rafmacalaba/datause-encoder-data huggingface dataset:
- 28 -

**Figure 13: Stock Of government securities by maturity – HUF million, Dec.  2004**

<!-- Start of picture text --> 3,500<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>2005 2008 2011 2014 2017 2020 2023 2026<br>Bonds Public Issues Bonds Private Placements Treasury Bills Retail Securities<br><!-- End of picture text --> <!-- Start of picture text --> Source: AKK.<br><!-- End of picture text -->

# **Figure 14: Average maturity and duration of government securities, 2002-2004**

<!-- Start of picture text --> 5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>2002Q1 2002Q3 2003Q1 2003Q3 2004Q1 2004Q3<br>Maturity -- Bonds Maturity -- All Securities<br>Duration -- Bonds Duration -- All Securities<br><!-- End of picture text --> Source: AKK.

57. The short average maturity of the stock of government debt looks particularly disappointing, because the lengthening of the maturity of government debt is one of the first outcomes expected from a pension reform. A more pronounced increase in maturity and duration would indicate that the Government is succeeding in developing this key market and building a yield curve for government securities, which provides the benchmark and the necessary preconditions for the development of other fixed income instruments with long durations such as mortgage, corporate, and infrastructure bonds.

58. The short duration of Government debt may not reflect a failure of the pension reform, however, but a deliberate strategic decision by the Debt Management Agency
for possible heterogeneity across country groups – industrial economies, where FDI is characterized by large inflows and outflows and a large share of M&A in total investment flows; developing countries, where the M&A share of total FDI is much lower, and outflows are dwarfed by inflows; and Latin America, where the FDI boom of the 1990s has been most closely associated with privatization of public enterprises.

The paper is organized as follows. Section 2 introduces the main concepts and data regarding the composition of FDI, and offers a brief overview of recent trends in the volume and structure of FDI across a large number of industrial and developing countries. Section 3 reports the results of causality tests between the M&A and greenfield components of FDI, and between each of them, domestic investment and GDP growth. Section 4 concludes.

# **2. FDI: Concepts, data and trends**

Direct investment undertaken by foreign firms in a host country (i.e., the country of the target firm whose assets are being acquired) can take the form of either _greenfield investment_ or _mergers and acquisitions_ (M&As), depending on whether the transaction involves mainly newlycreated assets coming under control of the foreign firms, or just a transfer of existing assets from local firms, respectively.

The fact that FDI represents just a financing flow, and not necessarily investment, is often overlooked. The same applies to greenfield investment, which does not necessarily reflect the acquisition of new fixed assets. Like FDI, greenfield investment includes all financial transfers from a multinational’s headquarters to its subsidiary (and back, in the case of outflows). These could take the form of equity or loan financing. While most financial transfers presumably

> 9 The received literature that investigates the relationship between FDI, domestic investment, and growth has focused on total FDI.  Studies of this type include Choe (2003), Basu, Chakraborty, and Reagle (2003), de Mello (1999), Ericsson and Irandoust (2001), Agosin and Mayer (2000), and Razin (2002).

5
- [62] robinson.j and e.yeh (2011) “Transactional Sex as a Response to Risk in Western Kenya,” _American Economic Journal: Applied Economics_ 3: 35-64.

- [63] romano.j.p and m.wolf (2016) “Efficient Computation of Adjusted p-values for Resampling-based Stepdown Multiple Testing,” _Statistics & Probability Letters_ 113C: 38-40.

- [64] shah.m (2013) “Do Sex Workers Respond to Disease? Evidence from the Male Market for Sex,” _American Economic Review: Papers & Proceedings_ 103: 445-50.

- [65] tankard.m (2016) Saving Money on Your Own or in Solidarity: An Experiment on Women’s Empowerment and Intimate Partner Violence, mimeo, Princeton.

- [66] thomas.a, t.nkuzimana, a.perez hoyos, and f.kayiyakire (2014) Impact of the West African Ebola Virus Disease Outbreak on Food Security, European Commussion, Joint Research Centre.

- [67] thomas.m.r, g.smith, f.h.g.ferreira, d.evans, m.maliszewska, m.aliszewska, m.cruz, k.himelein and m.over (2015) The Economic Impact of Ebola on sub-Saharan Africa: Updated Estimates for 2015, Washington DC: World Bank.

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45
# Casting the Tax Net Wider:

# Experimental Evidence from Costa Rica

Anne Brockmeyer, Marco Hernandez, Stewart Kettle, and Spencer Smith<sup>*</sup> Keywords: informality, tax evasion, rms, communication experiment. JEL codes: H25, H26, H32, O10.

> *Brockmeyer: The World Bank, email: abrockmeyer@worldbank.org; Hernandez: The World Bank, email: marcohernandez@worldbank.org; Kettle: Behavioural Insights Team, email: stewart.kettle@behaviouralinsights.co.uk; Smith: Department of Economics, University of Oxford, email: spencer.smith@economics.ox.ac.uk. We thank Christopher Adam, Miriam Bruhn, David McKenzie, Simon Quinn, Gabriel Tourek, and seminar/conference participants at the Pacic Conference for Development Economics, the Centre for the Studies of African Economies Conference, the Oxford University Development Lunch, and the Centre for Business Taxation Seminar for helpful comments. We are grateful to the Ministry of Finance and the General Directorate of Taxation of Costa Rica for their collaboration. In particular, we thank Fernando Rodriguez Garro and Carlos Vargas Duran, as well as to Laura Badilla Castro, Lorena Chacon Sanchez, Graciela Garcia Santamaria, Mercedes Padilla Delgado, Manuel Enrique Ramos Campos, Karla Salas Corrales, Ronald Solorzano Vega, and Giovanni Tencio Pereira. All errors are our own.
The revextue and incidence effects noted above<sup>are then combined and</sup> summarized in a single measure that allows<sup>different sectors to be ranked</sup> with respect to the efficiency-cum-equity<sup>cost of raising revenue.</sup> These **rankings are used to** compare traditional<sup>versus general equilibrium based</sup> **approaches to incidence** analysis. This is<sup>done by ascertaining that would</sup> **happen to the rankings** first if, as is<sup>common in partial equilibrium</sup> incidence analysis, it were assumed that<sup>all tax increases are fully forward</sup> **shifted into user** prices, and if second,<sup>as is sometimes the case in partial</sup> equilibrium analysis, it were assumed<sup>that all tax increases are fully</sup> backward shifted into factor returns. Incidence<sup>judgments are more heavily</sup> influenced in the first case by the<sup>characteristics of the socio-economic</sup> groups that consume the product on which<sup>the tax is to be increased and in</sup> the second case by the characteristics of those<sup>employed in its production.</sup> The change in the rankings in the three cases:<sup>full forward shifting, full</sup> backward shifting and the "central"<sup>case of a combination of forward and</sup> backward shifting of taxes is considerable<sup>and highlights the critical</sup> importance of labor market specifications<sup>and substitutability assumptions</sup> in production in arriving at judgments about tax<sup>policy proposals.</sup> The second part of the paper uses a tax policy<sup>model based on data</sup> from China to examine the desirability<sup>of recommending broad uniformity of</sup> tax rates over a large number of sectors.<sup>While such a rule of thumb leads</sup> to generally acceptable outcomes in market-based<sup>economies, it requires</sup> substantial re-examination in decentralizing<sup>socialist economies, such as</sup> China, where some production is centrally<sup>planned, and subject to price</sup> controls, a **d** some is subject to decentralized decision-making and **transacted at market prices.** The planning authorities allocate certain inputs to enterprises and require the delivery<sup>of certain outputs; these</sup> **transactions occur at controlled** prices.<sup>However, enterprises may sell any</sup>
45 Graph 19

# U.S. TERM SPREAD AND STOCK PRICE OVERVALUATION IN LAC

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# **6.1 Panel Analysis**

Our analysis may be subject to some omitted variable bias if countries with high statistical capacity share commonalities unrelated to their number qualitative articles. Below, rather than controlling for the number of qualitative articles, we add country fixed effects and year dummy variables to our OLS regressions, so that only within-country variation over time is used to identify our main relationships. Standard errors are clustered at the country level. Note that this specification may remove relevant variation if some countries have a constant high output of papers using data due to a constant high statistical capacity and vice versa.

Using this specification, the relationship between academic output using data and log GDP per capita is similar as previously estimated, with an elasticity between around 0.4 and 0.6 depending on the specification (Table 4). The relationship with population is not statistically significant under this specification in most cases. The SPI overall scores are not statistically significant either under this specification, but because the SPI overall scores only are available starting in 2016, the time series is relatively short. To overcome this shorter time series, an “extended SPI” index is created that incorporates data from the older Statistical Capacity Indicator (SCI), which the World Bank has produced since around 2004.<sup>4</sup> Using the longer time series in the extended SPI, a 10 unit increase in the extended SPI overall score is associated with an increase in academic output using data of around 0.1%. The estimated coefficient is statistically significant at the 5% level and similar in magnitude to the cross-sectional results that control for the number of qualitative articles. The extended SPI data sources score is also statistically significant at the 5% level.

_Table 4. Longitudinal Relationships between Number of Papers Using Data and Statistical Performance_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Log GDP per capita|0.58**|||0.51|0.58**|0.62**|
||(0.20)|||(0.33)|(0.22)|(0.22)|
|Log Population||0.18||1.09|0.40|0.38|
|||(0.35)||(0.69)|(0.37)|(0.37)|
|SPI Overall Score|||0.00|0.00|||
||||(0.00)|(0.00)|||
|SPI Overall Score<br>(Extended Series)|||||0.01+<br>(0.00)||
|SPI Data Sources Score<br>(Extended Series)||||||0.00+|

4 The SCI does not contain as many indicators as the SPI (25 indicators versus 51 in the SPI), and not all the indicators are similar. Eleven indicators in the SCI overlap with the SPI: national accounts base year, consumer price index, balance of payments manual in use, government finance accounting, population census, agriculture census, health surveys, household consumption/income survey, CRVS availability, and the IMF SDDS status of the country. Using these indicators, the extended SPI series contains 35 indicators with data back to 2004. These include 5 indicators covering data use, 1 covering data services, 16 covering data products, 8 covering data sources, and 4 covering data infrastructure. The correlation between the extended SPI and the official SPI overall score is around 0.90.

20
Policy Research Working Paper 10723

# **Abstract**

Unequal access to economic opportunity for individuals with different innate characteristics, such as ethnicity or parents’ socioeconomic status, is often seen as both morally undesirable and bad for economic growth. This paper estimates inequality of opportunity, or the share of inequality explained by birth characteristics, across 18 countries in Sub-Saharan Africa. For many countries, this is the first time inequality of opportunity is measured. The paper uses nationally representative household survey data harmonized to allow for cross-country comparisons. Using consumption per capita as the outcome, the findings show that inequality of opportunity in Sub-Saharan Africa is stark and more pronounced than previously estimated. On average, inherited circumstances explain more than half of inequality in the region. Estimates range from 40 to 60 percent in most countries and reach 74 percent in South Africa. The findings show that birthplace, parents’ education, and ethnicity tend to be the most significant contributors, but there is large variation in the importance of circumstances across countries. This represents the most comprehensive estimate of inequality of opportunity to date in the poorest and one of the most unequal regions in the world, and it underscores the pressing need for policy makers to intensify their efforts to address inequality of opportunity to foster societies that are more equitable and unlock the full potential for growth in the region.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at jlebow@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> Home | GATS RTACommitments Services Trade Policy Database __Statistics User Guide<br>Services Trade Policy Database and STRI<br>@Policy Information Ostri ®<br>Economies (i)<br>All Economies .<br>Sectors and Subsectors (i)<br>All Sectors °<br>Mode of supply<br>All Modes ~<br>Measure Categories \U<br>All Measure Categories ba<br>Measure Categories i)<br>ail<br>~<br>4-(7] All items<br>+ (] A. Conditions on market entry<br>h- (4) Ad Forms of entry {including foreign equity limits)<br>h- (] A2 Quantitative measures (for firms and natural persons)<br>+ (4) A.3 Conditions on licensing/investment screening/qualifications relating to market entry (for-<br>~ (4) Notification of investment required<br>~ (4) Registration required<br>h- (4) Screening of investment<br>h- (4) License or authorization required (incl. concessions)<br>~ (4) Minimum capital requirement<br>~ (4) Nationality or citizenship required for license to practice<br>~ (4) Prior or permanent residency required for license to practice<br>h- (4) Domicile required for licence to practice<br>~ (4) Consolidated supervision by their home regulator<br>~ (4) Period of time since an applicant's incorporation in its home country before obtaining a|<br>: : ~bo (4)[4] AutomaticLicence: other recognition requirements of foreign license granted ¥<br>< ><br><!-- End of picture text -->
Policy Research Working Paper 10026

# **Abstract**

Over the past decade, humanitarian assistance and social protection have increasingly emerged as a policy response tool to support crisis-affected populations facing conflict or natural disasters. This paper presents a descriptive literature review of non-contributory humanitarian assistance interventions in low-and-middle income countries. It uses evidence from twenty-one experimental or quasi-experimental studies to understand the effects on five outcome categories: (i) basic needs, (ii) financial outcomes, (iii) gender, (iv) human development, and (v) social cohesion. The findings show that gender, human development, and social cohesion are the least explored outcomes in humanitarian contexts. Moreover, evidence is scarce on the comparative performance of different modalities (for example, cash vs. in-kind), targeting mechanisms, cost-effectiveness of alternative interventions, heterogeneity analysis, and longer-term effects of interventions. The paper makes the case that there is a high dividend to be earned from conducting more impact evaluations in humanitarian settings.

This paper is a product of the Development Impact Evaluation Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at itrako@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In both private factory and cooperative factory areas, cane farmers tended to have slightly larger farms than did farmers who did not grow cane (e.g., 2.79 acres vs. 2.66 acres in cooperative factory areas). Cane farmers also tended to be slightly older than farmers who did not grow cane (49.5 years vs. 48.5 years) in cooperative reserve areas. This was also the case in private factory areas (49.2 years vs. 47.7 years). In both private and cooperative factory areas, cane farmers were slightly more likely to own tractors than non-cane-growing farmers were. Specifically, 12.8 percent (13.3 percent) of cane farmers owned a tractor in cooperative (private) factory areas compared with 7.0 percent (8.3 percent) of non-cane farmers.

Summary statistics for the sugar factories in AP are calculated using data from annual NFCSF and STAI publications (table 2). Factory-specific floor prices are based on prices received by cane farmers for 2007–8 for the nine factories in the survey area. The mean pol% of cane reported in the table is computed across all sugar factories in AP over the 1992–3 through 2006–7 processing years. Privately owned factories have, on average, a higher yield of sugar content than their cooperative counterparts and face a higher floor price.

The hypotheses regarding cane production costs are tested by estimating the farm per unit cultivation cost measured in Rs/metric ton as a function of farm size, factory area-specific dummy variables, and region- or district-specific dummy variables to control variation across different geographic areas. The first two models distinguish between farmers in private factory areas and farmers in cooperative factory areas. The latter two models distinguish among farmers in private factory reserve areas, farmers in private factory non-reserve areas, and farmers in cooperative factory areas. In all models, the base factory area is cooperative; the coefficients on

> 14 Electricity for farming is subsidized by the state government at no cost to farmers, so there is no electricity cost in cane production.

18
month are associated with sales the following month. Correcting for this mistiming increases the correlation with reported profits to around 0.70. We conclude from the more detailed analysis of measurement issues that the reported profit is the best measure of the firm’s profitability, and we use those data for the remainder of this paper.

The baseline survey also gathered detailed information on the replacement cost of assets used in the enterprise, and whether they were owned or rented. 99 percent of assets excluding land and buildings are owned by the enterprises. The majority of assets owned by the enterprises are land and buildings. In the baseline sample, these average 121,000 rupees ($1200), though about a sixth (15 percent) of firms report they own no assets in this category. The firms also reported an average of 14,400 ($145) rupees worth of machinery and equipment and 13,000 ($130) in inventories.

In each subsequent round of the survey, we then asked firms to report on the purchase of new assets, the disposition of assets by sale or damage, and the repair and return to service of assets. These are used to calculate a change in assets over the quarter. Combined with the data from each previous quarter, these data allow us to estimate investment levels by category for each quarter of the survey. Note that the data are not adjusted for depreciation of machinery and equipment. Given the relatively short period over which the data are gathered and the high percentage of assets invested in inventories, we do not see this as a major concern. The survey also asks the current value of inventories of raw material, work in progress and final goods each quarter.

We began with 405 firms in the baseline survey, and still had 384 of the firms in the survey by the fifth wave, an attrition rate of only 5.2 percent. However, only 388 of the 405 firms reported profits in the first wave, 390 in the second wave, 381 in the third, 366 in the fourth, and 366 in the fifth. We concentrate our analysis on the unbalanced panel of 383 firms reporting at least three waves of profit data. Fifteen of the firms assigned to the control group and 7 of the firms assigned to the treatment group attrited from the sample before reporting at least three waves of profit data. We show our results are robust to this attrition.

10