rafmacalaba/datause-encoder-data huggingface dataset:
|Table A7: Heterogeneous impacts of|_Mais Educação_by|municipal spending on educa|Test score<br>tion and culture|
|---|---|---|---|
|Dependent variable|Dropout rates|Test score mathematics|<br>Portuguese|
||(1)|(2)|(3)|
|Panel A|: Impacts on fifth-|grade variables||
|Dummy_Mais Educação_* year 2011|1.119|–3.974**|–1.987|
||(1.325)|(1.553)|(1.413)|
|Dummy_Mais Educação_* year 2009|0.974|–4.292***|–2.426*|
||(1.114)|(1.465)|(1.358)|
|_Mais Educação_* spending on|0.00272|0.0112|–0.00186|
|education and culture|(0.00207)|(0.00776)|(0.00680)|
|_R_ <sup>2</sup>|0.666|0.786|0.789|
|Observations|2,988|2,988|2,988|
|Dummy year 2009|Yes|Yes|Yes|
|<br>Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B|: Impacts on ninth-|grade variables||
|Dummy_Mais Educação_* year 2011|–0.296|–3.636**|–0.311|
||(1.125)|(1.808)|(1.692)|
|Dummy_Mais Educação_* year 2009|–0.140|–5.194***|–2.140|
||(0.968)|(1.663)|(1.647)|
|_Mais Educação_* spending on|8.26e-05|–0.00331|–0.00633|
|education and culture|(0.00413)|(0.00877)|(0.00840)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Municipal spending on education and culture was deflated by IPCA, and is in 2007 prices. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is Portuguese grades in _Prova Brasil,_ in column (2) mathematics grades in _Prova Brasil,_ and in column (3) dropout rates. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
**<u>Table 1.  Para-tariff Restrictions</u>**

|**Items**|**July 1985**|**Dec 1988**|**Dec 1989**|**Dec 1990**|
|---|---|---|---|---|
|Free|<br>4,757|0|4,192|5269|
|Restricted|350|4,724|535|0|
|Banned|196|539|539|0|
|Source: BC|RP||||

The tariff structure generated extreme positive and negative levels of effective protection. Effective protection was 261% for articles of clothing, 189% for dairy products and 134 % for shoes. Several primary and export sectors faced negative effective protection.

Sector officials made the decisions on tariffs and the inclusion or exclusion of products in the lists of banned or restricted imports, while the Central Bank assigned the exchange rates applicable to each import item. There was no unified autonomous technical authority to review protection or no protection requests and to ensure uniform criteria and minimal political interference. In practice, the high level of official discretion contributed to uncertainty, inefficiency and corruption.

# _(d) The New Economic Policy_

From its outset, the new government enforced an aggressive structural reform program to liberalize markets.

The early measures focused on eliminating hyperinflation. In August 1990, a “shock” adjustment was made to rapidly set controlled prices free, removed almost all subsidies, launched a programmed reduction in monetary expansion, and established a managed floating exchange rate. Inflation was substantially reduced in 1991, even when the instant death experimented by other hyperinflations did not take place.

In spite of the emphasis on stabilization, structural changes that became the framework for trade reform were launched in 1990.

3
5 representative sample of refugees or IDPs) or event/transaction datasets whose unit of analysis is not the household or individual. If datasets do not have a representative sample of refugees or IDPs or their unit of analysis is not the household or individual, these datasets are flagged as false positive and thus excluded from our study. We identify 43 false positive datasets from the UNHCR MDL and removing them leaves us with 369 datasets from the UNHCR MDL.

Figure 1: Methodology to identify FDP datasets from UNHCR and WB MDLs <!-- Start of picture text --> Extract the metadata • 412 datasets<br>of all existing datasets from UNHCR<br>MDL; 1,927<br>from UNHCR and WB<br>datasets in<br>MDLs WB MDL*<br>Remove false • 369 datasets<br>positives from from UNHCR<br>UNHCR MDL MDL**<br>Apply nlp4dev • 358<br>and keyword "relevant"<br>search to filter datasets<br>dateasets from from WB<br>WB MDL MDL<br>Remove WB MDL • 62 "relevant"<br>datasets cross- datasets<br>listed in UNHCR from WB<br>MDL MDL<br>Remove false • 6 datasets<br>positives from from WB<br>WB MDL MDL***<br>Combine the • 369 (from UNHCR<br>remaining datasets<br>from UNHCR and MDL) + 6 from WB<br>WB MDL MDL = 375 datasets<br><!-- End of picture text -->

* This refers to all micro-level datasets from the 53 low-income and middle-income countries analyzed for this study.

** Not all datasets in UNHCR MDL are micro-level datasets or sample explicitly from refugees or IDPs and should be manually removed.

**Most of the relevant datasets in WB MDL prove false positive because while having some references to relevant topics such as migration and displacement in the metadata (which is typically tagged as relevant in nlp4dev), they do not specifically mention sampling explicitly from refugees or IDPs.

Third, we identify micro-level datasets from the WB MDL that have a representative sample of refugees or IDPs. A large majority of datasets in the WB MDL do not sample specifically from refugees or IDPs, calling for a methodology to systematically filter these datasets and identify those that do have a representative sample of FDP. To this end, we apply three different filters to first identify “potentially relevant” datasets from the WB MDL:
policy makers currently have little systematic information about targeting performance on which to base their analysis of residential utility subsidies as a utility policy or social policy option.

This article seeks to contribute to closing this gap by systematically examining the targeting performance of residential utility subsidies using data from 13 water utilities and 27 electricity utilities around the world. A conceptual framework that decomposes the determinants of targeting performance is employed to identify those factors that most strongly influence the empirical results and predict targeting performance for various types of subsidies. We begin with an overview of the types of utility subsidies that exist and their prevalence among water and electricity utilities. The article then focuses on the analysis of the benefit and beneficiary targeting performance of consumption and connection subsidy schemes.

The results indicate that the most common forms of residential utility subsidies – quantity-based consumption subsidies such as increasing block tariffs -- are highly regressive. Most poor households are excluded from these subsidies, and the majority of benefits accrue to the non-poor. Prospects for achieving a progressive incidence of subsidies are better when other targeting mechanisms are used, such as means-testing. However, low levels of coverage and metering severely compromise the ability of any consumption subsidy scheme to reach the poor. Where coverage of standard in-house water and electricity service levels is low, subsidies targeted only to consumers of lower-levels of service or connection subsidies are better alternatives, but the performance of these subsidy models is highly dependent on how poor households respond to the subsidy offer. Even connection subsidies and servicelevel targeted subsidies may not reach many poor households if price is not the principal barrier to connection for poor households or if demand for alternative services (such as public taps or low voltage electricity service) is low.

# **Typology and prevalence of subsidies**

We distinguish in this analysis between two broad categories of utility subsidies – consumption subsidies and connection subsidies. Consumption subsidies reduce the charge for consuming water or electricity service, while connection subsidies are a one-time reduction in connection charges. Consumption subsidies are available only to existing utility customers. Connection subsidies are by definition for unconnected households only.

Consumption and connection subsidies may be targeted or untargeted. Untargeted subsidies occur when there is general underpricing of utility services, such as when certain costs are not passed on to customers.

3
Table 2: Effects of Electricity Access on Structural Transformation

||||_Dependent v_|_ariable:_|||
|---|---|---|---|---|---|---|
||GVA Share,|Agriculture|GVA Share,|Industry|GVA Share|, Services|
||_OLS_|_IV_|_OLS_|_IV_|_OLS_|_IV_|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Electricity|_−_0.035<sup>_∗∗∗_</sup>|_−_0.704<sup>_∗∗_</sup>|0.066<sup>_∗∗∗_</sup>|_−_0.438<sup>_∗∗_</sup>|_−_0.031<sup>_∗∗∗_</sup>|1.143<sup>_∗∗∗_</sup>|
|Access|(0.006)|(0.289)|(0.009)|(0.199)|(0.008)|(0.388)|
|Population|0.072<sup>_∗∗∗_</sup>|0.0001|_−_0.042<sup>_∗∗∗_</sup>|0.003|_−_0.030<sup>_∗∗∗_</sup>|_−_0.004|
|Density, log|(0.007)|(0.011)|(0.012)|(0.008)|(0.011)|(0.014)|
|Generation|0.013<sup>_∗∗∗_</sup>|0.091<sup>_∗∗∗_</sup>|_−_0.017<sup>_∗∗∗_</sup>|0.039<sup>_∗_</sup>|0.004|_−_0.131<sup>_∗∗∗_</sup>|
|Capacity|(0.002)|(0.034)|(0.003)|(0.023)|(0.003)|(0.045)|
|Observations|69,521|12,408|69,521|12,408|69,521|12,408|
|R<sup>2</sup>|0.881|0.001|0.887|0.0002|0.902|0.0002|
|Adjusted R<sup>2</sup>|0.874|_−_0.333|0.881|_−_0.334|0.897|_−_0.334|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust standard errors in parentheses. All regressions are weighted by municipal GVA and include municipality and time fixed effects.

30
We then turn to the impact of the earthquake on the food consumption score. First, we want to test whether this score depends on the pre-disaster level of assets as well as on asset losses, that is  ’ > 0 and  ’ < 0. Second, we consider the possibility that in absolute value, asset losses have a lower impact on food consumption score than the pre-disaster level of assets  ’ > |  ’|. Indeed, in connection with the earthquake, households should have enforced coping strategies that enable them to buffer the impact of that shock. Finally, we also want to test whether assistance (in particular food assistance) has an impact on the food consumption score that is  > 0.

For identification purposes, the post-earthquake asset index is measured in February. This will avoid endogeneity bias in equation (1) due to the fact that assets losses might be lower for households who benefited from assistance programs. Furthermore, the housing damage due to the earthquake (variable H) might be a valid instrument for EA in equation (3). Indeed, this variable should not be related to FCS otherwise than through EA. In the sequel, we will thus test this assumption and further discuss the impact of EA on FCS.

# **4. Data and Empirical Findings**

# _(a) Data sources and methodology_

A post-earthquake food security-oriented survey was conducted in June 2010 by the CNSA in collaboration with its main partners (ACF, FEWS-Net, Oxfam GB, FAO, UNICEF and WFP). The sampling used for the household survey is a probabilistic cluster method, using two stages: (i) enumeration sections (geographical areas) and camps and (ii) households. 2003 census data is used to select the enumeration sections, with a probability proportional to population size. Eight households are then selected randomly in each section. Camps are selected using the International Organization for Migration (IOM) data; the number of camps selected was proportional to the size of the communes. The sampling method yielded 1901 interviewed households, located in the disaster areas (camp and non-camp sites) as well as in some nondirectly affected areas. Geographic strata covered by the EFSA II survey are presented in Figure 3 (in Appendix).

To randomly select households, different methods were used for the urban households, the rural households and the camps. For urban households, survey investigators observe and mark the location of households on a street map that does not contain socio-economic infrastructure, and the households are randomly selected. For rural households, previously 9
# Annexes

# General Explanation

Annex 1 describes how IECSE combined the IDS 1987 benchmark results with information obtained by Bank and Fund missions, in order to produce tentative time series through 1990 and sometimes 1991, for use in this study pending CIR's final report. One advantage of IDS' 1-0 approach is that it permitted the unwrapping of the residual category commonly found on the expenditure side of material product system (MPS) accounts, covering both government and the resource balance. This revealed an important differences in some cases, notably the treatment of FTDs among FSU economies.

Annex 2 places the _Atlas_ method in a conceptual framework, identifies assumptions that don't seem applicable in the present case(s), and explains conceptual refinements that are should mitigate the problems. It also provides a rigorous explanation of the conceptual framework lying behind the traditional _Atlas_ method and then attempts to identify where its applicability seems doubtful, in the case of economies with pervasive price controls.

The fact that FTDs affected individual FSU republics differently suggests that the economies emerging from each republic begins with their own "tailored" set of multiple exchange rates. Beneath what may seem like accounting issues, there were genuine transfers of resources, among the republics that formed the FSU; dissolution of the Union severed the FTD transfer mechanism. The significance of this depends on the extent to which each economy depended on inter-republic trade, as well as the extent and speed of transition to market mechanisms in each. Basic information on this is available in the study by Michalopoulos and Tarr (1992). Annex 3 recasts the underlying data to suggest how ruble-dollar conversion factors would look if each FSU economy's trade, with each other (inter-republic) and the rest of the world (extra-Union) were adjusted to just compensate for removal of FTDs. This is suggested by the variety of import and export rates computed separately for each FSU economy. The ruble-dollar rate implied by the SACF is shown across-the-board, as the rate for nontraded goods. New tools will have to be developed before the incidence of terninating Union-wide FTDs can be gauged.I Depending on the path taken in the coming months, it should be possible to focus on one of the "impact" statements implied by this study or to prepare somewhat more realistic "incidence" estimates using a regional input-output framework being developed as an outgrowth of this study.

Annex 4 details the SACF procedures underlying the estimates ultimately recommended in this study, which links HPEs to the rest of the world through a combination of dollar exchange rates for other upper middle income economies benchmarked in 1985, the 1985 ICP lAn integrated framework of input-outpu **t** ables, including bilateral trade flows among the 15 FSU economies, is in preparation. The proposal document is available on request.

26
meta controls. To avoid introducing researcher biases in choosing this estimate, the single estimate is the mean elasticity reported in the study.

## Urban cost estimation

We estimate urban cost elasticities across developed and developing countries using city-level OLS regressions of (log) urban cost against (log) population density (equation 3). We use three measures of city-level urban costs: congestion (hours lost due to traffic), pollution (PM2.5 emissions) and crime (homicide rate).

A developing country dummy is included to reflect difference in urban costs in _levels_ , and interact the dummy with population density to estimate the difference in urban cost _elasticities_ across income groups. We normalize density relative to the mean value, such that constant terms can be interpreted as the levels at the mean city density.

Specifically, we estimate: 1. n 2 3. l (3) ii oo ii ii ii ii ii where llnnsscc is the log urban cost for city i, urban costs reflect either the log average annual additional = δδ + δδ nnhiill + δδ . lleennssiiccdd + δδ ll ∗lleennssiiccdd + uu hours spent driving in rush hours in 2018, log PM2.5 emissions for 2014 or log homicide rates in 2015. ii l is a dummy variable reflecting cities in developing countries, llnnsscc is the log population density for city i in 2015. Developing countries reflects non-high-income status as defined in 2015, ii ii using the World Bank classification. ll lleennssiiccdd

# 5. Meta-analysis: Estimation of Agglomeration Benefits

This section presents the results of the meta-analysis of agglomeration elasticities. We first examine potential publication bias, present overall meta-estimates and then examine the role of heterogeneity by study characteristics.

## a) Publication Bias

The results from Funnel Asymmetry Test (FAT) for publication bias, are presented in Table 3. For all estimation methods we find robust evidence of a highly significant positive relationship between the estimated agglomeration elasticity and the corresponding standard error, suggestive of publication bias.

**_Table 3: Funnel Asymmetry Test for Publication Bias_**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|Estimation Method:|Meta RE|Meta FE|WLS|
|Standard error (publication bias)|1.225***|3.824***|3.824***|
||(0.087)|(0.030)|(0.566)|

16
The scope of provisions that aim at protecting forest resources varies significantly across agreements.<sup>5</sup> RTAs can include more general language to promote the conservation and sustainable use of biological diversity and sustainable forestry management. Or they can commit members to specific actions such as measures to combat illegal logging and related trade. For instance, the RTA between the United States and Peru contains an annex on forest sector governance setting out detailed commitments promoting sustainable management of forest resources and combating trade associated with illegal logging, among other things. The agreement has specific requirements such as the identification within the Peruvian government of a focal point, with appropriate and sufficient authority and staff to investigate violations of laws and regulations for forest sector governance. The RTA between the EU and Cameroon contains similar, although not as detailed, provisions on forestry governance and trade in timber and forest products. Both agreements also include references to other international instruments that will support the objective of improved conservation, such as members’ participation to multilateral environmental agreements (MEAs). Finally, both agreements foresee provisions on technical assistance and capacity building. In the case of the EU-Peru RTA, the agreement recommended a voluntary partnership agreement under the EU’s action plan on forest law enforcement, governance and trade (FLEGT), which Cameroon signed shortly after the entry into force of the RTA.

# **2.2 Deforestation**

Forest loss around the globe is driven by agricultural land expansion (both for small holder agriculture as well as commercial production), forestry practices, fires, and urban expansion (Curtis _et al._ , 2018). Tropical deforestation is overwhelmingly driven by agricultural expansion (Gibbs _et al._ , 2010), and accounts for a large share of global greenhouse gas emissions (Baccini _et al._ , 2012).

To measure the impact of forest loss arising from RTAs, we utilize the Global Forest Change dataset by Hansen _et al._ (2013). These data provide high resolution estimates of year 2000 forest cover and annual estimates of forest loss for the entire terrestrial surface of the earth. Following Abman and Lundberg (2020) and Leblois _et al._ (2017) (among many others), we aggregate these

> 5See Monteiro (2016) for details.

8
Recent estimates suggest that globally about 1.5 billion people live in multidimensional poverty (UNDP, 2015). Over the last decade or so, there has been a veritable explosion of literature on the measurement of multidimensional poverty. Following the initial Atkinson and Bourguignon (1982) paper and important later contributions of Tsui (2002), Bourguignon and Chakravarty (2003) and Alkire and Foster (2007, 2011a), the literature on both the theory and applications of multidimensional poverty measurement has grown rapidly.<sup>1</sup> With regards to applied work, measures within the “counting” approach (which focus on counting the number of dimensions in which people are deprived) have been influential, in particular the class of measures proposed by Alkire and Foster (2011a). One indicator of this influence is the publication of the Multidimensional Poverty Index (MPI) – which is a special case of the Alkire-Foster (hereafter AF) class of measures – for more than a 100 countries in the United Nations Development Program’s annual Human Development Report since 2010 (UNDP, 2010). This influence is only likely to grow with the recent adoption by the UN of the new set of Sustainable Development Goals (SDGs) which encompass a wide range of welfare dimensions many of which are also central to multidimensional poverty measurement.<sup>2</sup> This paper revisits the measurement of multidimensional poverty from an axiomatic perspective, taking the AF measures as a point of departure. At its core, it presents axiomatic arguments for a class of “distribution-sensitive” measures. The arguments centre on the specific formulation of the dominance axioms<sup>3</sup> , in particular, the transfer and the rearrangement axioms. The paper proposes stronger versions of these dominance axioms – a stronger version of the transfer axiom requiring regressive transfers (defined later) to be poverty-increasing, while also introducing a cross-dimensional convexity axiom requiring multiple deprivations to matter more than the sum of their parts, which is shown to be equivalent to a stronger version of the rearrangement axiom.

2
\.,' HG3~.s­ · l,_h~ _P6J_ fJD·S-~ c / 2-

# Financial Innovation and Money Demand: Theory and Empirical Implementation*

by Patricio Arrau and Jo8e de Gregorio Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|TheModel|3|
|.3.|TheLack of Cointegration of Traditional MoneyDemandEquations|9|
||3.1Data<br>3.2Estimations|11<br>12|
|4.|··EstimatingFinancialInnovation|13|
|5.|Monte Carlo Exi>ertments|22|
|6.|Conclusions|24|
|Ref|erences|27|
|Tab|les|30|
|Fig|ures<br>SECTORAL LIBRARY<br>INTERNATIONAL BANK<br>FOR|36|
|App|endix A Individual Optimiiation<br> <br>RECONSTRUCTION AND DEVELOPMENT|38|
|App|endix BDataSourcesandUnit Root Tests<br>FEB071991|40|
|App|endix CVAREstimations for ChileandMexico|42|

* The first version of this paper was written while the second author was a consultant at the Debt and International Finance Division, World Bank. We are grateful to Ricardo Caballero, Jim Hanson, Raul Labaan, Leo Leiderman, and Sweder van Wijnbergen for helpful comments and discussions. Special thanks to Carmen Reinhart for detailed comments to a previous version.