rafmacalaba/datause-encoder-data huggingface dataset:
# 1. Introduction

The accumulation of human capital through formal education has two possible routes. One route is a more general education that aims to provide general knowledge in a variety of subjects, typically allowing for more flexibility in choosing a profession later on in life. The other route is technical-vocational education and training (TVET), which aims to prepare students/trainees for specific occupations in the labor market. TVET provides young individuals with more opportunities for insertion into the labor market in the short run but can generate more difficulties in finding employment at advanced ages due to the more specific skill set acquired from this education (Hanushek, Woessmann, and Zhang, 2011).

In Brazil, there is increasing education of the workforce and declining wage differences between high and low-skilled workers. In this context, technical-vocational education is presented as an important educational policy option.<sup>2</sup> Acquiring a suitable qualification level to work in the labor market is a common requirement to obtain a job (Aedo and Walker, 2012; Menezes Filho, 2012). A TVET course can provide students with the appropriate skills to enter the labor market in possibly a shorter period, and with higher productivity compared with regular education classes. However, usually technical and vocational education has a higher cost than regular education, so it is important to weigh its costs and benefits to ultimately quantify its returns. This paper analyzes the individual wage returns from completing a TVET course compared with a more general education. We also analyze the heterogeneity of these impacts and the probability of entering the labor market or tertiary education for individuals with a technical-vocational education background compared to those with a more general education.

We explore data from the professional education supplement of the _Pesquisa Nacional por Amostra Domiciliar_ – PNAD – (National Household Sample Survey) published by the _Instituto Brasileiro de Geografia e Estatística_ – IBGE (Brazilian Institute of Geography and Statistics) in 2007. To our knowledge this database is the best survey available for this purpose given the extent of information on the socioeconomic characteristics of the individuals who attended a technicalvocational education course as well as the details about the various types of this education. Furthermore, PNAD is a representative database for Brazil capturing both the formal and the informal sectors.

In the empirical work, we will focus on three types of technical-vocational education and training programs: i) technical education at the upper secondary level; ii) technical education at the

> 2 See trends documented with _Pesquisa Nacional por Amostra Domiciliar_ – PNAD - (National Household Sample Survey) in 1992, 1997, 2002, 2007, and 2012 and presented by Almeida et al. (2014). See Almeida et al. (2014) regarding education and wage trends in Brazil.

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model has still some limiting assumptions, it does move the discussion closer to empirical realities and toward a better understanding of the specificities of regional economics.

Regional analysis has reached a stage in which theory has formed the foundation for a better understanding of how spatial development happens, incorporating some of the frictions that we see in reality and that neoclassical theory ignores. Yet, regional economic development theory still falls short of complete spatial-awareness – of explaining both why and where things are happening. Policy proposals for regional development have followed a similar process of “evolution,” sometimes developing hand in hand with theory and sometimes despite it. In the next section, a brief review of some past and current policies will be provided, all premised on the notion that market failure, in the form of uneven development, requires some form of intervention.

# **1.1 An overview of policy proposals**

In the 80 years since the publication of Beveridge’s monograph, there has been a variety of regional policy proposals aimed at the reduction of interregional disparities. Many of these have focused on limited development in more prosperous areas while others focused on enhancing the competitive potential of the least prosperous regions of a country. An excellent but somewhat dated overview may be found in Armstrong and Taylor (2000) with some of the more recent policies reviewed by McCann (2015), with a special focus on the United Kingdom and the European Union.

## **_The Early Regional Policies – Sticks and Carrots_**

In the 1960s, especially in the United Kingdom, regional policies oscillated between those termed “Carrot” and those characterized as “Stick.” The former provided incentives for firms to locate establishments in less prosperous regions; these incentives ranged from outright grants to tax abatements and funds for the training/retraining of employees. In analysis of the Department of Regional Economic Expansion policies in Canada, Woodward (1974a, 1974b, 1975) characterized these policies as windfalls; in many cases, the firms had already decided to locate in less prosperous areas (especially in the Atlantic provinces) and so the incentives provided little benefit 7
**Table 2: Key features of the land registration process Ethiopia’s four main regions**

||**Tigray**|**Amhara**|**Oromia**|**SNNPR**|
|---|---|---|---|---|
|**Basic information**|||||
|Year started|1998|2003 (pilots 2002)|2003|2005 (pilots 2004)|
|No of woredas / kebeles|34|106|200|104|
|No of kebeles|630|2895|6000|4431|
|Households registered<sup>41</sup>|632,000 (88%)|2.4 mn. (79%)|2.4 mn.|700,126 (40 %)|
|No. of certificates issued|n.a. (delays)|1.3 mn.|n.a. (delays)|Only in pilot areas|
|Fee for certificate|B 3|Free of charge|B 5|B 2|
|Type of certificate|Blue cardstock A4<br>(“form 3”)|Passport sized booklet listing<br>rights & obligations|Green booklet|Green booklet|
|Women’s rights|Certificate in name of<br>head; no photo.|Certificate joint: 2 photos|One certificate per<br>family; 1 photo|Joint cert. W. 2 names<br>& photos<sup>42</sup>|
|CPRs demarcated|No (only pilots)|Yes|No|Kebele discretion|
|**Procedures**|||||
|Initial training by|Kebele; woreda staff|Woreda officials|Woreda|Kebele; training centers|
|Written materials used|Few copies of<br>Proclamation|Posters (delayed)|Yes; proclamation &<br>book|No due to language<br>problems|
|Supervision of data|Students backed up|Woreda survey team (3) &|Expert advice from|Rural development|
|collection|by technicians|Land Adm. team (1 + 2)|woreda on demand|agent|
|Data collection in field|Not always|Yes; not always done|Yes|Yes|
|Presentation of results|Discussed in kebele<br>council|Meeting with woreda official<br>present|Yes, details at<br>discretion of LAC|Yes, details at<br>discretion of LAC|
|**Registers & certificates**|||||
|Plot map/sketch|No|No, except in pilot kebeles|No, pilot proposal|No|
|Certificates written by|Kebele, signed by<br>kebele chairman.|Woreda LA team|Kebele; signed by<br>woreda chairman|Woreda LA expert|
|Registry book written by|Kebele|Woreda LA team|Woreda LA expert|Woreda LA expert|
|Book kept at kebele?|Yes|Yes|No, only copies of<br>data collection forms|Yes|
|Book kept at woreda|Yes|Yes|Yes, very condensed<br>(onlyfewplots/hh)|Yes, data at household<br>level(clumsysize)|
|**Updating procedures**|||||
|General format|Discretion of kebele|Interplay between kebele &<br>woreda`|Woreda book with<br>columns for transfers|Woreda book still being<br>developed|
|Procedure for inheritance|Transfer of certificate<br>or return & new issue|New certificate; old cert. not<br>closed; no cross-reference|New certificate; no<br>clear procedure yet.|<br>Not clear|
|Divorce|Wife gets half the<br>land but no certificate|Wife gets half the land, but<br>no certificate|Wife gets share of<br>land|Wife gets half the land|
|Recording of leases|Unclear|Not in book|Not in book|Not in book|

_Source:_ Field visits to regional EPLAUA offices and 24 kebeles in all four regions; Sept. 2005.

> 41 Numbers as of Aug. 2005, the latest date for which information was available.

> 42 Additional wives receive a joint certificate with their name first and their photo included.

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# **Figure 2. Indicators of market volatility and anxiety**

<!-- Start of picture text --> Equity - VIX index Currencies and commodities - common factors of<br>volatilities 1/<br>90 5<br>80<br>4<br>70 Exchange rates<br>3<br>60 Commodity prices<br>50 2<br>40 1<br>30<br>0<br>20<br>-1<br>10<br>0 -2<br>Apr-06 Jan-07 Oct-07 Jul-08 Apr-09 Apr-06 Jan-07 Oct-07 Jul-08 Apr-09<br>Credit - TED spread in basis points Overall market volatility - common factor 2/<br>500 5<br>450<br>4<br>400<br>350 3<br>300<br>2<br>250<br>1<br>200<br>150 0<br>100<br>-1<br>50<br>0 -2<br>Apr-06 Jan-07 Oct-07 Jul-08 Apr-09 Apr-06 Jan-07 Oct-07 Jul-08 Apr-09<br><!-- End of picture text --> Source: Bloomberg Note: 1/ Exchange rate common factor from a factor analysis of the annualized coefficient of variation of daily exchange rates (EUR, JPY and GBP); for commodity prices a factor analysis of the annualized coefficient of variation of daily prices (agriculture, energy and metals) 2/ Overall market volatility common factor from a factor analysis of VIX, daily exchange rates (EUR, JPY and GBP), commodity prices (agriculture, energy and metals) and the TED spread.

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Further, our evidence that IMF forecasts overshoot performance more in weakly governed countries than compared to the estimated effect from discoveries in general supports our interpretation that our results are driven by countries growth under-performance rather than analyst exuberance alone. Where we examine the estimated effect of discoveries in strong institution contexts we find no significant effect on any of our main outcomes of interest, namely forecast growth, actual growth or the growth forecast error, further supporting this interpretation.

# **2.2 Empirical specification**

We are interested in both how discoveries impact growth rates relative to their counter-factual, and how these effects might differ across countries with different quality of political institutions. We compare the growth forecast errors in the period immediately following a resource discovery to those prior to the discovery and further away in time. To identify the effect we measure the within-country variation in growth forecast errors, controlling for time invariant country characteristics via country fixed effects, as well as common time-varying effects via year fixed effects. For our main specification we regress our oil discovery on the subsequent growth performance: defined in terms of forecast growth, actual growth and the resulting growth Under the null hypothesis, the estimated coefficient on the difference between forecast growth and actual growth should equal zero. In other words, the forecast growth effect of a giant oil discovery should not differ from the growth path the country subsequently experiences. Where this estimated coefficient is less than zero, average forecasts exceed the average observed growth experience, implying oil discoveries have proved worse for growth, over the period of 6 years, than we expected.

We analyze a cross country panel between 1990 and 2010. In every year (t) and country (n) we compare cumulative average t+1-t+5 year growth forecasts as reported in year (t) with actual growth trajectory for the same t+1-t+5 period. We label the difference of growth forecast and actual growth “diff”. For example, in 1990 the IMF WEO reported an average expected growth rate of 5.1% for country Z for the 1991 to 1995 period. The actual average growth rate turned out to be 4.7% in the same country and period. Hence the difference (shortfall) in growth rates for country Z in 1990 is - 0.4%. Forecast(Z,1990) = 5.1%, Actual (Z, 1990) = 4.7%. Diff (Z, 1990) = - 0.4%.

We utilize the forecast growth rate, the actual and the differential across various specifications.

Our analysis looks at whether the lagged variable of oil discoveries affect growth and growth forecasts, and by extension the forecasting gap or error.

Our preferred treatment variable is two year lags of oil discoveries, based on both evidence from 11
GTAP database but was distorted by a normally distributed random error term with zero mean and the size of standard deviation as large as 5 times the “true” trade data. The solution from the model is compared with the benchmark data set for both the inter-regional shipment and intersector transaction flows.

In the second experiment, we use the region-specific input-output coefficients as constant in the model. We re-estimate the interregional shipment data as the first experiment, and compare the model solution with the benchmark data set for the inter-regional trade data only.

In the third experiment, we assume the interregional shipment pattern is known with certainty and we use the three region’s weighted average IO coefficients as initial estimates to estimate the region-specific input-output accounts.

In the fourth experiment, Batten’s model was used to estimate the interregional shipment and individual region’s IO flows. In the fifth to the seventh experiments, experiments 1-3 were repeated by using the extended model. Solutions from both models are compared with the “true” interregional trade and inter-sector IO flow data in the aggregated GTAP data set. The assumptions, initial estimates and expected model solution are summarized in table 1.

# **(** **_Insert Table 1 here_ )**

# **_Measures to Evaluate Test Results_**

Each experiment produces a different set of estimates, and it is desirable to know how much each set of estimates differs from the true, known data. However, it is difficult to use a single measure to compare the estimated results. Since there are so many dimensions in the model solution sets, a particular set of estimates may score well on one region or commodity but badly on others. It is meaningful to use several measures to gain more insight on the model performance in different experiments. Generally speaking, it is the proportionate errors and not the absolute errors that matter; therefore, the "mean absolute percentage error" with respect to the true data will be calculated for different commodity and regional aggregations. Consider the following aggregate index measure for intra/inter-regional trade flows: 17
# **B‐ Oil importers**

<!-- Start of picture text --> Jordon: Credit Cycles and GDP Correlogram Morocco: Credit Cycles and GDP Correlogram<br>-10 -5 0 5 10 -10 -5 0 5 10<br>Lag Lag<br>Lebanon: Credit Cycles and GDP Correlogram Tunisia: Credit Cycles and GDP Correlogram<br>-10 -5 0 5 10<br>Lag<br>-10 -5 0 5 10<br>Lag<br>Yemen: Credit Cycles and GDP Correlogram Syria: Credit Cycles and GDP Correlogram<br>-10 -5 0 5 10<br>Lag -10 -5 0 5 10<br>Lag<br>1.00 1.00 1.00 1.00<br>0.50 0.50 0.50 0.50<br>0.00 0.00 0.00 0.00<br>-0.50 -0.50 -0.50 -0.50<br>corr-growth of real credit cycle and growth of GDP -1.00 -1.00 corr-growth of real credit cycle and growth of GDP -1.00 -1.00<br>1.00 1.00 1.00 1.00<br>0.50 0.50<br>0.50 0.50<br>0.00 0.00<br>0.00 0.00<br>-0.50 -0.50<br>-0.50 -0.50<br>corr-growth of real credit cycle and growth of GDP -1.00 -1.00<br>corr-growth of real credit cycle and growth of GDP-1.00 -1.00<br>1.00 1.00 1.00 1.00<br>0.50 0.50 0.50 0.50<br>0.00 0.00<br>0.00 0.00<br>-0.50 -0.50<br>-0.50 -0.50<br>corr-growth of real credit cycle and growth of GDP -1.00 -1.00<br>corr-growth of real credit cycle and growth of GDP -1.00 -1.00<br><!-- End of picture text --> 45
**<u>Table 3: Time and mode of land acquisition as well as output by distance to farm/resettlement blocks</u>**

||<br>**Total**||<br>**D**|<br>**istance to blo**|<br>**ck**||
|---|---|---|---|---|---|---|
|||**0**|**< 5 km**|**5-10 km**|**10-20 km**|**> 20 km**|
|**Panel A: Listing information**|||||||
|**Location**|||||||
|Dist. to next block (km)|11.26|0.00|1.93|7.30|14.57|55.45|
|Size of next block|32,092|53,946|30,849|18,695|19,159|7,905|
|Avg. size of listed parcel (ha)|17.35|19.10|15.49|16.42|18.05|15.34|
|**Time of land acquisition**|||||||
|Before 1995|0.47|0.53|0.50|0.47|0.35|0.40|
|1996-2000|0.19|0.18|0.21|0.15|0.23|0.17|
|2001-2005|0.12|0.11|0.11|0.12|0.14|0.12|
|2006-2010|0.10|0.08|0.07|0.11|0.15|0.12|
|2011-2015|0.08|0.06|0.06|0.09|0.08|0.14|
|2016-2020|0.04|0.03|0.05|0.06|0.05|0.05|
|**Mode of acquisition**|||||||
|From chief|0.44|0.22|0.56|0.61|0.55|0.52|
|From Government|0.23|0.39|0.13|0.12|0.24|0.08|
|Inherited|0.19|0.15|0.18|0.21|0.15|0.35|
|Purchased|0.12|0.23|0.11|0.05|0.05|0.03|
|Title|0.22|0.38|0.16|0.16|0.14|0.07|
|Offer letter|0.18|0.26|0.12|0.11|0.22|0.09|
|No. of parcels|6,584|2,337|1,227|976|1,191|853|
|<br>**Panel B: Survey information**|||||||
|<br>Titled land|0.21|0.37|0.12|0.09|0.17|0.07|
|If no, would like a title?|0.81|0.83|0.83|0.86|0.81|0.63|
|If yes, would like to pay for it?|0.95|0.96|0.96|0.94|0.95|0.91|
|<br>Willing to pay for title (ZMK/ha)|225.90|299.64|199.84|207.19|166.16|233.62|
|<br>No. of parcels (owned)|2,645|921|535|423|509|257|
|Crop output (ZMW/ha)|3,264|3,326|2,933|3,112|3,115|4,284|
|<br>Purch. inputs (ZMW/ha)|1,917|2,084|1,795|1,851|1,647|2,206|
|Family labor (d/ha)|85|68|80|96|101|109|
|Cultivated area (ha)|6.20|6.31|6.77|6.10|5.90|5.34|
|Maize|0.50|0.52|0.52|0.49|0.47|0.48|
|Sunflower|0.10|0.12|0.09|0.09|0.10|0.11|
|Groundnuts|0.13|0.12|0.14|0.14|0.11|0.15|
|Soybeans|0.19|0.16|0.16|0.21|0.24|0.19|
|<br>Cotton|0.04|0.02|0.04|0.05|0.06|0.04|
|Tobacco|0.01|0.02|0.02|0.01|0.01|0.01|
|Beans, sweet potato & others|0.00|0.04|0.04|0.04|0.00|0.02|
|<br>No. of parcels (cultivated)|2,314|821|453|379|444|217|

_Source:_ Own computation from 2020 Zambia Eastern Province Farm Survey 21
For a pollutant-specific ernissions charge p, conversion of (5.2) to an emissions response function is straightforward under the assumption of cost minimization. For the first of four pollutants, a cost-minimizing plant should equate pi to (dC/dE,), given the volume of wastewater and influent for pollutant 1. This yields the following emissions equation: <!-- Start of picture text --> (5.3) E1 = file ao] 1l plIA ,1I-l rl EIi fl 81<br>1 1 ~~~~i=2 i<br><!-- End of picture text --> In the following section, we use Equations (5.1) - (5.3) to analyze the cost-effectiveness of the current system.

# **6. ABATEMENT COSTS AND POLICY ALTERNATIVES aN CHINA**

# 6.1 Marginal Cost Variations by Sector, Scale and Abatement Rate

**In** environmental economics, assumed variation in plant-level marginal abatement cost schedules fuels the standard critique of regulation based on uniform end-of-pipe emissions standards. The received theory suggests that use of market-based instruments, such as emissions charges or tradable permits, will realize significant efficiency gains by confronting all polluters with an identical 'price of polluting' at the margin. Of course, the theory says nothing about the actual magnitude of variations in marginal abatement costs. In practice, these have to be large enough to convince policy makers that the efficiency benefits of market-based instruments will outweigh the costs of the transition to a new regulatory regime.

Our results suggest that the magnitudes are very large. In Table 6.1 we draw on Equation (5.2) to estimate marginal costs of abatement (in annual dollars/ton) by pollutant, for plants in five sectors (Food, Textiles, Paper, Oil Refining, and Chemicals) and three abatement size classes. The impact of these variables on marginal cost is dramatically illustrated by a few comparisons. Within sectors at constant abatment rates, the MAC ratio between small and large facilities can be as high as 30:1 (e.g., TSS and COD in Food Processing). Across sectors, MAC ratios at the same scale and abatement rate can be as high as 20:1 (e.g., large-scale Food Processing and Textiles for TSS). Finally, MAC ratios in the same sector and scale class can be as high as 15:1 for 90% and 10% abatement (e.g., BOD in small-scale Paper). The implication, confirmed in Section 6.4 below, is that very large savings could be realized in an emissions charge regime which equalized the marginal 'price of pollution' across plants.

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