rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

Domestic revenues in several African countries are scant and below the minimum level of 20 percent allowing them to achieve development goals (UNDP, 2010). In those countries, low domestic revenue limits the ability of governments to invest in development programs, improve economic efficiency, and reduce poverty (Baunsgaard and Keen, 2010; Agénor et al., 2008). Although developing countries are the hardest hit by taxpayer non-compliance (Fuest and Riedel, 2009), little is known about the determinants of tax compliance and tax morale in those countries, especially in Africa (D’Arcy, 2011; Fjeldstad and Semboja, 2001).

Tax morale is strongly associated with tax compliance, as highlighted by Luttmer and Singhal (2014). The authors define tax morale as "... an umbrella term capturing nonpecuniary motivations for tax compliance as well as factors that fall outside the standard expected utility framework". In addition to capturing citizens’ intrinsic motivation to pay their taxes, tax morale also reflects, the feeling of being guilty or shame for failure to comply with tax obligations. This paper examines the role of trust in public institutions and the neighborhood on tax morale in four African countries: Algeria, Ghana, Morocco, and Nigeria. First, I discuss the theoretical foundations of the relationship between both dimensions of trust and tax morale based on the standard model of tax evasion from Allingham and Sandmo (1972). Further, to isolate the effects of trust in public institutions and the neighborhood on tax morale, I use individual-level data from the sixth wave of the World Value Survey (WVS) covering the 2010-14 period. Lastly, I employ an empirical methodology that accounts for potential endogeneity. The identification strategy relies on the instrumental variables (IV) method and takes advantage of the historical data on slavery at the tribe level and the literature on the cultural heritage of trust.

The slavery data are from Nunn (2008a) and Nunn and Wantchecon (2011) who document the number of slaves exported by the ethnic groups during both the transatlantic and the Indian Ocean slave trade. I first match these historical data with the ethnic group of each respondent in the WVS. Then, following Nunn and Wantchecon (2011) I build a variable cap1
are perceived, which in turn tends to influence utilization and public support for publicly financed health care. The alternative to perceptions surveys is household surveys that are more costly but can provide more extensive evidence and context on how the sector is performing in meeting citizen preferences.

Corruption perception surveys by the World Bank, AfroBarometer, AmericasBarometer, and Transparency International among others, focus on specific sectors including health, and provide perceptions of the extent of corruption in the sector. Results are typically reported either as the share of citizens, business people, experts, or public officials reporting worse-than-neutral corruption outcomes, or as an average of all scores.

Perceptions of corruption in the health sector vary across countries (Figure 7). For the countries for which there are data, the largest shares of households perceiving the health sector as corrupt are in Sierra Leone, Honduras, and Colombia and range from 50-56 percent of respondents, to the “smallest” in Indonesia (20 percent) and Zambia (24 percent).

# **FIGURE 7. SHARE OF HOUSEHOLDS AND PUBLIC OFFICIALS PERCEIVING HEALTH AS CORRUPT, 2001-2006 (PERCENT)**

<!-- Start of picture text --> 80<br>70<br>60 Households<br>Public Officials<br>50<br>40<br>30<br>20<br>10<br>0<br>% of households and public officials reporting corruption in health<br>(2001) (2006) (2002) (2002)<br>Indonesia Zambia (2003) Mozambique (2004) Ghana (2000) Paraguay Peru (2001) Benin (2006) Colombia Honduras Sierra Leone (2002)<br><!-- End of picture text --> Source: World Bank Country Diagnostics (various years).

Respondents in different positions generally have different experiences with and insights into corruption. For instance, a public official may be aware that there is purchasing of positions whereas households may be more aware of the necessity of informal payments to gain access to health services. For all countries in Figure 7 except 56
Policy Research Working Paper 9700

# **Abstract**

This paper estimates the impacts of regulating the use of sanitary and phytosanitary and technical barriers to trade measures through preferential trade agreements on exports of firms in Chile, Colombia, and Peru along the firm size spectrum. The analysis exploits novel data from the World Bank Deep Trade Agreements database and customs covering the universe of exporting firms in each country over 1996–2015. The paper uses a firm-product gravity equation with a stringent set of fixed effects and controls for the overall depth of the preferential trade agreements and product-specific bilateral tariffs. The findings show that firms’ exports increase significantly in destination markets with preferential trade agreements, including a larger number of sanitary and phytosanitary and technical barriers to trade provisions, and the effect is stronger for smaller firms. Provisions for the harmonization of sanitary and phytosanitary regulations in preferential trade agreements also have greater benefits for the exports of smaller firms, and so do preferential trade agreements, including stronger transparency provisions for sanitary and phytosanitary and technical barriers to trade regulations. The results are robust to dropping larger exporters and highly concentrated export sectors to address endogeneity. The benefits of sanitary and phytosanitary and technical barriers to trade provisions are mainly driven by sectors with more heavily-regulated products. Entry into new product markets and increases in export quality partly explain the rising exports of smaller firms. Finally, the estimated impacts are similar regardless of the income level of the preferential trade agreement partners.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment 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 afernandes@worldbank.org, klefebvre@worldbank.org, and nrocha@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
# **Agriculture and food security**

- Econometric analysis of agricultural price and production variation based on accessibility to markets

- Econometric analysis of food insecurity based on accessibility to markets

- Identifying areas ecologically suited for high‐value cash crops but poorly connected to markets

- Researching what degree of development project success is due to accessibility challenges vs. program design flaws

# **Transportation and infrastructure investment**

- Optimizing facility and road placement using accessibility criteria

- Calculating improvements in accessibility (cumulative person hours) and health/economic impacts from proposed new facilities or roads

- Integration with global accessibility modeling tools like OpenRouteService for more automated, interactive modeling of accessibility in Nepal

- Extending the model to reflect the quality or reliability of services

- Detecting road condition deterioration by comparing recorded GPS speeds over roadways to modeled speeds

# **Governance**

- Modeling facility catchment areas to assess overuse/ underuse of facilities based on populations within them

- Detecting possible corruption or misreporting using the above

- Breaking down accessibility to various services by ethnicity, caste and/or religion

# **Health care**

- Assessing the impact of accessibility on health care utilization and outcomes, by health care service type

# **Economic development**

- Overlapping access to financial institutions with population disbursement and poverty rates to highlight areas where mobile banking may be more financially sustainable than brick‐and‐mortar formal institutions

# **Disaster response**

- Highlighting areas where cash‐based assistance for disaster relief may underperform due to low access to formal financial institutions

We intend to address some of these topics in follow‐up research. As a first step, we are in the process of regressing HRVS data sets related to recovery from shocks on the remoteness data.

We recognize that the data resulting from this research are just as important as the analysis. Providing others useful tools to address accessibility quantitatively was an explicit aim of this research given its foundational importance in Nepal. To this end, we are publishing all output spatial data on the Nepal GeoNode, the code and ArcGIS toolboxes used for analysis on Github, and the resulting maps and charts via a series of provincial dashboards. We can also provide the charts and maps in bulk on request; interested parties are encouraged to contact the authors. We therefore hope to advance the state of understanding accessibility in Nepal and enable more precise analysis of its impacts on economic and developmental challenges. It is our sincere wish that other researchers, policy makers and development partners use, adapt, improve and offer feedback on the model data outputs. We look forward to the results of these efforts.
gender equality. To this end, it is important to note that gender equality does not mean that women rule over men; rather, it guarantees a level playing field that lacks all forms of discrimination against women.

# **Footnotes**

# (1) See Duflo (2005) for a detailed survey.

(2) Several complementary theories have been proposed to explain the profound transformation in the role of women in the family and the workplace in developed countries during the last century. These explanations range from the liberation effect of new consumer durables that greatly decreased the amount of work required to run a household (e.g., washing machines, vacuum cleaners), as suggested by Greenwood, Seshadri, and Yorukoglu (2004), to the revolutionary effect of the oral contraceptive, which facilitated women’s investments in their careers, as argued by Goldin and Katz (2002). The expansion of the service sector with a strong demand for white-collar jobs and skill-biased technological change has also significantly facilitated the increase in the female labor supply (Goldin 1990).

(3) The expansion of women’s rights is related to the evolution of laws that allowed women to own and control separate property, to write contracts, to own and control their earnings, and to maintain custody of their children.

(4) This marital arrangement accounts for approximately one-third of all marriages in rural Pakistan.

(5) See Maccoby and Mnookin (1992); Cancian and Meye (1998); Fox and Kelly (1995).

# **Acknowledgements**

I would like to thank Ana Maria Muñoz and Aline Coudouel at the World Bank and Emmanuel Jimenez and three anonymous referees for their comments and useful suggestions. Corresponding author: L. Farré, Institut d'Anàlisi Econòmica, IAE-CSIC, Campus UAB, 08193, Bellaterra, Barcelona, Tel: (+34) 93 592 9779. Email: <u>lidia.farre@iae.csic.es. The author is a research fellow at IZA, CReAM, and INSIDE</u> and acknowledges the support of the Barcelona GSE Research Network, the Government of Catalonia, and the Spanish Ministry of Science (grant ECO201129663).

29
|**ULATION**|(6)<br>(7)<br>CRT<br>Raven<br>-0.019<br>0.041<br>(0.185)<br>(0.168)<br>-0.001<br>-0.110<br>(0.263)<br>(0.184)<br>0.019<br>-0.151<br>(0.289)<br>(0.235)<br>0.948<br>0.522<br>295<br>295<br>entheses. All out-<br>ude as controls: a<br>f observations are due|
|---|---|
|**NAL REG**<br>**ENCE**|(5)<br>Locus<br>of<br>Control<br>-0.466***<br>(0.123)<br>-0.039<br>(0.200)<br>0.426*<br>(0.249)<br>0.087<br>295<br>evel are in par<br>egressions incl<br>es in number o|
|**EMOTIO**<br> **FOR VIOL**|(4)<br>Negative<br>Valence<br>Diference<br>-0.487**<br>(0.207)<br>0.001<br>(0.316)<br>0.488<br>(0.356)<br>0.170<br>308<br> course-school l<br>ation 1.0. All r<br>ects. Diferenc|
|**E ASP ON**<br>**PENSITY **|(3)<br>Positive<br>Valence<br>Diference<br>-0.569*<br>(0.345)<br>-0.312<br>(0.257)<br>0.257<br>(0.425)<br>0.546<br>308<br>d errors at the <br> standard devi<br>blocks fxed ef|
|**S OF TH**<br>**IAL PRO**|(2)<br>Valence<br>-0.566**<br>(0.232)<br>-0.137<br>(0.197)<br>0.429<br>(0.332)<br>0.196<br>308<br>pped standar<br>ean of 0 and <br>stratifcation-|
|**EFFECT**<br>**TS’ INIT**|(1)<br>Arousal<br>(stress)<br>-0.202<br>(0.176)<br>0.340*<br>(0.179)<br>0.542**<br>(0.219)<br>0.013<br>308<br>ely. Bootstra<br>evel with a m<br>violence and|
|**TABLE 6. HETEROGENEOUS **<br>**BY PARTICIPAN**|i. Efect on low-violence treated students<br>ii. Efect on high-violence treated students<br>iii. Diference of efects between high-low<br>treated students [ii] - [i]<br>iv. _p_-value Ho: Diference = 0<br>Observations<br>***, **, * signifcant at 1%, 5%, and 10%, respectiv<br>comes have been standardized at the control-course l<br>second-order polynomial of student’s propensity for <br>to variation in the response rate for each outcome.|

46
<!-- Start of picture text --> Barge arrival<br>Gate in same 'Actual barge departure' next Barge arrival<br>Discharge from vessel<br>Rail arrival<br>Barge Departure previous 'Actual barge arrival' next Gate out<br>Loaded on vessel<br>Rail departure<br><!-- End of picture text -->

# Potential biases with the data

Despite the data being massive and provided by reputable industry sources, the data used in this analysis may imperfectly reflect the reality on the ground. Potential issues include:

- Although there are rigorous procedures to input the time stamps, the process is not fully automatized in some countries and may depend on practices by local operators, more so for aviation and postal data than for shipping. The very few countries for which there is a strong suspicion this is the case have not been included, as per the recommendation of the data partners (see appendixes by mode on the cleaning process). The published KPI only use data that the data partners consider reliable.

- The shipping data by TradeLens is a very large sample of movement of containers (in the double digit millions). There may be a selection bias in that more efficient operators tend to use advance digital tracking solutions. There is a also a possibility of bias due to some shipping line being more present in the sample than others. However, expert opinion is that it is very unlikely. Furthermore, expert reading of the dwell time data across ports suggests that the dwell time data is consistent with local knowledge when available.

- In some cases, the end of the import process registered in the data may not be the actual delivery to the importer. For instance, small developing economies including islands states may not move containers inland but rather unstuff them and store the goods in bonded facilities. The current system does not detect these old-fashioned logistics practices, which are progressively disappearing.

- Representativeness of transport corridor data for landlocked countries: For rich landlocked countries in Europe, moving containers inland is not representative of their international supply chain. In Europe, goods are cleared mostly at entry into the EU. Thus lead time data from container tracking is not representative of the cross-border processes of these countries and may exaggerate the actual delays. For instance, trucking of imports to landlocked countries in Europe is not directly measured given direct trucking and clearance at the port of entry. In developing landlocked countries, trucks (or rail) are going to inland ICDs,<sup>16</sup> thus the data is representative.

> 16 Except for some landlocked developing economies, such as the West Bank and Gaza, or SACU countries, where goods are cleared at the port of entry.

18
# **<mark>How Well Do Internet-Based Surveys Track Labor Market Indicators in Middle-Income Countries?</mark>**<sup>**¥**</sup>

**<mark>Vidhya Soundararajan, Sidiki Soubeiga, David Newhouse, Amparo Palacios-Lopez, Utz Johann Pape, Michael Weber</mark>**

> ¥ We thank the RIWI corporation for sharing the data from their online surveys for this paper. The data were collected through the Random Domain Intercept Technology (RDIT), patented by RIWI corporation.
The trigonometric representation is also convenient in terms of its flexibility in accommodating time-varying seasonality. Symmetrically with the trend representation in equation an 11-vector of independent innovations. In terms of the general specification (4), this may be shown to be equivalent to the Harvey (1989) representation: where σ<sup>*</sup> denote virtual conjugate processes, the 6-vector ζ is a seasonal innovation process and ζ<sup>*</sup> is * its virtual conjugate with Var(ω _jt_ ) = Var(ω _jt_ ) .<sup>14</sup> Harvey (2006) argues that, in practice, very little is lost by additionally imposing Var(ω _jt_ ) = Var(ω1 _t_ ) ( _j_ = 2,,6) so that the variation in the seasonals is accounted for by a single additional parameter. This is the approach adopted in the estimates that follow. Estimations are done using the STAMP package (Koopmans et al, 1999).

The conditional seasonal gap estimate is obtained as (max (st) - min(st)) obtained from the STAMP procedure.<sup>15</sup> To calculate the contribution of seasonality to overall price volatility, (3) is

> where λ = α2 +β2 is the seasonal amplitude and ω = tan<sup>−</sup> 1<sup></sup>  α  is the phase shift relative to the cycle  β 

 _m_ π  defined by the cycle cos  .<sup>13</sup> If seasonality does have this sinusoidal structure, the seasonal gap is  6  approximately λ.

14 The conjugate process reverses the sign of one of the components (here the sine function) in the process of interest. Use of the conjugate notation allows expression of a cyclical second order difference equation in σ _jm_ * ' as a first order process in the vector (σ _jm_ σ _jm_ ) . 15 For those crop-location pairs for which seasonality is variable, the seasonal gap is measured in 2012 which is the final year of the sample.

10
**Gertler, Paul J., Sebastian Martinez, Patrick Premand, Laura B. Rawlings, and Vermeersch. Christel M.J.** 2011. _Impact Evaluation in Practice_ : World Bank.

**Goldsmith, Arthur H., Jonathan R. Veum, and William Darity.** 1997. "The Impact of Psychological and Human Capital on Wages" _Economic inquiry_ , 35(4): 815-829.

**González-Velosa, C., Ripani, L. and Rosas-Shady, D. (2012** ). ¿Cómo mejorar las oportunidades de inserción laboral de los jóvenes en América Latina? Banco Interamericano de Desarrollo, Unidad de Mercados Laborales y Seguridad Social, Nota Técnica IDB-TN-345. Available online at: <u>http://idbdocs.iadb.org/wsdocs/getdocument.aspx?docnum=36976183.</u>

**Groh, Matthew, Nandini Krishnan, David McKenzie, and Tara Vishwanath.** 2012. "Soft Skills or Hard Cash? The Impact of Training and Wage Subsidy Programs on Female Youth Employment in Jordan.". World Bank Policy Research Working Paper Series.

**Heckman, J., R. LaLonde, and Smith J.** 1999. "The Economics and Econometrics of Active Labor Market Programs." In _Handbook of Labor Economics_ , ed. O. Ashenfelter and David Card, 1865-2097. Vol. III. Elsevier, Amsterdam.

**Hirano, Keisuke, Guido W. Imbens, and Geert Ridder.** (2003). “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score.”Econometrica, 71(4): 1161–89.

**Hopenhayn, Martin.** 2002. "Youth and Employment in Latin America and the Caribbean:  Problems, Prospects and Options" , ECLAC Working Paper.

**International Labor Organization (ILO).** Global Employment Trends for Youth. 2013.

**International Labor Organization (ILO).** Youth Employment Statistics in Nepal. 2014. <u>http://apyouthnet.ilo.org/stats/youth-employment-statistics-in-nepal/at_download/file1 (accessed</u> September/1).

**International Labor Organization (ILO).** Labour market transitions of young women and men in

Nepal. Work 4 Youth Publication Series No. 12. May 2014.

**Kling, Jeffrey R. and Jeffrey B. Liebman.** 2004. _Experimental Analysis of Neighborhood Effects on Youth_ : John F. Kennedy School of Government, Harvard University.

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Paper No. 2018; RWI Discussion Paper No. 37.

**Leuven, E. and B. Sianesi.** 2003. "PSMATCH2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing  and Covariate Imbalance Testing" , 4.0.6 17may2012.(http://ideas.repec.org/c/boc/bocode/s432001.html).

**Linn, M. W., R. Sandifer, and S. Stein.** 1985. "Effects of Unemployment on Mental and Physical Health" _American Journal of Public Health_ , 75(5): 502-506.

**Macours, Karen.** 2011. “Increasing inequality and civil conflict in Nepal”. _Oxford Economic Papers_ , 63 (1).

24
**Figure 3: The Yasuda(Fuji) Group**

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12
# **APPENDIX II – Derivation of the empirical specification following HMR, 2004**

The basic set-up of the model is that of Krugman (1980), where the demand side is represented by CES preferences of the form With demand given by Prices are a constant mark-up over marginal cost due to the assumption of Dixit-Stiglitz monopolistic competition, so a firm with productivity level a, will charge the mill price of: Firms face two types of export costs, iceberg trade costs τij > 1 as well as fixed trade costs fij > 0 measured in units of labour, whereby the latter can be thought of as representing technical barriers to trade. A firm that exports from i to j, has export sales and profits (earned in j) The cut-off level of profitability is implicitly characterized by the condition that the least productive exporting firm will just break even, i.e. for this borderline firm operating profits will equal fixed costs exactly: Bilateral trade volumes can then be characterized as follows: firms) (8) We can then derive the value of country i’s imports from j given the demand function and the pricing equation as 31