rafmacalaba/datause-encoder-data huggingface dataset:
value added tax, and labor taxes) and exclude personal income tax. The main data source is the World Bank’s Enterprise Survey Database. The dataset covers the years from 2002 to 2012. Tax complexity is measured with two alternative variables: time to comply with tax requirements and the number of tax payments, both of which are from the World Bank’s Doing Business database. In this paper we try to identify empirical determinants of tax corruption, including tax complexity indicators, through different regression analyses. In the benchmark regression specification, tax corruption is the dependent variable, while tax complexity indicators and control variables are included as independent variables. The control variables include political and institutional determinants of tax corruption, as well as judicial determinants. A GMM technique is applied to investigate the impact of these variables on tax corruption due to the possibility of an endogeneity problem.

The regression findings support the existence of a strong link between tax corruption and the indicators of tax complexity. After obtaining the estimated coefficients, different experiments are run to understand the economic significance of the tax simplification variables on tax corruption. The results show that while a 10 percent drop in the number of tax payments leads to an approximately 4 percent cut in tax corruption, the same amount of decrease in the hours to comply with tax requirements reduces tax corruption by 6 percent. The combined effects of the two tax simplification variables (10 percent cuts in both variables) are predicted to be even stronger, leading to a 9.6 percent cut in tax corruption. To check for robustness, regional differences and the income level of countries are controlled.

We find that tax corruption responds more to the changes in the tax simplification variables in the Latin America and Caribbean and Sub-Saharan African regions. Similarly, a stronger positive link is observed between tax corruption and tax simplification for lowerincome countries. The empirical results, indicating that tax simplification has a strong impact on tax corruption, have important policy implications. Lowering corruption in tax administration is possible by simplifying the tax regime, often in various easy, non-controversial ways, many of which do not even need legislative changes. The paper attempts to provide a road map for tax 4
- Amarante, V., and R. Arim. 2015. _Desigualdad e Informalidad: Un An ́alisis de Cinco Experiencias Latinoamericanas_ . Santiago, Chile: Comisi ́on Econ ́omica para Am ́erica Latina y el Caribe.

- Araujo, B. C., and L. S., Paz. 2014. “The Effects of Exporting on Wages: An Evaluation Using the 1999 Brazilian Exchange Rate Devaluation.” _Journal of Development Economics_ 111: 1–16.

- Artu ̧c, E., S. Chaudhuri, and J. McLaren. 2010. “Trade Shocks and Labor Adjustment: A Structural Empirical Approach.” _American Economic Review_ 100(3): 1008–45.

- Atkinson, A. B. 2008. “More on the Measurement of Inequality.” _Journal of Economic Inequality_ 6(3): 277–83.

- Attanasio, O., P. K. Goldberg, and N. Pavcnik. 2004. “Trade Reforms and Wage Inequality in Colombia.” _Journal of Development Economics_ 74(2): 331–66.

- Autor, D. H., A. Manning, and C. Smith. 2016. “The Contribution of the Minimum Wage to U.S. Wage Inequality over Three Decades: A Reassessment.” _American Economic Journal: Applied Economics_ 8(1): 58–99.

- Autor, D., and D. Dorn. 2013. “How Technology Wrecks the Middle Class.” _The New York Times_ , August 24.

- Autor, D., D. Dorn, and G. Hanson. 2013. “The China Syndrome: Local Labor Market Effects of Import Competition in the United States.” _American Economic Review_ 103(6): 2121–68.

- Autor, D., D. Dorn, G. Hanson, and J. Song. 2014. “Trade Adjustment: Worker-Level Evidence.” _Quarterly Journal of Economics_ 129(4): 1799–1860.

- Autor, D., L. Katz, and A. B. Krueger. 1998. “Computing Inequality: Have Computers Changed the Labor Market?” _Quarterly Journal of Economics_ 113(4): 1169–1213.

- Autor, D., L. Katz, and M. Kearney. 2008. “Trends in US Wage Inequality: Revising the Revisionists.” _Review of Economics and Statistics_ 90(2): 300–23.

- Azevedo, J., G. Inchauste, and V. Sanfelice. 2013. “Decomposing the Recent Inequality Decline in Latin America.” Policy Research Working Paper 6715. Washington, DC, United States: World Bank.

- Azevedo, J., M. D ́avalos, C. Diaz-Bonilla, B. Atuesta, and R. Casta ̃neda. 2013. “Fifteen Years of Inequality in Latin America: How Have Labor Markets Helped?” Policy Research Working Paper 6384. Washington, DC, United States: World Bank.

- Bacchetta, M., J. P. Bustamante, and E. Ernst. 2009. _Globalization and Informal Jobs in Developing Countries._ Geneva, Switzerland: International Labour Organization and World Trade Organization.

- Bastos, P., J. Silva, and J. Verhoogen. 2018. “Export Destinations and Input Prices.” _American Economic Review_ 108(2): 353–92.

37
In the second to fifth rows of Table 4 we report the coefficients for the trade and FDI variables in the regressions on the gender wage gap. As measures we use aggregate trade (in current and constant prices) and FDI net inflows (in current prices) as a percentage of GDP. Also we use a measure of sectoral trade based on the World Bank Trade and Production Database which contains data on trade, production and tariffs for 67 developing and developed countries at the industry level over the period 1976-1999.<sup>17</sup> Occupations within the ILO October Inquiry can be linked with the ISIC codes in the Trade and Production Database given that each occupation belongs to a specific sector (ILO 1995). The use of sectoral trade data allows us to exploit the more direct link between sectoral trade and the gender wage gap for occupations in a sector. On the other hand, the number of observations is strongly reduced as the Trade and Production Database is limited to the manufacturing industries. The different regressions have different numbers of observations because of this difference in availability.

The second to fifth rows of Table 4 show that the effect of trade is generally negative (although not always significant), that is, the gender wage gap tends to fall with the openness of the economy. This result confirms what we already have seen in Figure 3. For FDI net inflows we find a more ambiguous pattern, with a negative and a positive but insignificant effect for the poorer and richer countries respectively. The use of the Sachs-Warner measure of openness gives an insignificant result as well (not reported). The R<sup>2</sup> is much lower for poorer countries, which may reflect greater measurement error in poorer countries and therefore heteroskedasticity. The standard errors are therefore corrected for clustering within country/occupation observations throughout the analysis.

Overall we can conclude that countries that trade more tend to have a lower occupational gender wage gap. In the following we will further investigate this basic result by analyzing possible specification, simulateneity and omitted variable bias.

_Specification bias: occupational heterogeneity, trade and the gender wage gap_ The impact of globalization on the gender gap may vary across occupations. First, the existing gap may vary across occupations, and globalization may be expected to have

> 17 See www.worldbank.org/research/trade.

18
13

**Table 4.1: Results for Targeting Performance of Zonal Subsidies in Panama**

||**Household**<br>**zonal**|**s receiving**<br>**subsidy**|**Household**<br>**receive zon**|**s failing to**<br>**al subsidy**|**To**|**tal**|
|---|---|---|---|---|---|---|
||**'OOOs**|**%**|**'OOOs**|**%**|**'OOOs**|**%**|
|Below extreme poverty line|1.0|68.6|16.2|3.8|17.2|4.0|
|Above extreme poverty line|0.4|31.4|408.2|96.2|408.7|96.0|
|Total|1.5|100.0|424.3|100.0|425.9|100.0|

The key explanation for these results is that the vast majority of extremely poor people in the IDAAN customer base were living in zones that were predominantly inhabited by people above the extreme poverty line. In other words, extreme poverty is comparatively diffuse in the urban areas of Panama.

This level of targeting performance was thought to be unacceptable for a water subsidy and the use of zonal subsidies was consequently ruled out in the case of Panama.

# **Individual Eligibility**

An alternative approach is to base the eligibility for the subsidy on an assessment of individual household circumstances. While reliable information about poverty levels at the zonal level may be obtained based on census or survey data, it is much harder to obtain a reliable estimate of household income and expenditure in order to assess eligibility for individual subsidies. Consequently, it is necessary to find proxy variables which are closely associated with poverty, but which may be easier to observe and harder to falsify.

In Panama, an extensive search for proxy variables was conducted by making crosstabulations between candidate proxies and poverty levels, using data from the national Living Standards Measurement Survey. The results for the most successful proxy variables are reported in Table 4.2. These were as follows:

- the presence in the house of poor-quality floor materials (cement, earth, bamboo);

- the absence of a connection to the telephone network;

- the head of household not educated beyond primary level;

- the toilet facilities located outdoors _and_ most basic in nature (i.e., pit or latrine).

A successful proxy variable must have both a high correlation with poverty and a low correlation with not being in poverty. As the table illustrates, it is difficult to find a single variable that performs well on both these counts.

- For example, low-quality floor materials are highly correlated with poverty; 88.6% of those in extreme poverty live in houses with low-quality floor materials. However, lowquality floor materials are also quite prevalent among those who are not poor, affecting as many as 56% of this group. Hence, this variable is effective in identifying who should be
|Municipal Government|||||
|---|---|---|---|---|
|Support|37|1.44|29|8|
|Other|100|3.89|75|25|

Source: Linked ENESTYC-EIA panel data.

The paper analyzes the SME programs that yielded the larger sample sizes. The programs are: 1) PAC-CIMO, included both in the ENESTYC 2001 and in the ENESTYC 2005 Surveys.

2) PROSEC, only recorded in the ENESTYC 2005.

3) PNAA, only recorded in the ENESTYC 2005.

4) Fiscal Support and Technological Innovation, only recorded in the ENESTYC 2005.

5) State government support, only recorded in the ENESTYC 2005.

6) Any program, referring to any program in the program module of the ENESTYC 2001 and of the ENESTYC 2005, as follows: _ENESTYC 2001 and 2005_ : COMPITE, CRECE, FIDECAP and FAMPYME.

_ENESTYC 2001:_ MEX-EX (México Exporta), Technical Assistance and Image — Campaign Program ( _Programa de Asistencia Técnica y Campaña de Imagen_ PATCI), PMT, PCI, and PAIDEC.

_ENESTYC 2005:_ Fondo PYME, PROMODE, Mixed or Sectoral Funds, Productive Chains, Financing, Crediexporta, PAT, Municipal Government Support, and other programs.

The objective was to explore the impact of several programs in groups by agency, and to analyze programs individually when data allowed, as follows:<sup>3</sup> 1) All STPS programs (CIMO-PAC), which include training, BDS, and productivity programs.

2) ME, which includes FIDECAP, FAMPYME, Fondo PYME, COMPITE, CRECE, PROMODE and PROSEC.

3) BANCOMEXT, which includes MEX-EX, PATCI, Crediexporta and PAT.

4) CONACyT includes PMT, PCI, PAIDEC, Fiscal Support and Technological Innovation and Mixed or Sectoral Funds.

> 3 The other option of grouping by type of program (training, technology upgrade, etc.) was not possible in Mexico, since most programs belong encompass more than one type of intervention (Table 12).

19
**Table** 6b: Country-Specific Effects of Decentralization on Preference Costs

|Variable|Construct|ion|Maintena|nce|Administr|ation|Total Eff|ect|
|---|---|---|---|---|---|---|---|---|
|Variable|coeff|t-stat|coeff|t-stat|coeff|t-stat|coeff|t-stat|
|Cameroon|_55:_|_5.53_||S0<br>_5:_|59613|.3|54|1|
|Ethiopia||3|29|6|9|**9**--<br>17|32|5.94-|
|South Africa|31|5.81|26|.69|25|.|32|1|
|Argentina|3_|5.09|28|5.7|5<br>2|5A5|40<br>-|7-80|
|Bolivia|6||5|.5|64|40|56|9.70|
|Brazil|54|5<br>-|-4<br>-|.|||:- 0||
|Chile||4||649||11|48|9|
|Colombia|53|7.92-||**10.20**|44|9.85 .|_9_|1196|
|Costa Rica|49|4|.44<br>:46|8i48|$: 3:|||8.18|
|Indonesia|-3|-0.25|-9|--. 84|1|0.29|4|0.81|
|Japan|2|0.35|-7|-1.55*|-97|-2.33|19|3.98|
|Korea|3|0.53|-0.65|-0.14|0.79|0.20|324|5527|
|Philippines|47|4.4|41|801|51|1|_45_-|19|
|New Zealand|8|1.15|3|0.55|9|2.07|12|_257_|
|Denmark|-37|-5|**_-:_**_:::::_<br>_4_|_70_<br>-112 2|-4'-||.32|-8 11|
|Finland|7|1.13|3|0.61|9--|2-_,09_|17|3<br>;.59|
|Germany|3|_-5,78_|--4|-7.907||||.-|
|UK|-35|_-51_|-45|-1:-1|-35<br>|:50|- -<br>28|6.49|
|Netherlands||6599|||22|5.20|-17|-3.60|
|Spain|_-5_|-0.81|-I3|-2.72|-4|-0.86|0.47|0.10|
|Sweden|_-5_|-0.88|-7|-1.61*|-4|-0.95|-2|-0.39|
|Austria|-3|58|3|83|37|89|2|-572|
|Belgium<br>|33::<br>|-3.88|-39|_-5.38_|-|-6.10<br>|-27<br>|-4<br>|
|Hungary|11.|||||2.99:|1|2.69|
|Iceland|4|7,9|47|10.30|43|977|5|1.4|
|Ireland-3||-38|-4|-86|4|-1.1|-2|_-54_|
|Italy|-34|-486|-42|_-7,58_|43|-112.4|-9||
|Poland|4|0.53|-2|-0.29|6|1.25|5|1.04|
|Switzerland|:-44<br>|i:2-16.24|0i4-43|-17.73|-43|-16.2|5: 3|14.94:|
|Uganda<br>-<br>|::<br>4**4**:|4.36|2|7.|43|106.2-|51||
|Zaire|58|5.17:|5:S3|7.317|6||9.32<br>56.94||
|Canada|10:|3452|9-||i3L<br>9-3.2|27<br>1|1:.|7|
|Saudi Arabia|13|||2.42|<br>22|||4.63|
|Bahrain|-12|-1.19..|<br>-18|. -3.44|-|-2|-5|-26|
|Time|-076|-<br>_-5.87_|-0.69|-. 30|-78|.|||
|GNP/capita<br>Constant|V.s.<br>.<br>54<br>|-0.22<br>.<br>600|v.s.<br>142-|-0.24<br>25.4|v.s.<br>158|-0.23<br>49|v.s.<br>J549|0.05<br>22|

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

** significant at 80% level of confidence.

22
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_Table 10: Determinants of applicant being invited for an interview_

|Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|Model 7|
|---|---|---|---|---|---|---|
|Female<br>0.0129**<br>(0.0055)<br>Accounting|0.0104<br>(0.0065)|0.0105<br>(0.0065)|0.0108*<br>(0.0065)<br>-0.0447***<br>(0.0093)|0.0104<br>(0.0065)|0.0106<br>(0.0065)<br>-0.0491***<br>(0.0087)|0.0069<br>(0.0077)<br>-0.0493***<br>(0.0087)|
|Marketing|||-0.0418***<br>(0.0098)||-0.0414***<br>(0.0094)|-0.0415***<br>(0.0094)|
|IT|||0.0020||0.0108|0.0115|
||||(0.0147)||(0.0174)|(0.0174)|
|Istanbul Asia||||||-0.0149*<br>(0.0079)|
|Ankara||||||<br>-0.0016<br>(0.0106)|
|_Total application size (100)_|||||||
|Female * Istanbul Asia||||||0.0160*<br>(0.0094)|
|Female * Ankara||||||-0.0067<br>(0.0117)|
|Female * Acct|||-0.0097*<br>(0.0051)||||
|Female * Mrkt|||0.0013<br>(0.0056)||||
|Female * IT|||0.0087<br>(0.0113)||||
|_Female * Total application size_|||||||
|Vacancy and CV sectors match||0.0220**||0.0213**|-0.0064|-0.0066|
|||(0.0097)||(0.0097)|(0.0119)|(0.0119)|
|Experience (in months)|0.0004|0.0005|0.0003|0.0004|0.0003|0.0003|
||(0.0006)|(0.0006)|(0.0005)|(0.0006)|(0.0005)|(0.0006)|
|Age<br>-0.0109**|-0.0139**|-0.0139**|-0.0133**|-0.0140**|-0.0134**|-0.0135**|
|(0.0050)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|(0.0064)|
|<br>Objective beauty|<br>0.0101|<br>0.0101|<br>0.0080|<br>0.0102|<br>0.0081|<br>0.0064|
||(0.0092)|(0.0092)|(0.0091)|(0.0092)|(0.0091)|(0.0091)|
|Subjective beauty|-0.0007|-0.0013|0.0004|-0.0012|0.0005|0.0003|
||(0.0046)|(0.0046)|(0.0045)|(0.0046)|(0.0045)|(0.0045)|
|Worked in one firm only|<br>0.0050|<br>0.0049|<br>-0.0005|<br>0.0048|<br>-0.0006|<br>-0.0001|
||(0.0052)|(0.0052)|(0.0052)|(0.0052)|(0.0052)|(0.0052)|
|Total application size (100)||||<br>-0.0004|<br>-0.0003|<br>-0.0003|
|||||(0.0003)|(0.0003)|(0.0003)|
|Constant<br>0.3428**|0.3906**|0.3883**|0.4089***|<br>0.3944**|<br>0.4139***|<br>0.4240***|
|(0.1346)|(0.1561)|(0.1562)|(0.1552)|(0.1562)|(0.1553)|(0.1554)|
|N.obs.<br>10748|10748|10748|10748|10748|10748|10748|
|R-squared<br>0.002|0.004|0.004|0.012|0.004|0.012|0.013|

**Notes:** Models 1 to 7 report the results from OLS regressions. Cluster-robust standard errors at the vacancy level are shown in parentheses. Dependent variable in all regressions is a dummy that takes on the value 1 if the applicant is invited for an interview. Variable _Female_ takes on the value 1 if applicant is female, 0 otherwise. Variable _Vacancy and CV sectors match_ takes on the value 1 if the vacancy is in the same sector with applicant’s current or previous sectors. Variables _Accounting_ , _Marketing_ and _IT_ denote the occupational clusters of vacancies (and so of applicants), and the baseline category is sales occupations. Variable _Total application size_ denotes the total number of applications for the vacancy. Symbols ∗∗∗, ∗∗, and ∗ indicate significance at the 1%, 5% and 10% level, respectively.
# FIGURE 1. COMPARISON OF MORTALITY EXPERIENCES OF VACCINATED AND UNVACCINATED CHILDREN AGED 7-35 MONTHS: KASONGO, ZAIRE, 1974-77

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# UNVACCINATED CHILDREN

SOURCE: TIlE KASONGO PROJECT TEAM, 1981
Policy Research Working Paper 9045

# **Abstract**

Water, sanitation, and hygiene interventions are increasingly recognized as essential for improving nutritional outcomes in children. Emerging literature describes the negative effects of poor sanitation on child growth. However, limited evidence has shown a link between water quality and nutritional outcomes. Similar to poor sanitation, it is plausible that water contaminated with E. coli could affect the nutritional status of children through various possible biological pathways, such as repeated episodes of diarrhea, environmental enteropathy, parasites, or other mechanisms that inhibit nutrient uptake and absorption. This study explores the relationship between contaminated water and stunting prevalence among children younger than age five years, using unique cross-sectional data from the 2012–13 Bangladesh Multiple Indicator Cluster Survey, which was one of the first nationally representative surveys to include water quality testing for E. coli. E. coli contamination in drinking water is measured at household and source points. Stunting is measured using height-for-age z-scores for children under five, where a child is considered stunted when he or she is two or more standard deviations below the median of the World Health Organization reference population. The results of multiple probit regression models indicate a 6 percent increase in the prevalence of stunting in children who are exposed to highly contaminated drinking water at household point compared with those exposed to low-tomedium contamination. When contamination is measured at the source level, the association is greater, with a 9 percent increase in the likelihood of stunting when exposed to a high level of contamination.

This paper is a product of the Water 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 gjoseph@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
a sample of countries that were attacked by removing countries representing the axis powers.<sup>9</sup> The results of this estimation are shown in row (3) of Table 3. The coefficient on the losses during WW2 is still significant and inversely related to the deaths from COVID-19, although the power of its significance declines.

We use the DFBETA influence statistics (Bollen and Jackman 1990) to identify the influential outliers in our regression. The method measures the difference, scaled by the estimated standard error of the coefficient, between the regression coefficient when the _i_<sup>th</sup> observation (country) is included and excluded.<sup>10</sup> (The Appendix provides the scatter plot of the values of DFBETA statistics.) Based on that criteria, Belarus (BRL) and Armenia (ARM) have a strong influence on the result of our estimations. The estimates of our main specification, excluding these countries from the sample, are shown in row (4) of Table 3.

We also conducted a falsification test to see if our results might be driven by unobservable confounding factors. The idea behind the falsification test is to demonstrate that the effect does not exist in the context where we expect no effect (see, for example, Rothstein 2010). We use the deaths from cardiovascular diseases, which predominantly affect the elderly population, as a placebo outcome. We assume that cardiovascular deaths are not affected by the levels of social capital and trust in society. Another measure of placebo outcomes is deaths from influenza and pneumonia during the season of 2017/2018. These diseases are similar to COVID19 in terms of contagion risks, but no social distancing and NPI were implemented to stop the infection despite the fact it killed more than 650,000 people worldwide in 2018 (Paget et al. 2019). We assume that influenza and pneumonia deaths are not associated with social capital and levels of trust in society that are required to internalize the negative externalities of the lockdowns. Thus, at least through these channels, the losses during WW2 should have no influence on contemporary deaths from cardiovascular or influenza and pneumonia diseases. This is confirmed in Table 3, which gives the coefficients on WW2 losses in rows (5) and (6); the coefficients are smaller by magnitude and statistically insignificant.

> 9 An emerging literature that studies heroic actions and altruism during the war finds that the majority of heroic acts happened when the combatants defended their land (vs. being on the attack), e.g., Franco et al (2011). A possible explanation of this phenomenon could be that it is based on the evolutionary mechanism of protecting of close kin (Rusch and Stormer 2015). Rusch and Stormer 2015). ).

> explanation of this phenomenon could be that it is based on the evolutionary mechanism of protecting of close kin (Rusch and Stormer 2015). Rusch and Stormer 2015). ).

> 10 Our criterion for selecting observations is that: |D , where _N_ is the sample size. ii DDEEDDAA | > 2/√NN

12