rafmacalaba/datause-encoder-data huggingface dataset:
as collateral, with the center’s permission, raising concerns about the fiscal impact of a real estate bubble.

**Table 9 Central and Subnational Overall Fiscal Balance in China, India, and Brazil**

**2008–10** <u>(as percent of GDP)</u>

|**(**_Country/level of government_|_2008_|_2009_|_2010_|
|---|---|---|---|
|_China_||||
|Central|6.0|5.2|6.7|
|Subnational|–6.6|–8.3|–8.3|
|General|–0.7|–3.1|–1.5|
|_India_||||
|Central<sup>~~a~~</sup>|–6.1|–6.6|–6.1|
|Subnational|–2.5|–3.3|–3.2|
|General<sup>~~a~~</sup>|–8.7|–10.0|–9.4|
|_Brazil_||||
|Central|–0.2|–3.1|–1.3|
|Subnational|–1.2|0.1|–0.3|
|General|–1.3|–3.0|–2.7|

_Sources:_ Official national data sources; IMF 2012. a. General government balances are as reported by IMF, with central deficit duly adjusted.

In sum, the experience of China, India, and Brazil in responding to the global crisis of 2008 presents a mixed picture and raises various country-specific concerns. China scores high in terms of the composition of the fiscal stimulus and its multiplier effect on the economy; it scores low in terms of prudent financial management, with excessive reliance on off-budget domestic bank financing. India and Brazil score low in terms of the composition of fiscal stimulus and its (low) investment content. India scores high in enabling state governments to contribute to the stimulus within the limits of a sustainable fiscal framework. Slack credit demand from the private sector was a factor that enabled Indian states to access domestic bank credit, which was a binding constraint for the states in Brazil.

Although Brazil scores high in terms of protecting real spending levels on social programs in 2009, the procyclicality of subnational fiscal response, resulting from procyclical central transfers and limited access to credit, together imply that SNGs faced increasingly tight resource constraints since 2011.

A study by the IMF (2012) compares pre-crisis (2005–07) and post-crisis (2008–10) trends in subnational government finance in Australia, Brazil, Canada, China, Germany, Mexico, and 25
Policy Research Working Paper 8099

# **Abstract**

Multidimensional poverty comparisons can be sensitive to the choice of welfare indicators, the weights assigned to the indicators, as well as the aggregate poverty measure used. This paper examines the robustness of trends in multidimensional poverty in the Philippines to these choices by presenting estimates for three alternative weighting schemes and three measures of multidimensional poverty. The weighting schemes range from uniform weights similar to those used in the global multidimensional poverty indexes produced by the United Nations Development Programme, to weights based on inverse incidence of different deprivations and those derived from the estimated relationship of deprivations to a survey-based measure of subjective welfare. The multidimensional poverty measures similarly range from the “dual cut-off” indexes analogous to the United Nations Development Programme’s global Multidimensional Poverty Index, to “union-based” indexes that count all deprivations, to indexes that are also responsive to the distribution of deprivations. Using data for 2004–13, the paper finds evidence of a significant decline in multidimensional poverty that is robust to these alternatives, although the magnitude of the decline in, and especially the dimensional contributions to, aggregate multidimensional poverty are quite sensitive to the alternatives considered.

This paper is a product of the Poverty and Equity Global Practice 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 author may be contacted at gaurav. datt@monash.edu.

_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
# **Reforming Subsidies: A Tool-kit for Policy Simulations**<sup>**1**</sup>

_Abdelkrim Araar_<sup>_2_</sup> _and Paolo Verme_<sup>_3_</sup>

**JEL** : C53, C63, E27, E37

**Keywords** : Micro-simulation, Subsidies, Stata

**Sector board** : Poverty (POV)

1 The paper has been prepared in the framework of the World Bank regional program for North-Africa and the Middle-East. The authors wish to thank Jonathan Walters (Director, Regional Programs and Partnerships, MENA, World Bank) for funding the study, Quentin Wodon (HDNED, World Bank) for peer review, Umar Serajuddin and Thomas Walker (MNSPR, World Bank) for testing the first version of the tool-kit and Jean-Yves Duclos (University of Laval) for useful advice and hospitality at the University of Laval during the early stages of the work. The views expressed in the paper do not represent the views of the World Bank or its directors. All remaining errors are the sole responsibility of the authors.

2 University of Laval and World Bank (consultant).

3 World Bank and University of Torino.
5 Table 1. Austl Increwnt in Femlo Life Expectancy

||Fmle p|rimry|
|---|---|---|
||school e|nrol ment|
|Femsle life|||
|expectancy<br>|Less than<br>|70 percent|
|at birth|70 percent|or more|
|35.0|0.33|0.39|
|37.5|0.35|0.44|
|40.0|0.38|0.47|
|42.5|0.41|0.50|
|45.0|0.43|0.52|
|47.5|<br>0.45|0.53|
|50.0|0.47|0.54|
|52.5|0.47|0.54|
|55.0|<br>0.48|0.53|
|57.5|<br>0.48|0.51|
|60.0|<br>0.47|0.48|
|62.5|0.45|0.45|
|65.0|<br>0.41|0.41|
|67.5|0.36|0.36|
|69.4|<br>0.31|0.31|
|71.1|0.27|0.27|
|72.6|<br>0.23|0.23|
|73.9|0.20|0.20|
|75.0|<br>0.16|0.16|
|75.9|<br>0.13|0.13|
|76.6|0.11|0.11|
|7,.5|0.09|0.09|
|78.5|0.07|0.07|
|79.5|0.06|0.06|
|80.5|0.05|0.05|
|81.5|0.04|0.04|
|82.5|0.03|0.03|
to missing. Lastly, when capital is missing, but its depreciation value is 0, depreciation is converted to missing because it is likely that newly imputed values for capital will be positive which implies positive depreciation of capital.

The easiest and probably the most frequently used methods to handle missing data points are casewise data deletion and mean substitution. If a case has any missing values, the entire record can be deleted or missing points can be substituted by mean values. However, Roth (1994) compared different approaches often used in empirical research and concluded casewise data deletion and mean substitution are inferior to maximum likelihood based methods such as multiple imputation.

To resolve the issue of missing data, we introduce a multiple imputation technique developed by Rubin (1978, 1987) and others. The multiple imputations usually generate five to ten complete data sets by filling in gaps in existing data with proper raw values. Raw values are drawn from their predicted distribution based on the observed ones. Then each complete data set can be analyzed by common statistical methods (e.g., regression). After conducting identical analysis multiple times, the results drawing upon imputed data sets are combined into one summary set of parameters.

We generated five complete data sets and used mean values to impute missing cases. The imputed values were evaluated again to check their consistency. When imputed values.were unreasonably small or large, we converted them back to missing and imputed again. The imputation procedure was repeated three times.

These plant level data are supplemented by district and metropolitan area level demographic and amenities data from the 1991 Census of India and detailed information on the availability and quality of transport infrastructure linking urban areas. The plant level data have been 21
52 patemalistic employers and governments tend to require pensions. Empirical evidence on the latter point appears In the next section.

27. See McGreevey (1990) and Grosh (1990) for a discussion of Latin American plans; among others, Atkins (1991), Diamond (1992) and Takahashi (1992) discuss Eastem European plan difficulties.

28. This discussion is taken from Gustman et al. (1992).

29. These responses have been estimated for males; women's retirement responses to economic factors may be somewhat smaller (Pozzebon and Mitchell 1989).

30. These conclusions are supported by a number of microeconomic retirement studies on the United States and Canada, capably reviewed by Quinn and others (1990). British retirement studies by Zabalza and others (1980) using microdata also tend to confirm these conclusions. English-language joumals offer fewer micro-data studies on retirement pattems in other OECD countries; aggregate time-serie **s** tudies include those by Zwelm0ller (1991) and others cited in Section I.

31. Information in this paragraph derives from Gustman and others. (1992).

32. On the other hand positive productivity effects of lower mobility due to pensions is difficul **t** o find (Allen and Clark 1987).

33. In the United States, Mitchell (1988) found that many workers did not adequately perceive their private pension plan benefit rules, and Bemheim (1988) reported that public social security retirement offerings were also widely misunderstood. Information apparently improved as workers approached retirement age. The extent to which worker reallocate labor across economic sectors, as well as tabor and leisure over their lifetimes, deserves far more study, particularly in developing countries.

34. In his literature review McGreevey (1990) refrains from drawing any conclusion about the size of the payroll tax effect on the demand for labor. Hamermesh (1992) discusses and payroll tax incidence in the developing country context and concludes that more research on this topic is required before a consensus can be drawn.

35. One study examined whether unemployment compensation beneffts were used to smooth the path to early retirement in the United States, and found little evidence in support of the thesis (Hamermesh 1979).

36. See Munnell (1986), Munnell and Emsberger (1989), and Munnell and Yohn (1990), for a survey of theories and empirical work in the area. The most well-known proponent of the view that social security reduces savings is Feldstein (1976).

37. There Is also considerable controvery regarding the question of whether the demographic aging of the population will increase or decrease tie demand for savings; see Auerbach and Kotlikoff (1991), among others.
This classification approach utilizes spatial and textural characteristics of the imagery at the neighborhood level. A combination of spatial features and spectral information was extracted from the original imagery using the open source MapPy python script library available on GitHub. A set of six spatial features and two spectral features was extracted using the python scripts, and are listed below:

- Differential Morphological Profiles (DMP) segment imagery according to an area’s degree of homogeneity.<sup>25</sup>

- Fourier Transform (FT) examines edges by decomposing the image into different spatial frequencies.<sup>26</sup>

- Gabor Filters are edge extraction filters at different scales and orientations.<sup>27</sup>

- The Histogram of Oriented Gradients (HOG) captures the distribution of structure edge orientations in a histogram.<sup>28</sup>

- Local Binary Pattern Moments (LBPM) is a texture algorithm that examines relationships between a center pixel and its neighboring pixels.  The layout of the pixel group represents the local pattern as flat areas, edges, corners, and line ends.<sup>29</sup>

- PanTex is a contrast texture designed specifically to identify buildings.  It is derived from the Gray Level Co‐Occurrence Matrix (GLCM), an algorithm that determines how often adjacent pixels have the same gray level value.<sup>30</sup>

- The Normalized Difference Vegetation Index (NDVI) is a simple spectral indicator that assesses whether a pixel has green vegetation.<sup>31</sup>

- The local mean of each of the original four bands and composite image was also computed.

Each of the features was computed with a block size (output resolution) and scale size (window size). Block size represents the output layer resolution. Because this analysis focuses on neighborhoods, rather than individual buildings, block sizes that produced output resolutions closest to 15m were used.<sup>32</sup> A block size of 32 produced an output classification resolution of 16m (0.5m input imagery X block size 32 = 15m output resolution). Scale size represents the window size, or the amount of pixels from which the spatial feature algorithm will extract contextual information. Three scale sizes, starting at the block size and increasing in octaves, were chosen (32, 64, and 128). Once all feature layers were computed, they were layered into a virtual stack.

# **Training Sites**

Training sites are small polygons that analysts draw over ‘stereotypical’ land area. Figure 5 below shows sample training sites for Commercial/industrial, Residential Irregular and Vegetation in Dar es Salaam. For each city, similar polygons were created throughout the image, resulting in roughly 40 training sites (each measuring approximately 15,000 sq meter) per class. Once these training sites were created, 500

> 25 Klaric et al. 2005. 26 Smith 1997. 27 Daugman 1985.

> 28 Dalal & Triggs 2005. 29 Wang & He 1990.

> 30 Pesaresi et al. 2008. 31 Jensen 2007.

> 32 Graesser et al. 2012.

12
WPS 3197

# **_Building and Running General Equilibrium Models in EViews_**

## B.  Essama-Nssah

Poverty Reduction Group (PRMPR) The World Bank Washington. D.C.

## Abstract

A crucial step in policy analysis involves computing consequences of policy actions. This paper shows how to implement numerically a general equilibrium model in EViews. Computable general equilibrium models are now commonly used in both developed and developing countries to assess the impact of external shocks or economic policies on the structure of the economy or the distribution of welfare. The current version of EViews offers a set of tools for building and solving simulation models in general. The same tools make it possible to conduct policy analysis within a general equilibrium framework. Based on the generalized Salter-Swan framework and macroeconomic data for Indonesia, the paper demonstrates how to process a social accounting matrix (SAM), specify and calibrate the model, and run simulations. The results replicate welfare and structural effects of shocks and policies consistent with the underlying conceptual framework. They also reveal the key role played by structural parameters, such as the elasticity of export transformation and that of import substitution, in determining the extent of structural adjustment to shocks, and the relevance of the policy response.

## JEL Classification: C63, C68

## World Bank Policy Research Working Paper 3197, January 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._
TABLE 2. Regression estimates, effects of childcare coverage on women employment

||Hous|ehold|Indivi|dual|Moth|ers|Other wo|men in HH|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Labor Force participa_|_tion_||||||||
|Coverage rate|0.230***||0.138***||0.144***||0.136***||
||(0.06)||(0.03)||(0.05)||(0.05)||
|Treated||0.049***||0.024***||0.026**||0.024**|
|||(0.02)||(0.01)||(0.01)||(0.01)|
|Mean|0.372|0.372|0.191|0.191|0.226|0.226|0.155|0.155|
|_Employment_<br>Coverage rate|0.103||0.063*||0.074*||0.053||
||(0.08)||(0.04)||(0.04)||(0.05)||
|Treated||0.036*||0.017*||0.017||0.017|
|||(0.02)||(0.01)||(0.01)||(0.01)|
|Mean|0.301|0.301|0.157|0.157|0.184|0.184|0.130|0.130|
|Individual controls|||Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|12454|12454|24517|24517|12417|12417|12100|12100|

_Note:_ The analysis is estimated among working age women living with young children aged 1 to 6, excluding households with infants under the age of 1. For household-level analyses, the outcome variable is the number of women in the labor force and the number of women employed. Standard errors in parentheses, clustered at district level; * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 _Source:_ World Bank: Listening to Citizens of Uzbekistan survey data 2018–2022; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

participation, the smaller effect on actual employment suggests that not all women entering the labor market are able to secure jobs. This discrepancy could be due to tight labor market conditions in Uzbekistan, where finding employment remains difficult despite an increase in job search activity among women.

# **5.2. Heterogeneity analyses**

To better understand how different types of households contributed to the overall employment effect and which groups benefited most from the policy reform, we conducted additional analyses focusing on different subgroups of women in the sample. These subgroups are based on income status, the availability of potential informal care 14
-3-

Table 2. Ratio of Exports to GNP, Selected Industrial Countries, 1970, 1980, 1984-86 a/ ( Unit; Percent )

|Country|1970|1980|1984|1985|1986|
|---|---|---|---|---|---|
|Japan|10.8|13.7|15.1|14.6|11.7|
|Australia|14.7|16.4|16.4|16.4|16.8|
|Austria|31.2|37.1|39.2|40.8|37.1|
|Belgium|51.5|61.0|78.3|76.1|69.2|
|Canada|23.1|29.2|29.5|29.3|28.3|
|Denmark|28.0|33.5|38.7|38.3|33.2|
|Finland|25.9|33.5|31.1|29.7|27.1|
|France|15.7|21.4|24.3|24.1|21.6|
|Germany, FR|21.2|26.4|30.5|32.4|30.0|
|Italy|15.4|21.7|23.2|23.2|20.4|
|Netherlands|44.6|52.6|62.2|63.7|54.1|
|Norway|42.1|48.9|48.7|47.8|38.3|
|Sweden|24.1|30.0|37.0|36.2|33.7|
|Switzerland|31.6|35.3|35.6|36.9|35.0|
|United Kingdom|23.1|27.7|28.9|29.2|26.2|
|United States|5.8|10.0|7.5|7.0|6.9|

a/ Exports of goods and nonfactor services.

Source: The World Bank, World Tables 1987, 4th ed., 1988
Consequently, a range of interventions can be effective at reducing dropout and increasing attainment depending on the context, from conditional cash transfers to providing information on returns to schooling, to training on socio-emotional skills (Barrera-Osorio et al 2011; Nguyen 2008; Jensen 2010; Fryer 2013; Avitabile and de Hoyos 2015; Heller et al 2016).

While understanding why students drop out is critical, the ability of policy makers to respond effectively depends on answering an even more fundamental question – who is most likely to drop out? This question may appear relatively easy to answer, particularly in countries, regions, or localities with high dropout rates, as one might assume dropouts are concentrated in particularly disadvantaged or dysfunctional schools, or among students with particular characteristics. However, dropouts are often spread throughout schools and not readily identifiable by single characteristics, reflecting the complexity of the issue as documented in the dropout typology literature mentioned above. For example, in Guatemala, one of the countries in this study, over half of sixth grade students who drop out in the transition to lower secondary are spread across 70% of the country’s primary schools, where the dropout rate is below 50%, and while 50% of students who score in the lowest quartile of a sixth-grade standardized exam drop out, so do 20% of those who score in the highest quartile.

Accurately identifying students at risk of dropping out in order to target effective interventions to where they are most needed is particularly important in contexts of limited resources and competing priorities, which describes most of the world’s education systems. Largely based on the rich administrative data available in many U.S. school systems, research on dropout prediction is providing an increasingly sound empirical base for accurately predicting who will drop out several months to several years before dropout occurs, and over 30 U.S. states currently have in place some form of “early warning” system (O’Cummings and Therriault 2015). Similarly, the majority of European countries report monitoring “early warning” signs of potential dropout through their management information systems, primarily at the school level, but in some cases nationally (European Commission 2013). For many middle-income countries, which have invested in setting up information management systems in recent years, answering the prediction problem of dropout is now becoming possible through the use of consistent student-level data. In both Guatemala and Honduras, for example, student and school-level data are now digitalized in 3
# **5. LABOR EARNINGS DETERMINANTS AMONG OCCUPATIONAL GROUPS USING REGRESSION MODELS**

In addition to a simple descriptive comparison, Mincerian earning functions were estimated. We use these functions because we know that payment differences depend on qualifications, level of education and other personal characteristics. The main objective of this analysis is to determine whether teachers are underpaid. To enrich analysis, workers were reclassified into four occupational groups: _Teachers in basic public schools_ (which includes teachers in primary public schools as well as teachers in secondary public schools), _Teachers in basic private schools_ (which includes the same levels as in the previous definition but in private schools), _Other government workers_ (which contains all the other occupational public groups, excepting teachers, with 12 years of formal schooling or more), and _Private sector workers_ (workers in the private sector, excepting _the agricultural group_ workers and for _the low-skilled group_ workers, with 12 years of formal schooling or more). These two latter groups were chosen in order to provide close comparison. Separate ordinary least squares regressions were computed for both groups of teachers and for the comparable groups. The analysis uses hourly labor earnings as the dependent variable and years of schooling, gender, region (urban-rural), experience (defined as age-years of schooling-6) and experience squared as explanatory variables.

Estimates are presented in the table below.

**Table 12. Determinants of hourly labor earnings, 1996**

||**Teacher in b**<br>**Public scho**|**asic**<br>**ols**|**Teacher in basic**<br>**Private schools**|**Other govern**<br>**Workers**|**ment**<br>|**Private se**<br>**Worke**|**ctor**<br>**rs**|
|---|---|---|---|---|---|---|---|
|Years of schooling|0.058<br>(3.464)|*|0.030<br>(0.998)|0.128<br>(9.245)|*|0.168<br>(13.518)|*|
|Gender (Male=1)|0.083||0.397<br>*|0.038||0.230|*|
||(1.191)||(2.249)|(0.546)||(3.564)||
|Experience|0.033|*|0.113|0.083|*|0.049|*|
||(2.705)||(1.312)|(5.039)||(5.483)||
|Squared experience|-0.0004|*|-0.002|-0.002|*|-0.001|*|
||(-1.976)||(-0.996)|(-3.708)||(-2.59)||
|Region (Urban=1)|-0.1233<br>(-1.561)||Dropped|0.051<br>(0.278)||0.452<br>(4.873)|*|
|Constant|1.2715<br>(3.831)|*|0.709<br>(0.812)|-0.561<br>(-2.049)|*|-1.543<br>(-7.349)|*|

Source: Authors' estimates based on ENIGH 1996 survey. * Significant at the 5% level T-stat in parenthesis.

19