rafmacalaba/datause-encoder-data huggingface dataset:
<mark>currencies. So, Ethiopia would likely face such retaliatory measures from its trade partners only when it reaches a higher stage of development and claims a more prominent role in manufacturing worldwide.</mark>

# **<mark>References</mark>**

Abed, G. T., Erbas, S. N, and Guerami, B. (2003). The GCC monetary union: Some considerations for the exchange rate regime. IMF Working Paper No. 66. Washington, D.C.: IMF.

Amsden, A. (2001). The rise of “the Rest”: Challenges to the West from late industrializing economies. Oxford University Press: Oxford.

Argy, V. (1990). Choice of exchange rate regime for a smaller economy: A survey of some key issues. In Choosing an Exchange Rate Regime: The Challenge for Smaller Industrial Countries, Ed. Victor Argy and Paul De Grauwe. Washington, D.C.: IMF.

- Berg, A., and Miao, Y. (2014). The real exchange rate and growth revisited: The Washington consensus strikes back? IMF Working Paper No. 57. Washington, D.C.: IMF.

Berg, A., Ostry, J. D., Zettelmeyer, J. (2012). What makes growth sustained? _Journal of Development Economics_ , 98(2): 149-166.

- Bhalla, S. S. (2008a). Economic development and the role of currency undervaluation. _Cato Journal_ 28(2): 313-40.

Bhalla, S. S. (2008b). Second among Equals: The middle class kingdoms of India and China. Washington: Peterson Institute of International Economics (June).

Bahmani-Oskooee, M., and Ratha, A. (2004). The J-curve: A literature review. _Applied Economics_ , 36:13771398.

Black, S. (1976). Exchange policies for less developed countries in a world of floating rates. _Essays in International Finance_ 119, Princeton University.

- Dollar, D. (1992). Outward-oriented developing economies really do grow more rapidly: Evidence from 95 LDCs, 1976–1985. _Economic Development and Cultural Change_ , 40(3): 523-544.

Dollar, D., and Kraay, A. (2003). Institutions, trade, and growth. _Journal of Monetary Economics_ , 50(1): 133-162.

Doornik, J. A., Hendry, D. F., and Pretis, F. (2013). Step-Indicator Saturation. Discussion Paper No. 658, University of Oxford.

- Dominguez, J. (2008). Explaining Latin America’s lagging development in the second half of the twentieth century: Growth, strategies, inequality and economic crises. In _Falling Behind_ , ed. Francis Fukuyuma. New York: Oxford University Press.

- Dorosh, P., Robinson, S., and Ahmed, H. (2009). Economic implications of foreign exchange rationing in Ethiopia. ESSP Discussion Paper 9. Addis Ababa, Ethiopia: International Food Policy Research Institute.

- Easterly, W., and Levine, R. (1997). Africa’s growth tragedy: Policies and ethnic divisions. _Quarterly Journal of Economics_ , 10, 1203-1250.

- Easterly, W. 2005. National policies and economic growth: A reappraisal. In _Handbook of Economic Growth,_ Ed. Philippe Aghion and Steven Durlauf. Amsterdam: Elsevier.

- Eichengreen, B. (2008). The real exchange rate and economic growth. Working Paper No. 4, World Bank PREM Network, Commission on Growth and Development.

- Eichengreen, B., and Gupta, P. (2011). The real exchange rate and export growth: Are services different? Policy Research Working Paper No. 6629, Washington, D.C.: World Bank.

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and demand will respond to price changes—even if the short-run elasticities are low, which would make prices stationary. If prices are stationary, however, it is optimal to maximize extraction when prices are high and to minimize it when prices are low. This would cause government revenues to be more volatile than price; if at the same time consumption is to stay largely unaffected, in accordance with the PIH, fluctuations in revenues should be absorbed largely by capital expenditure and the fiscal balance.

The theory of optimal resource abstraction assumes that the resources in the ground are finite and known. This is very much a matter of debate, particularly in relation to oil, where new technology such as fracking has made more reserves accessible.

In terms of assessing fiscal performance, a natural test is whether resource wealth is associated with higher government saving or, in other words, whether there is less government consumption expenditure relative to revenue, and whether the saving takes the form of a stronger fiscal balance or higher capital expenditure. This is the issue that we address here. A second aspect of the resource curse issue, which we do not discuss here, is the possibility that even when the revenues are largely saved, they may nevertheless be wasted—for example, in ill-conceived or inefficiently implemented investment projects.

# **3. Resource Wealth and Fiscal Performance**

In this section we examine the correlation between resource wealth and a number of fiscal variables, using annual data for low- and middle-income countries back to 1996. Fiscal data are scarce before that date, so the sample can be extended further back for only a very few countries.

We capture resource wealth by the share of exports represented by fuel or ores and metals, transformed into dummy variables that are equal to 1 if the export share is above a threshold level. For fuels, we use a threshold of 25 percent; for ores and metals, the threshold is 10 percent. Each of these dummy variables is equal to 1 for just over 20 percent of the sample, but it is rare for both of these dummies to be 1 (2.5 percent of the sample). Using dummy variables for export shares exceeding a certain threshold rather than the export shares themselves focuses the analysis on the differences between resource-rich and resource-poor countries, rather than on the degree of resource richness. This binary approach eliminates the variation among resource-rich countries, but it has the important advantage that it prevents the results from being dominated by a few extreme cases where practically 100% of exports consist of fuel.

4
# **4 Econometric Results**

# **_City-Level Outcomes_**

Table 5 reports the city upgrading effects on city-level outcomes, especially government activities since the upgrading. Compared with a fixed effects model by Fan, Li, & Zhang (2012), the propensity score model with or without year fixed effects yields similar results. Thus, we confirm the findings in Fan, Li, & Zhang (2012) that government spending share in productive activities (as defined by spending in construction and providing support to agricultural production) decreases following upgrading. The share of agricultural tax in total tax also falls – this is intuitive since cities began to shift away from agricultural production.

In addition, newly-upgraded cities do not necessarily outperform similar counties in terms of rates of GDP growth in the post-upgrade period of 1998-2004. Similar to Fan, Li, & Zhang’s (2012) fixed effects model, findings using a propensity score matching method suggest that at the countylevel, upgrading does not necessarily lead to higher growth.

We also try and identify the rate of new firm entrants in newly-promoted counties compared to the counterfactual counties that were not promoted. We find that there is a significant difference between the two, suggesting that new firms favor cities to counties, even if the former are only recently promoted. Correspondingly, tax from business income for cities has increased more significantly than their similar counties.

**Table 5:  City-level outcome of city upgrading using PSM**

||**PS Model**|**PS + Fixed**<br>**Effects**|**Fan, Li, &**<br>**Zhang 2012**|
|---|---|---|---|
|_City-level Govt. Activities Outcome_||||
|# of public employees|414.9**|407.5**|995***|
|Share of productive expenditure|-0.00577**|-0.00575**|-0.026***|
|Share of agriculture tax|-0.0586***|-0.0588***|-0.053***|
|Post-upgrade average GDP growth|-0.000693|-0.000797|-|
|# of firm births|4.371***|4.844***|-|
|Logtax from business income|0.515***|0.517***|-|
|Controls|Block FE|Block FE|County FE|
|||Year FE|Year FE|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. The pscore model uses variables of the three upgrading requirements and their interactions to generate propensity score.

10
**_Perceived discrimination._** In a number of countries, estimated proportion of informal employees who consider themselves belonging to a discriminated group, is well above similar proportion among both formal employees and informal self-employed (Figure 15), once again supporting the hypothesis that exclusion or poverty escape motive plays an important role in the way how employees end up working without a contract. Table 7 provides evidence that within European regions the differences between informal and formal employees are statistically significant (at the country level the relevant sub-samples are too small).

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>LV BG RU SI EE SK PL CZ HU RO UA FI SE NO DK ES GR IL PT CY IT FR UK DE NL BE CH AT IE<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% belonging to a discriminated group<br><!-- End of picture text -->

**Figure 15 Perceived discrimination among formal and informal workers, by country. 2004-2009**

_Note:_ The sample size of employees without contracts is less than 30 for IT, SE and FI; between 50 and 90 for CZ, RO, SK, HU, EE, BE, FR, DE and CH; between 100 and 745 for other countries. _Source:_ Calculation with ESS data

**_Union experience._** Another indicator which helps to understand where the informal workers are coming from is trade-union membership (current or past).  Informal workers with union experience have clearly been working in the formal sector some time ago (and those with current membership probably plan to return). Figure 16 compares  union coverage (current or past) among formal and informal workers. It appears that transition from formal to informal sector is quite common in Nordic countries, Cyprus, Israel, Belgium, Ireland, UK, Germany, and Austria, where from 35% to 80% of informal employees (and, except for Cyprus, similar fractions for informal self-employed) come from the unionized sector. In Israel, Greece, Portugal, and France these proportions are lower (around 20%), but still similar to the ones found among formal workers, again suggesting that the two sectors are not isolated from each other.

35
agricultural employment measured by the number of male persons employed in agriculture as wage . rkers and the nonfarm employment measured by the total (male and female) persons employed in rural nonfarm activities. In the census employment is measured by occupational status of main workers, i.e., individuals are asked whether they worked in agriculture or nonagriculture for atleast 183 days in the last year prior to the census period. The reason for including female labor in rural nonfarm activities is that women are more active in nonfarm than farm activities. Rural nonfarm activities include activities such as mining and quarrying, manufacturing, processing, servicing, and repairs, construction, trade and commerce, transport, storage and communications, and other services. The crop and wage data are drawn from the data series for 85 districts covering a period of 21 years from 1961 to 1981 used for another study (Binswanger, Khandker and Rosenzweig, 1988). However, because of lack of comparable employment definition used in the census, the employment data from 1961 populatioa census could not be included. The number of districts covered in this paper is 85 which are randomly selected from 13 states of India.

The price variables are the aggregate crop price index and fertilizer price index. The price indices are deflated by the consumer price index for agricultural workers using 1975 as the base year. The infrastructure (physical, financial and human) variables include the government irrigation (i.e., area irrigated by canal and tank), the number of regulated markets, the number of rural and semi-urban commercial bank branches, the number of villages electrified, the number of villages with primary school and the road length. All the infrastructure variables are normplized by the district's total geographic area. The persistent timeinvariant agroclimatic endowments and locational factors are the length of
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking road indicators, 2006**

||Unit|Low-income<br>countries|Tanzania|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km<sup>2</sup><br>of arable land|86.6|47.1|507.4|
|Unpaved road density|km/1000 km<sup>2</sup><br>of arable land|504.7|482.6|1,038.3|
|GIS rural accessibility|% of rural pop within 2 km from all-season<br>road|21.7|24.0|59.9|
|Paved road traffic|Average Annual<br>Daily Traffic|1,049.6|1,797|2,786.0|
|Unpaved road traffic|Average Annual<br>Daily Traffic|62.6|99.8|12.0|
|Paved network condition|% in good or fair condition|80.0|94.7|79.0|
|Unpaved network condition|% in good or fair condition|57.6|69.1|58.3|
|Perceived transport quality|% firms identifying as major business<br>constraint|23.0|14.1|10.7|
|Overengineered network|% of total network|26.0|22.0|20.0|

_Source:_ Gwilliam and others 2009. Derived from AICD database, http://www.infrastructureafrica.org/aicd/tools/data.

**Figure 8. Benchmarking the fuel levy, 2006**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>optimal levy for actual fuel levy optimal levy for implicit fuel levy<br>maintenance plus maintenance<br>rehabilitation<br>US$ cents per liter<br><!-- End of picture text --> _Source:_ Gwilliam and others 2009.

16
Figure 1 <!-- Start of picture text --> Change in General Govemment Expenditures<br>in Transition Countries<br>*0 1988-89 0 1993-94<br>70<br>60<br>50<br>40<br>30<br>20<br>oi3 I c ) = C |0 ~ 0 00000 0 1, 02,1 0 ;0 1 C C<br>10<br>20 | - 0 n - a | l |<br>0 10 - rJ . : C C- IL - 0| 0<br>22 ~~~~~~~~~~ 2 :25' c C E ~~~~ ~. 2<br>E 0~~~~~~~~<br><!-- End of picture text -->

Notes: 1. Within each group countries are sorted according to their level of government expenditure in 1993-94. 2. The figures for 1988-89 assign to currently existing countries the averages ofthe their former federal states.

# A Typology of Changing Expenditures

There are multiple reasons for the changing patterns, reflecting just as much successful reforms in the economic role of govemments as well lack of restructuring in its social-security responsibilities, or, simply, a drastic fall in government revenues. While we are aware of all the risks associated with sweeping generalizations, we offer a four-fold classification of the typology of changes in public expenditure in RESEs, based on an assessment of their reforming of major spending programs, particularly those that proxy the level of government economic interventionism and the role of government in social and welfare sectors.

6
exaggerated due to artificially depressed current water tariffs, the difference is significantly large that piped water would continue to remain attractive, even if water tariffs increased substantially. In the case of propane, the comparison is not so favorable, with a weekly value of time savings of around Q.9 (US$1.25), compared with a typical weekly energy bill of around Q.18.

**Table 5.4 Time allocation and wood collection**

(Number of minutes spent yesterday in each activity among those who spent time on them)

|||Ur<br>|ban<br>|||Ru<br>|ral<br>||
|---|---|---|---|---|---|---|---|---|
||Fem|ale|M|ale|Fem|ale|M|ale|
||7-15|>15|7-15|>15|7-15|>15|7-15|>15|
|**Paid work**|||||||||
|Did not collect|33|167|34|360|19|85|55|342|
|Collected|0|82|50|275|9|47|34|263|
|Difference|***|***||***|*|***|**|***|
|**Unpaid work**|||||||||
|Did not collect|39|57|49|81|75|74|172|159|
|Collected|48|43|103<br>|164<br>|75|82|165|216<br>|
|Difference|||*|**||||***|
|**Study**|||||||||
|Did not collect|312|314|296|299|302|282|311|296|
|Collected<br>Difference|382<br>**|368|302|178<br>**|285|313|299|243|
|**Housework**|||||||||
|Did not collect|136|373|44|58|194|448|34|33|
|Collected<br>Difference|263<br>***|416|48|73|251<br>***|466|62<br>***|43<br>***|
|**Errands/shopping**|||||||||
|Did not collect|10|23|9|12|7|15|6|10|
|Collected|34<br>**|29|6|14|10|20|9|11|
|Difference|||||||||
|**Leisure and other**|||||||||
|Did not collect|913|750|951|778|916|763|945|799|
|Collected|781|658|940|777|846|709|903|776|
|Difference|***|***|||***|***|**|**|

_Notes:_ Means of those who collected and did not collect wood are significantly different at: *** 99%, ** 95% and * 90%. Minutes spent collecting wood among those who did it. The symbols describe cells that are significantly different from the one to the right at:<sup>^^^</sup> 99%,<sup>^^</sup> 95%,<sup>^</sup> 90%.

_Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala Those devoting such significant amounts of time to wood and water collection activities must presumably do so at the expense of other activities. It is therefore interesting to explore which types of activities households who engage in wood and water collection (Tables 5.4 and 5.5) curtail. Paid work and leisure (including sleep) seem to be the activities that are cut back the most in order to accommodate wood and water collection. Interestingly, the amount of time devoted to study by children who do and do not engage in these activities is not significantly different in most cases.

39
|Crops with purchased seeds (number of crops)|-.237*|-.133|-.279**|.261|
|---|---|---|---|---|
||(.136)|(.144)|(.111)|(.159)|
|Fertilizer or pesticides (=1)|-.007|.023|.019|-.052|
||(.018)|(.036)|(.031)|(.046)|
|Number of crops grown by the household|-.075|.077|-.152|.622**|
||(.251)|(.269)|(.182)|(.259)|
|Marketable crop farming (=1)|.102**|-.022|.178***|.036|
||(.042)|(.054)|(.040)|(.049)|
|Marketable crop selling (=1)|.111***|-.022|.201***|.007|
||(.037)|(.040)|(.040)|(.045)|
|Tree farming (=1)|.021|-.028|.034|.043|
||(.041)|(.058)|(.024)|(.039)|
|**Panel C: Acces**|**s to extension**<br>|**services**<br>|||
||Round 1 x<br>RCBP|Round 1 x RCBP x<br>Femalehead|Round 2 x<br>RCBP|Round 2 x RCBP x<br>Femalehead|
|Contact with extension officer for advice or information (=1, head)|-.005|.074|.005|-.009|
||(.030)|(.057)|(.016)|(.012)|
|Contact with extension officer was regular (=1, head)|.057|.072|-.014|.005|
||(.045)|(.066)|(.018)|(.030)|
|Rated DA advice very useful (=1, head)|.131***|.121|-.063|-.094|
||(.048)|(.093)|(.047)|(.076)|
|Received advice from peer (=1, head)|-.026|.094|.057|.004|
||(.042)|(.060)|(.050)|(.072)|
|Received advice from contact farmers (=1, head)|.116**|-.020|.010|.041|
||(.050)|(.058)|(.051)|(.071)|
|Received advice from extension officers (=1, head)|-.017|.078|-.005|-.039|
||(.027)|(.055)|(.019)|(.049)|
|Received advice from an NGO (=1, head)|-.018|-.029|.042**|-.018|
||(.018)|(.026)|(.020)|(.032)|

Notes: Standard errors in parentheses, clustered at the kebele level. * indicates significance at 10% level, ** at 5% level, *** at 1% level. Each row shows the coefficients of the regression of the outcomes in the left-hand side column on interactions of treatment and time (Round 1 x RCBP, Round 2 x RCBP), and the interaction of the time indicator, treatment variable and sex of the household head (Round 1 x RCBP x Female head, Round 2 x RCBP x Female head). All regressions control for region, time period, and the following contemporaneous household variables: presence of a female spouse of the head, household size, number of men in the household, number of elderly in the household, number of adults, number of adolescents, number of children, dependency ratio, number of literate household members, number of students in household, number of rooms in the dwelling, roof material is iron sheets. Outcomes shown in Panel C and "Tried new agricultural activities in the past year" are individual respondent level variables, with only household heads included in the sample.

21
Several attempts have been made to evaluate technical education programs in Mexico.<sup>3</sup> Using a rigorous impact evaluation method, this paper re-examines the performance and evolution of the College of Professional Technical Education (CONALEP) system. CONALEP is the backbone of Mexico's skills training structure and has become the most important government technical education system.

This paper is organized as follows. Section II reviews briefly the Mexican labor market. Section III describes the Technical Education System in Mexico and the place of CONALEP within this system. Section IV discusses the evolution of the CONALEP system. Section V reviews CONALEP's past evaluations. Section V also introduces the CONALEP graduate tracer survey, the National Employmept Survey (ENE), and the National Employment, Training, and Salary Survey (ENECE) used in this study. Section VI discusses the CONALEP benefit results compared to a well-designed control group. Section VII discusses the CONALEP benefits of the reformed program (the introduction of the modular course, among others). Section VIII presents the cost-benefit analysis. Section IX offers conclusions. The annexes at the end of this paper include the most relevant quantitative results that support the paper's findings.

# H. **The Labor Market**

Crisis and change have marked the past twenty years of Mexico's economic development. Many crises have had important impacts on labor markets. In the early 1980s, Technological Education (COSNET). This Council applies other tests in the SEIT schools to measure students' formal reasoning and the ability to learn mathematics. In addition, each institution designs its own proficiency examination. The "technological" area uses as criteria 7 points in the learning examination (in a 0 to 10 scale), a minimum of 18 correct answers out of 32 in the over-all knowledge examination and 12 correct answers out of 24 in the test to assess capacity for learning mathematics. SEP, in the "Informe de Labores 1997-1998," reports that 234,925 students took this exam. Of them 3,231 (1.3 percent) were rejected from upper-secondary education, not having the knowledge and capacities for entry requested by the educational institutions.

> 3 See World Bank, 1997, _Mexico: Training Assessment Study._ Carnoy B. et. al., 2000, "Aprendiendo a trabajar: Una revisi6n del Colegio Nacional de Educaci6n Profesional Tecnica y del Sistema de Universidades Tecnol6gicas de Mexico."

6
2006, with the target of reaching 100 schools. The collection of information was done directly at the school in an unannounced visit, and questionnaires were administered to students in grades three through eleven, their math and Spanish teachers, and the school’s principal.

The survey company, SEI (Sistemas Especializados de Informacion), was able to gather the information from 97 schools: three schools (two treatment and one control) declined to participate in the data collection activities. Because the follow-up survey was completed two years later, we focus on students who were in grades through nine at baseline – students in grades ten and eleven at baseline would have either dropped out or graduated by the time the follow-up survey was administered. The first three columns of Table 1 contain a tabulation of these schools by research group. In total, the distribution of schools and students is even. The sample contains forty-nine control schools and forty-eight treatment schools, and 3,889 control students and 4,327 treatment students in grades three to nine. Dividing the sample by gender and grade shows a similarly even distribution of students. Across all of the groups, the largest difference is the 182 additional male students in the treatment group.

During May and June of 2008, the same survey company resurveyed the same 97 schools, focusing on the students who were in grades three through nine in 2006. The final sample comprises 5,201 students that were found at follow up; 3,015 students were attritors, yielding an attrition rate of 37 percent. Discussions with local principals suggest that the high rate of attrition is primarily due to high rates of migration in the rural areas chosen for the research project. A high attrition rate alone does not violate the internal validity of a study as long as the types of students who attrit are similar in each research

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