11|0.12|0.20|0.26|0.19|

# Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1% Dependent variable is whether has tended youngsters the week before the survey. Regressions also control for child age, orphanhood, parents education, ethnicity, religion, household assets and village fixed effects (not reported).

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55

# **V. THE ORGANIZATION AND VOLUME OF SECOND PILLAR**

Each employer can choose between different organizational structures. These range from setting up a completely autonomous and independent pension fund to outsourcing the scheme entirely to an insurance company. Independent pension funds and insurance companies are subject to different laws and thus different legal restrictions along several dimensions, which can be expected to have an impact on the financial structure of the pension scheme.

The second pillar is characterized by a large number of pension funds. Although their number has declined considerably over time (Table 15), there is still high fragmentation. The number of individuals covered by the pension funds is more difficult to determine as some workers/retirees participate in more than one fund (in case of multiple employers).

|**Table **|**15 Numb**<br>|**er of Pensi**<br>|**on Funds a**<br>|**nd Memb**<br>|**ers**<br>||
|---|---|---|---|---|---|---|
||1994|1996|1998|2000|2002|1994<br>2002<br>Δ|
|Total Pension<br>Funds|12,851|11,572|10,409|9,096|8,134|-36.71%|
|Pension Funds with<br>members|4,727|4,285|3,806|3,418|3,170|-32.94%|
|Members(in m)|3,24|3,15|3,14|3,23|3,31|2.22%|

Source: Bundesamt für Statistik (2004b), p.32, T8 This chapter outlines the different structures of pension funds, their characteristics, and their relative importance. It suffers from data limitations due to the large fragmentation of the second pillar and somewhat outdated standards on reporting and accounting. These shortcomings will be especially important in the section on the volume of the contributions and the accumulated old-age capital. A sizable part of the chapter is dedicated to the incidence of underfunding within occupational pension plans that has become an important policy issue since the turn of the millennium.

# **5.1 Institutions and Organization**

Setting up a pension fund involves decisions on different organizational issues like the application for _registration_ , the choice of its _legal_ and _administrative form_ , and finally, on how _risk_ should be covered. This section illustrates these three issues in turn.

As mentioned in Chapter 2.2, by law the BVG insures income above the coordination offset and below a defined upper bound. In addition, pension providers can also offer insurance on a voluntary basis for income above and below the threshold levels: Most of them do it for income exceeding the upper level, but very few provide coverage for lower income levels. Companies that cover the mandatory part need to be registered. Those that only provide supplementary services do not. In 2002, 30 percent of all pension funds were
We ensure representativeness of our worker panel database by showing that the demographics and job characteristics of workers in the 10% worker random sample are close to those of workers in the complete RAIS database (see Panels A and C in Appendix Table A1).<sup>16</sup>

# **III. Impact of Firms’ GFC Foreign Shocks on Workers**

## _A. Empirical Design_

Our analysis tracks workers over time after the GFC, comparing the evolution of labor market outcomes for workers in firms that experience a larger versus a smaller adverse foreign shock due to the GFC. Our measures of firm foreign shocks due to the GFC - henceforth designated as ‘GFC firm shocks’ - are constructed as a combination of aggregate shocks with measures of firms’ shock exposure. For the aggregate shocks we exploit quasi-experimental variation in foreign demand across destination countries due to lower GDP growth caused by the GFC. For the firms’ shock exposure, we consider pre-GFC firm export portfolio weights across destinations built from customs data.<sup>17</sup> We define GFC firm shocks as firm-specific exportweighted destination market GDP decline given by: where _j_ is a firm, _d_ an export destination, _GDPgrd_ 2008 _t_ corresponds to the real GDP growth rate in destination _d_ between 2007 and 2008, and _wjd_ 2007 is the share of firm _j_ exports to destination _d_ in total firm _j_ exports in 2007.<sup>18</sup> The minus sign included in Equation (1) eases interpretation by allowing to capture the impact of a _decline_ in GDP in the firm’s destinations as a result of the GFC. Higher values of the GFC firm shock indicate that the firm experienced a more adverse demand shock induced by the GFC. For example, a firm with a shock equal to 1 (i.e., its destinations’ GDP declined on average by 1%) is more adversely affected by the GFC than a firm with a shock equal to -2 (i.e., its destinations’ GDP grew on average by 2%).

From the point of view of a firm or a worker in Brazil, a country that did not contribute

> 16To further probe the worker database quality we estimate Mincer regressions for monthly real wages including a polynomial on worker age following Fr ́ıas et al. (2022), worker tenure at the firm, 4-digit industry and year fixed effects and either dummies for males and for higher education or worker fixed effects. The estimates show expected patterns: e.g., a male wage premium of 26% and a higher education premium of 85% (see Appendix Table A2).

> 17Brazil’s customs agency is not authorized to share its customs data, but counterparts in that agency constructed our GFC firm shocks using code we provided and we merged those to our worker and firm panel databases based on unique firm identifiers.

> 18The 2007-2008 real GDP growth rates are taken from the World Development Indicators.

10
Public provision of the program to firms was justified both with reference to the overall policy objective of improving productivity, as well as due to the presence of several market failures that prevent firms from improving management on their own. A first issue is that of information: many badly managed firms do not know they are badly managed, with data from the World Management Survey showing that Colombian managers perceive their firms to be slightly better managed than U.S. firms, when the reality is substantially worse management.<sup>3</sup> Secondly, even if firms know they need to improve, they may be unable to identify which providers can offer good services, may lack the financial resources to pay for consulting, and a lack of insurance may prevent them from investing in an activity with uncertain payoffs.

A total of 218 firms applied for the program. Of these, 180 were accepted in the preliminary step, with the remainder rejected for being too small, or for only being distributors rather than manufacturers of parts. Eleven firms then dropped out, so 169 firms formed the group to take part in the first, diagnostic, phase of the project. Following the diagnostic, we dropped firms with fewer than 10 workers, to leave a sample of 159 firms for the experiment.

# **2.2 Random assignment and firm characteristics**

Firms were randomly assigned to three groups of 53 firms each. Since the number of firms in each group would be small, we aimed to improve balance on observables by forming matched triplets of firms, choosing this grouping in a way to minimize the Mahalanobis distance between firms in a triplet in terms of their geographic location, size, labor productivity, and management practices.<sup>4</sup> This took place in November 2013, after the diagnostic phase (described below). Then within each triplet, firms were randomly allocated to a control group and two treatment groups: an individualconsulting treatment group and a group-consulting treatment group.

Table 1 provides some summary characteristics of the firms, along with their means by treatment group status. The mean (median) firm has been in business for 24 (23.5) years, with only 20 percent having been in business for fewer than 10 years. A key feature of the data is that firms are heterogeneous in terms of size and product produced. Firms had a mean of 59 and median of 40

> 3 Colombian firms had an average WMS score of 2.50 in 2014 (described below), but an average perceived score of 3.76. In contrast, U.S. firms had an average WMS score of 3.32, and perceived score of 3.57.

> 4 Location consisted of Cundinamarca and Valle regional dummies; firm size consisted of dummies for small (10 to 50 workers) and medium size (51 to 310 workers), as well as for the number of employees; management practices consisted of indices for practices in human resources, production, logistics, marketing and finance; as well as for seven individual management practices identified as priority areas in many diagnostic plans.

5