rafmacalaba/datause-encoder-data huggingface dataset:
# **V. Transit Subsidies**

# **A.  Public Buses**

As suggested in Table 1, the Transport division of BEST has historically operated at a loss. In fact, as illustrated in Figure 2, revenues have not covered operating costs for any of the years pictured. The fare subsidy shown in Figure 2 ranges from 12 to 30 percent for the last fifteen years. Parts of BEST’s transport losses are covered by the profits made by its electricity supply division. However, borrowing and government subsidies have been needed since, as Appendix Table A.4 indicates, the undertaking has operated at a net loss for the past three years. In computing bus subsidies we use figures from 2005-2006, which imply that fares would have to rise 29.6% to cover costs.

**<u>Figure 2.  Costs, Revenues and Subsidies, BEST, Selected Years</u>**

<!-- Start of picture text --> 12000 40<br>35<br>10000<br>30<br>8000<br>25<br>6000 20<br>15<br>4000<br>10<br>2000<br>5<br>0 0<br>Depreciation (Rs. Million)<br>Operating Expenses Not Covered by Earnings (Rs. Million)<br>Operating Expenses Covered by Earnings (Rs. Million)<br>Subsidy (%)<br>1989-901990-911991-921992-931993-941994-951995-961996-971997-981998-991999-002000-012005-06<br>Percent<br>Rs. Million<br><!-- End of picture text --> 13
PRE Working Paner Series

||||Aut{rfor|Contact<br>1apar|
|---|---|---|---|---|
|WPS568|Higher Wages for Relief Work Can<br>Make Many of the Poor Worse Off:<br>Recent Evidence from Maharashtra'<br>'EmploymentGuarantee Scheme'|Martin Ravallion<br>Gaurav Datt<br>s Shubham Chaudhuri|January 1991|C. Spooner<br>30464|
|WPS569|Domestic Purchase Requirements for<br>Import License Allocations in Mali|Wendy E. Takacs|January 1991|S. Fallon<br>37947|
|WPS570|Debt Concentration and Secondary<br>Market Prices|Raquel Fernandez<br>**Sijle**Ozler|January 1991|S. King-Watson<br>31047|
|WPS571|Credit's Effect on Productivity in<br>Chinese Agriculture: A<br>Microeconomic Model of<br>Disequilibrium|Gershon Feder<br>Lawrence J. Lau<br>Justin Y. Lin<br>Xiaopeng Luo|January 1991|C. Spooner<br>30464|
|WPS572|Capital Positions of Japanese<br>Banks|Edward J. Kane<br>Haluk Unal<br>Asli Damirguc-Kunt|January 1991|S. King-Watson<br>33730|
|WPS573|Malaysian Labor Markets Under<br>Structural Adjustment|Dipak Mazumdar|January 1991|M. Schreier<br>36432|
|WPS574|Public Policies and Saving in<br>Developing Countries|Vittorio Corbo<br>Klaus Schmidt-Hebbsl|January 1991|S. Jonnakuty<br>39074|
|WPS575|Household Saving in Developing<br>Countries: First Cross-Country<br>Evidence|Klaus Schmidt-Hebbel<br>Sleven B. Webb<br>Giancarlo Corsetti|January 1991|E. Khine<br>39361|
|WPS576|Lessons from Tax Reform:<br>An Overview|Wayne Thirsk|January 1991|A. Bhalla<br>37699|
|WPS577|Africa's Rising Inflation: Causes,<br>Consequences, and Cures|Ajay Chhibber|February 1991|B. Chomun<br>39413|
|WPS578|The Bank's Use of Technical<br>Assistance for Institutional<br>Development|Beatrice Buyck|January 1991|E. Madrona<br>37496|
|WPS579|Chile's Experience with Stabilization<br>Revisited|Vittorio Corbo<br>Andr6s Solimano|January 1991|E. Khine<br>39361|
|WPS580|Do Natural Resource-Based<br>Industrialization Strategies Convey<br>Important (Unrecognized) Price<br>Benefits for Commodity-Exporting<br>Developing Countries?|Alexander J. Yeats|January 1991|J. Jacobson<br>33710|
|**Table 5B: Im**|**pact of SO**<br>|**FES on jobs on**<br>|**the side, Maste**<br>|**r students, SOF**|**ES data**<br>||
|---|---|---|---|---|---|---|
||N|<RD|>RD||PROBIT||
|DS=0.08|(1)|(2)|(3)|(4)|(5)|(6)|
|±0.01|86|0.333|0.522|0.188|0.089|0.077|
|||||(0.120)|(0.170)|(0.213)|
|±0.02|154|0.344|0.623|<br>0.279<sup>***</sup>|<br>0.289<sup>***</sup>|<br>0.312<sup>***</sup>|
|||||(0.079)|(0.097)|(0.101)|
|±0.03|211|0.326|0.618|<br>0.292<sup>***</sup>|<br>0.332<sup>***</sup>|<br>0.332<sup>***</sup>|
|||||(0.069)|(0.084)|(0.088)|
|±0.04|297|0.356|0.532|<br>0.176<sup>***</sup><br>(0.059)|<br>0.207<sup>***</sup><br>(0.074)|<br>0.187<sup>**</sup><br>(0.077)|
|DS=0.15|||||||
|±0.01|65|0.518|0.556|0.038|-0.186|-0.019|
|||||(0.179)|(0.250)|(0.266)|
|±0.02|86|0.500|0.500|0.000|0.013|-0.020|
|||||(0.128)|(0.160)|(0.182)|
|±0.03|130|0.512|0.521|<br>0.009|<br>-0.007|<br>-0.067|
|||||(0.091)|(0.114)|(0.129)|
|±0.04|173|0.461|0.500|0.039<br>(0.080)|0.026<br>(0.098)|-0.036<br>(0.106)|
|DS=0.30|||||||
|±0.02|34|0.720|0.667|-0.053<br>(0.181)|||
|±0.03|43|0.667|0.615|-0.051|||
|||||(0.160)|||
|±0.04|48|0.706|0.643|<br>-0.063<br>(0.150)|||
|_Controls_|||||||
|Socio-ec.||||No|Yes|Yes|
|background|||||||
|Educational||||No|No|Yes|
|background|||||||

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> The results from the econometric analysis based on the survey data are presented in Table 6. For other factors than the treatment effect, the coefficient on student’s family income is very small and insignificant. The student’s age has a positive effect on the probability of having a job on the side. A one year older student has a 2.3%-point higher probability of work. Students in Economics and Administration (Area II) and Engineering and Natural Sciences (Area III) less frequently take part-time jobs.

26
|**Table 2: Households’ status in th**<br>|**e semi-form**<br>|**al credit m**<br>|**arket by**<br>|**rationing**|**mechanism**<br>|||||
|---|---|---|---|---|---|---|---|---|---|
|**Rationing mechanism**|**En**|**tire sampl**|**e**||**Gojam**|||**Wollo**||
||**Total**|**Appli**|**ed?**|**Total**|**Appli**|**ed?**|**Total**|**Appli**|**ed?**|
|||**No**|**Yes**||**No**|**Yes**||**No**|**Yes**|
|Percentage share||77.06|22.94||73.41|26.59||80.26|19.74|
|**Type of rationing**||||||||||
|Price rationed/unconstrained|0.33|0.22|0.72|0.31|0.18|0.66|0.35|0.25|0.79|
|Risk rationed|0.46|0.60||0.45|0.61||0.47|0.58||
|Quantity rationed<sup>a</sup>|0.14|0.11|0.21|0.17|0.13|0.28|0.11|0.10|0.12|
|Transaction cost rationed<br>|0.02|0.02||0.02|0.03||0.02|0.02||
|Uncategorized, yet constrained<sup>b</sup>|0.05|0.05|0.07|0.05|0.05|0.06|0.06|0.05|0.09|
|Number of households|1587|1223|364|741|544|197|846|679|167|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07. **Note:**<sup>a</sup> For applicants, quantity rationed households include those whose applications were partially approved or fully rejected. Of the partially approved ones, only 16% (12 out of 75), 14.6% (8 out of 55), and 20% (4 out of 20) were quantity rationed in the pooled, Gojjam and Wollo samples, respectively. As shown in table 1, very few loans in the sample were fully rejected. b Uncategorized applicants are those who received the full amount they requested but still wanted to borrow more at prevailing loan terms.

17
TABLE 13: TRAINER CHARACTERISTICS ON STUDENT PSYCHOLOGY (ERROR COMPETENCE) _y = Error competence score of students after training_

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|||Error<br>competence<br>(1-5)|Error<br>competence<br>(1-5)|Error<br>competence<br>(1-5)|Error<br>competence<br>(1-5)|Error<br>competence<br>(1-5)|
|**phics**|Age|-0.00540*<br>(0.00300)|-0.00145<br>(0.00264)|-0.00150<br>(0.00710)|0.00304<br>(0.0109)|-0.00229<br>(0.0127)|
|**ra**|Female|0.117*|0.0805|0.183|0.143|0.431**|
|**mog**||(0.0662)|(0.0586)|(0.116)|(0.124)|(0.169)|
|**De**|Years at school|-0.0175|-0.00515|-0.0292|-0.0346|-0.0669**|
|||(0.0107)|(0.0131)|(0.0183)|(0.0215)|(0.0273)|
|**preneur**<br>**Model**|Ever been a business||0.200***|0.142**|0.200***|0.341***|
|**Entre**<br>**Role**|owner||(0.0656)|(0.0590)|(0.0630)|(0.0963)|
|**e skills**|Digitspan score|||0.0137<br>(0.0245)|0.0282<br>(0.0402)|0.0212<br>(0.0469)|
|<br>**itiv**|Raven test score|||-0.0434**|-0.0257|-0.0348|
|stics<br>**ogn**||||(0.0183)|(0.0158)|(0.0271)|
|teri<br>**n-c**|Personal intitative|||-0.0935|-0.182*|-0.147|
|arac<br>**no**||||(0.113)|(0.0990)|(0.181)|
|Ch<br>**and**|Error competence|||0.173|0.166|0.145|
|iner<br>**ve**||||(0.134)|(0.185)|(0.166)|
|Tra<br>**Cogniti**|Prosocial orientation|||0.153<br>(0.113)|0.146<br>(0.164)|0.521<br>(0.379)|
||How satisfied are you||||-0.0107|-0.0490|
|**tion**|with your job?||||(0.0547)|(0.0525)|
|**atisfac**|Given a choice, would<br>still be a teacher||||-0.0629*<br>(0.0328)|-0.101***<br>(0.0364)|
|**Job s**|Career satisfaction||||0.0767**<br>(0.0357)|0.0583<br>(0.0367)|
|**VET**|Years as teacher|||||0.0263<br>(0.0172)|
|**ars at T**<br>**college**|Years at TVET Colleges|||||0.0221<br>(0.0199)|
|**Ye**|Perceived|||||-0.0212|
||entrepreneurial skills|||||(0.0850)|
||Average score for|4.432|4.432|4.432|4.432|4.432|
||students at baseline|(0.0270)|(0.0270)|(0.0270)|(0.0270)|(0.0270)|
||Observations|564|564|564|564|564|

_Notes:_

* significant at 10% level ** significant at 5% level *** significant at 1% level

> (1) OLS regressions sequentially add explanatory variables from columns (1) to (5)
# **What Drives Firm Productivity Growth?**

Paloma Anos-Casero* and Charles Udomsaph** World Bank Keywords: total factor productivity, business environment, transition economies JEL Codes: D24, O12, P27

*Paloma Anos-Casero is Senior Economist at the World Bank (email: <u>panoscasero@worldbank.org). Charles Udomsaph is consultant (STC) at the World Bank (email:</u> cudompsaph@worldbank.org)
Policy Research Working Paper 6013

# **Abstract**

Once the current political crisis in Yemen has been resolved, it will be ever more urgent to speed up progress, including Millennium Development Goal (MDG) achievements. Drawing on simulations with the Maquette for MDG Simulations (MAMS), a model for strategy analysis, and a linked microsimulation model, this paper addresses Yemen’s MDG challenges. A first simulation set considers scaled-up government actions with the aim of fully achieving the 2015 international MDG targets with required additional financing from foreign or domestic sources. The main finding is sobering but not surprising: given the required expansion of MDG—related services, on-time achievement of key MDG targets does not appear to have been a realistic objective even if the government, hypothetically, would have expanded services with grant aid financing starting from 2005; macroeconomic stability, government efficiency, and the production of tradables would all have suffered due to the size of spending and aid increases as well as the resulting real exchange rate appreciation. The results suggest that countries, instead of relying on international targets, should set MDG targets grounded in their own reality. In light of these results, the authors designed a second simulation set that is focused on the remaining period up to 2015, and on what may be feasible once the current conflict has been settled. The simulations introduce moderate increases in foreign aid or government allocative efficiency. The government uses the resulting fiscal space for spending and service expansion in infrastructure and human development without losses in productive efficiency. The results suggest that, under these conditions, substantial improvements could still be achieved.

This paper is a product of the Development Economics Prospects Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hlofgren@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**49**

|**Country**|**Spell**|**Headcou**|**nt ($1)**|**Pov. ga**|**p ($1)**|**Pov. gap s**|**qu. ($1)**|**Gi**|**ni**|
|---|---|---|---|---|---|---|---|---|---|
|||**Start**|**End**|**Start**|**End**|**Start**|**End**|**Start**|**End**|
|Uganda|1992-1996|30.54|23.68|9.60|5.95|4.22|2.16|43.19|37.40|
|Uganda|1996-1999|23.68|26.85|5.95|7.74|2.16|3.20|37.40|43.11|
|Ukraine|1988-1992|0.06|0.02|0.07|0.01|0.19|0.01|23.31|25.71|
|Ukraine|1992-1999|0.02|2.92|0.01|0.62|0.01|0.26|25.71|28.96|
|Ukraine|1995-1996|2.06|0.00|0.64|0.00|0.39|0.00|39.29|33.18|
|Uruguay|1981-1989|0.91|0.58|0.50|0.34|0.56|0.40|43.65|42.33|
|Uruguay|1989-1996|0.58|0.56|0.34|0.18|0.40|0.12|42.33|43.76|
|Uruguay-Urban|1996-1998|0.56|0.61|0.18|0.20|0.12|0.12|43.76|45.18|
|Uruguay-Urban|1998-2000|0.61|0.20|0.20|0.05|0.12|0.02|45.18|44.56|
|Uzbekistan|1988-1993|0.00|3.28|0.00|0.46|0.00|0.11|24.95|33.27|
|Uzbekistan|1998-2000|19.16|17.32|8.12|4.26|4.70|1.86|45.35|27.03|
|Venezuela, RB|1981-1987|7.52|6.60|1.46|1.04|0.38|0.22|55.82|53.45|
|Venezuela, RB|1987-1989|6.60|2.95|1.04|0.87|0.22|0.45|53.45|44.08|
|Venezuela, RB|1989-1993|2.95|2.66|0.87|0.58|0.45|0.22|44.08|41.68|
|Venezuela, RB|1993-1995|2.66|9.43|0.58|2.86|0.22|1.31|41.68|46.84|
|Venezuela, RB|1995-1996|9.43|14.69|2.86|5.62|1.31|3.17|46.84|48.79|
|Venezuela, RB|1996-1997|14.69|9.65|5.62|2.88|3.17|1.27|48.79|48.80|
|Venezuela, RB|1997-1998|9.65|14.31|2.88|6.58|1.27|4.08|48.80|49.53|
|Vietnam|1993-1998|14.63|3.08|2.55|0.48|0.65|0.10|35.68|35.40|
|Yemen, Rep.|1992-1998|3.55|10.21|1.11|2.30|0.67|0.85|39.45|33.44|
|Zambia|1991-1993|64.64|73.57|38.91|42.66|28.78|29.55|60.05|52.61|
|Zambia|1993-1996|73.57|72.63|42.66|37.75|29.55|23.88|52.61|49.79|
|Zambia|1996-1998|72.63|63.65|37.75|32.65|23.88|21.07|49.79|52.60|
|Zimbabwe|1991-1995|33.32|56.12|8.96|24.17|3.34|13.04|39.42|50.12|

Note: Start- or end-years specified with a 0.5-digit were rounded up for the calculation of the spell length.
empire (Mali and Guinea). Pairwise comparisons do not reveal significant differences in terms of European settlement, tax revenue, or public expenditures (see table S1.1 in the supplemental appendix). One exception is perhaps that the Mali border districts exhibited higher population density. As we shall see in the following subsection, this feature has been reversed since then.

# **Geography**

First, we check the continuity of the density of the distance to the border. The usual RD tests for sorting (Lee 2008, McCrary 2008) must be adapted to our context. We cannot only count the number of primary sample units because the sample stratification and sample rates differ between country surveys. For each bandwidth and each border size, we compute standardized “relative PSU weights” by dividing the original weights by their mean. The test then detects whether relatively more people are found closer to the border on one side compared to the other. The first row in Table 1 shows the result of this test in the case of the Ghana border. No border discontinuity is detected; one minor exception is the polynomial method at a bandwidth of 50 km. The same is found for Mali and Guinea at 75 km distance (see appendix table A.1), which will be our preferred bandwidth everywhere.

14
With this defined, the expected value of the location effect conditional on the residuals of the households within the location can be obtained: as well as the variance: EB prediction is expected to perform well in the presence of large _η_ , if many of the locations are covered by the survey, and if the distributions of the error terms approximate a normal distribution (Van der Weide, 2014).

# **4.2 Monte Carlo Simulations**

ELL (2003) use various specifications in their paper. The options and methods differ depending on how the variance for the decomposed error terms is obtained. The simulation may be done via parametric drawing of the parameters, and via bootstrap. The PovMap manual (Zhao, 2006) only details the parametric approach. Nevertheless, the newest version of the software has incorporated a bootstrap approach to obtaining the parameters. The steps for the simulation are:

- ˆ ˆ

- 1. Obtain GLS coefficients by drawing from _β_<sup> ̃</sup> _GLS ∼ N βGLS,_ Var _βGLS_ . For reasons that will � � ��

- become apparent in the following steps, this approach is only possible when the user chooses ELL variance estimation.

- (a) A different approach relies on obtaining bootstrapped samples of the survey data used in the first stage, this is repeated for every single simulation. The latter method yields a set of _β_<sup> ̃</sup> for every single simulation.

2. Under the ELL variance estimation approach the cluster component of the error term, _η_ ̃ _c_ is obtained by drawing from _η_ ̃ _c ∼ N_ �0 _,_ ˆ _ση_<sup>2</sup> � where we:

- (a) Draw _σ_ ˆ _η_<sup>2</sup><sup>_∼Gamma_</sup> � _σ_ ˆ _η_<sup>2</sup><sup>_,_Var</sup> � _σ_ ˆ _η_<sup>2</sup> �� 26

- i. With each _σ_ ˆ _η_<sup>2inhanditispossibletodrawthelocationeffectsfrom</sup><sup>_η_ ̃</sup><sup>_c∼N_</sup> �0 _,_ ˆ _ση_<sup>2</sup> �.

> 26As described in Demombynes et al. (2008), Var<sup>�</sup> _σ_ ˆ _η_<sup>2</sup> � may be estimated by simulation as detailed above, or from equation

> 9. In its present form the only available option is that from equation 9.

11
households) and the extension of the service network [access to electricity can be as low as 4% in Chad; see World Bank (2009)].

Furthermore, households react to the relative prices of the alternative source of energy (Heltberg 2005; Wu _et al_ 2004). Therefore, satisfying the minimum energy requirements may require different amounts of money, depending on accessibility and on price configurations. This is the _specificity_ issue (Ravallion and Bidani 1994), whose relevance is somewhat reduced if the affordability index is computed to answer questions like “Were the service accessible, could the households afford to meet their minimum energy requirements using it?” In fact, the normative capabilities approach might justify paying attention not only to meeting the minimum energy requirements, but also – because of the health, safety and environmental externalities - to the mix of fuels used. The minimum energy requirement varies with the energy efficiency of the accommodations and the lighting, cooking or heating technologies. This adds a further dimension to the specificity issue, because the household production functions are – at least in the short run – constrained by the stock of durables. Considering the same input of energy when the efficiencies of the household production functions are different violates the horizontal equity principle, because the welfare of the households is different. A different way to look at the problem is to consider the cost heterogeneity of the complementary capital investments required to make productive use of the energy (Foster and Tre 2000).

A similar line or reasoning can be followed for other public services. As for water, the identification of the subsistence threshold is probably easier, but still the problems of multiple sources and capital costs need to be solved. In fact, water can be provided by conventional piped distribution, private wells, public standpipes, private vendors or collected directly from nature. As in the case of energy, the degree of substitution and complementarity among different sources is related not only to the relative prices, but also to the quality and reliability of the service and its own cost of access.

For telecommunications and transport the definition of the subsistence threshold should refer to the minimum quantity of these services necessary to the individuals to fully participate in the society they belong to. For transport this _“could be interpreted in terms of the distance required for essential travel, such as commuting to work, travelling to school and visiting the local market”_ (Foster and Tre 2000). This, again, 20
(score of +1) or pro-employer (-1) or neutral (0) over the period 1958 to 1992. Given the limited time-series variation within states, Hasan, Mitra, and Ramaswamy (2007) build on the BesleyBurgess data set to cumulate the coded scores in net years into a time-invariant index of the general direction in labor regulations, classifying states with anti-employee amendments as those with flexible labor markets and the others as inflexible labor markets. They argue that the scores on cumulative amendments between 1980 and 1997 do not vary much over time within states, with eight of the states showing no amendment activity since 1980. Dougherty (2009) further reports that only 8 amendments (in 3 states) have been recorded since 1990, and only one amendment passed in 2004 appears to be of material importance to labor market outcomes. Gupta et al. (2008) build a composite index based on a simple majority rule across the indicators proposed in Besley and Burgess (2004), Bhattacharjea (2006), and Dougherty (2009), and classify five states to have flexible labor regulations, (Andhra Pradesh, Karnataka, Rajasthan, Tamil Nadu, and Uttar Pradesh), three states to have inflexible labor market regulations (Maharashtra, Orissa, and West Bengal), and the remaining seven to be neutral (Assam, Bihar, Gujarat, Haryana, Kerala, Madhya Pradesh, and Punjab).<sup>21</sup> Following the composite classification in Gupta et al. (2008), we create a **Flexible State** dummy that takes the value 1 for states with flexible labor regulation and 0 for states with rigid or neutral labor regulations as shown in panel B of Table 2. We make one change to the Gupta et al. classification of using the pre-2000 state boundaries in classifying states. So our flexible states classification includes Uttaranchal (which split from Uttar Pradesh in 2000) and our inflexible states classification includes Jharkhand and Chattisgarh that were formerly part of Bihar and Madhya Pradesh, respectively).

> 21 The labor market regulation index is not available for the following states and union territories: Jammu & Kashmir, Chandigarh, Nagaland, Manipur, Tripura, Meghalaya, Daman & Diu, Dadra & Nagar Haveli, Pondicherry, Lakshadweep, and Andaman & Nicobar Islands.

19