rafmacalaba/datause-encoder-data huggingface dataset:
corruption, while the same amount of decline in TAXTIME is expected to lower tax corruption by 6 percent. The second panel of Table 12 reports the results for the higher-income group. A 10 percent reduction in either TAXTIME or TAXPAY cuts the tax corruption ratio by 1.9 percent and 2.3 percent, respectively, but the effects are limited for this group of countries.

**Table 12 – Income groups: Impact of Tax Simplification on Tax Corruption (in percentage terms)**

||10% drop in<br>taxpayments|10% drop in tax<br>time|10% drop in tax<br>payments and<br>tax time|
|---|---|---|---|
|**LOW-INCOME AND LOWER MIDDLE-I**|**NCOME COUNTRIES**|||
|**Equation (4) from Table 11**|-11.155|..|..|
|**Equation (5) from Table 11**|..|-7.976|..|
|**Equation (6) from Table 11**|-8.486|-6.031|-14.516|
|**UPPER MIDDLE-INCOME AND HIGH-IN**|**COME COUNTRIES**|||
|**Equation (10) from Table 11**|-1.984|..|..|
|**Equation (11) from Table 11**|..|-2.350|..|
|**Equation (12) from Table 11**|-1.882|-1.600|-3.482|

Source: Authors’ calculations.

These results are encouraging. Most of World Bank Group client countries belong in the first group – the lower and lower-middle income countries. The model predicts that working on tax reforms that will reduce complexity of tax systems is highly beneficial in terms of an impact on reducing tax corruption.

# **4. Simplifying Tax Regimes to Lower Tax Corruption – Policy Implications and Specific Measures**

It is interesting that the results obtained in the above analysis are similar to the findings of several other studies, for example, Fisman and Gatti (2006). They used data from the World Business Environment Survey, also a firm level survey carried out in 1999 and 2000 across 61 countries, with about 100 firms interviewed in each country. They modeled time spent with 38
# **Table 5: Water shortages – Heckman selection model**

||**Labor Productivity**<br>|**Water Used for**<br>|
|---|---|---|
||**(logs)**|**Business Activity**|
||**coef/se**|**coef/se**|
||**(1)**|**(2)**|
|Total duration of water shortages last month (in days)|-0.041***||
||(0.012)||
|Firm located within household or fixed premises & permanent structure Y/N||0.281***|
|||(0.051)|
|Log of total number of employees in regular month|-0.235|0.316***|
||(0.193)|(0.070)|
|Largest owner has secondary education or higher: Y/N|0.038|-0.181**|
||(0.202)|(0.081)|
|Manager experience (years)|0.003|-0.011**|
||(0.006)|(0.004)|
|Largest owner has a job in formal sector or looked for one over last 2 years Y/N|0.129|0.093|
||(0.166)|(0.111)|
|Largest owner started another business in the last three years Y/N|-0.126|0.037|
||(0.082)|(0.049)|
|Firm uses electricity for activity Y/N|0.196*||
||(0.103)||
|Firm uses machinery in its current operations Y/N|-0.035|-0.097|
||(0.156)|(0.165)|
|Firm uses vehicles in its current operations Y/N|0.729***|0.173***|
||(0.107)|(0.063)|
|Manufacturing firm Y/N|0.369***|0.166**|
||(0.119)|(0.079)|
|Produces or sells under contract from another business or person Y/N|0.305*|-0.118|
||(0.166)|(0.074)|
|Firm uses bank account to run business Y/N|0.066|-0.053|
||(0.143)|(0.071)|
|Firm has a loan at this time Y/N|0.108|0.060|
||(0.109)|(0.079)|
|Firm pays informal payment to remain unregistered Y/N|-0.135|0.076|
||(0.130)|(0.100)|
|Firm experienced crime last month Y/N|0.140|-0.113|
||(0.178)|(0.094)|
|Firm pays for security Y/N|-0.230|-0.238**|
||(0.333)|(0.113)|
|Constant|3.620***|-1.643***|
||(1.184)|(0.469)|
|/athrho|1.243***||
||(0.297)||

29
|he dependent variable<br>publicly available data<br>olumn 3); in individual<br>uarterly disbursements<br> reported) and include<br>ables are winsorized at|(11)<br>All haven|2.4143*<br>(1.3702)<br>2.2195<br>(2.0202)<br>2,345<br>0.1021<br>YES<br>YES|
|---|---|---|
|t data. T<br>ens with <br>e data (C<br>erest is q<br>DP (not <br>d aid vari|(10)<br>Jersey etc<br>**and period**|-2.0679<br>(2.0260)<br>1,304<br>0.0808<br>YES<br>YES|
|le deposi<br> non-hav<br>y availabl<br>ble of int<br>ange in G<br>posit an|(9)<br>Belgium<br>**ty by haven**|-1.0347<br>(0.7264)<br>2,075<br>0.0828<br>YES<br>YES|
|icly availab<br>n 1); in all <br>ith publicl<br>atory varia<br>entage ch<br>els. The de|(8)<br>Lux<br>**Heterogenei**|2.7165**<br>(1.1969)<br>1,796<br>0.0873<br>YES<br>YES|
|with publ<br>ta (Colum<br>n-havens w<br> the explan<br>rterly perc<br> in log-lev|(7)<br>Swiss|2.8725*<br>(1.5627)<br>2,334<br>0.0812<br>YES<br>YES|
|s obtained <br>ailable da<br>ge in all no<br>egressions, <br>or the qua<br>e diference|(6)<br>ratio<br>**1990-2018)**|4.6067**<br>(1.7131)<br>2,315<br>0.0743<br>YES<br>YES|
|ws result<br>ublicly av<br>the chan<br> all the r<br> control f<br>d with th|(5)<br>non-haven<br>**me period (**|-2.4209<br>(1.4551)<br>2,319<br>0.0841<br>YES<br>YES|
|table sho<br>s with p<br>nd above<br>). Across<br>gressions <br>roximate<br>.1)|(4)<br>haven<br>**Longer ti**|2.3821*<br>(1.3295)<br>2,345<br>0.1021<br>YES<br>YES|
|**data.** The <br> in all haven<br>e data over a<br>olumns 4-10<br>GDP. All re<br>nges are app<br>0.05, * p_<_0|(3)<br>ratio<br>**-2010)**|4.0755**<br>(1.5999)<br>1,641<br>0.0746<br>YES<br>YES|
|**deposit **<br> deposits <br>availabl<br>e data (C<br>f annual <br>tage cha<br>01, ** p_<_|(2)<br>non-haven<br>**ation (1990**|-2.0686<br>(1.2631)<br>1,645<br>0.0855<br>YES<br>YES|
|**public **<br>hange in <br>h publicly<br>y availabl<br>raction o<br>s. Percen<br>*** p_<_0.|(1)<br>haven<br>**Valid**|2.4599*<br>(1.3198)<br>1,669<br>0.1138<br>YES<br>YES|
|**Results with **<br>erly percentage c<br>in all havens wit<br>ters with publicl<br>orld Bank as a f<br> time fxed efect<br>99th percentile (||%GDP)<br>%GDP) × l(pre-2010)<br>%GDP) × l(post-2010)<br>vations<br>ared<br>ry FE<br>FE|
|**Table 7: **<br>is the quart<br>(Column 2),<br>banking cen<br>from the W<br>country and <br>the 1st and||Aid (<br>Aid (<br>Aid (<br>Obser<br>R-squ<br>count<br>time|

29
the literature. At the grid-cell level, the marginal economic return of an additional drop of rainfall is highest in arid areas and slowly decreases as the weather becomes wetter. The first 500mL of precipitation each year increases GDP per capita growth by 1 percent; precipitation between 500mL and 1,000mL a year raises growth by about an extra 0.8 percent. Eventually, extra precipitation brings no additional GDP per capita growth (Fig Sup 1). The temperature curve in the baseline regression is similar to that found in a variety of studies and peaks at 20 degrees Celsius as observed in micro studies (BHM 2015).

Next, we explore the consequences of aggregating weather and economic outputs data at a coarser spatial resolution. In column 2, we show the consequences of aggregating data at the Administrative 1 level (one level below a country; i.e., a state in the United States). We determine population-weighted levels of total annual precipitation, average temperature and average GDP per capita. Results at the administrative 1 level (state) are similar in magnitude and significance to those at the cell level. They suggest that the first 500mL of precipitation brings an additional 0.8 percent of GDP per capita growth, and that passing from 500mL to 1,000mL increases against growth by 0.6 percent. The concave relationship between temperature and GDP per capita growth is also retained.

In column 3, we aggregate data at the country level, following the same weighting procedure. This time the results vanish for rainfall, consistent with findings from DJO, BHM and other studies. However, temperature remains significant and is consistent with previous findings. At the country level, the optimal level of temperature is also found to be 13.9�C, as in BHM.

Robustness checks are presented in the Supplementary Material. In Tables Sup 2, 3 and 4, we test different specification models at the cell, administrative 1 and country levels: first, we exclude the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather; second, we exclude China and the United States; third, we include region by year fixed effects instead of year fixed effects; fourth, we control for region fixed effects but exclude country-year trends; fifth, we eliminate year fixed effects; sixth, we include linear instead of quadratic country time trends; and seventh, we account for one lag and three lags of GDP growth. Results are robust to a large number of alternate specifications. In Table Sup 5, we estimate our main equation 1 on trimmed outliers. We consecutively exclude the top/bottom 1% and the top/bottom 5% of the observations with the highest levels of precipitation. Results are unchanged, with an increasing concave relationship between rainfall and GDP growth, implying that the results are not driven by extremes.

In Table Sup 6, we check that our findings are not driven by the use of interpolated GDP values. We estimate equation 1 with the sub-national GDP per capita data used as inputs by Kummu, Guillaume and Taka (2018b) 6
19, Table 10: By How Much Would You Raise Wages Without The PPWW?

|Inmreasc|A (no. of firmsj|AAA (no. of finms)j|
|---|---|---|
|64-10%|2-|5|
|11 -15 %|4|3|
|:16-20 %|2|7|
|20-30%||3|
|>30%||3|

Table I0 quantifies this further. It indicates by how much firns expected wages to raise wages in the absence of the PPWW scheme. The cable again indicates that managers saw the PPWW as an effective restraint on wage behavior. The conclusiGn seems clear the PPWW did have an impact on wage developments: wage inflation in Poland would have been significantly higher without it.

_Summing up_ The overall assessment is somewhat mixed. There is no indication char the government as shareholder acted directly to chan,ge management behavior for tie better, and the possibility of tax arrears undermined efforts to impose hard budget constraints. But this damage was limited by the facts that tax arrears cannot exceed tax liabilities and that firrns that could. did pay taxes (the more profitable AAA firms). And, most importantly, the governrmen had favorable effects on governance through indirect channels. Most crucial was the stoppage of open-ended subsidies, which would have destroyed any leverage other claimholders might have had. By minimizing subsidies, the government created the possibility for other claimholders to exercise control. And our survey evidence indicates that the other indirect channel through which the government influenced corporate behavior. the PPWW scheme, did lead to significant wage restraint, contrary to common views on this issue.

3.2 _Banks and Corporare Governance_ The Polish banking system has developed with break neck speed. As latI as 1988, Poland did
Table 1 **:** Heavily-Indebted Countries: Share of Public and rublicly-Guaranteed Debt in Total Debt **l/**

|||1982|||1988||
|---|---|---|---|---|---|---|
||Public|Total|Share|Pgblic|Total|Share|
|Argentina|15.9|<br>27.1|58.6|47.5|49.3|96.4|
|Bolivia|2.8|<br>3.0|95.7|4.1|--<br>4.3|95.9|
|Brazil|51.7|<br>74.8|69.1|89.9|101.4|88.6|
|Chile|5.2|<br>14.0|37.5|13.7|16.1|85.3|
|Colombia|6.0|<br>7.2|83.4|13.8|15.4|90.0|
|Cote d'Ivoire|5.1|<br>,6.2|81.4|7.9|11.6|68.2|
|Ecuador|3.9|<br>5.5|70.5|9.0|9.1|98.7|
|Mexico|51.6|<br>59.7|86.4|80.6|86.5|93.1|
|Morocco|10.2|<br>10.5|97.6|19.4|19.6|99.0|
|Nigeria|9.1|<br>10.4|87.4|29.3|29.9|98.2|
|Peru|7.0|<br>8.6|80.7|12.5|13.9|89.8|
|Philippines|8.9|<br>12.1|73.3|23.0|24.0|95.9|
|Uruguay|1.7|<br>1.9|89.2|3.0|3.0|97.2|
|Venezuela|12.4|<br>17.4|71.3|24.6|28.9|85.2|
|Yugoslavia|5.5|<br>16.3|33.4|14.0|18.6|74.8|
|Total HICs|196.9|274.8|71.7|392.5|431.9|90.9|

1/ Totals are in millions of US S; shares are in percent.

Source: World Debt Tables, 1991-92.
# Table 8: **Regression Results** – Debt Relief and Productivity

This table reports the effect of debt relief on productivity. Within a panel, each column reports results from a separate regression. The dependent variables are (i) total agricultural revenue and (ii) agricultural revenue per acre of cultivated land, both measured as averages over the first two post-program crop seasons. All additional controls and fixed effects are as defined in Table 5. Robust standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01.

||Model 1||Model 2||Model 3||Model 4||
|---|---|---|---|---|---|---|---|---|
||Revenue|Revenue|Revenue|Revenue|Revenue|Revenue|Revenue|Revenue|
|||per acre||per acre||per acre||per acre|
|_Panel A: Full Sample_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|100% Relief|-0.22***|-0.19***|-0.12**|-0.13**|-0.19***|-0.18***|-0.13**|-0.13**|
||[0.07]|[0.06]|[0.06]|[0.05]|[0.05]|[0.05]|[0.06]|[0.05]|
|Observations|2,494|2,444|2,494|2,444|2,075|2,055|2,494|2,444|
|R-squared|0.13|0.16|0.12|0.15|0.29|0.2|0.12|0.15|
|_Panel B: Restricted Samples_|||||||||
|Audited accounts|||||||||
|100% Relief|-0.26***|-0.20***|-0.16**|-0.12+|-0.22***|-0.18***|-0.16**|-0.13+|
||[0.07]|[0.06]|[0.07]|[0.07]|[0.07]|[0.06]|[0.07]|[0.07]|
|Observations|1,597|1,567|1,597|1,567|1,319|1,311|1,597|1,567|
|R-squared|0.13|0.18|0.12|0.16|0.29|0.21|0.12|0.16|
|Reduced bandwidth sample|||||||||
|100% Relief|-0.23**|-0.15**|-0.12|-0.10+|-0.20***|-0.19***|-0.13+|-0.11+|
||[0.08]|[0.06]|[0.07]|[0.06]|[0.06]|[0.06]|[0.07]|[0.06]|
|Observations|1,812|1,770|1,812|1,770|1,496|1,481|1,812|1,770|
|R-squared|0.12|0.16|0.11|0.15|0.3|0.21|0.11|0.15|
|Interviewer effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*district effects|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Respondent controls|No|No|No|No|Yes|Yes|No|No|
|Local linear control fctn|No|No|Yes|Yes|Yes|Yes|No|No|
|Quadratic control fctn|No|No|No|No|No|No|Yes|Yes|

39
21 However, the text also includes a broad exception that permits each party to “adopt and maintain the safeguards _it deems appropriate_ to ensure the protections of personal data and privacy.” This italicized text intends to create a self‐judging exception as to what is needed to ensure privacy protection. This exception resembles the broad national security exception included in trade agreements, which are similarly self‐judging. The proposed EU approach also stands in contrast to how the exception for privacy is dealt with in the GATS – which as outlined above, requires demonstration that the exception is “necessary” and complies with the chapeau requirement that the measure is non‐ discriminatory and least trade restrictive.

The Commission text goes even further and seems to carve‐out rules and safeguards for the protection of personal data and privacy, including on cross‐border transfers of personal data, from being part of the regulatory cooperation commitments in its FTAs. Given the importance of regulatory cooperation and the potential role for trade agreements to support progress here, such an approach is a missed opportunity.

**V. INTERNATIONAL REGULATORY COOPERATION ON PRIVACY AND DATA FLOWS**

In this section, we discuss two efforts at regulatory convergence, the OECD and APEC privacy guidelines and rules, respectively, and one effort at the recognition of conformity assessment procedures, the EU‐ US Safe Privacy Shield.

_The OECD Privacy Guidelines_ In 2013, the OECD released _Guidelines Governing the Protection of Privacy and Trans‐Border Flows of Personal Data_ . The Guidelines were produced in response to the significant changes in personal data collection since the rise of a global internet and the opportunities and risks of harm from online data collection and processing (OECD 2013). They are an update of the OECD’s 1980 Privacy Guidelines and based on the Fair Information Practices Principles (FIPPS) developed in the United States. They also build on other OECD internet‐related work such as the OECD Recommendation on Principles for Internet Policy Making 2011.

The Privacy Guidelines are a minimum set of principles governing the collection, storage and use of personal data to guide the development by Members of domestic privacy protection regimes. Many of these principles are reflected in the EU Data Directive, including:

- data are to be obtained by lawful means and where appropriate with the consent of the data subject;

- personal data should be accurate, complete and up‐to‐date;

- the purpose for collecting the data should be specified and the use of the data limited to fulfilling that purpose;

- personal data should not be disclosed within the consent of the data subject; and

- individuals have the right to obtain personal data from the data controller.

The Privacy Guidelines also expand on the so‐called accountability principle as it applies to the data controller. Specifically, it requires the data controller to have in place a privacy management program that gives effect to these principles. The Guidelines require that privacy management programs are
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⋅ The term ρ W.FDI is the spatial autoregression term. The matrix W is the spatial lag weighting matrix estimated based on the geographical contiguity between governorates and ρ is a parameter to be estimated measuring the strength and sign of the spatial relationship of FDI inflows.

# **5. Estimation results**

Table 5 presents the results of the GMM regression of regional FDI on a set of economic, institutional and geographical determinants. We note that the lagged FDI variable is positive and statistically significant in all specifications of the model confirming the high persistence effect of this variable. This result indicates that past FDI values inflows have a significant influence on current FDI activities. This finding is supported by the work of Campos and Kinoshita (2003) who point out that in presence of agglomeration effects, past FDI inflows will be a good predictor of current FDI even after controlling for other classical factors. According to the same authors, historically, FDI is found to agglomerate more often than financial investments partly because this type of investment is more of a long-term capital investment that is irreversible in the short term. The statistically significant and positive coefficient of regional GDP as a proxy for the living standard and market size of the region lends support to our hypothesis above that regions with high GDP (mainly clustered along the littoral) tend to be more attractive to foreign investors. According to recent government statistics, the size of the markets along the coastal regions is substantially larger than the rest of the country and represents about 65% of the total population of the country, with 3 out of 4 local firms being established in these regions. The proximity from key government institutions that facilitates the access to information and reduces the transaction costs for doing business, the presence of a relatively well-developed infrastructure network and the abundance of relatively well-educated labor force in the coastal regions could also explain this result. This clustering effect of FDI is not surprising. According to Walsh and Yu (2010), historically foreign firms have a tendency to gather together in specific regions for several reasons: (a) leverage production process linkages among projects, (b) benefit from an established international business climate, and (c) take advantage of the economies of scale and positive spillovers from existing investors.

Regional incentives appear to have a positive influence on the inflows of FDI to the regions. Fiscal incentives (e.g. tax cuts/exemptions and other workers' legislations advantages) tend to provide attractive business operating conditions to foreign investors particularly in a vertical FDI framework
marriage-only penalty are quantitatively similar, and widest in Nepal and Maldives. As expected, the estimates for the “ignore child” sample are larger than the no child sample for all countries. Still, the relatively small gap in these estimates implies that the reduction in female labor force participation around the time of marriage is driven specifically by – – employment restrictions placed on married women likely due to social norms rather than their childcare burdens.

In contrast, men enjoy a marriage premium in South Asia. Table 1, Panel B, shows the estimates for the men’s sample. The estimated premium for men ranges from 13.2 to 17 p.p., with an aggregate effect size of 12.8 p.p. These impacts are consistent with the extensive literature on the marriage earnings premium in the US (Hersch and Stratton, 2000). As with women, the difference in the estimates between the no-child and ignore-child samples is small. Taken together, the findings in Table 1 demonstrate a marriage penalty for women and marriage premium for men in South Asia. These effects are driven primarily by the act of marriage itself, rather than the time and financial costs associated with childrearing.

# **3.2 Event-studies**

Figure 1 plots event-study coefficients on indicators for leads and lags of the marriage year, relative to _t_ = _−_ 1, by country. The pre-marriage event coefficients are all precisely estimated zeroes across each country, suggesting no trends in married women’s labor force participation leading up to marriage, and no evidence of anticipation effects. The reduction in labor force participation kicks in immediately in all countries, with a _t_ = 0 reduction in the nochild sample ranging from 7.3 to 13.2 p.p. across the region (dynamic point estimates are available in Table A4 and Table A5). As expected, the no-child dynamic estimates in Figure 1 are almost always above the ignore child sample estimates. Still, the confidence intervals typically overlap, and the magnitudes in the differences are very small for all countries except Maldives.

Lower labor force participation among married women without children persists up to 5 years after marriage in India and Maldives. In contrast, female employment rates appear to revert back to pre-marriage levels by _t_ = 5 in the no-child sample for Bangladesh and Nepal. Still, it is important to recall that the event-study estimates in the no-child sample 12