rafmacalaba/datause-encoder-data huggingface dataset:
**tradItional academic secondary** school, vocational schools are simply not **cost effective.2/ Similarly,** a number of studies have shown vocational **schools to be a high** cost form of training for the skilled trades, in **relation to alternative** more closely job-based training modes, such as the **traditional apprenticeship.3/** So the place of vocational schooling within **both the schooling** and training systems is under attack.

We present this case study appraising the economic outcomes of **vocational secondary** schools within this context. It is felt that an **examination of the** Israel experience, where so much relative importance has **been accorded to** vocational schooling, should be of interest, both in its **own right and also** in adding to the roster of case studies round the world, **on these issues.** In Israel well over half of all secondary school pupils **(and over 40 per cent** of all youth of secondary school age) attend **vocational secondary** schools. Outside of the East European counties, where **national economic planning** dictates that the educational system at the **secondary level is dominated** by vocational schools, only a handful of **countries exhibit as large** a vocational secondary school sector.<sup>4</sup> / **Moreover, the proportion** of secondary school pupils attending vocational **schools has shown a secular,** and continuing, rise over time, thus departing from comparative international norms (Benavot, 1983).

2 This literature has been reviewed in Zymelman (1976), Psacharopoulos **(1987) and Tilak** (1988). Two recent World Bank studies of vocational **education in Peru** (Moock and Bellew, 1988) and the Ivory Coast (Grootaert, **1988) are confirmatory of these results.**

**3 For a discussion,** see Metcalf (1985).

**4 This small group of countries includes Belgium** and the **Netherlands, Argentina, and El** Salvador: **see** UNESCO year book.

-2-
firms disproportionate weight in aggregate GDP as well as the fact that we do not observe the firm in the year it disappears.

A common finding across our initial analysis is that foreign-owned firms have a higher likelihood of exit compared to domestically owned firms, while manufacturing firms also have a higher probability of exit relative to service sector firms. Given the deteriorating economic environment and increased political uncertainty during the period under consideration, which contributed towards the erosion of foreign and manufacturing firms previously privileged positions in the economy, these findings are in line with our expectations. Regarding the role of firm size in determining exit, we find a monotonic relationship, that is, larger firms are less likely to exit. This result supports the similar findings of Frazer (2005) and Soderbom et al. (2006) based on samples of manufacturing firms in Ghana, Kenya, and Tanzania. Finally, in our regressions we also find a flat baseline hazard (captured by the λs), which implies no duration dependence and suggests that we are adequately controlling for unobserved heterogeneity.

Table 2 presents the relationship of GDP growth and firm exit.<sup>21</sup> We find that, on average, a 1% increase in GDP growth lowers the likelihood of exit by 1.7%. In an effort to understand the business cycle role on firm survival, we use interaction terms to test for different impacts across sectors, size, and ownership. However, over our various specifications we do not find any differential effects, suggesting that economic growth broadly affects the private sector.

Table 3 considers separate regressions by industry in an effort to understand how robust the coefficients in Table 2 are.<sup>22</sup> In columns (1) and (2) we first consider the tradables sector firms and find that firms located in Abidjan face a higher probability of exit (around 27% higher), while foreign-owned firms also face a significantly higher likelihood of exit compared to domestically-owned firms. Micro and small-sized firms continue to face a significantly higher likelihood of exit compared to firms with at least 150 employees. We find that a 1% increase in GDP lowers the probability of exit by 2%, however, when we control for growth interaction effects (column 2), GDP growth is no longer significant. In columns (3) and (4) we consider manufacturing sector firms. Ownership and location do not have a significant effect on firm

> 21 The baseline hazard function is defined as domestically-owned firms with at least 150 employees, located outside of Abidjan, and operating in the service sector.

> 22 The baseline hazard function is defined as large firms, domestically owned, operating outside of Abidjan.

19
_Annex 2 Table 1: Food prices (ln) and program saturation in nationally representative data; Model with cutoff at saturation of 50 percent_

||Share of<br>food<br>expenditures|Saturation|Saturation*Above<br>cutoff|Above cutoff|N|<br>Saturation +<br>Saturation*Above<br>cutoff|
|---|---|---|---|---|---|---|
|Rice, raw regular|||||||
|milled|0.191|0.061 ***<br>(0.014)|0.088 *<br>(0.049)|-0.022<br>(0.030)|8370|<br>0.149 ***<br>(0.047)|
|Other raw rice and<br>grains|0.074|-0.030|-0.301|0.034|8555|<br>-0.332|
|||(0.111)|(0.371)|(0.228)||(0.351)|
|Roots and tubers|0.011|0.247 ***|0.357 *|-0.196|8487|<br>0.604 ***|
|||(0.055)|(0.197)|(0.120)||(0.187)|
|Eggs (fresh)|0.018|0.083 ***|0.185 ***|-0.094 **|8551|<br>0.268 ***|
|||(0.018)|(0.062)|(0.038)||(0.059)|
|Eggs (processed)|0.001|0.097 ***|0.135|-0.076|6449|<br>0.232|
|||(0.026)|(0.155)|(0.091)||(0.152)|
|Fish (fresh)|0.058|-0.016<br>(0.015)|0.090 *<br>(0.054)|-0.042<br>(0.033)|8530|<br>0.074<br>(0.051)|
|Fish (processed)|0.023|-0.075 *<br>(0.042)|0.162<br>(0.152)|-0.096<br>(0.092)|8479|<br>0.087<br>(0.144)|
|Meats (fresh)|0.035|0.002<br>(0.013)|0.053<br>(0.044)|-0.007<br>(0.027)|8510|<br>0.055<br>(0.042)|
|Meats (processed)|0.011|1.779 ***<br>(0.359)|-5.163 ***<br>(1.383)|2.781 ***<br>(0.836)|8242|<br>-3.384 **<br>(1.334)|
|Fruits (fresh)|0.021|-0.211 ***<br>(0.034)|0.275 **<br>(0.118)|-0.106<br>(0.072)|8512|<br>0.064<br>(0.111)|
|Vegetables (fresh)|0.046|0.030<br>(0.018)|0.180 ***<br>(0.062)|-0.061<br>(0.038)|8561|<br>0.210 ***<br>(0.058)|
|Rice and grains|||||||
|processed|0.021|0.169 ***<br>(0.054)|-0.509 ***<br>(0.180)|0.290 ***<br>(0.110)|8555|<br>-0.340 **<br>(0.170)|
|Coffee, cocoa, tea|0.025|0.093 **<br>(0.046)|-0.023<br>(0.156)|0.040<br>(0.096)|8559|<br>0.070<br>(0.148)|
|Sugar|0.016|-0.541 ***<br>(0.029)|0.390 ***<br>(0.097)|-0.163 ***<br>(0.060)|8529|<br>-0.151 *<br>(0.092)|
|Milk Products|0.007|0.140 **|-0.008|-0.095|8117|<br>0.132|
|||(0.063)|(0.237)|(0.144)||(0.227)|

note: *** p<0.01, ** p<0.05, * p<.1. Standard errors in parenthesis. Each row corresponds to a separate regression. Observations are at the village-food item-year level and pooled across the years 2009, 2012, and 2015. All models include year and village fixed effects. Prices are calculated as unit values, see text for details. Source: 2009, 2012, and 2015 Family Income and Expenditure Surveys and Department of Social Welfare Administrative Data on _Pantawid_ Program Enrollment 72
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Hogan, W. W. (1998). _Competitive Electricity Markets: a Wholesale Primer_ . Cambridge, Mass.

39
trajectory required for 8 percent real GDP growth (which translates to 7.2 percent per capita growth) over 2011-2015. The national savings rate has to increase to 41 percent by 2015 in order to deliver this growth rate.

# **Figure 6. Growth  Scenario 1**

TFP Growth 1.75 percent GDP Growth per Capita Target = 7.2% over 2011-2015 (a). (b).

<!-- Start of picture text --> 0.45 10.0%<br>0.40<br>0.35 8.0%<br>0.30<br>0.25 6.0%<br>0.20<br>4.0%<br>0.15<br>0.10<br>2.0%<br>0.05<br>0.00<br>0.0%<br>-0.05 2010 2015 2020 2025 2030<br>2010 2015 2020 2025 2030<br>National Saving Private Saving Public Saving Per Capita GDP Growth GDP growth rate<br><!-- End of picture text --> _Source_ : Hevia and Loayza (2011) How will the required savings rate of 41 percent for Sri Lanka be split between private and public savings? With private savings determined endogenously, most of the action will need to come from raising public savings, as shown in the figure. Public savings will need to increase from negative levels in 2010 to close to 20 percent of GDP in 2015 and eventually plateau around 23 percent of GDP. Going beyond 2015, the simulation assumes that the 41 percent national savings rate will be maintained into the future. However, as a consequence of diminishing marginal returns to capital, the growth rate of GDP declines to 4 percent by 2030 even if the savings rate of 41 percent is maintained. Indeed, to keep real GDP growth at 8 percent over the long run, the national savings would have to continuously rise above 41 percent. Box 5 provides an explanation for why the savings rate must keep rising to achieve a given growth target.

20
- [3] Arellano, M (1989) A Note on the<sup>Anderson-HsiaoEstimatorfor Panel Data.</sup> Economics Letters, 31: 337-341.

- [4] Ashton, JK (2000) Cost Efficiency in<sup>the UK's Water and SewerageIndustry',</sup> Applied Economics Letters, 7: 455-458.

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- [6] Baltagi BH (1995) Econometric Analysis<sup>of Panel Data.New-York: JohnWileyand</sup> Sons, 1995.

- [7] Baltagi BH, and Griffith JM (1988)<sup>A GeneralizedError Component Model with</sup> Heteroskedasticic Disturbances. International<sup>Economic Review29,745-753.</sup>

- [8] Battese, GE, and Coelli TJ (1988). Prediction of<sup>farm-leveltechnicalefficiencieswith</sup> a generalized frontier production and panel<sup>data.Journalof econometrics38, 387-</sup> 399.

- [9] Battese, GE, and Coelli, TJ (1992). Frontier production<sup>functions,technical</sup> efficiency and panel data: with application<sup>to paddyfarmersin India.Journal</sup>

- of productivity analysis 3, 153-169.

- [10] Bauer PW, Berger AN, Ferrier GO, and<sup>HumphreyDB (1998)Consistency</sup> Conditions for Regulatory Analysis of<sup>FinancialInstitutions:A Comparisonof</sup> Frontier Efficiency Methods. Journal of<sup>Economicand Business,50:85-114.</sup>

- [11] Bhattacharyya A, Harris TR, Narayanan R, and Rafflee<sup>K (1995)Technical</sup> Efficiency of Rural Water Utilities. Journal of Agricultural<sup>and Resource</sup> Economics 20, 373-391.

- [12] Bishop M and Thompson D (1992) Regulatory Reform<sup>and ProductivityGrowthin</sup> the UK's Public Utilities', Applied Economics, 24:<sup>1181-1190.</sup>

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18
# **Table A10:  Global E-Governance Index (EGV)**

**Data Provider** Brown University Center for Public Policy **Description** University located in Providence, United States **Website** www.insidepolitics.org **Data Source** Global E-Governance Index **Type** Expert assessment **Respondents** Research team led by Professor Darrell M. West **Frequency** Annual since 2002 **Coverage** Global sample of countries **Public Access** Yes **Description** This source reports an assessment of the quality of e-government based on reviews of official government websites.  Features assessed include online publications, online database, audio clips, video clips, non-native languages or foreign language translation, commercial advertising, premium fees, user payments, disability access, privacy policy, security features, presence of online services, number of different services, digital signatures, credit card payments, email address, comment form, automatic email updates, website personalization, personal digital assistant (PDA) access, and an English version of the website.  Assessments are scored on a 100-point scale with 72 points for availability of publications and databases and 28 points for the number of online services available.

||2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|Global E-governance Index|X|X|X|X|X|X|X|..|..|..|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Control of Corruption**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Country Coverage**|**196**|**196**|**196**|**195**|**192**|**195**|**194**|**..**|**..**|**..**|
|**Year of Publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

49
and remittances) may hinder access to more efficient resources like better seeds, sprayers or animals (i.e., oxen). By the same token, access to local infrastructure and public goods and capital can increase yields per hectare. Further, there is an important role played by agricultural extension services and technical advice on crop husbandry, land use, and general agricultural assistance that allow farmers to achieve higher productivity. A similar role can be attached to social capital and learning externalities.

We are not only interested in the direct determinants of agricultural productivity, but also in the impacts of the marketing reforms. There are several channels that can be advanced. The provision of credit and of inputs on loan, which may allow farmers to better combine factors of production, may depend on the phases of the reform. During the collapse of the outgrower scheme, credit became more expensive to farmers thus hindering productivity. When the scheme improved, credit became cheaper, probably causing productivity to increase. Similarly, the privatization of the ginning industry may cause firms to adopt better cotton seeds (with higher yields) and more efficient pesticides and fertilizers. This would work as technological advances that firms pass-through to farmers, leading to increases in farm productivity. Finally, the outgrower scheme involved an improvement in the provision of extension services, particularly in terms of information about marketing. This could help eliminate some uncertainty about the crop. In addition, more efficient extension services, providing advice on crop husbandry and know-how, can help farmers increase yields.

# 4 Data and Estimation Strategy

In this section, we describe the data and we develop the empirical model to estimate the impacts of the cotton marketing reforms on farm productivity.

## 4.1 The Post Harvest Survey

We use farm surveys called the Post Harvest Survey (PHS). These data are collected by the Zambian Central Statistical Office (CSO). The surveys are not panel data but rather a set of repeated cross-sections. We have annual data available for the period 1997-2002.

8
combination of factors, including exhuLastion of benefit eligibility, or an increase in unrecorded employment. Furthermore, many workers losing jobs fail to enter or re-enter the unemployment register, due to lack of eligibility for benefits, or because of access to eamings outside the wage labor sector. Further, available data on planned separations and hirings by firms in the LMFS also forecast a further drop in the rate of job destruction in 1994 (assuming the decision to close some of the huge loss-makers is further delayed) rather than a rise of the re-employment probability for the unemployed.

Given this environment, the stagnant pool characterization of unemployment is thus unlikely to change in the near future. And there are several other particularities that are important. They include, (i) a low exit probability. The 3.0/4.0% monthly exit rate would suggest an expected completed duration well in excess of two years in steady state.

(ii) Little or no feedback to wages, as indicated earlier in Table 9. A similar conclusion is suggested by the regional data given in Table 15. The partial correlation coefficients between the rate of change of industrial wages (taken from CSO statistics for the 20 counties) on the one hand, and the rate and change of unemployment, on the other, are differently signed. In other words, regional Phillips-curves, if they exist at all in Hungary, suggest a slightly slower wage increase where unemployment is rising, but no observable negative feedback from high unemployment to lower wage claims. With the close-to-zero parameters and low significance levels the correct conclusion might be that, in contrast to some other countries where correlations between regional unemployment and wages can be easily shown on simple scatters, Hungary as yet does not experience any such feedback 2.

(iii) Mismatch; the lack of feedback to wages may, to a great extent, be attributed to regional and skill mismatch. Fazekas (1993) has shown that the highest rates of unemployment are experienced in remote rural areas and highly industrialized, typically single-employer, micro-regions, where vacancies are locally minimal. Fazekas and Kollo (1994) in addition find, on the basis of the LME survey, that demand for retraining increases when the fimn has hard-to-fill vacancies, decreases if the local labor force has a higher educational level, but only slightly decreases when there is a higher local rate of :nemployment, after controlling for the 22 Blanchard et al (1994) 34
depends on parameters and aggregate variables and is common to all firms.

From equation 3, there are important aspects of this model to keep in mind. First, at any given point in time, _τ_<sup>_ρ_</sup> _exp_ ( _z_ ) is a summary statistic of the size of a firm. This observation will help solve for the general equilibrium. Second, that expression makes clear the role of the firm-specific wedge _τ_ : for values of _τ_ > 1 (a subsidy), the firm will be making profits higher than for the frictionless benchmark _τ_ = 1, while for values of _τ_ < 1 (a tax), the firm will generate lower profits than for the benchmark. In addition, conditional on their levels of productivity, firms with subsidies will be relatively larger than firms with firm-specific taxes.

# **3.3. Entry, Exit, and Dynamics**

In this model’s economy, there are exogenous and endogenous sources of firm exit. At the beginning of each period, every firm is exposed to an exogenous death shock with probability _δ_ and survives with probability 1 − _δ_ . Surviving firms can either choose to exit or continue to operate and pay a fixed cost of production _f_ . The size of a firm over time depends on idiosyncratic shocks and on two endogenous mechanisms: innovation and bribing. A firm that decides to keep operating can choose to spend resources (innovate) to improve its productivity or to engage in rent-seeking behavior through bribes to try to improve its firm-specific revenue tax or subsidy. In the following subsections, I describe these mechanisms individually.

## **3.3.1. Innovation**

Productivity in this model evolves as in Atkeson and Bursteinn (2010). The productivity of a firm evolves depending on the firm’s investment in R&D and on firm-specific shocks. A firm with state ( _z_ , _τ_ ) that decides to keep producing in period _t_ can invest _exp_ ( _z_ ) _cφ_ ( _φ_ ) (in labor units) in R&D in order to increase the likelihood of improving its productivity in period _t_ + 1. In particular, if a firm spends _exp_ ( _z_ ) _cφ_ ( _φ_ ), it has a probability _φ_ of increasing its productivity to _z_ + Δ _z_ and a probability 1 − φ of decreasing its productivity to _z_ − Δ _z_ , where Δ _z_ is the step size of the productivity support. I assume that the function _cφ_ ( _φ_ ) is convex in _φ_ .

15
Table 1.8. -Turkey: Registered Unemoloyed by Age Grouns* (percents of total number)

||Age group|
|---|---|
|Year|14-19<br>14-24|
|1986|13.2<br>55.1|
|1985|14.0<br>54.4|
|1984|14.7<br>55.0|
|1983|14.5<br>53.9|
|1982|14.9<br>54.5|
|1981|16.4<br>53.2|
|1980|19.1<br>55.3|
|1979|17.7<br>53.8|

* At placement offices at end of year Source: Yearbook of Labor Statistics, ILO.

_io_