rafmacalaba/datause-encoder-data huggingface dataset:
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Table 11 cont'd.

||Educational<br>||Unit Cost/Yea<br>|r<br> <br>|Source|
|---|---|---|---|---|---|
|Country/Year|Level|USS|Local Curr.|<br>Index||
|Mexico<br>1984|Primary<br>Secondary<br>- General<br>- Vocational<br>University|135<br>589<br>1,035|22,663<br>98,846t<br>173,658|100<br>436<br>766|Based on SEP (1985), p.8.|
|Paraguay<br>1990|Primary<br>Secondary<br>- General<br>- Vocational|50<br>144|61,623<br>176,259|100<br>286|Public Ex=<br>1ditue Reviews The<br>Social S<br>n,Report No.<br>10193-PA,TheWorld Bank,<br>1992.|
||University|710|872,685|1,416||
|Uruguay<br>1989|Primary<br>Secondary|256|154,900|100|Psacharopoulos<br>and VElez<br>(1992).|
||- General|306|185,100|119||
||- Vocational|450|272,200|176||
||University|614|371,500|240||
|Venezuela|Primary|198|7,668|100|Fiszbein and Psacharopoulos|
|1989|Secondary|347|13,420|175|(1991).|
||<br>- General|315|12,170|159||
||- Vocational|471|18,198|237||
||University|1,625|62,795|819||

# Source: Annex 1.

Notes: Y Primary education unit cost comes from Public Sendina on Social Prosram=,while secondary educadon unit costs come from Secondary Education and Trainina in Bmzil: Adaotin to New Economic Realiis. llhzr Educaion Reform in Brazil provides the unit cost of higher education. Information for both pnmary and secondary education has to be inflated to 1989 current prices.

Unit cost for nine years of primary education.

I Weighted average of academic and vocational tracks _(4.5:_ I ratio, respectively from Unesco, Statisti Xmuboic, 1990) for 3 years of secondary education.

FUnit cost refers to upper secondary education.
|||**Table A.1**|**4: Difference b**|**etween dual**|**and non-dual**|**providers' treat**|**ment of SPs(**|**public sam**|**ple only)**|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**<br>**Effort**|**(3)**|**(4)**|**(5)**<br>C<br>**Diagnosis**|**(6)**<br>C|**(7)**|**(8)**|**(9)**<br>|**(10)**<br>Correct<br>**Treatment**|**(11)**|**(12)**|**(13)**|
||Time spent|Checklist|IRT Score|Gave<br>diagnosis|orrect<br>diagnosis<br>(conditional)|orrect<br>diagnosis<br>(unconditional)|Correct<br>treatment|Palliative<br>treatment|Unnecessary<br>treatment|<br> <br>treatment<br>Only|Antibiotic|<sup>Number of</sup><br>medicines|<br>Referred<br>patient|
|**Panel A: Dualpractice sample, w**|**ith SP, case an**|**d district fi**|**xed effects**|||||||||||
|Is a dual provider|-0.659***|-3.881*|-0.329|-0.022|0.021|0.014|-0.007|-0.029|-0.029|-0.017|-0.106|-0.212|-0.015|
||(0.210)|(2.045)|(0.218)|(0.057)|(0.100)|(0.034)|(0.064)|(0.072)|(0.064)|(0.026)|(0.077)|(0.209)|(0.049)|
|R-squared|0.268|0.131||0.187|0.319|0.113|0.340|0.211|0.096|0.044|0.142|0.153|0.159|
|Number of observations|252|252|103|252|73|252|161|161|161|161|161|161|161|
|Mean of non-dual observations|2.343|22.788||0.326|0.207|0.067|0.295|0.705|0.836|0.033|0.541|2.885|0.131|
|Mean of dual observations|1.562|17.677||0.270|0.341|0.092|0.380|0.630|0.830|0.020|0.480|2.800|0.080|
|Mean of sample|1.838|19.482||0.290|0.288|0.083|0.348|0.658|0.832|0.025|0.503|2.832|0.099|
|**Panel B: Dualpractice sample, w**|**ith SP, case an**|**d district fi**|**xed effects**|||||||||||
|Is a dual provider|-0.677***|-4.285*|-0.420*|-0.065|-0.092|-0.020|-0.007|-0.005|-0.073|-0.012|-0.153*|-0.293|-0.050|
||(0.248)|(2.291)|(0.253)|(0.071)|(0.140)|(0.042)|(0.077)|(0.090)|(0.073)|(0.029)|(0.088)|(0.267)|(0.060)|
|Age of provider|-0.017*|-0.077|-0.001|-0.001|-0.003|-0.002|-0.004|-0.007|0.001|-0.002|-0.006|-0.028**|0.000|
||(0.009)|(0.101)|(0.011)|(0.003)|(0.008)|(0.002)|(0.004)|(0.004)|(0.003)|(0.001)|(0.004)|(0.012)|(0.002)|
|Gender of provider (1=Male)|0.097|1.275|-0.083|-0.073|-0.027|-0.023|0.032|0.160|0.147|-0.039|0.245**|0.434|-0.162|
||(0.342)|(3.264)|(0.368)|(0.100)|(0.202)|(0.062)|(0.127)|(0.120)|(0.106)|(0.058)|(0.123)|(0.349)|(0.102)|
|Patient load during visit|-0.031|0.495|0.036|0.008|-0.019|-0.000|-0.010|0.033**|0.004|-0.006|0.002|0.006|0.020|
||(0.040)|(0.584)|(0.031)|(0.011)|(0.048)|(0.010)|(0.019)|(0.016)|(0.023)|(0.005)|(0.026)|(0.086)|(0.017)|
|R-squared|0.310|0.143||0.198|0.375|0.145|0.360|0.259|0.202|0.099|0.255|0.248|0.275|
|Number of observations|215|215|90|215|62|215|137|137|137|137|137|137|137|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant. Observations are standardized provider-patient interactions. In column (13) the dependent variable is total number of medicines recommended to the patient (dispensed and/or prescribed).
# **1 Introduction**

There is widespread evidence of both a wage and an employment premium in exporting vis-`a-vis non-exporting firms (Bernard and Jensen, 1999; Bernard, Jensen, Redding and Schott, 2007). This paper investigates differences the _composition_ of employment of exporting firms. In particular, it asks whether exporting firms hire more engineers relative to blue-collar workers than non-exporting firms. This can happen because firms produce goods of varying quality and exporters tend to produce higher quality goods. As in the literature, more productive firms become exporters (Melitz, 2003). In our framework, goods are produced with tasks using assignment rules (Acemoglu and Zilibotti, 2001; Acemoglu and Autor, 2011; Costinot and Vogel, 2010). Production involves different tasks, such as managing, output and design, and maintenance services. Exporters and non-exporters fill managerial tasks with skilled workers and fill maintenance services tasks with unskilled workers. However, output and design can be performed by engineers or by blue-collar workers. Engineers can deliver higher quality goods than blue-collar workers because they are more efficient in input supervision, assembly, and general product attributes that determine quality (Acemoglu and Zilibotti, 2001; Bastos, Silva and Verhoogen, 2014; Bernard and Jensen, 1997; Brambilla, Lederman, and Porto, 2012; Bustos, 2014; Caron, Fally and Markusen, 2014; Kugler and Verhoogen, 2012; Verhoogen, 2008; Yeaple, 2005). For given factor prices, more productive firms can afford to enter export markets, choose to produce on average higher quality products, and hire on average a higher share of engineers relative to blue-collar workers than less productive, non-exporting firms.

To test these predictions, the empirical analysis exploits detailed information of firms’ demand for different tasks contained in the Chilean Encuesta Nacional Industrial Anual (ENIA). The results from an instrumental variable estimator show that Chilean exporters indeed utilize a higher share of engineers over blue-collar workers than non-exporting firms. The results provide causal evidence of the skilled tasks demanded by exporters relative to non-exporters in Chile.

The rest of the paper is organized as follows. Section 2 sketches a simple model of exports, quality, and the demand for engineers and blue-collar workers. Section 3 discusses the main empirical results. Section 4 concludes.

2
that might have led to the observed (de)industrialization patterns. Spefically, we look at whether resource booms such as oil have induced sustained real exchange rate appreciation that could be linked to slower or less industrialization in resource rich countries. Finally, we also test implications of the Dutch disease and resource-curse hypotheses to the outcome of SSA’s deindustrialization, although this analysis is limited to only four countries with significant oil-exporting sectors.

Table 6 presents the regression results based on equation (2) with and without the additional covariates specified above in Columns (1) and (2), respectively. The results in Table 6 suggest that personal remittances may be an important correlate of deindustrialization in sub-Saharan Africa. In particular, higher personal remittances are associated with lower manufacturing output shares in SSA.

**Table 6: Manufacturing output shares regressions (additional covariates)**

||(1)|(2)|
|---|---|---|
|Population (logarithm)|2.803***<br>(0.965)|2.848***<br>(0.965)|
|Population squared (logarithm)|-0.099***<br>(0.035)|-0.092***<br>(0.034)|
|GDP per capita (logarithm)|2.528**<br>(1.169)|2.047**<br>(1.001)|
|GDP per capita squared (logarithm)|-0.145**<br>(0.074)|-0.117*<br>(0.064)|
|Foreign direct investment, net infows (% of GDP)||-0.002<br>(0.002)|
|Personal remittances, received (% of GDP)||-0.003***<br>(0.001)|
|Observations|1031|1031|

Standard errors in parentheses. Results based on fixed effects fractional logistic regressions in equation (2). _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 010 Table 7 presents results for the non-oil GDP sample. For an apples-to-apples comparison, we estimate the same regression specification on the non-oil GDP sample but using first, the usual manufacturing output shares in GDP (RMVA) and second, the share of manufacturing value added in non-oil GDP (RMVANOIL). As shown in Table 7, the results are qualitatively the same regardless of the deindustrialization measure used in terms of statistical significance. Although 22
12 investment in developing countries. s For example in Japan, Germany and Sweden the share of environmentally sensitive industries in total outward FDI declined between mid-70s and late 80s. In the US it first increased in the 70s but declined slightly in the 80s. Data for many developing countries shows that in a number of countries the share of environment-sensitive industries in inward foreign direct investment has decreased somewhat in the 80s (UN 1992).

**Direction of Trade.** Industrial countries continue to export environmentally sensitive goods to other high standard countries suggesting that, overall, the industrial countries have been able to meet the higher standards in their export markets. Successful exporters, such as Austria and Germany have increased the share of other industrial countries in their exports of environmentally sensitive goods (Table 7). Only Japan and the United States have increased the share of developing countries in their total exports. This could mean that the Europeans have better adjusted to the changed product market conditions. Or it could just be a reflection of the growth in importance of the Asian market to the United States and Japan.

Changes in the developing countries' share in industrial country imports of environmentally sensitive goods was mixed in the sample countries. Among the European countries developing country share in imports of environmentally sensitive goods went down in most countries. Curiously, developing country share was substantially up only in Japan and the United States despite the higher share of non-tariff barriers than in Europe, and lower levels of environmental costs than many European countries.

I **For a** discusaion of **a** number of studies **and** recent data **see** World Investment Report (1992).
**B. Sensitive items:** To measure changes in the composition of respondents, we use items from the second set of questions in the midline survey to construct two sensitive outcomes.<sup>25</sup> We asked the same questions in baseline and endline surveys for the impact evaluation.

- _Stress:_ To measure participants’ perception of stress in the midline survey, we asked them, “How stressed did you feel last week?” The response options were _Very Stressed, Moderately Stressed, Mildly Stressed, Not Stressed at All_ . An individual has “High Stress” if she responds, “Very Stressed.” In the baseline and endline surveys, we measured participants’ stress using the Depression, Anxiety, and Stress Scale (DASS-21) instrument (Lovibond and Lovibond, 1996). We use the seven items from the stress subscale. Each item is measured on a scale of 0–3 points ( _Never, Rarely, Almost Always, or Always_ ). We estimate the total stress score following Lovibond and Lovibond (1996); that is, a participant has “High Stress” if the score is greater than 18.

- _Perceptions of effectiveness of physical violence toward children:_ We asked participants, “Do you think physical punishment is an effective method to discipline children?” The response options were _No, it is never effective; It is rarely effective; It is sometimes effective; It is effective most of the time; It is always effective_ . The same question and response options were included in baseline, midline, and endline surveys.

**C. Consistency in self-reports of sensitive items:** To measure consistency in the reporting of either of the two sensitive outcomes at baseline or in the endline survey, we generated dummy indicators that take the value of 1 if the answer in the midline survey is equal to the one reported at baseline and in the endline survey, respectively.<sup>26</sup>

**D. Sociodemographic information:** In addition to the outcomes described above, we merged the midline survey with information collected during the enrollment of participants or baseline survey concerning their age, gender, education, age of children in her care, municipality of residence, and mobile phone us-

> 25More details on the construction of these outcomes—sources of data, items (original scale and translation in Spanish), and description—are in Table A9 in the Appendix. 26See Table A8 in the Appendix for more details.

12
was applied as proposed through 2016 (the last year of our data set), with the exception of increases for the first three residential blocks, to protect vulnerable consumers. There has been less government buyin on reforms in Kenya, where tariffs planned by the Regulator have been delayed three times in the past five years, due to the insistence of the government. In 2013, the government chose to provide a subsidy to the distribution utility (KPLC) instead of allowing an increase in connection charges.

<!-- Start of picture text --> Figure 18: Economy-wide Factors and Cost Recovery<br>a) Cost Recovery and GDP per Capita b) Changes in Cost Recovery and<br>Economy-Wide Governance<br> 8,000 0.60<br>R2 = 0.0707 R2 = 0.0916<br>Dominican 0.50<br> 7,000 Republic Peru 0.40 TajikistanColombia<br>Colombia<br> 6,000<br> 0.30<br> 5,000 0.20 Peru<br>Tanzania Uganda<br> 0.10 Dominican Kenya<br> 4,000 Republic<br>Morocco - Senegal Vietnam<br>India -Rajasthan India -Odisha<br> 3,000 Philippines (0.10) India -AP Ukraine<br>India -Andhra Egypt, Arab Ukraine<br> 2,000 Pradesh Rep. Pakistan VietnamIndia -Odisha (0.20) Morocco<br>RajasthanIndia - Senegal Kenya (0.30) Philippines Pakistan<br> 1,000 Tanzania<br>Uganda (0.40) Egypt, Arab<br>Tajikistan<br> - (0.50) Rep.<br>20% 40% 60% 80% 100% 120% -50% 0% 50% 100%<br>Full-Cost Recovery Change in Full-Cost Recovery<br>GDP per Capita<br>Change in World Governance Indicators<br><!-- End of picture text --> _Source:_ World Bank staff.

# **#10: Full-cost recovery is less strongly associated with investment levels and improvements in sector outcomes than financial cost recovery**

67. The last part of the analysis focuses on the original objectives of cost-recovery reforms: Attracting investment to meet growing demand and improving service outcomes.

68. In contrast to the Standard Model’s logic, the analysis of the 17 case studies finds a much stronger link between sector outcomes and the most basic indicator of utilities’ cost recovery—financial operating cost recovery (A1) adjusted for bill collection losses—than full-cost recovery and sector outcomes. This is true for all three major indicators of sector outcomes analyzed for this study: (a) Transmission and distribution losses (Figure 19a-b), the number of outages per month (Figure 19c-d), and an indicator of the country’s or state’s ability to meet growing demand (Figure 19e-f). This suggests that financial viability of power utilities should be elevated as a reform objective by itself—as opposed to a side-effect of cost recovery reforms—particularly in power sectors that (i) are going through periods of high investment needs, e.g., to meet demand growth or expand access to electricity service; (ii) are facing issues in efficiency and quality of service; or (iii) are situated in economies with low consumer purchasing power and low overall governance quality, where full-cost recovery is inherently harder to achieve (see discussion of boundary conditions under Observation #9).

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conditional trade instruments allowed by the GATT. Only a few sectors listed are considered as carbon-intensive emitters by experts.

The “excessive” length of the Commission’s list deserves close attention since it reveals the inability of the Commission to resist to protectionist pressures, leaving little doubt about the ultimate capture of climate change policies by vested industrial interests if the climate community is not vigilant.

**Table 5.  Industries with “significant risk of carbon leakage” (EU Commission)**

|Sectors<br>[a]|Use of<br>contingent<br>protection[d]|Sectors<br>[b]|Use of<br>contingent<br>protection[d]|Sectors<br>[c]|Use of<br>contingent<br>protection[d]|
|---|---|---|---|---|---|
|Aluminum|D***|Ceramics||Boards|D***|
|Cement|D***|Chemicals|D***|Expanded clay||
|Steel & iron|D+C+S***|Glass|D**|Manganese|D***|
|||Pulp & paper|D***|Man-made fibres|D***|
|||Copper||Nickel|D*|
|||Magnesite|D***|Starch|D***|
|||Potassium|D**|Textiles|D***|
|||Tyres<br>Zinc|D+S***|||
|||Aviation|[e]|||

Source: European Commission [2009]. Author’s list for conditional measures of protection. For the detailed list, see Annex A. Notes: [a] Industries quoted as “currently being analyzed”. [b] Industries quoted as “having provided preliminary information”. [c] Industries quoted as “having announced that they will provide information”. [d] ***: intensive petitioners of complaints. **,* : less frequent petitioners of complaints. D: antidumping complaints, C: antisubsidy complaints, S: safeguard complaints. [e] Important role of public procurement. Industries underlined are the ones generally considered as the carbon intensive sectors most prone to carbon leakage.

The drift from climate change concerns (which should be the core ones) to trade concerns revealed by Table 5 occurred relatively recently. It mostly emerged when the EC Directive 2003/87 was modified by the EC Directive 2009/29. These modifications included the definition of criteria for defining sectors with significant risk of carbon leakage. The criteria adopted defined three major groups of sectors [European Commission 2009]:

- Sectors with a “particularly high” increase (more than 30 percent) of the ratio between the costs due to carbon regulations and the gross value added.  Clearly, this group deserves attention from a climate change perspective.  But, it accounts for only 2 clearly identified sectors (cement and lime) in the Commission’s list.

- Sectors with a large trade intensity (above 30 percent) defined as the ratio between the sum of imports and exports and the sum of gross value added and imports.  Clearly, this group has little to do with climate change concerns—but a lot to do with vested interests eager to use climate change for plain protectionist purposes, or protection for

24