rafmacalaba/datause-encoder-data huggingface dataset:
# Table A2. FLFP, individual & household characteristics and local labor market variables in <u>2018. OLS estimates</u>

||(1)|(2)|
|---|---|---|
|_Individual & household characteristics_<br>|||
|Age|0.0407|0.0408|
||[0.00103]***|[0.00102]***|
|Age squared|-0.000533<br>|-0.000534<br>|
||[1.55e-05]***<br>|[1.54e-05]***<br>|
|Primary level|0.0206|0.0227|
||[0.00233]***|[0.00197]***|
|Secondary level|0.112|0.114|
||[0.00644]***|[0.00643]***|
|University level|0.301<br>***|0.303<br>***|
||[0.0126]|[0.0124]|
|Indigenous person|0.0513<br>|0.0433<br>|
||[0.00581]***|[0.00658]***|
|Married|-0.174|-0.173|
||[0.00459]***|[0.00459]***|
|Number of children 0-3|-0.0398|-0.0398|
||[0.00267]***|[0.00267]***|
|Number of children 4-6|-0.0201|-0.0202|
||[0.00181]***<br>|[0.00180]***<br>|
|Number of children 7-12|-0.00763|-0.00782|
||[0.00106]***|[0.00103]***|
|Number of people > 12|-0.0109|-0.011|
||[0.000440]***|[0.000422]***|
|Presence of grandmother|0.0297|0.0292|
||[0.00212]***|[0.00208]***|
|Household head|0.105<br>|0.104<br>|
||[0.00384]***|[0.00397]***|
|Spouse works|-0.00621<br>[0.00396]|-0.0088<br>[0.00367]**|
|Income stability|0.156|0.156|
||[0.00445]***|[0.00444]***|
|At least one person receives remittances regularly|-0.0663|-0.066|
||[0.00265]***<br>|[0.00250]***<br>|
|Household Home & WASH index|0.00781|0.00818|
||[0.00169]***<br>|[0.00162]***<br>|
|Urban|0.0754|0.0724|
||[0.00564]***|[0.00551]***|
|Use internet in the last 3 months|0.115|0.113|
||[0.00426]***|[0.00409]***|
|Washing machine|0.0347|0.0329|
||[0.00463]***|[0.00432]***|
|_Local labor market variables_|||
|Male-to-Female employment ratio|-0.0141|-0.0155|
||[0.00310]***|[0.00377]***|
|Log of per capita GDP|0.0342|0.0455|
||[0.0113]***|[0.0247]*|
|Crime rate|-2.086<br>|-1.799<br>|
||[1.493]|[1.744]|
|Share of male employment in primary sector|0.0185<br>|0.00249<br>|
||[0.0440]|[0.0548]|
|Share of male employment in construction|-0.0982|-0.0986|
||[0.0717]|[0.0789]|
|Share of male employment in white services|0.0619|0.0111|
||[0.0765]|[0.0825]|
|Share of male employment in other services|0.0273|0.00988|
||[0.0601]|[0.0632]|
|Constant|-0.744|-0.818|
||[0.114]***|[0.235]***|
|Observations|1,696,260|1,696,260|
|R-squared<br>|0.255<br>|0.256<br>|
|Department fixed effects|No|Yes|

Sources: 2018 National Census of Guatemala and 2018 municipality statistics (FUNDESA and INE). Notes: Women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves. Robust standard errors clustered at the municipality level between brackets. *** p<0.01, ** p<0.05, * p<0.1.

31
**Table 11.** **<u>Ordered probit regression of road quality rating</u>**

||Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|Coef.|Std. Err.|
|---|---|---|---|---|---|---|---|---|---|---|
|ln_age_|-0.567|(0.162) ***|||||||||
|ln_traffic_|||-0.328|(0.099) ***|||||0.200|(0.257)|
|ln_securityafter_|||||-0.429|(0.133) ***|||-0.679|(0.391) *|
|ln_rainafter_|||||||-0.557|(0.146) ***|-0.290|(0.291)|
|ln_cost_|0.035|(0.324)|0.077|(0.340)|0.073|(0.311)|0.053|(0.322)|0.028|(0.332)|
|ln_length_|-0.151|(0.259)|-0.128|(0.269)|-0.088|(0.260)|-0.154|(0.260)|-0.106|(0.274)|
|ln_lane_|3.329|(0.915) ***|4.086|(0.966) ***|3.017|(0.907) ***|3.631|(0.920) ***|3.904|(0.987) ***|
|ln_thickness_|0.390|(0.176) **|0.365|(0.176) **|0.387|(0.175) **|0.393|(0.180) **|0.287|(0.171) *|
|ln_gravel_|0.071|(0.074)|0.081|(0.083)|0.088|(0.079)|0.065|(0.075)|0.086|(0.087)|
|ln_bitumen_|-0.055|(0.075)|-0.057|(0.075)|-0.061|(0.075)|-0.054|(0.075)|-0.056|(0.074)|
|ln_earth_|0.006|(0.076)|0.050|(0.079)|-0.040|(0.080)|0.006|(0.075)|0.024|(0.084)|
|ln_brick_|0.063|(0.112)|0.088|(0.119)|0.026|(0.131)|0.073|(0.118)|0.069|(0.160)|
|ln_gabion_|0.141|(0.097)|0.082|(0.093)|0.141|(0.095)|0.131|(0.097)|0.121|(0.096)|
|ln_excavate_|-0.110|(0.049) **|-0.144|(0.048) ***|-0.115|(0.049) **|-0.122|(0.050) **|-0.144|(0.047) ***|
|ln_cement_|0.010|(0.090)|-0.004|(0.096)|0.015|(0.092)|0.013|(0.093)|-0.039|(0.108)|
|D(_postqualify_)|0.396|(0.657)|-0.005|(0.673)|0.152|(0.661)|0.338|(0.655)|-0.151|(0.682)|
|Obs.|136||128||133||136||125||
|Wald chi2|181.11||185.06||188.48||196.97||201.52||
|Pseudo R2|0.482||0.512||0.480||0.485||0.522||
|No. of dummy variables|:||||||||||
|Project districts|18||18||18||18||18||

Note: The dependent variable is the quality rating. Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively. Due to multicollinearity, ln _Age_ is omitted from the last column model.
# **Table 4.1. Measures of relative poverty by settlement area**

|Region|Povert|y Incidence|(headc|ount)|Gap||||Severit|y|||Povert|y Relative|Risk Ra|tio***|Urban/Rural<br>Poverty|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||||||||||||Incidence|
|C|Urban|Capital|Other|Rl|Urban|Capital|Other|Rl|Urban|Capital|Other|Rl|<br>Urban|Capital|Other|Rl|<br>Ratio|
|ountry|(all)|City|Urban|ura|(all)|City|Urban|ura|(all)|City|Urban|ura|<br>(all)|City|Urban|<br>ura|<br>|
|EUAccession||||||||||||||||||
|Hungary|17.29|13.21|18.89|24.80|3.68|2.60|4.10|5.71|1.20|0.80|1.36|2.03|0.86|0.66|0.94|1.24|0.70|
|Lithuania|13.64|8.22|15.28|33.45|3.13|1.60|3.60|9.28|1.07|0.44|1.26|3.62|0.68|0.41|0.76|1.67|0.41|
|Poland|13.34|2.04|14.10|30.58|2.82|0.20|3.00|7.58|0.92|0.03|0.98|2.74|0.67|0.10|0.70|1.53|0.44|
|Balkans/ EE||||||||||||||||||
|Albania|15.11|13.27|15.79|23.40|3.36|2.75|3.59|4.83|1.18|0.93|1.27|1.48|0.76|0.67|0.79|1.17|0.65|
|Bosnia*|14.19|...|23.98|20.56|2.89|...|5.79|4.94|0.92|...|2.11|1.65|0.71|...|1.20|1.03|...|
|Bulgaria|14.87|8.17|16.68|30.64|4.16|1.32|4.94|11.34|1.76|0.33|2.14|5.83|0.74|0.41|0.83|1.53|0.49|
|Kosovo|18.69|...|...|20.72|4.94|...|...|4.78|1.85|...|...|1.66|0.94|...|...|1.04|0.90|
|Moldova|21.85|8.28|34.99|18.95|6.15|1.72|10.43|4.80|2.61|0.58|4.57|1.86|1.09|0.41|1.75|0.95|1.15|
|Romania**|11.44|4.32|12.84|30.24|2.60|0.64|2.98|7.83|0.95|0.18|1.10|2.92|0.57|0.22|0.64|1.51|0.38|
|Serbia**|16.05|13.23|17.28|25.12|3.33|2.76|3.58|6.15|1.09|0.88|1.18|2.31|0.80|0.66|0.86|1.26|0.64|
|Caucasus||||||||||||||||||
|Armenia**|22.42|20.88|23.89|16.60|4.86|4.95|4.77|3.79|1.72|1.93|1.52|1.35|1.12|1.04|1.19|0.83|1.35|
|Azerbaijan|23.88|16.69|30.13|15.60|5.49|3.02|7.64|3.91|1.85|0.84|2.73|1.48|1.19|0.83|1.50|0.78|1.53|
|Georgia**|20.98|16.59|24.96|18.97|5.93|4.18|7.51|6.35|2.61|1.71|3.42|3.20|1.05|0.83|1.25|0.95|1.11|
|Central Asia||||||||||||||||||
|Kazakhstan|14.81|4.41|16.31|26.18|3.39|0.68|3.79|6.05|1.17|0.16|1.31|2.11|0.74|0.22|0.82|1.31|0.57|
|Kyrgyz Rep.|14.09|7.03|19.71|23.23|2.56|0.95|3.85|4.72|0.76|0.23|1.18|1.52|0.70|0.35|0.98|1.16|0.61|
|<br>Tajikistan|16.78|5.05|21.65|20.92|4.55|1.37|5.87|5.53|1.87|0.61|2.40|2.28|0.84|0.25|1.08|1.05|0.80|
|<br>Turkmenistan|9.53|...|12.45|27.95|2.20|...|2.87|8.02|0.78|...|1.01|3.31|0.48|...|0.62|1.39|0.34|
|Uzbekistan|15.60|5.59|18.63|22.64|3.30|1.18|3.94|5.63|1.12|0.40|1.34|2.41|0.78|0.28|0.93|1.13|0.69|
|Slavic||||||||||||||||||
|Belarus**|17.44|6.41|20.95|25.75|3.54|1.33|4.25|5.64|1.17|0.51|1.38|1.88|0.87|0.32|1.05|1.29|0.68|
|Russia|16.53|8.11|18.21|29.16|5.32|2.32|5.92|10.70|2.58|1.11|2.88|5.65|0.82|0.40|0.91|1.45|0.57|

The use of small type designates cells with fewer than 30 counts.

*    For Bosnia, the categories are urban, mixed and rural **  Per adult equivalent  consumption ***Relative to country poverty incidence (first column) Source: see Table 2.1

20
17

|Table 2.4. Annual Credit Reaqirement for Agricultural In<br>........................<br>r...1e<br>qures inves tm.<br> <br>|vestment (Percentage of 1992-93 Agricultural GDP)<br>..............<br>G.............ent.A...GDPr<br>t..e.<br>.. .......**.**...<br>|
|---|---|
|Agricultural growth rate<br>Assumed percentage poi<br>5|nts of investment financed through non-formal sources<br>6<br>7|
|4<br>7|6<br>5|
|6<br>13|12<br>11|
|8<br>19<br>Source:..........<br>A uthors'...........<br>estim ates**..........**<br>...<br>.......................................|18<br>17<br>.................................................................................................................|

An important finding of this report is that at least in the 1989-90 to 1994-95 period, farmers have not been credit constrained. the sector has grown at a healthy annual average rate of 5.1 percent (ignoring the abnormal year, 1992-93) supported by institutional disbursed credit of 5.6 percent of the agricultural GDP, which is close to the required rate to support growth. Yet the 1985 surey, based on farmers' direct response to questions, estimates the gap between credit need and availability at 3: 1. Although a considerable improvement over the 1973 survey, which reported the gap at 7: 1, it is considerably larger than that reported in Table 2.4. There may be several explanations. First, farmers may want to shift credit demand from the more expensive non-institutional sources to the cheaper institutional ones. Second, farmer estimates of credit need may be based on best farmer practices. Yet another explanation is that smallholders with poorer access to credit from formal sources may indeed be credit constrained.

The critical policy issues thus are: (i) continuing to make credit available at current levels (or higher levels if a faster rate of growth is desired) while ensuring the financial survivability of the lending institutions; (ii) ensuring that credit reaches the smallholders as well so that they can emulate the best practice fanners; and (iii) providing for the credit needs of non-fann business enterprises, women in rural areas, households engaged in livestock business and the rural poor. The next section and chapter M review the existing practice and institutional arrangements concerning these issues and then chapter IV outlines the policy interventions to achieve these policy objectives.
# Do Index Insurance Programs Live Up to Their Promises? Aggregating Evidence from Multiple Experiments<sup>_∗_</sup>

Pauline Castaing Jules Gazeaud

## **JEL Classification:** C11, G22, G52 , 012, 013, Q12.

**Keywords:** Index Insurance, Agricultural Decisions, External Validity, Bayesian Hierarchical Modeling

> _∗_ Pauline Castaing: The World Bank. E-mail: pcastaing@worldbank.org; Jules Gazeaud: J-PAL MENA, The American University in Cairo. E-mail: jgazeaud@povertyactionlab.org. We thank Frederick Amon-Armah, Catherine Araujo, Tanguy Bernard, Stefan Dercon, Esther Gehrke, Patrick Guillaumont, Alain de Janvry, Antoine Leblois, David McKenzie, Georgios Manalis, Rachael Meager, Muhammad Meki, Adam Osman, Elisabeth Sadoulet, Olivier Sterck, Quentin Stoeffler, Julie Subervie, Marteen Voors, Witold Wiecek, and conference, seminar, and workshop participants at CSAE, EUDN, GLaD, ICDE, J-PAL, and NOVAFRICA for helpful comments. We are grateful to all the authors who kindly agreed to share their data for this project: Erwin Bulte, Michael Carter, Francesco Cecchi, Shawn Cole, Ghada Elabed, Wouter Gelade, Xavier Giné, Catherine Guirkinger, Dean Karlan, Neha Kumar, Robert Lensink, Nicholas Magnan, Simrin Makhija, Ana Marr, Francesca de Nicola, Robert Osei, Isaac Osei-Akoto, David Spielman, Quentin Stoeffler, Christopher Udry, Marcel van Asseldonk, Ruth Vargas Hill, James Vickery and Patrick Ward. We are particularly indepted to Francesco Cecchi, Ghada Elabed, Sofia Gallo, Quentin Stoeffler, and Patrick Ward for helping us to understand the data, to Olivier Santoni for his help with the rainfall data, and to Rachael Meager and Witold Wiecek for developing the _baggr_ R package and for answering our questions. Viewpoints and errors are our own. Part of this research is based on work conducted while the authors were affiliated with CERDI, CNRS, Université Clermont Auvergne (Castaing and Gazeaud), and NOVAFRICA, Nova SBE, Universidade NOVA de Lisboa (Gazeaud). Our pre-analysis plan is available at https://osf.io/zpesh/?view_only=97433abd5ea945f48c25d3963758eb6a .
extracted as a 15-year monthly panel between 2006 and 2020.

# **3.4 Taliban Control**

Afghanistan’s war between the government and the Taliban for two decades led to the country’s changing profile of territorial control. Areas under the control of the Taliban increased since the reduced presence of U.S. forces in 2011, with the entire country under the Taliban government since August 2021. We extract data on district control in 2018 from FDD’s Long War Journal that maintains real-time data on district control in Afghanistan: contested, government-controlled, Taliban-controlled. A district is marked as contested if the district center is under the government, but the Taliban control most areas outside the district center. A district is marked under Taliban control if the district center is under the Taliban.<sup>13</sup> In our study sample, 134 districts (39 percent sampled households) in Afghanistan were contested in 2018, 124 districts (52.5 percent sampled households) were under the government, and 30 districts were under the Taliban control (8.5 percent households). Figure 1 maps data on district control in 2018.

# **3.5 Measure of Weather Shock**

We construct the measures of weather shock at the cluster level, the lowest administrative unit, from which ten households were randomly sampled. Although we combine the data for coordinates of every household in IE-LFS 2019-20, each household in a given cluster is almost surely going to experience the same weather conditions.<sup>14</sup> Further, weather variables in the last growing season are more likely to affect incomes and consumption in the current period. With this in mind, we construct measures of weather shock for the 2018 growing season to analyze its effects on 2019-20 consumption.<sup>15</sup> There are several ways to define the functional form of the weather variable. Most studies either focus on levels or level changes in weather indicators. Another common approach is to use frequencies at which weather realizations fall into different categories (e.g., days with temperature between 0<sup>_◦_</sup> _−_ 10<sup>_◦_</sup> , etc.). This approach is advantageous as it avoids

> 13More information available at `https://www.longwarjournal.org/mapping-taliban-control-in-afghanistan`

> 14The Survey contains information on 1495 clusters in 275 districts of 34 provinces; roughly about 60 households were interviewed per district. The cluster is the lowest administrative unit, the primary sampling unit in IE-LFS 2019. And the district is the administrative unit above the cluster.

> 15To identify the growing months in the year, we rely on the agricultural module from the 201112 round of the Afghanistan IE-LFS (known as Afghanistan Livelihoods Conditions Survey, 2011-12) conducted at the cluster level. This survey collected detailed information about the most grown crops in the cluster and the annual cropping cycle for the crop. We first identify the most grown crop for each region, which helps determine the relevant growing months.

9
cost functions. Given the panel structure of our data, we address these issues applying the methods developed in Blundell and Bond (2000), which build on Arellano and Bond (1991) and Arellano and Bover (1995). In particular, we estimate the cost and production functions using a first difference instrumental variable approach. By computing first differences we control for firm unobservable and time invariant characteristics. By using lagged values of inputs to instrument current differences in inputs (together with lagged differences in inputs to instrument current levels) we account for any correlation between input choices and transitory productivity or cost shocks. Our instruments are valid as long as the first differences of transitory shocks in the production and cost functions do not exhibit high order serial correlation. In our empirical application we allow for first order serial correlation but not for higher order serial correlation.

Several interesting facts emerge from our empirical analysis. First, in line with the previous literature (e.g., Pischke, 2005, Bassanini, Booth, De Paola and Leuven, 2005) our estimates of the effects of training on productivity are quite high: an increase in the amount of training per employee of 10 hours per year, leads to an increase in current productivity of 0.6%. Increases in future productivity are dampened by the rate of depreciation of human capital but are still substantial. In a rough comparison, this estimate is below other estimates of the benefits of training in the literature (e.g., Dearden, Reed and Van Reenen, 2005, Blundell, Dearden and Meghir, 1999). If the marginal productivity of labor were constant (linear technology), an increase in the amount of training per employee by 10 hours would translate into foregone productivity costs of at most 0.5% of output (assuming all training 4
# _Tier 1 Emission Standards in the United States_

In 1990, Congress amended the Clean Air Act. Emission standards were further tightened to counter the additional pollution resulting from the increase in vehicle stock. Published as a final rule on June 5, 1991, Tier 1 standards were implemented between 1994 and 1997. Effective in 1994, the NOx standard was set at 0.6 gpm for cars (USEPA, 1999). The Tier 1 vehicle emission standards (0.25 grams per mile non-methane hydrocarbons (NMHC) for light duty vehicles, which were introduced progressively from 1994 onwards in the United States, became obsolete after the 2003 model year with a phase-in implementation of Tier 2 standard schedule from 2004 to 2009 (Gwilliam et al., 2004).

# _Tier 2 Emission Standards in the United States_

The EPA proposed Tier 2 tailpipe emissions standards in 1999 that were to be implemented in 2004. For the first time, both cars and light duty trucks were subject to the same national pollution control system. The same emissions standards apply to all vehicle weight categories. For example, cars, minivans, light-duty trucks, and SUVs have the same emission limit. Tier 2 set the new standard at 0.07 gpm for NOx, a 77 to 86 percent reduction for cars. In order to take full advantage of vehicle emission control technologies, the EPA also proposed a reduction in average sulfur levels to 30 parts per million (ppm) (USEPA, 1999) from the then average of more than 300 ppm. As a comprehensive national control program meant to regulate vehicles and their fuel as a single system, the Tier 2 Emission Standards pursue significant emission reductions (Gwilliam et al., 2004). Tier 2 regulations are more stringent than Tier 1 requirements, and they further extend the application of the standards to include some of the heavier vehicle categories that were not included in Tier 1 standards (Dieselnet, 2005).

In order to understand how the Tier 2 program works, it is necessary to understand the EPA’s classification of light duty vehicles and trucks. Vehicles and trucks under 8500 lb gross vehicle weight rating (GVWR) are classified as light duty vehicles.

16
<!-- Start of picture text --> 40<br>35<br>30<br>24.5 25.6<br>25<br>20<br>15<br>10<br>6) [<br>2005 2006 2007 2008 2009 2010 2011 2012 2013<br>@ Mining as share of total budget Mining as share of total fiscal revenues<br><!-- End of picture text -->
The numerator measures the share of activity _j_ in overall activity income in country _c_ ’s exports. The denominator calculates the same share across all countries. If the index is above one for a particular activity _j_ , country _c_ is said to be specialized in exporting that activity.

The AS indices can be used to track activity specialization in a country over time. Figure 3 provides heat maps for the change in the AS indices during the period 2000-2018 for four countries at different levels of development. Changes in the AS indices for Cambodia are relatively minor compared to the changes in the other countries. The AS for various occupations in hotels and restaurants (industry number 22) and in inland transport (23) industries went up. But in many manufacturing industries, AS indices barely changed for any occupational class. In contrast, AS indices in Vietnam changed frequently with major declines in mining (2), food (3) and leather manufacturing (5) industries, while in many other manufacturing industries and in utilities (17) and water transport (24) AS indices increased. In particular, AS for machine operators (occupation class 10) increased, but also for clerical support workers (7) and personal service workers (8). In Mexico AS developments diverged across occupations: AS for various occupations went up, for example in food manufacturing (3), petroleum refining (8) and transport equipment (15), but at the same time there were strong declines in AS for legislators (1) and managers (2) in the same industries. In China, a clear shift in specialization away from activities in goods producing industries (1-16) towards activities in services production (17-35) is visible. The strong increase in the AS of sales workers (9) is remarkable, also in most goods producing industries. Overall, it is noteworthy that in all countries AS indices within the same industry do not necessarily move in the same direction for all occupations. Both increases and declines in AS for particular classes are frequently found within the same industry illustrating that shifts along the extensive and intensive margins both play a role in determining export activity specializations.

15
|**Netherlands**|106.39|0.05|5.32|1.79|0.34|
|---|---|---|---|---|---|
|**Russia**|344.17|0.05|17.21|4.73|0.27|
|**Austria**|98.96|0.10|9.90|3.45|0.35|
|**UK**|480.80|0.15|72.12|20.14|0.28|
|**Mean**|328.79|0.10|36.86|10.36|0.30|

_Data Source: Central Bank_ _Table 4: Estimated revenue loss from reduced withholding on royalties (2014)_

|**Country**|**Flow in 2014**<br>**_Royalties_**<br>**_(Million USD)_**|**Differential**<br>**_with standard_**<br>**_withholding_**|**Direct Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Total Effect**<br>**_Tax revenue_**<br>**_foregone_**<br>**_Million USD)_**|**Efficiency**|
|---|---|---|---|---|---|
|**Cyprus**|62.77|0.05|3.14|0.02|0.01|
|**Russia**|40.09|0.05|2.00|0.39|0.20|
|**UK**|85.55|0.15|12.83|3.07|0.24|
|**Switzerland**|84.40|0.05|4.22|‐0.31|‐0.07|
|**US**|59.87|0.05|2.99|0.08|0.03|
|**Mean**|66.53|0.07|5.04|0.65|0.08|

_Data Source: Central Bank_ Our estimates suggest, for instance, that resetting the withholding tax rate on interest payments to Cypriot lenders could result in almost US$ 80 million in additional revenues. However, in the long run, financing would likely be restructured and originate from other countries and we estimate that only 27% of the initial increases of an isolated increase in the rate with a specific country would persist. For royalty payments, for which zero withholding rates are most common, this effect is even more pronounced. The ratio of total revenue increases as a share of mechanical effects is just 8% on average.

# **4. An investigation of indirect costs and treaty abuse**

Treaty based withholding rates likely affect government revenues both directly and indirectly. As presented above, the direct effect may be an increase in revenues due to a change in the applicable tax rate. Indirectly, a change in withholding tax rates may also change the costs of shifting income abroad 18