rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7600

# **Abstract**

This paper studies tax withholding on business sales, a widely used compliance mechanism which is ignored by public finance theory. A withholding scheme, whereby the payer in a transaction collects tax from the payee, is introduced in a standard evasion model with third-party reporting. If the taxpayer can fully reclaim the tax withheld, withholding is irrelevant to her evasion decision. However, in the presence of costly reclaim, low salience of enforcement or extensive margin compliance gaps, withholding can raise tax payment. To show this empirically, the study exploits a ten-year panel of income tax and sales tax records from 400,000 firms in Costa Rica, and over 20 million third-party information and withholding reports. First, an anatomy of compliance shows that firms are relatively compliant with third-party information on the extensive, intensive and payment margin. An event study then shows that coverage by third-party reporting leads to a 20–50 percent increase in reported taxable income. Finally, a difference-in-difference and regression discontinuity estimation of a withholding rate increase isolates the impact of withholding. A doubling of the withholding rate leads to a 33 percent increase in sales tax payment among treated firms and an 8 percent increase in aggregate sales tax revenue. The mechanisms are reduced misreporting and incomplete reclaim of the tax withheld. The large compliance impact of withholding rationalizes its widespread use in low compliance contexts.

This paper is a product of the Macroeconomics and Fiscal Management 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 authors may be contacted at abrockmeyer@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
# Aid Tying and Donor Fragmentation

Stephen Knack<sup>a,</sup><sup>_∗_</sup> , Lodewijk Smets<sup>b</sup>

> _aWorld Bank, Washington DC_

> _bInstitute of Development Policy and Management, University of Antwerp, Belgium_

## **Abstract**

This study tests two opposing hypotheses about the impact of aid fragmentation on the practice of aid tying. In one, when a small number of donors dominate the aid market in a country, they may exploit their monopoly power by tying more aid to purchases from contractors based in their own countries. Alternatively, when donors have a larger share of the aid market, they may have stronger incentives to maximize the development impact of their aid by tying less of it. Empirical tests strongly and consistently support the latter hypothesis. The key finding - that higher donor aid shares are associated with less aid tying - is robust to recipient controls, donor fixed effects and instrumental variables estimation. When recipient countries are grouped by their scores on corruption perception indexes, higher shares of aid are significantly related to lower aid tying only in the less-corrupt sub-sample. This finding is consistent with the argument that aid tying can be an efficient response by donors when losses from corruption may rival or exceed losses from tying aid. When aid tying is more costly, as proxied by donor country size and income, it is less prevalent. Aid tying is lower in the Least Developed Countries, consistent with the OECD Development Assistance Committee’s recommendation to its members.

_Keywords:_ aid, development assistance, collective action, corruption

## **1. Introduction**

A consensus in the international aid community holds that tying aid to purchases from the donor country reduces its effectiveness. More recently a consensus has also emerged on the importance of reducing aid fragmentation and the transactions costs it imposes on recipient countries. This study explores possible trade-offs and complementarities between these two objectives. We test two opposing hypotheses about the relationship between aid fragmentation and aid tying. Some observers caution that reducing aid fragmentation can reduce the bargaining power of a recipient country government relative to that of its remaining donors. If reducing the number of donors implies they have more monopoly

> _∗_ Corresponding author

> _Email addresses:_ `sknack@worldbank.org` (Stephen Knack), `lodewijk.smets@ua.ac.be` (Lodewijk Smets)
Table 7: Impact of Conflict Intensity on Schooling Enrollment and Attainment, 15-18 year-olds

||(1)<br>Years of<br>Education<br>Female 15-18|(2)<br>Years of<br>Education Male<br>15-18|(3)<br>Enrollment<br>Female 15-18|(4)<br>Enrollment Male<br>15-18|
|---|---|---|---|---|
|Explained Variable|Years of<br>education|Years of<br>education|Enrolled|Enrolled|
|Specification|Eq. (3)|Eq. (3)|Eq. (3)|Eq. (3)|
|District casualties until start of|0.5322*|-0.0977|-0.0024|0.0062|
|survey|(0.2988)|(0.2818)|(0.0475)|(0.0411)|
|=1 if Rural|-1.1541***|-1.0202***|-0.1109***|-0.0547**|
||(0.2132)|(0.1895)|(0.0273)|(0.0229)|
|=1 if Head has primary education|0.7790***|0.0797|0.0940***|-0.0054|
||(0.1433)|(0.1512)|(0.0183)|(0.0229)|
|=1 if Head has secondary|2.9949***|2.1377***|0.3160***|0.2208***|
|education|(0.1830)|(0.1437)|(0.0264)|(0.0240)|
|=1 if Head has higher education|4.1748***|3.2704***|0.4228***|0.3586***|
||(0.2728)|(0.2726)|(0.0331)|(0.0442)|
|Constant|3.9172***|5.4001***|0.5180***|0.7323***|
||(0.2240)|(0.1952)|(0.0312)|(0.0274)|
|Panel variable|District|District|District|District|
|Age at Interview dummies|Yes|Yes|Yes|Yes|
|DHS 2006 x Region x Age<br>dummies|Yes|Yes|Yes|Yes|
|Observations|3912|3420|3912|3420|
|No. of Clusters|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.2339|0.2100|0.1685|0.1450|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Sample only includes individuals surveyed in the Nepal DHS 2001 and 2006 and aged 15-18 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were not covered, namely Dolpa, Jajarkot, Rolpa and Rukhum. The small districts of Manang and Mustang were not surveyed either, but these districts did not experience any casualty during the conflict. Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

43
areas where scarcity in training and education may exist because the existence of a wage differential due to training may be interpreted as rent (to skilled labor).

**_Education_** _in_ **_Ghana._** The system of education in Ghana consists of up to 17

years of pre-university education: six years of primary school, followed by four years of middle school and seven years of secondary school. After pre-university education, higher education includes professional. polytechnic, or university education.<sup>6</sup>

**Table 1. Education by Gender and** Resiidence **in Ghana (percent)**

||Ge|nder|Res|idence|
|---|---|---|---|---|
|Educational level<br>completed|Female|Male|Accra|Other|
|None|15.1|8.0|5.4|15.9|
|Primary school|1.6|3.8|2.9|4.3|
|Middle school|31.4|48.9|46.5|48.8|
|Secondary school|18.4|12.4|14.7|9.2|
|Vocational school|23.8|11.4|13.6|15.2|
|Professional and technical<br>schools|8.7|14.3|15.2|6.1|
|University|1.1|1.3|1.8|0.6|
|Total|||||
|Percent|100|100|100|100|

_Note:_ The values show the percentage of each category of workers with each level of education. For example, 15.1 percent of the females in the sample have not completed any level of education. _Source:_ Round Three of the 1994 Regional Prograrn on Enterprise Development for Ghana.

5 See Barro (1991) and Mankiw, Romer, and Weil (1992).

> 6 In 1987, the education sector was reformed and pre-tertiary education was reduced to a total of 12 years.

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# **Table 10: Estimations of contract volumes formed on oDesk with workers in India**

||D|ependent varia|ble is share of|oDesk contract|s formed with|workers in Ind|ia|DV is India's<br>|DV is share<br>|
|---|---|---|---|---|---|---|---|---|---|
||Base<br>estimation|Including<br>distance<br>covariates<br>only|Weighting<br>by log<br>population|Unweighted<br>estimation|Adding<br>worldwide<br>oDesk<br>contracts|Adding<br>trade with<br>India<br>control|Excluding<br>UAE<br>outlier firm|share of<br>dollar value<br>of contracts<br>for country|of company<br>contacts with<br>Indian ethnic<br>name|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Indian diaspora share of|1.090***|0.728***|0.969***|0.850*|1.135***|1.004***|0.531**|0.694*|2.577***|
|country population|(0.197)|(0.156)|(0.364)|(0.429)|(0.218)|(0.236)|(0.204)|(0.360)|(0.188)|
|Indicator for geographical distance|0.071**|0.041|0.090**|0.087**|0.069**|0.077**|0.059*|-0.033|0.004|
|to India of 5,000-10,000 kilometers|(0.030)|(0.026)|(0.043)|(0.044)|(0.031)|(0.033)|(0.030)|(0.056)|(0.058)|
|Indicator for geographical distance|0.095***|0.088***|0.100**|0.092**|0.119***|0.102***|0.087***|0.058|-0.074|
|to India of >10,000 kilometers|(0.029)|(0.030)|(0.039)|(0.041)|(0.027)|(0.033)|(0.028)|(0.063)|(0.047)|
|Log population|-0.009||-0.016*|-0.017*|0.010|-0.009|-0.010|0.011|0.000|
||(0.007)||(0.009)|(0.010)|(0.008)|(0.007)|(0.006)|(0.013)|(0.007)|
|Log GDP per capita|-0.042**||-0.051*|-0.045|-0.008|-0.041*|-0.034|0.010|-0.078**|
||(0.022)||(0.027)|(0.029)|(0.025)|(0.023)|(0.022)|(0.039)|(0.035)|
|Log telephone lines per capita|0.004||0.002|-0.004|0.005|0.004|-0.005|-0.056|0.052|
||(0.034)||(0.039)|(0.038)|(0.030)|(0.034)|(0.034)|(0.055)|(0.041)|
|Overall fit of project profile with|0.078**||0.070|0.054|0.085**|0.076*|0.074*|0.015|-0.032|
|India's worker profile|(0.039)||(0.047)|(0.054)|(0.039)|(0.040)|(0.039)|(0.057)|(0.083)|
|Log count of oDesk contracts|||||-0.027***|||||
|worldwide|||||(0.009)|||||
|Trade with India as share of GDP||||||0.660<br>(1.046)||||
|Observations|92|92|92|92|92|92|92|92|92|
|Mean of DV|0.341|0.341|0.341|0.341|0.341|0.341|0.338|0.372|0.096|
|Relative effect (1% diaspora share)|0.032|0.021|0.028|0.025|0.033|0.029|0.016|0.019|0.268|

Notes: Country-level regressions estimate traits associated with a larger share of work being contracted to India. Regressions weight by log number of worldwide contracts formed on oDesk, unless otherwise noted, and report robust standard errors.
The natural resources exports over total exports variable shows less similarity between the two estimation techniques. In cross section, it is uniformly negative and appears insensitive to the addition of any of the controls, or concentration<sup>measures or</sup> IIT measures. This would seem to suggest some intrinsic effect of a high natural resource concentration in exports that is not accounted for by any of the usual channels.<sup>However,</sup> again, the panel results cast some doubt on this conclusion. The variable enters negatively and significantly with the basic conditioning variables, however in both cases the Sargan statistic rejects the adequacy of the instruments, casting some doubt<sup>on the</sup> estimates. Giving the results the benefit of the doubt, the influence of natural resource exports over exports, as well as the evidence of misspecification, weakens with<sup>the</sup> introduction of the second conditioning set and it is difficult to know whether this<sup>reflects</sup> a previously unreliable result, or that a high resource concentration in exports<sup>has a</sup> deleterious effect on capital accumulation. The effect, and evidence of questionable instruments, disappears completely with the addition of the terms of trade variable<sup>and</sup> never reappears, thus suggesting that it is not so much natural resources per se, but the fact that their terms of trade fell during this period. Including macro stability, the Herfindahl index of export concentration and IIT variables do not substantially alter<sup>the</sup> finding that resource exports/exports is not a statistically significant variable. Finally, although we do not show this specification in Table 3b, it is worth noting that with<sup>the</sup> basic controls, plus factor accumulation, the inclusion of the export Herfindahl index alone eliminates the negative effect of export concentration in natural resources, but the Sargan test for that model remains unsatisfactory. Arguably concentration per se is negatively correlated with growth, but concentration in natural resources in particular,<sup>is</sup> not.

The broadly similar pattern of the coefficients across conditioning sets<sup>to that of</sup> ihe export Herfindahl raises the question of whether resource exports over exports is,<sup>in</sup> iact, simply a weak proxy for export concentration more generally. This is supported<sup>by</sup> the complete disappearance of the variable when the Herfindahl is added to the<sup>basic</sup> conditioning set. In the absolute most generous interpretation, the negative<sup>impact of</sup> natural resources is not happening through productivity growth as Sachs and Warner 13
**Do Gender Norms Become Less Traditional with Displacement? The Case of Colombia**<sup>*</sup>

Eliana Rubiano-Matulevich<sup>+</sup> _Keywords:_ gender norms, conflict, internal displacement. _JEL codes:_ J16, D10, D74, O15, F22.

The authors of this paper conducted their research under Gender Dimensions of Forced Displacement project. The project is co-led by Lucia Hanmer and Diana Arango under the guidance of Hana Brixi, Global Director, Gender Unit, The World Bank Group.

* This work is part of the program ‘Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership’. The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR. + World Bank Group, Washington DC, USA. The author would like to thank Lisa Andersson, Franziska Gassmann, Khalid Koser, Ortrun Merkle, Zina Nimeh, Isabel Ruiz, Melissa Siegel, and Jennifer Waidler for their thorough review of earlier versions of this paper. The author is grateful to Diana Arango, Gabriel Demombynes, Kathryn Falb, Lucia Hanmer, Michelle Hynes, Patricia Justino, and Jeni Klugman, for their insightful comments and suggestions. Ragui Assad, Daisy Demirag, Jocelyn Kelly, Caroline Krafft, Ezequiel Molina, Maria Beatriz Orlando, Julieth Santamaria, Sergio Rivera, and Migration Days workshop participants at Maastricht University provided valuable comments to earlier versions.
capital goods are taxed. Because the capital account is exogenous, rates of return across countries can differ over time and across simulations. The model solves only for relative prices. The numeraire, or price anchor, in the model is given by the export price index of manufactured exports from high-income countries. This price is fixed at unit value in the base year and throughout time.

The new version of the LINKAGE model, Version 6, is based on the latest release of the GTAP dataset, Release 6.0.<sup>6</sup> Compared with Version 5 of the GTAP dataset, Version 6 has a 2001 base year instead of 1997, updated national and trade data, and, importantly, a new source of the protection data. The new protection data come from a joint CEPII (Paris)–ITC(Geneva) project. The product of this joint effort, known as MAcMap, is an HS-6 detailed database on bilateral protection that integrates trade preference, specific tariffs, and TRQs.<sup>7</sup> In summary, the new GTAP database has lower tariffs than the previous database because of the reform efforts between 1997 and 2001—for example, China’s progress toward WTO accession and continued implementation of the Uruguay Round Agreement—and the inclusion of bilateral trade preferences.

The version of LINKAGE used for this study comprises a 27-region, 25-sector aggregation of the GTAP dataset.<sup>8</sup> There is a heavy emphasis on agriculture and food, which make up 13 of the 25 sectors, and a focus on the largest commodity exporters and importers.

> 6. GTAP is an international consortium of trade researchers from universities, research institutions, and national and international agencies. It is based at Purdue University. The GTAP Center provides four key resources to the trade community. First is an integrated and consistent international database for trade policy analysis. The current version is composed of 87 country and region groupings and 57 economic sectors. The second is a publicly available global trade model, also known as the GTAP model. (Note The LINKAGE model is distinct from the GTAP model, though it uses the same underlying database.) The third is an annual course in applied trade modeling. Finally, GTAP organizes and cohosts the annual Conference on Global Economic Analysis. More information on the GTAP Center and project can be found at http://www.gtap.agecon.purdue.edu.

> 7. More information on the MAcMap database is available at http://www.cepii.fr/anglaisgraph/bdd/macmap.htm and from Bouët and others (2004).

> 8. Details on the aggregation are available in appendix A of this paper.

5
adults, but it can also influence expectations about opportunities and well-being in the future. The results of this analysis indicate that the VCT offer is associated with a lower marriage rates and initiation of fertility among poorer young women that report some likelihood to being infected and wealthier young men. This is ex-post analysis that was not in the experimental design. Therefore, while informative, the results should be cautiously interpreted.

# **_Evidence on Testing_**

There are a handful of studies purposively designed to examine the impact of testing on risky sexual behaviors.<sup>3</sup> Delavande and Kohler (2012), in investigating this phenomenon with a sample of adults in Malawi, exploit a random assignment in vouchers for cash to be redeemed upon retrieval of one’s test result from temporary VCT sites. They examine the effect of testing on a range of risky behaviors, and show that selectivity into testing is important for inferring the impact of testing. Learning one is HIV-positive results in fewer partners and more condom use up to two years later. Using the same voucher experiment as Delavande and Kohler, Thornton (2008) finds that individuals who receive a positive test result are more likely to purchase condoms two months later. However, she does not find a significant effect for those who learned a negative test result. She also does not find that beliefs on one’s status prior to testing matters in terms of impact of testing.

Other studies have also considered the role of beliefs about own infection, and the updating of beliefs after learning a test result. Gong (2012), using data from Nairobi, Kenya and Dar es Salaam, Tanzania, finds that individuals who learn that they are positive (and did not think so before that test) are more likely to engage in risky sexual behavior after being tested,

> 3 Gersovitz (2011) discusses studies that explore the implications of testing with non-random testing. See also Potts (2008).

5
country one can find rural health clinics completely without drugs while the government (or donor) financed medicines are easily available on the black market. For example, over 70 _percent_ of the government supply of drugs disappeared in Guinea in 1984 (Foster, 1990). Various studies in Cameroon, Uganda, and Tanzania estimated that about 30 percent of publicly supplied drugs were misappropriated, in one case as much as 30 to 40 percent of public supply was "withdrawn for private use" by staff (World Bank, 1994a).

# _A) Evidence on quality_

There are a number of empirical studies of how quality of is linked to demand for public facilities (e.g. Akin, Guilkey, and Denton, 1995, Lavy and Germain, 1994, Lavy, et. al., 1996, Mwabu, Ainsworth, and Nyamete, 1993, Thomas, Lavy, and Strauss, 1996) and this literature is well reviewed in Alderman and Lavy (1996). Even though the measures of quality are not always satisfactory, the findings are that demand is responsive to quality. An example of the problematic nature of some of the measures is that the absence of various types of drugs is often used to indicate poor quality. Shortages, however, could be caused by high demand and hence it is hard to draw inferences about the causal relationship (a problem generally acknowledged by the authors). In addition, there may be important discrepancies between _de facto_ and _de jure_ measures of quality, a result highlighted by Thomas, Lavy, and Strauss (1996) who show the differential impact on health outcomes of the actual versus the official number of staff.

distance/transport problems (20 percent), lack of clinic fees (14 percent), _harassment by nursing staff /fear of nurses_ (1 1 percent).

20
# **S1.2 Harmonized 12-Month Survey Panels from Labor Force Surveys**

The informality section complements the analysis using harmonized household survey data with analysis using 12-month survey panels from Labor Force Surveys. Data for Argentina cover 2003-2014; for Brazil, 2005-2011; and for Mexico, 2005-2015. The data were harmonized by the World Bank and the Center for Distributive, Labor and Social Studies (CEDLAS, for its acronym in Spanish) at the Universidad Nacional de La Plata in Argentina. Because these panels were developed as part of the Labor Force Surveys (Encuesta Permanente de Hogares-Continua (Argentina), Pesquisa Mensal de Emprego (Brazil), and Encuesta Nacional de Ocupaci ́on y Empleo (Mexico), the geographic coverage is limited to the main metropolitan areas (31 in Argentina, 6 in Brazil, and 32 in Mexico). For further details on the data see for example Cerutti et al. (2019). Table S1.2 shows the number of workers included in the panel in each year.

Table S1.2 Summary Statistics, 12-Month Survey Panels from Labor Force Surveys

||Num|ber of work|ers|
|---|---|---|---|
|Year||||
||Argentina|Brazil|Mexico|
|2004|2,461,869|-|-|
|2005|2,628,739|2,539,639|1,770,798|
|2006|2,742,351|2,515,159|1,611,663|
|2007|2,795,994|2,618,458|1,591,197|
|2008|2,844,509|2,708,733|1,552,292|
|2009|2,918,944|2,715,050|-|
|2010|2,959,980|2,648,601|-|
|2011|3,122,041|2,704,008|1,613,610|
|2012|3,040,144|-|1,552,809|
|2013|1,525,840|-|1,859,417|
|2014|1,650,562|-|1,440,487|
|2015|-|-|1,505,865|

_Note:_ All totals from the household surveys are computed applying sampling weightsexpansion factors provided by the institutes of statistics.

49