rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Fiscal Balance Current Account Account Balance<br>35.0 (%6 of GDP} sa [6 ofGDP} ofGDP}GDP}<br>m2005 m2006 m2007 m2008 m2009<br>30.0 ao 2005 m2006 m2007 m2008 m2009<br>25.0<br>30<br>20.0<br>a9 15.0 5 20<br>s<br>Pe 10.05.0 sFSFS 10QQ<br>o.0 fan) & ra - & «<br>(20.0)(5.0) ¥ osse °& *él < (20)<br>(15.0) (30)<br>Source:IMF Regional Economic Gutlook, MENA and Central Asia, May 2009 Source: World Bank World Development indicotors and IMF Regional Economic<br>Note: Egypt, Syria- General GovernmentBalance Outlook, MENA snd Central Asis, May 2009 May 2009 2009<br>Real GDP Growth Consumer Price Inflation<br>(unweighted average of annual % change)<br>12.0 130AA (unweighted average average of annual % change)<br>m2005 m2006 2007 = 2008 m2009 ©2005 m2006 m 2007 m2008 m2009<br>15.0<br>10.0<br>14.0<br>8.0 12.0<br>10.0<br>® 6.0<br>we 80<br>40 6.0<br>40<br>2.0 2.0<br>0.0 a0<br>Egypt Jordan Lebanon Morocco Tunisia Algeria Libya Syria (2.0) Egypt Jordan Lebanon Morocco Tunisia = Algeria Libya Syria<br>Source:IMF World Economic Outlook April2003 Source: World Bank WD WD<br><!-- End of picture text --> <!-- Start of picture text --> Current Account Account Balance<br>sa [6 ofGDP} ofGDP}GDP}<br>ao 2005 m2006 m2007 m2008 m2009<br>30<br>5 20<br>sFSFS 10QQ<br>fan) & ra - & «<br>(20)<br>(30)<br>Source: World Bank World Development indicotors and IMF Regional Economic<br>Outlook, MENA snd Central Asis, May 2009 May 2009 2009<br><!-- End of picture text --> <!-- Start of picture text --> Consumer Price Inflation<br>130AA (unweighted average average of annual % change)<br>©2005 m2006 m 2007 m2008 m2009<br>15.0<br>14.0<br>12.0<br>10.0<br>we 80<br>6.0<br>40<br>2.0<br>a0<br>(2.0) Egypt Jordan Lebanon Morocco Tunisia = Algeria Libya Syria<br>Source: World Bank WD WD<br><!-- End of picture text -->
**Table 3: Determinants of Enterprise Employment and Sales Growth, 2000-2005**

||Dependent gr|owth variable:|
|---|---|---|
||Annual growth|Annual growth of|
||of labor days|sales 2002-|
|Explanatory variables|2000-2004|2004|
||(1)|(2)|
|ln age|0.400*|0.057|
||(2.22)|(0.62)|
|ln age squared|-0.156*|-0.047|
||(-2.18)|(-1.07)|
|ln age cubic|0.020*|0.008|
||(2.19)|(1.03)|
|ln size|-0.329**|-0.990**|
||(-19.6)|(-3.65)|
|ln size squared|0.228**|0.179**|
||(19.5)|(3.54)|
|ln size cubic|-0.037**|-0.011**|
||(-16.3)|(-3.51)|
|ln size x ln age|-0.021**|0.005|
||(-3.42)|(0.42)|
|Formally registred|0.009|0.009|
||(1.70)|(0.47)|
|Constant|-0.119|1.871**|
||(-0.82)|(3.93)|
|Regional dummies|YES|YES|
|Observations|722|828|
|Adjusted R-squared|0.65|0.18|
|Robust t statistics in parentheses|||
|* significant at 5%; ** significant at 1%<br>Source: 2005 Tanzania RICS|||

The relationship between size and age on growth is nonlinear. The results are stable in the sense that using average instead of initial size in the regressions to address the problem of transitory fluctuations of enterprises (Mazumdar and Mazaheri, 2003) does not significantly change the sign or significance of the coefficients. In addition, sample censoring does not seem to bias the results significantly. Since most proprietors do not keep records, they can only estimate their sales or profits, even at the present time. It is likely that measurement errors of sales growth make the regression to perform poorly. By contrast, the number of working days performs well as measured by an R<sup>2</sup> of 0.65. Changes in working days are a more robust measure of enterprise growth in rural areas. For rural entrepreneurs that do not keep records, this measurement is easy to remember.

18
Table 11: Inflation rate (it) and exchange rate (Xt) by country.

||Bra|zil|Mex|ico|Ind|onesia|Moza|mbique|
|---|---|---|---|---|---|---|---|---|
||it (%)|Xt|it (%)|Xt|it (%)|Xt|it (%)|Xt|
|1996|9,6|0,99|27,7|0,13|6,0|0,00042|48,5|0,089|
|1997|5,2|0,95|15,7|0,14|10,3|0,00023|7,4|0,091|
|1998|1,7|0,89|18,6|0,14|77,6|0,00023|1,5|0,088|
|1999|8,9|0,60|12,3|0,15|1,9|0,00027|2,9|0,083|
|2000|6,0|0,61|9,0|0,16|9,3|0,00021|12,7|0,076|
|2001|7,7|0,50|4,4|0,17|12,5|0,00021|9,1|0,059|
|2002|12,5|0,45|5,7|0,17|9,9|0,00026|16,8|0,059|
|2003|9,3|0,45|4,0|0,15|5,2|0,00029|13,4|0,065|
|2004|7,6|0,50|5,2|0,15|6,5|0,00027|12,7|0,076|
|2005|5,7|0,61|3,3|0,15|17,1|0,00029|7,2|0,077|
|2006|3,1|0,69|4,1|0,16|6,6|0,00033|13,2|0,076|
|2007|4,5|0,78|3,8|0,16|6,6|0,00032|8,2|0,079|
|2008|5,9|0,84|6,5|0,16|10,2|0,00030|14,5|0,093|
|2009|4,3|0,81|3,6|0,14|2,8|0,00035|3,8|0,085|
|2010|5,9|0,96|4,4|0,15|7,0|0,00039|12,4|0,076|
|2011|6,5|1,04|3,8|0,15|3,8|0,00039|11,2|0,096|
|2012|5,8|0,92|3,6|0,15|4,3|0,00037|2,6|0,099|
|2013|5,9|0,87|4,0|0,15|7,7|0,00031|4,3|0,096|
|2014|6,4|0,84|4,1|0,15|8,4|0,00033|2,6|0,093|
|2015|10,7|0,65|2,1|0,13|3,4|0,00031|3,6|0,075|
|2016|6,3|0,66|3,4|0,11|3,0|0,00032|17,4|0,055|
|2017|3,0|0,72|6,8|0,12|3,6|0,00032|15,1|0,062|
|2018|3,8|0,64|4,8|0,12|3,1|0,00030|3,9|0,066|
|2019|4,3|0,61|2,8|0,12|2,7|0,00032|2,8|0,064|
|2020|4,5|0,48|3,2|0,11|1,6|0,00031|3,1|0,059|

Source: own elaboration with data from the International Financial Statistics (IMF).

47
# **Reference:**

Aaronson, D, Barrow, L and Sander, W. Teachers and Student Achievement in the Chicago Public High Schools. _Journal of Labor Economics_ 2007; vol. 25: 95-135 Annual Status of Education Report (ASER) 2005, 2006, 2007, Pratham Foundation http://www.pratham.org/aser-report/ Banerjee A, Banerji R, Duflo E, Glennerster R, Khemani, S. Can information campaigns spark local participation and improve outcomes? A study of primary education in Uttar Pradesh, India. Policy research working paper. 2006; 3967: World Bank.

Chaudhury N, Hammer J, Kremer M, Muralidharan K, Rogers H. Missing in action: teacher and health worker absence in developing countries. _Journal of Economic Perspectives_ 2006; 20(1):91-116.

Darling-Hammond, L. Teacher quality and Student Schievement. _Education Policy Analysis Archive_ 2008, 8(1): <u>http://epaa.asu.edu/epaa/v8n1.html</u> Duflo, E, Hanna, R, Ryan, S. Monitoring Works: Getting Teachers to Come to School. Working Paper, 2007: MIT.

Lazear, E. Educational Production. Working Paper, 1999; W7349: NBER.

Hanushek, E. The Evidence on Class Size. Occasional Paper, 1998: University of Rochester.

Hanushek, E, Woessmann, L. The role of education quality for economic growth. Policy Research Working Paper Series. 2007; 4122: World Bank.

Pandey P, Goyal, S, Sundararaman, V. Community Participation in Public Schools: The Impact of Information Campaigns in Three Indian States. **Policy research working paper. 2008; WPS 4776: World Bank.** Miller, R, Murnane, R, Willett, J. Do Teacher Absences Impact Student Achievement? Longitudinal Evidence from One Urban School District. Working Paper, 2007, W13356, NBER.

NCERT (National Council of Educational Research and Training), India. Learning Achievement of Class III Students- A Baseline Study under SSA. 2006 OECD: Education at a Glance. 2005.

_Public Report on Basic Education in India (PROBE)_ , New Delhi, 1999, Oxford University Press.

36
**–** **<u>Table 5: Comparison of Means Village Infrastructure (2007)</u>**

|||**Baseli**|**ne (2007)**<br>|||
|---|---|---|---|---|---|
||**Compariso**|**Treate**|**Differenc**|**Stde**|**Pval**|
|Private primary school exists|18%|19%|0%|0.06|0.95|
|Private middle school exists|18%|29%|11%|0.06|0.10|
|Private secondary school exists|11%|9%|-2%|0.04|0.67|
|Any overhead tank exists|69%|75%|6%|0.06|0.36|
|No. of government school|1.7|1.9|0.24|0.14|0.10|
|No. of teachers in govt. schools|9.0|11.8|2.75|1.07|0.01|
|No. of anganwadis|1.8|1.9|0.13|0.13|0.31|
|No. of employees in anganwadi|3.5|3.9|0.39|0.29|0.19|
|No. of handpumps|4.7|3.5|-1.21|0.68|0.08|
|No. of tubewells|2.8|3.0|0.25|1.19|0.83|
|No. of water sources constructed- last 12|0.5|1.4|0.83|0.68|0.22|
|No. of roads|24.7|24.6|-0.09|2.66|0.97|
|No. of gravel roads|2.2|2.0|-0.17|0.57|0.76|
|No. of roads constructed- last 12 months|1.4|2.5|1.15|0.47|0.01|
|Number of roads free of garbage|10.0|10.6|0.63|2.19|0.77|
|Number of roads with a drain|7.4|8.6|1.30|1.41|0.36|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

**–** **<u>Table 6: Comparison of Means Village Activity and Works (2007)</u>**

|||**Bas**|**eline (2007)**|||
|---|---|---|---|---|---|
||**Comparison**|**Treated**|**Difference**|**Stdev**|**Pval**|
|Activity in last 12 mons - Roads|43.0%|42.9%|-0.1%|0.080|0.986|
|Activity in last 12 mons -|5.0%|5.1%|0.1%|0.036|0.978|
|Activity in last 12 mons - Schools|4.0%|8.2%|4.2%|0.039|0.286|
|Activity in last 12 mons - Health|9.0%|5.1%|-3.9%|0.043|0.369|
|Activity in last 12 mons - Water|47.0%|42.9%|-4.1%|0.076|0.586|
|Activity in last 12 mons - Sanitation|22.0%|20.4%|-1.6%|0.061|0.794|
|Activity in last 12 mons - Electricity|41.0%|27.6%|-13.4%|0.074|0.072|
|Activity in last 12 mons - Irrigation|2.0%|9.2%|7.2%|0.037|0.057|
|Agr. extension officers visit|51.0%|58.2%|7.2%|0.079|0.366|
|Taluk Engineer visits|39.0%|28.6%|-10.4%|0.080|0.198|
|EO visit|33.0%|25.5%|-7.5%|0.074|0.315|
|NGO active in village|10.0%|9.2%|-0.8%|0.045|0.857|
|Traditional Panchayat active|63.0%|58.2%|-4.8%|0.072|0.504|
|Male agricultural wage|57|53|-4|2.109|0.056|
|Female agricultural wage|32|30|-2|1.654|0.330|
|Oligarchy|0.10|0.10|0.00|0.017|0.876|

***Note:** The standard errors for the difference in means are clustered at the level of the Gram Panchayat.

32
# **Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania**

Kathleen Beegle, World Bank Joachim De Weerdt, EDI Tanzania Jed Friedman, World Bank John Gibson, University of Waikato

**Keywords:** consumption, expenditure, survey design **JEL** : C83, D12

* This work was supported by the World Bank Research Committee. We would like to thank two anonymous referees, seminar participants at IFPRI, the ASSA annual meetings, and the Survey Design and Measurement in Development Economics conference for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries.
bonds are positive across pre-merger and announcement months. These studies, however, do not distinguish between absolute and systemic size as potential determinants of market discipline on a bank.

Demirgüç-Kunt and Huizinga (2010b) consider absolute and systemic size as separate determinants of the market-to-book value of a bank’s equity for a sample of international sample of banks over the 1991-2008 period. Bank market-to-book value is found to be negatively related to either size measure, while it is more negatively related to systemic size in countries with large public deficits, as evidence that systemically large banks can be too large to save. The present paper’s finding that the sensitivity of bank interest expenses to bank risk is greater for systemically large banks also suggests that these banks can be too large to save, and thus it is consistent with the evidence from bank stock pricing in our earlier paper.

The remainder of this paper is organized as follows. Section 2 discusses the data. Section 3 presents the empirical results. Section 4 concludes.

# **2. The Data**

In this study, we examine an international sample of banks over the 1991-2009 period. Income statement and balance sheet information on individual banks is taken from Bankscope. A full list of variable definitions and data sources is given in the Appendix. Our sample is restricted to publicly-listed banks to ensure relatively high data quality. With data from many countries, we can distinguish between a bank’s absolute size and its systemic size, i.e. its size relative to the national economy. To measure a bank’s absolute size, we define the assets variable as the log of total assets in constant dollars. To gauge systemic size, we take a bank’s liabilities-to-GDP ratio, denoted liabilities over GDP. This ratio corresponds is to a country’s maximum expenditure in a bank bail-out relative to its GDP, if all of a bank’s assets go completely sour.

In Table 1 with summary statistics, we see that liabilities over GDP has a sample mean of 0.046. Figure 1 provides additional information about the overall distribution of liabilities over GDP. Specifically, this figure plots the percentages of banks with liabilities over GDP in consecutive intervals of size 0.05 up to a value of 1, and also in a remainder category of banks with liabilities over GDP exceeding 1. Most banks are shown to be rather small relative to GDP, 6
**Table 3. Summary statistics of key indicators from the ground and from outer space**

||Obs|Mean|Std. Dev.|Mean|Std. Dev.|
|---|---|---|---|---|---|
|||2|001||2011|
|**Sample**||||||
|Population size (_thousands_)|1,277|38.73|371.39|46.79|392.48|
|Population density (_people per square_<br>_kilometer_)|1,277|2252|3983|3085|6057|
|Built‐up share (_percent_)|1,277|9.91|17.46|13.05|19.99|
|Lit‐up share (_percent_)|1,277|34.06|41.92|52.97|45.45|
|Built‐up area (_square kilometers_)|1,277|1.65|10.80|2.07|11.84|
|Lit‐up area (_square kilometers_)|1,277|4.74|20.87|6.83|23.24|
|**Universe**||||||
|Population size (_thousands_)|564,052|1.75|27.59|2.10|33.53|
|Population density (_people per square_||||||
|_kilometer_)|564,052|580|2339|693|2700|
|Built‐up share (_percent_)|564,052|0.50|3.47|0.81|4.43|
|Lit‐up share (_percent_)|564,052|2.57|14.18|8.57|25.93|
|Built‐up area (_square kilometers_)|564,052|0.04|1.13|0.05|1.35|
|Lit‐up area (_square kilometers_)|564,052|0.13|2.46|0.40|3.09|

# _A model for the probability of being urban_

Following the data modeling tradition, we specify an explicit probabilistic model on whether an administrative unit is urban or rural in practice: where U� is the subjective assessment of unit i , p� is the expected likelihood that a unit is urban given the values of its key indicators X� , and F�. � is the cumulative distribution function. The estimation of equation (2) requires that a functional form be assumed for F�. � . Here we use the standard logit model linking the urban status of a unit in a specific year with the values of its key indicators at a similar point in time. The justification for this choice, as opposed to the also standard Probit model, is its higher goodness of fit and better prediction accuracy. The results obtained using the Probit model are available upon request.

A linear function of X� is assumed for the function F�. � . The benchmark specification combines data from the ground and from outer space using population size, population density, built‐up share, and lit‐up share as the key indicators. The model is also run retaining only indicators from the ground or from outer space, either individually or combined (table 4).

17
# **Economic and Welfare Effects of the Abolition of Health User Fees: Evidence from Uganda**

**Abstract:** Household level data for Uganda for 1999/2000 and 2002/03, before and after the abolition of user fees for public health services, are used to explore the impact of this policy on different groups’ ability to access health services and morbidity outcomes. We find that the policy change improved access and reduced the probability of sickness in a way that was particularly beneficial to the poor. Although the challenge of maintaining service quality remains, aggregate benefits are estimated to be significantly larger than the estimated shortfalls from the abolition of user fees.

Keywords: User fees, health outcomes, health care services, public and private health care centres

## **1. Introduction**

Few interventions in the area of public service provision have been as controversial and subject to as fierce a debate as the imposition of user fees for health services. Some think that at least a moderate level of cost recovery is justifiable to put social services on a more sound footing and that potential negative side effects from a modest user fee for health treatment are a small price to pay for the increased accountability and improvement in service quality that can be achieved through such a measure. Others hold equally firm views to the opposite, thinking that charging for treatment will strongly discriminate against the poor who, especially in an economy that is still largely based on subsistence, will be hard pressed to come up with the cash required to make such payments. Even though, on theoretical grounds, both outcomes are possible, what happens in practice will have far-reaching implications for the ability of the poor to access health care and thus to realize their full economic potential. To make informed policy decisions, empirical evidence to identify, in any given setting, the relevant factor, will therefore be critical.

Indeed, a large number of studies from developing countries have aimed to provide empirical evidence to support claims regarding user fees for health services. However, in many cases the analysis is based on the level of access to public health services rather than on changes that have been associated with a specific reform that aimed to change such access, thereby making it more difficult to distinguish between pre-existing structural characteristics of the system and a certain policy (Gwatkin, 2000). Furthermore, the scarcity of good and comprehensive socio-economic data on health access has forced analysts in many instances to rely on administrative data sources that tend to be insufficient to make behavioral inferences or to allow in-depth assessment of the incidence of access to health services that are critical in order to judge the impact of a specific policy on equity. Finally, analyses that focus only on those who actually 1
13 _Table 3. Net benefits of Ivorian public forward sales (Model 1)_

|Calculation of gross benefits||
|---|---|
|Average price gain_(PVAM-_spot)|1.9%|
|Quantity exported (mmt)|1,000|
|Average CIF PVAMprice ($/mt)|1,507|
|Average CIF spot price ($/mt)|1,479|
|Average FOB price ($/mt)<br>|1,387|
|Total gross benefits (millions of $)|28.1|
|Costs of PVAM(millions of $)||
|CAISTAB operating costs|16.7|
|Excess marketing costs (5% of FOB)|69.3|
|Net benefits (millions of $)|-57.9|
|Net benefits as % of producer income|-7.6%|

_Note::_ Table may contain rounding errors.

_Source:_ Marquet (1997), CAISTAB (1998) and authors' calculations.

The "excess marketing costs" are defined as the difference between the levels specified in the _bareme_ and the levels that would be observed in competitive markets. The excess arises out of the _bareme_ negotiations between CAISTAB and exporters14 The regulated costs within the _bareme_ are, as explained previously, a necessary feature of the _P VAM_ because exporters cannot hedge their future domestic costs, including the price at which they buy from farmers, through market transactions; hence, they must fix those costs through an administered price structure, such as the _bareme._ In theory marketing costs within the _bareme_ could be determined competitively and the excess would then be zero; in practice, they are fixed during secret negotiations between CAISTAB officials and exporters. This price-fixing is insulated from competition so that exporters' declared costs, and the profit margins based on declared costs, are inflated. The inflated margins have two parts -- one is a pure transfer from producers to exporters, and is not a social cost; the other is a social cost that would be eliminated through competitive pricing. It is only the second part that we define as "excess marketing costs".

Excess marketing costs are in fact likely to exceed 5 percent of FOB. Ivorian marketing costs appear to be at least double those found in cocoa producing countries with liberalized marketing systems, where the sum of marketing costs is about 15

> 14 Cameroon prior to the 1994 reforms had a cocoa marketing system very similar to Cote d'Ivoire's based on the concept of the _Caisse de Stabilization._ As in Cote d'lvoire the system used the _bareme_ to determine the producer price by deducting marketing costs and margins. The system also used forward sales aiming at stabilizing producer prices. In Cameroon after market liberalization and the elimination of the marketing agency ONCPB, cocoa producer prices rose by 40-50 percent relatively to those in Cote d'Ivoire. According to Gilbert (1997), two thirds of this difference is due to reductions in Cameroonian marketing costs with the reminder being due to lower taxation.
|**Appendix 3: Attrition by Group**|||All Firms|||New Firms||E|xisting Firm|s|
|---|---|---|---|---|---|---|---|---|---|---|
||Sample<br>Size|First<br>Survey|Second<br>Survey|Third<br>Survey|First<br>Survey|Second<br>Survey|Third<br>Survey|First<br>Survey|Second<br>Survey|Third<br>Survey|
|Panel A: Information available on<br>|whether o<br>|r not they<br>|operate a f<br>|irm<br>|||||||
|Experimental Winners|729|0.846|0.930|0.885|0.840|0.922|0.869|0.856|0.942|0.910|
|Control Group|1112|0.752|0.906|0.825|0.756|0.901|0.816|0.738|0.924|0.852|
|Experimental Sample|1841|0.789|0.916|0.848|0.785|0.908|0.835|0.799|0.933|0.882|
|National and Zonal Winners|475|0.834|0.924|0.878|0.864|0.873|0.831|0.824|0.941|0.894|
|RD Booster Sample|823|0.694|0.792|0.829|0.678|0.788|0.822|0.718|0.799|0.839|
|Panel B: Responded to the Survey|||||||||||
|Experimental Winners|729|0.812|0.915|0.870|0.796|0.900|0.847|0.838|0.939|0.906|
|Control Group|1112|0.719|0.872|0.805|0.723|0.863|0.800|0.703|0.901|0.821|
|Experimental Sample|1841|0.756|0.889|0.831|0.748|0.876|0.816|0.773|0.921|0.865|
|National and Zonal Winners|475|0.806|0.918|0.869|0.831|0.856|0.805|0.798|0.938|0.891|
|RD Booster Sample|823|0.667|0.776|0.821|0.650|0.776|0.816|0.693|0.777|0.830|

54