rafmacalaba/datause-encoder-data huggingface dataset:
|**extension)**|||||||
|---|---|---|---|---|---|---|
|**Rainfall**|0.019|0.020|0.011|0.016|0.025*|0.013|
|**Relative Humidity**|-0.003|0.011|0.012|0.008|0.012|0.010|
|**Temperature**|0.002|0.033|0.029|0.033|-0.050*|0.029|
|**Wind Speed**|-0.007*|0.004|-0.002|0.003|0.003|0.003|
|**Workday**|-0.031|0.027|0.019|0.023|0.006|0.024|
|**Summer**|-0.295**|0.103|-0.099|0.078|-0.252**|0.088|
|**Winter**|-0.037|0.085|0.008|0.053|-0.003|0.068|
|**Diwali**|0.019|0.085|0.052|0.052|0.025|0.104|
|**Observations**|25260||25260||25260||

Source: Authors' analysis based on data described in the text.

The dependent variable is an indicator of whether the observation is missing (=1 if missing and =0 otherwise). The explanatory variables are the same as in Table 4 with the addition of season fixed effects. Std. Errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level, and *** at 1 percent level.

**Table S1.6b Predicting Missing Observations at Siri Fort: Entire Series, 2004-2006**

||**NO2**||**CO**||
|---|---|---|---|---|
|**Share Missing in Entire Series**|27||52||
||**Coefficient**|**Std.**<br>**Error**|**Coefficient**|**Std.**<br>**Error**|
|**Red Line (second extension)**|0.526**|0.231|-0.008|0.200|
|**Yellow Line (introduction)**|0.060|0.119|0.302**|0.138|
|**Yellow Line (first extension)**|-0.342**|0.172|0.082|0.137|

66
||Business start-up support|Outsiders|Increase inflow into employment|
|---|---|---|---|
|**Provide**<br>**employment-**<br>**conditional income**<br>**support**|Public works|Outsiders|Decrease poverty<br>Strengthening labor market attachment of<br>participants<br>Increase inflow into employment|
||Employment-conditional “in-<br>work” benefits|Outsiders and<br>insiders|Decrease poverty<br>Decrease “working poor”<br>Increase inflow into employment<br>Reduce outflow from employment|
||Sanctions on insufficient job<br>search efforts|Outsiders|Intensify work search efforts<br>Increase inflow into employment|
|**Provide human**<br>**capital**<br>**enhancement**|On-the-job training<br>Classroom training|Outsiders<br>Outsiders|Increase inflow into employment<br>Increase productivity<br>Increase inflow into employment<br>Increase productivity|
|**Improve labor**<br>**market matching**|Job search assistance|Outsiders|Improve job search efficiency<br>Increase inflow into employment|
||Employer intermediation<br>services|Outsider and insiders|Improve job sear efficiency<br>Improve matching quality<br>Increase inflow into employment|
||Counseling and monitoring|Outsider|Improve job search efficiency<br>Increase inflow into employment|

Source: Brown and Koettl, 2012 Note: “Insiders” refers to those who are currently employed, “outsiders” to the unemployed, long-term unemployed, discouraged, informal workers, and inactive.

The study underlines that ALMPs aiming at retaining employment – such as wage subsidies, short work etc. - should be used only for very short periods and in severe recessions. In the case of an increase in energy prices – given the likely permanent nature of the shock – ALMPs aimed at retaining employment in energy-intensive industries are not sustainable. The ALMPs that support recovery from a crisis by putting in place incentives to create new jobs are more cost-effective. Moreover, these types of ALMPs could play a critical role in fostering employment in new, cleaner industries.

In fostering employment, ALMPs that invest in human capital and improve labor market matching are more likely to play a more effective role. Recent studies have shown that longer-term evaluations provide evidence on more positive impacts of policies, whereby training programs, which long have been regarded as too expensive; can be indeed cost-effective from a longer-term perspective. They are especially effective the nearer they are to regular jobs and targeting disadvantaged outsiders (Brown and Koettl, 2012).

Across OECD countries, job subsidies and other demand-side employment incentives have become more important, while direct job creation programs (e.g., public works) have been gathering less funding. Startup incentives for new businesses are only a small part of total spending, but they have been very sizable during the early transition phase in Eastern European countries. An increasing share of active spending 31
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Table A.1: Data Sources

|**Variable**|**Definition**|**Frequenc**|**y**<br>**Source**|
|---|---|---|---|
|Output|Real gross domestic product (GDP)|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private consumption|Real personal consumption expenditure|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Private investment|Real private gross fixed capital formation|Quarterly||
|Government consumption|Real government consumption expenditure<sup>a</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), OECD, Haver Analytics|
|Government investment|Real government gross fixed capital formation<sup>a</sup>|Quarterly|OECD, Haver Analytics, Eurostat|
|Real effective exchange rate|Real effective exchange rate<sup>b</sup>|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), BIS|
|Current account|Current account as percent of GDP|Quarterly|Ilzetzki, Mendoza, and Vegh (2013), WEO|
|Government debt|General government debt as percent of GDP|Annual|WEO|
|Fiscal balance|Overall fiscal balance as percent of GDP|Annual|WEO|
|Government consumption-to-GDP ratio|Government consumption as percent of GDP|Annual|WDI|
|Government investment-to-GDP ratio|Government investment as percent of GDP|Annual|WDI|
|Interest rate|Short term nominal interest rate|Quarterly|Ilzetzki,Mendoza,and Vegh(2013)|

Note: The main source for the quarterly series is Ilzetzki, Mendoza, and Vegh (2013). This database which ends around 2008 is extended by splicing from different sources as mentioned in the table. a This refers to general government for most countries while for a few countries central government is taken. See Ilzetzki, Mendoza, and Vegh (2013). b The narrow index wherever available is taken while the remainder uses the broad index. Details are available upon request.

<u>Table A.2: Distribution of Fiscal Positions</u>

|**Percentile**|5|10|15|20|25|30|35|40|45|50|55|60|65|70|75|80|85|90|95|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Debt-GDP Ratio**|12.4|17.3|24.8|28.7|32.2|35.9|38.5|40.7|42.9|45.1|47.9|51.4|56.1|60.1|65.5|71.3|80.5|92.4|107.4|

Note: The table shows the percentile values of fiscal position, taken to be annual government debt-to-GDP ratio, from the sample of advanced and developing economies during the period 1980-2014.

39
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by country _d_ against Brazil for product _p_ . (Threat)kd,t−1 is a binary indicator that takes a value of one if Brazil filed an AD investigation against country _d_ in the three-digit ISIC industry category _k_ in the previous year. We consider it plausible that firms in the same industry may be connected via unions, trade associations, and other business organizations. Thus, retaliation may be reflected at the industry level, although AD filings target a narrow product line.<sup>8,9</sup> In addition, this variable is zero for countries that have not implemented an AD law because they have no capacity to retaliate. We treat the European Union countries as a single country because AD is a European Union-wide policy.<sup>10</sup>

# **_<<B>>_** _Other Considerations_

We include firm-product-destination fixed effects in our specifications to control for firm-level unobservables, such as managerial ability, to account for the heterogeneity in the quantities’ units of measurement, which are used to create the unit export values and to control for time-invariant, country-specific factors, such as distance.<sup>11</sup> Moreover, we use the total export revenues of the firm, which measure not only the size but also the market power in the international market.<sup>12</sup> In addition, as stated in Verhoogen (2008) and Hallak and Sivadasan (2008), larger and more productive firms pay higher wages and employ better inputs, resulting in higher quality in exports, which are sold for higher prices. Although we do not have information on domestic sales, we use the “number of workers” obtained from the linked employment data to represent the size of the firm.<sup>13</sup> Recent literature (including, but not limited to, Hallak 2006, Kneller and Yu 2008, Harrigan and Deng 2008, Manova and Zhang 2009, Bastos and Silva 2010), on the other hand, has documented that the unit value of shipments increases with distance, size and the income of 8
WPS4060

# **Distributional Effects of WTO Agricultural Reforms in Rich and Poor Countries***

## **by**

**Thomas W. Hertel** and Roman Keeney**

**Purdue University Maros Ivanic GOIC, Doha**

**and L. Alan Winters The World Bank**

### World Bank Policy Research Working Paper 4060, November 2006

_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._

*Preliminary version of a paper prepared for the 44<sup>th</sup> Panel Meeting of _Economic Policy_ , Helsinki, Finland, October 20–21, 2006. The Annexes are available from <u>https://www.gtap.agecon.purdue.edu/resources/download/2816.pdf.</u>

** Corresponding author: T. W. Hertel, Center for Global Trade Analysis, Purdue University, 403 West State Street, W. Lafayette, IN 47907; <u>hertel@purdue.edu</u>
and industry-level DVAR. In particular, for those ...rms that import more than their needs, which we call excessive importers, using (4) may underestimate their DVARs and in the extreme case result in negative DVARs.<sup>24</sup> On the other hand, for those ...rms that buy imported materials from other processing ...rms locally, which we call excessive exporters, using (4) may overestimate their DVARs, and in the extreme case bias the DVARs towards 1. To address the issue of indirect importing, we ...rst use balance-sheet data to identify both the excessive importers and exporters.

We de...ne excessive importers as those ...rms that import more than their total material costs as recorded in the NBS Annual Survey of Industrial Firms (2000-2007), given that total material costs should equal to the sum of imported materials and domestic materials.<sup>25</sup> These excessive importers import more than their total materials and are dropped from our sample. To identify excessive exporters, we ...rst identify all registered ordinary (nonprocessing) exporters that only export in a single industry. Unlike processing exporters, ordinary exporters are not required by China’s Customs to sell all outputs abroad. They can use imported materials to produce for both domestic and foreign sales. In addition, ordinary exporters need to pay import tari¤s and thus should have less incentive to import materials. The DVAR of ordinary exporters should be on average higher than that of processing exporters in the same industry. Thus, we use the 25th percentile of ordinary exporters’DVARs as an upper bound for processing exporters’DVAR, and identify all processing ...rms that have a DVAR higher than this cuto¤ as excessive exporters. Our ...rm-level regression results

> 24In the raw data, about 10 percent of the single-industry ...rms have negative net exports.

> 25Without a common ...rm identi...er shared by the two data sets, we use ...rm names to merge the customs transaction data with the NBS Annual Surveys of Industrial Firms. For rare cases that have duplicate ...rm names, we use the ...rm’s address to improve the merging. See Ma, Tang, and Zhang (2014) for details about the merging procedures. Tables A2 and A3 in the Appendix present the representation of the merged and ...ltered samples, relative to the original customs sample. In terms of the number of exporters, about 39% of the single-industry processing exporters from the customs data sets can be merged with the NBS data, and about 22% survive our ...lters that remove excessive importers and exporters. In terms of export value, our ...nal sample covers over 46% of exports based on the original customs data.

11
(Burchfield, Overman, Puga and Turner, 2006). The unit of analysis is the _pixel_ or picture element, which determines the size of the smallest landscape feature that can be distinguished and mapped. Typical sizes are 30 meter × 30 meter or 1 kilometer × 1 kilometer grids. However, these grid cells are not individual agents and do not make decisions. Hence, using data at this level often involves an aggregation across decision makers. This aggregation may lead to an ‘ecological fallacy’ of drawing inferences about the behavior of individuals from analyses based on grouped or area-level data (Freedman, 2004). Instead, under the general principle of matching the spatial scale of the decision process and the scale at which measurement is carried out (Anselin 2002) it may be better to survey individual decision-making agents and use GPS to then link them to other spatial data.

This linking of different layers of data takes place in a Geographic Information System (GIS). While GIS can be seen simply as a tool for combining, manipulating and displaying spatial information that may have been captured in a variety of ways, including GPS, a broader view sees an emerging geographic information science (Goodchild, 1992). This science may enable researchers to produce more measurements than just the distance between features and to discover new relationships for geographically referenced information. Some of the literature that we review here relies more heavily on GIS than on GPS but still serves as an example of the types of analyses that could be facilitated by a greater use of GPS in household surveys.<sup>6</sup> In this paper we review four ways that GPS can help lead to better economics and better policy: (i) through constructing instrumental variables that can be used to understand the causal impact of policies, (ii) by helping to understand policy externalities and spillovers, (iii) through better understanding of the access to services, and (iv) by improving the collection of household survey data. We then discuss some pitfalls, unresolved problems, and ongoing research issues.

# **FOUR WAYS USING GPS CAN LEAD TO BETTER ECONOMICS AND BETTER POLICY**

# **_1. GPS can be used to help identify causal impacts_**

The majority of empirical work in development economics aims to identify the effect of a particular variable of interest, _X,_ on a particular outcome, _Y_ . For example, Deininger and Minten

> 6 Recent reviews of the use of GIS in economics that are based largely on developed countries are Overman (2006) and Bateman et al. (2002).

- 4 -
**Table 1: Cotton Exports**

||**_1990-94_**|**_1995-99_**|**_2000-04_**|**_2005-09_**|**_1990-94_**|**_1995-99_**|**_2000-04_**|**_2005-09_**|
|---|---|---|---|---|---|---|---|---|
||**_------------_**<br>|**_------ Thousa_**<br>|**_nd tons ------_**<br>|**_------------_**<br>|**_-----------_**<br>|**_------- Share_**<br>|**_(%) -----------_**<br>|**_-----------_**<br>|
|**United States**|1,539|1,418|2,479|2,978|26.0|25.1|37.6|37.5|
|**European Union**|233|329|345|312|3.9|5.8|5.2|3.9|
|**Brazil**|48|6|171|435|0.8|0.1|2.6|5.5|
|**C-4**|289|485|558|490|4.9|8.6|8.5|6.2|
|_enin_|_76_|_136_|_141_|_98_|_1.3_|_2.4_|_2.1_|_1.2_|
|_urkina Faso_|_60_|_95_|_164_|_219_|_1.0_|_1.7_|_2.5_|_2.8_|
|_had_|_50_|_73_|_61_|_40_|_0.8_|_1.3_|_0.9_|_0.5_|
|_Mali_|_104_|_180_|_192_|_134_|_1.8_|_3.2_|_2.9_|_1.7_|
|**Others**|3,811|3,403|3,048|3,719|64.4|60.3|46.2|46.9|
|**WORLD**|**5,919**|**5,641**|**6,601**|**7,934**|**100**|**100**|**100**|**100**|

**Source:** United States _Department of Agriculture_

**Note:** Cotton used to be a key contributor to Chad’s export revenues. Today, Chad’s key merchandise export is crude oil.

16
<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>Lowess conflict on year Mean of conflict by year<br>.3<br>.25<br>.2<br>Proportion of Countries in Conflict<br>.15<br><!-- End of picture text --> FIGURE A4: TIME TRENDS IN CONFLICT Notes: The y-axis is the proportion of all countries experiencing a conflict in each year. Dots are the average of an indicator for whether a country experiences conflict in a given year, the trend line shows the lowess plot of this variable over time. Data are from the NQ replication file.

47
# Electronic Payment Technology and Tax Compliance: Evidence from Uruguay’s Financial Inclusion Reform<sup>_∗_</sup>

Anne Brockmeyer & Magaly S ́aenz Somarriba Originally published in the Policy Research Working Paper Series on _February 2022._ This version is updated on _July 2022._ To obtain the originally published version, please email prwp@worldbank.org.

_JEL classification:_ H26, H32, G18, O16.

_Keywords:_ Credit/debit card payment, tax compliance, third-party reporting, VAT.

> _∗_ Brockmeyer (anne.brockmeyer@ifs.org.uk): Institute for Fiscal Studies, University College London, World Bank and CEPR; S ́aenz Somarriba (msaenzsomarriba@worldbank.org): World Bank and University of Delaware. We are deeply indebted to the Uruguayan authorities for an outstanding collaboration. We particularly thank Felipe Quintela and Fernando Pelaez at the General Directorate of Taxation, Ariel Cancio and Florencia L ́opez from the Ministry of Economy and Finance, and Martin Vallcorba. We are grateful to La ́ısa Rachter de Sousa Dias for excellent research assistance and to Deeksha Kokas for help with the Findex data. We thank Juliana Londo ̃no-V ́elez for excellent contributions in the first part of this project. Marcelo Bergolo, Naomi Feldman, Dirk Foremny, Rafaella Giacomini, Sean Higgins, Michael Keen, Leora Klapper, Dennis Kristensen, Joana Naritomi, Panayiotis Nicolaidis, Eduardo Olaberria, Daniel Prinz, Dorothee Singer, Joel Slemrod, Tavneet Suri, Alisa Tazhitdinova, Javier V ́azquez-Grenno and seminar/conference participants at the World Bank, NTA, AEA, the Gates Foundation, Chr. Michelsen Institute, IEB Barcelona, the IGC and IFS-UCL-STICERD provided helpful comments. This work was funded by the World Bank through the Research Support Budget and the Macroeconomics, Trade and Investment Global Practice, and by UK aid from the UK government through the Centre for Tax Analysis in Developing Countries (TaxDev). The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.
functions are more akin to other “core government functions”—e.g., revenue collection, maintaining law and order—and draw on similar principles for their management.<sup>1</sup>

# **Section I. The New Public Management**

Until the early 1980s, the public sector in most countries was monopolistic, centralized, hierarchical, inflexible, unresponsive to users and insulated from the private sector and other agencies outside government. With budget crises and the realization of significant inefficiencies in the public sector in the early 1980s, and with the coincident rise in theories and disciplines such as public choice theory, principal-agent theory and transaction cost economics, it was recognized that traditional approaches to public administration were in need of reform. A range of reforms—collectively referred to as the new public management or NPM—rapidly formed a new model of state management. At the heart of these reforms was a shift from government by control to government by contract. This typically involved changes in organizational structure (e.g., moves toward managerial autonomy or corporatization of public entities) and introduction of market processes (e.g., through formal privatization or market-simulating reforms within the public sector, such as purchaser-provider splits and decentralization), and it came to imply a redefinition in the government’s role from that of direct service provider to one of stewardship, oversight and regulation (Batley 1999a). The poster-country for these reforms was New Zealand, where sweeping reforms were carried out in the late 1980s—sparing no sector—and which remains as the most comprehensive example of a NPM-motivated reform process to date (Bale and Dale 1998).

The concepts of NPM resonated with health policy specialists and had a major influence on health reforms in the 1980s and 1990s. In most cases the reform effort made no distinction between curative and preventive services and applied similar prescriptions to both. Some concepts—such as purchaser-provider splits, hospital autonomy and decentralization—were effective and took hold for curative services. The greatest success was reported in industrialized countries with high levels of administrative capacity and political stability, such as Singapore and New Zealand, though developing countries such as Ghana, Zimbabwe, Sri Lanka and Thailand also attempted them to various degrees (Russell, Bennett, and Mills 1999). For public health and preventive services, however, impact evaluations—which weren’t conducted until the late 1990s—revealed a less positive picture. In this section we examine new public management strategies for public health under three headings: true market reforms, i.e., those involving user charges and provider competition; pseudo-market reforms, e.g., purchaser-provider splits, contracting and other market-simulating reforms; and decentralization.

> 1 For a more general discussion of issues of public management for service delivery, see the recent World Development Report (World Bank 2003).

2