rafmacalaba/datause-encoder-data huggingface dataset:
# **5.3.2 Wallsten (2002)**

Wallsten (2002) concentrates more on regulation per se. The concern of this paper is whether or not the establishment of a regulator _before_ privatization (defined as partial or total sale of shares of the incumbent telecom company) provided benefits in terms of higher investment and larger privatisation revenues.

Like Fink et al, the paper estimates fixed effects models on a data set of 200 countries over the period 1985-99. The models estimated date the establishment of the regulator using ITU data – many were only established after 1995. Unlike Fink et al or Pargal, there are no data on competition or liberalization so that the effects of privatization and regulation may be exaggerated.

One other feature of the Wallsten data is that the sample included major OECD countries (including the US, the UK, France, Germany, etc) and there are no estimates reported for developing countries only and there are no dummies for international regions. Hence, the results reported may be unreliable to the extent that different factors are relevant (or more important) in rich than in poor countries (e.g. on quality of regulation)<sup>53</sup> . The estimated equations included the standard control variables of per capita GDP and population.

One interesting feature is the use of self-reported answers on whether regulators were independent. The specific question to which countries sent replies to the ITU was whether “the Regulatory Authority [is] independent from political power”<sup>54</sup> . As Wallsten points out, this is a very ambiguous question with clear incentives to provide an answer of independence - and virtually no penalties against providing such an answer. Wallsten rightly points outs that this is emphatically not an appropriate way to collect reliable data on the quality of regulation<sup>55</sup> .

Bearing these qualifications in mind, the estimation results are nevertheless supportive of a positive impact of regulation on investment. The key results are:

- For the full sample of 185 or more countries, a separate regulator is positively associated with the number of cellular subscribers but not investment in fixed services.  (This could well be a liberalization effect if competition – and competition in mobile services – were introduced at the same time or soon after the establishment of the regulator).

- Installing a regulator before privatization is positively and significantly associated with investment – mainlines, mainlines per capita, telecom investment (almost entirely investment by incumbents) and number of mobile subscribers.

- The problematic “regulatory independence” variable was _negatively_ associated with the investment variables, sometimes significantly so.

- For the 33 countries which had partially or wholly privatised their incumbent telephone company, having a regulator in place before privatization was

> 53 DPS (2002) found that different equations between developed and developing countries were crucial for understanding regulatory staff numbers.

> 54 See Wallsten op cit, p 10.

- 55 See Wallsten op cit p 10-11.

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countries that over the next couple of decades half of the jobs could be automated. Arntz et al. (2016) follow a task-based approach and allow for heterogeneity of workers’ tasks within occupations and demonstrate that the threat from technological advances is less pronounced, with a much smaller percentage of jobs in OECD countries being automatable in the future.

In our paper, we measure ICT adoption at the firm level with the use of complex software including client management, production, and business software. To our knowledge only one study, Bloom et al. (2014), examines the impacts of complex software on firm organizational decisions, although it does not consider the impact on the demand for different types of skills. Their conjecture is that the use of business software reduces the costs for workers to access information, and this allows workers to solve more problems and rely less on the training of specialists. Their evidence for firms in the U.S. and Europe confirms that indeed the use of business software increases decentralization within the firm, leading to more autonomy and a wider span of control for local plant managers. Iacovone and Pereira-Lopez (2017) adopt an indirect approach whereby they consider the use of business software as a possible mechanism to explain the positive impact of ICT adoption on the demand for skilled workers estimated for Mexican firms. They show that ICT adoption leads to organization adjustments in the form of the use of business software.

# **3. Data and Summary Statistics**

# **3.1 Data Sets and Definitions**

<mark>In the paper, we exploit several data sets. First, we exploit a nationwide longitudinal firm-level survey, ELE, which is representative of all economic activities in Chile except for public administration, health, education, domestic service, and extraterritorial organizations.</mark><sup>4</sup> <mark>We use the 2007 and 2013 waves of the survey. Appendix A provides technical details on the ELE survey.</mark> <mark>The ELE survey is unique to answer our research questions as it collects rich information on firm technology adoption, ranging from the use of computers, to the u</mark> se of complex software, and to access to the internet.<sup>5</sup> The use of complex software captures the use of client management, production or business software packages. This type of software can perform complex tasks within firms such as the planning of production levels (based on expected demand and stocks), product

> 4 A small number of recent studies rely on ELE survey data (2007 and 2009 rounds) to study links between ICT and innovation (Santoleri, 2015; Alvarez, 2016) and innovation and wages (Cirillo, 2016). 5 All variables from the ELE survey are defined in Table B1 (Appendix B).

8
Table 8: IV estimates of the effect of ICT use on wages: Manufacturing

||(1)|(2)|(3)|
|---|---|---|---|
|**Dependent variable:** ∆**ln(Wage of whi**|**te-collar wo**|**rkers)**||
|∆Share of labor that uses computers|0.0156***|0.0150***|0.0106***|
||(0.0029)|(0.0029)|(0.0016)|
|F- frst stage|40.43|38.71|106.26|
|Sargan p-value|0.1451|0.1527|0.1338|
|∆Share of labor that uses Internet|0.0130***<br>(0.0022)|0.0125***<br>(0.0022)|0.0138***<br>(0.0021)|
|F- frst stage|73.39|70.25|73.93|
|Sargan p-value|0.2717|0.2869|0.1664|
|∆ln(stock of computer equipment/worker)|0.4147***<br>(0.0925)|0.6035***<br>(0.1872)|0.9054***<br>(0.3222)|
|F- frst stage|26.36|11.19|6.69|
|Sargan p-value|0.1514|0.1241|0.1311|
|**Dependent variable:** ∆**ln(Wage of blue**|**-collar wor**|**kers)**||
|∆Share of labor that uses computer|0.0194***<br>(0.0027)|0.0185***<br>(0.0027)|0.0100***<br>(0.0015)|
|F- frst stage|40.43|38.71|106.26|
|Sargan p-value|0.5471|0. 4724|0.1468|
|∆Share of labor that uses Internet|0.0225***|0.0216***|0.0157***|
||(0.0033)|(0.0033)|(0.0026)|
|F- frst stage|73.39|70.25|73.93|
|Sargan p-value|0.3492|0.3667|0.8176|
|∆ln(stock of computer equipment/worker)|0.6351***<br>(0.1110)|0.9118***<br>(0.2324)|0.7571***<br>(0.2563)|
|F- frst stage|26.36|11.19|6.69|
|Sargan p-value|0.6448|0.2970|0.1273|
|**Controls**||||
|∆ln(capital/worker)|No|Yes|Yes|
|Age dummies|No|No|Yes|
|Size dummies|No|No|Yes|
|Observations|11,438|11,438|11,438|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

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Next, we eliminate δ _M_ _<u>rw</u>_ from (C5) by multiplying (1 −α ) and rearranging the equation: _M rw_ Inserting (C10) in (C7) and substituting Δα using (C8), we get: Prices for preferred imports (from EU) can be rewritten as: Where _Px_ is the export price excluding tariff t. taking the total derivative of (c13), we get: (C14) δ _peu_ = δ _Px_ 1( + _t_ ) + _Px_ δ _t_ Dividing (C14) by (C13): Next, using (C16) in (C12) and α= 1 −λ , we get: Finally, separating (C17) into trade creation (TC) and Trade diversion (TD), we obtain: 20
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by factors related to measurement errors and methodological differences, which add to the difficulties of isolating the same cohort in several consecutive censuses. The burdens accentuate as research moves from general to specific interests in attempt to capture the characteristics of a specific set of migrants. Table 2 in sub-section 3.2 provides examples of counterintuitive increases in cohort sizes. Normally, these should decline over time due to return migration and natural causes.

Previous studies relying on US census data make inferences about the selective nature of return migration, but do not incorporate it explicitly in estimations of assimilation.<sup>32</sup> For this purpose, researchers typically turned to alternative data sources, as described in section 2.

The approach taken in this paper is a very simple one and consists of calculating the rate of attrition for the skilled immigrants in a particular cohort over the decade that is closest to their arrival in the US. As the next step, the countries with the highest rates of return migration are identified and excluded them from the samples. Finally I re-estimate equation (1) and plot the predicted variables as described in sub-section 3.3.<sup>33</sup> To illustrate, I will focus on the case where the occupational placement improvement, defined as Education Index 1, is evaluated for the 1985-1989 arrivals. The ranking of countries by the rate of change in the number of skilled members of the 1985-1989 period of arrival leads to the identification of several mostly developed states<sup>34</sup> which seem to have experienced the highest levels of out-migration during 1990 and 2000. As shown in Figure 5, the relationship between the predicted occupational placements obtained after eliminating those countries from the sample is similar to what was presented in Figure 2 of sub-section 3.3. The conclusion that results do not change after eliminating countries with significant out-migration incidence is robust to using alternative cohorts and definitions of the occupational placement index.

# **_3.4.2. Pseudo-Panel Analysis: Biases Due to Using Repeated Cross-Section Datasets_**

As explained before, the sample of people responding to the long-form questionnaires of the US Censuses is drawn anew each decade from the entire population of the country. Hence, the validity of the inferences stated in this paper may be affected by the comparison of different

> 32 Duleep and Regets (1997) take a step in the right direction by suggesting a method for testing the sensitivity of results to biases caused by sampling error and migration.

> 33 The dependent variables are not logged prior to estimating the equations.  There is no significant difference between results based on a log-functional form as opposed to original variables.  The log specification was primarily introduced to facilitate the convergence analysis.

> 34 Australia, Belgium, Brazil, Canada, Chile, France, Germany, Israel, Japan, Mexico, Netherlands, New Zealand, Sweden, Turkey, United Kingdom.

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work by Cadot, Carr`ere, and Strauss-Kahn (2011) and Jaud, Kukenova, and Strieborny (2018), we measure this distance by the Euclidean distance between the vector of the exporting country’s relative factor endowments and the vector of the exported product’s relative factor intensities: The Heckscher-Ohlin theory maintains that exported products should intensively use those factors of production that are abundant and therefore cheap in the exporting country. Consequently, the relative factor intensity of the exported products should be as close as possible to the relative factor endowment of the country that exports them. In the above equation, _κc_ , _hc_ and _lc_ are the exporting country’s endowments in physical capital, human capital and land, all relative to the fourth production factor - labour (e.g., _κc_ ˆ represents the capital/labor ratio in a country _c_ ). _κk_ , _h_<sup>ˆ</sup> _k_ and<sup>ˆ</sup> _lk_ are the corresponding relˆ ative factor intensities of the exported product (e.g., _κk_ represents capital/labor intensity for a product _k_ ). Since the production factors are measured in units that are not directly comparable, we standardize the differences between the product’s factor intensities and the country’s factor endowments to have zero mean and unit variance (“std” in Equation 1 stands for “standardized”).<sup>12</sup> Our approach should be seen as complementary to the Ricardian theory that stresses technology as an alternative source of comparative advantage in international trade. Our focus on factor endowments is motivated both by data availability and existing evidence that Ricardian forces of technological differences do not bias tests of the Heckscher-Ohlin model. Online Appendix A2 discusses these issues in more detail.

The data on relative factor endowments and factor intensities is taken from Cadot, Tumurchudur, and Shirotori (2009). While finding proxies for factor endowments at the country level is relatively straightforward (physical capital constructed according to the perpetual inventory method, years of schooling, hectares of arable land, number of workers), factor intensities at the product level are not easily observable. Cadot,

> 12 The resulting metrics _distanceck_ is time-varying as countries’ relative factor endowments evolve over time, with the time subscript in Equation 1 omitted for simplicity.

9
# I. JORDAN’S GROWTH OF EDUCATIONAL ATTAINMENT AND SCHOOL SUPPLY

Educational attainment in Jordan witnessed dramatic growth in the second half of the twentieth century. The evolution of the average years of schooling of males and females by year of birth is depicted based on data from JLMPS 2010 (figure 1).<sup>3</sup> As shown, there was a rapid increase in educational attainment across cohorts for both males and females. For males, the increase occurred earlier, with their mean years of schooling rising from under six years for the 1940 birth cohort to ten years for the 1955 cohort. It then stagnated at about 11 years for cohorts born between 1960 and 1975, only to start rising again for younger cohorts. The increase in female mean years of schooling occurred later and was more sustained. The mean years of schooling starts as low as fewer than two years for the 1940 cohort and rises rapidly to about ten years for the 1965 cohort. Although the rate of increase of female schooling slows after that, the mean years of schooling for women exceeds that of their male counterparts for the youngest cohort born between 1975 and 1985.

To investigate whether this dramatic increase in educational attainment corresponds to the expansion of public schools in the country, the average number of basic and secondary public schools per 1,000 individuals in subdistrict of birth for the JLMPS 2010 sample is depicted (figure 2). The data on the number of public schools is obtained from the school census carried out in 2010 by the Jordanian Ministry of Education.<sup>4</sup> The focus on public schools is because they

> 3. Mean years of schooling for older cohorts may be biased upward due to selectivity resulting from the likely higher mortality of less educated individuals.

> 4. Since these figures are based exclusively on surviving schools in 2010, they ignore school closures and school conversions from one type of school to another. Basic schools currently go from first to tenth grade. Before 1994, they were subdivided into primary schools (going from first to sixth grade) and preparatory schools (going from seventh to ninth grade).

8
The first step towards integrated risk management approaches and corresponding regulation and supervision would be the identification and standardization of the common themes and elements in the various standards and codes. Commonly mentioned in many of the codes are the importance of independence of the supervisor and local standard setters, adequacy of financial and human resources, and proper regulatory governance; the need for transparency, disclosure and governance of financial markets participants; and the importance of certain key infrastructure elements such as clearing/settlement and payments systems (and have been highlighted, among others, in the 2004 IMF review of regulatory systems aimed at identifying common regulatory themes). The second step would be to try to develop a more common approach in strengthening regulatory regimes. This could involve separate assessments of the elements common across all sectors rather than through the now typical sector-specific assessments. Subsequently, policy and technical assistance should aim at strengthening foremost these common elements. A third, accompanying step would be to acknowledge the greater relative importance of some codes and other institutional infrastructure to financial system functioning. Clearly, accounting and auditing, and governance have come to be recognized to be core ingredients as regulatory agencies are moving towards more sophisticated risk-based regulatory techniques in a more financially integrated world. The whole institutional infrastructure for assets identification and treatment ⎯ e.g., the availability of credit information, the registration and perfection of collateral ⎯ and the framework for financial restructuring, including reorganization and insolvency, have also moved more to the forefront.

There are also some specific lessons. For one, there may be an argument for having higher transparency requirements for banks and other financial institutions than for nonfinancial corporations. Accurate reporting on a detailed and timely basis of financial performance and material events can improve market discipline. However, while in general improved transparency has been part of the re-regulation agenda, it has encountered some tradeoffs for financial institutions. Too little transparency is surely unwelcome, but too much transparency has been argued to be potentially volatilityinducing. This has come to the forefront with the choice of more mark-to-market IAS 13
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