rafmacalaba/datause-encoder-data huggingface dataset:
- 52 -

# B. Tax Planning and Tax Competition

Prior to examining Thai tax policy in light of the objectives above, we consider tax planning (or tax arbitrage) and tax competition, which affect some of the conclusions reached in the previous section.

1. Tax Planning. Tax planning refers to the multinational's effort to minimize taxes, including the shifting of the tax base from high to low tax jurisdictions. Some of the strategies include transfer pricing, debt restructuring and the allocation of overhead costs so that expenses are deducted in high-tax jurisdictions and income is reported in low- tax jurisdictions. Tax planning therefore reduces tax revenue accruing to the high-tax country. As a result, countries use othex taxes, such as withholding taxes on individuals and companies, to protect revenue.

What is less well known is that tax planning can make a company tax in a high-tax country quite generous to multinational investment. With tax planning, a multinational deducts expenses at a higher statutory tax rate in one jurisdiction and reports income in a low-tax jurisdiction. In effect, capital can be subsidized once tax planning is taken into account. The effects of tax planning on government revenue and multinational investment in the host country is ameliorated by the deferral and accrual taxation adopted by some capital exporters. With accrual taxation, and to a lesser extent, deferral taxation, it is more difficult to shift income from high-tax to low-tax countries since the transaction triggers more taxes paid to the home country. The gains from tax planning therefore vary across multinational companies depending on their residence.

2. Tax Competition. The second issue Ls "tax competition" which refers to tax policies chosen by different countries in competition with each other to attract foreign capital.<sup>13</sup> There are two important aspects j_/ Much of this discussion is borrowed from Mintz and Tulkens (1989).
the handling of import transactions, and the data was collected for 153 freight forwarding firms.<sup>1</sup> One of the challenges faced in this data collection exercise was the identification of the universe of freight forwarding firms in Serbia from which a representative sample of firms could be drawn. Table 1 shows the composition of our final survey sample and the initial sub-universes and response rates whose details are provided in what follows. We initially obtained a list of 36 firms that are members of the Serbian Freight Forwarder Association and firms identified by logistics experts in the field as relevant in providing freight forwarder services. Given their relevance, we screened and attempted to survey all firms in this list, and in our final survey sample 11% of firms are these ‘relevant’ freight forwarders. Additionally, we obtained from the National Bank of Serbia a list of 1,311 registered firms in transport and trade related activities (i.e., rail and road transport, cargo handling, etc.) with information on size measured by their number of employees.<sup>2</sup> We conducted a screening of the firms in both lists to identify those that qualify as international freight forwarders and found that 31% of the firms complied with the screening criteria. Of those 61% were surveyed.

Based on the recent evidence that international trade is highly concentrated in the hands of a few very large firms, we screened and attempted to survey all medium-sized and large freight forwarders (with 50-250 employees and more than 250 employees).<sup>3</sup> With a response rate of 77%, these freight forwarders account for 7% of our final sample. A random sample of small freight forwarders was screened and 61% of those identified as international freight forwarders were successfully interviewed. The distribution of freight forwarding firms according to their size is representative of the universe of freight forwarding firms, as shown by Appendix Figures A.1 and A.2.

> 1 The questionnaire can be obtained from the authors upon request.

> 2 The registered firms are those with valid contact information available.

> 3 Cebeci et al. (2012) and Freund and Pierola (2015) show that in any typical developing country the bulk of exports is conducted by a small share of the top exporting firms.  We attempted to sample all medium and large freight forwarder firms because it was likely that the distribution of activity across freight forwarders was also skewed, and the survey confirmed that it is.

5
Table 2: Politicians vs. Bureaucrats - Selection Traits

||Bureaucrat|Politician|Dif.|p-value|N|
|---|---|---|---|---|---|
|_Panel A: Integrity measures_||||||
|OK to spread rumors|0.05|0.11|0.06|0.00|2124|
|OK to borrow without permission|0.02|0.04|0.02|0.01|2124|
|Not a sin to exaggerate credentials|0.12|0.17|0.05|0.00|2124|
|Follow authorities orders|0.14|0.22|0.07|0.00|2124|
|No blame if group does the same|0.04|0.09|0.05|0.00|2124|
|OK to take credit for others’ ideas|0.20|0.23|0.02|0.21|2124|
|Some people lack feelings to be hurt|0.11|0.13|0.02|0.18|2124|
|People are mistreated for a reason|0.31|0.36|0.05|0.02|2124|
|Integrity Index|0.12|-0.20|-0.32|0.00|2124|
|_Panel B: Non cognitive measures_||||||
|Extraversion|2.88|3.07|0.19|0.00|2126|
|Conscientiousness|3.34|3.24|-0.09|0.00|2126|
|Openness|3.11|3.11|-0.01|0.75|2126|
|Emotional Stability|2.88|2.87|-0.01|0.72|2126|
|Agreeableness|2.94|3.06|0.12|0.00|2126|
|Big fve index (mean of z-scores)|-0.04|0.08|0.12|0.01|2126|
|Grit|2.72|2.68|-0.04|0.07|2126|
|Decision making|3.26|3.13|-0.13|0.00|2126|
|Hostile Bias|3.05|2.91|-0.14|0.00|2126|
|Other Non-Cognitive Index (Grit,Decision,Hostile)|0.12|-0.21|-0.34|0.00|2126|
|Non-Cognitive index (Big Five + Others)|0.02|-0.04|-0.07|0.13|2126|
|_Panel C: Public Sector Motivation_||||||
|Attraction to Policy Making|3.51|3.51|0.00|0.83|2126|
|Commitment to Public Interest|4.08|4.07|-0.01|0.55|2126|
|Social Justice|4.09|4.08|-0.00|0.80|2126|
|Civic Duty|4.05|4.10|0.05|0.00|2126|
|Compassion|3.75|3.67|-0.07|0.00|2126|
|Self-Sacrifce|4.05|4.14|0.08|0.00|2126|
|PSM Index|-0.01|0.02|0.04|0.43|2126|
|_Panel D: Cognitive and risk profle measures_||||||
|Altruism - percentage shared|0.29|0.32|0.04|0.00|2125|
|Choose dominated risk option|0.10|0.13|0.03|0.04|2126|
|Risk-aversion (1-5)|3.72|3.91|0.19|0.01|1869|
|Cognitive ability - Number of digits remembered|5.52|5.25|-0.28|0.00|2126|

_Note:_ This table compares average characteristics of politicians and bureaucrats. Column (1) reports the average characteristic for all politicians, while column (2) does the same for bureaucrats. Details about the construction of variables are presented in the Annex. Column (3) presents the difference between politicians’ and bureaucrats’ means. Column (4) presents the p-value of a test with null hypothesis of equal means. Column (5) reports the number of observations with non-missing values in each test.

44
92

# **IENXURE VII**

# **TBE PUECTIOONIN** OF WDP IN JAIPUR DISTRICT

Jaipur was one of the first districts to have been identified for the WDP. With an area of 14,000 sq. km. it has a population of 3,420,574 persons. It has a sex ratio of 1104 males to 1000 females, and rate of rural female literacy that is the lowest in the state - 0.16%.

Distinctive features of the district having a bearing on WDP: Jaipur is the capital and the hub of political activity in Rajasthan. Therefore, a program like WDP has a high visibility and comes under surveillance from a cross-section of politicians, research organizations, and journalists. It also has a number of women's organizations in the sphere of research and field-work. Hence, crimes against women, women's rights, child-marriage acquire political significance and get publicity. Beix.g the capital city, Jaipur is also the administrative headquarter for WDP. The Department of Women and Children, the State IDARA, aad IDS are all located in Jaipur.

The oraanization of WDP: Jaipur has 76 Sathins and 1'^ Prachetas. The PD is a medical doctor who left her government jt. to join WDP. The responsibility for the district IDARA has been entrusted to the Rajasthan Adult Education Association. It has well-known skills in developing material for training pi -rams, and working with a large number of people including jouri. 'sts, teachers, government officers and field workers. The same organization functions as the State IDARA.

Functioning of WDP in JaiDur: Jajams in Jaipur district have been held fairly regularly once a month. They are attended by the staff of IDS, IDARA and the PD. An analysis of the jaja"' reports of Jaipur district from June 1987 to March 1988, shows t.:at drought rel!of has been the most important subject of discussion both in jajams and in the informal women's groups. The other important subjects for discussion have been accessibility of water, fodder, pensions and health, in that order. The group action has been concentrated more on the malpractice on the sites of drought relief works, and on the problems of individual women. It would not be wrong to say that group action in Jaipur district has been influenced by the more radical and feminist ideology of the workers in IDS, IDARA, and the PD.

One Shivir has been held in Jaipur (in February 1986). The topic was Paisnayati Raj or local self-governing institutions. The planning for the shivir was a thorough process, with a number of pre-shivir meetings held to decide the topic, seek relevant information, and work out the logistics. An analysis of the shivir brought up the following details that serve to give a better understanding of the shivir and its functions:
<!-- Start of picture text --> 5<br>4<br>— Brazil<br>§ Colombia<br>i= Ethiopia<br>2 Mexico<br>= Philippines<br>oH South Africa<br>3 — Vietnam<br>| ee<br>2004 2006 2008 2010 2012 2014<br><!-- End of picture text -->

<!-- Start of picture text --> 2.6<br>2.4<br>— Brazil<br>E Colombia<br>|e 22 Ethiopia<br>2 Mexico<br>= Philippines<br>oH 2 South Africa<br>— Vietnam<br>1.8 — we<br>_—<br>—<br>—_<br>—_—<br>—<br>1.6<br>2004 2006 2008 2010 2012 2014<br><!-- End of picture text -->
18 regarded as fundamentally organized around patron-client, charismatic, or coercive forms of political mobilization.

**Table 1:  Party organization and public policy**

**<u>(</u>** _<u>p</u>_ -values in parentheses, standard errors clustered by country **<u>)</u>**

|Dependent<br>variables:|Gross Secondary<br>School<br>Enrollment|Gross Primary<br>School<br>Enrollment|Bureaucratic<br>Quality<br>(ICRG)|Corruption<br>(ICRG)|
|---|---|---|---|---|
|**Panel A**: All country<br>elected. Coefficients<br>|-years, controlling fo<br>for other controls n<br>|r whether govern<br>ot reported (see n<br>|ments are com<br>ote)<br>|petitively<br>|
|Competitive<br>elections, 0-1|1.13<br>(.55)|4.0<br>(.15)|.29<br>(0.103)|0.17<br>(0.17)|
|Average of<br>programmatic<br>dummy variables<br>over all parties|6.22<br>(.01)|6.03<br>(.08)|.64<br>(0.00)|1.01<br>(0.00)|
|**Panel B**: Sample res<br>elections. Coefficient|tricted to country-ye<br>s for other controls|ars with governm<br>not reported (see|ents elected in c<br>note)|ompetitive|
|Average of|9.56|7.26|.66|.92|
|programmatic<br>dummy variables<br>over allparties|(.001)|(.08)|(.01)|(0.00)|
|**Panel C**: Sample res<br>elections. Coefficient|tricted to country-ye<br>s for other controls|ars with governm<br>not reported (see|ents_not_elected<br>note)|in competitive|
|Average of<br>programmatic<br>dummy variables<br>over all parties|3.93<br>(.23)|7.66<br>(.11)|.33<br>(0.08)|.79<br>(0.00)|

Note : Table based on Tables 4 and 5, Keefer (2011). Results of ordinary least squares regressions that control for real, purchasing power parity-adjusted income per capita; the percent of the population that is rural; the percent that is 15 years old or younger; land area; and total population (not reported). Competitively-elected governments are those in which both legislative and executive elections had multiple parties competing and in which no party or candidate received more than 75 percent of the vote or seat share (data from the Database of Political Institutions). Sample sizes in Panel A range from approximately 500 for the education variables to approximately 1300 for the International Country Risk Guide variables.

For example, the almanacs, and therefore the data in the DPI, tend to distinguish whether programmatic tendencies are the product of leaders’ autonomous decisions, or are supported by the party. Venezuela provides a useful illustration. In particular, although
# **Mover-stayer model for non-heads, Logit estimates**

Number of obs = 8166562 Wald chi2(6) = 293.58 Prob > chi2 = 0.000 Pseudo R2 = 0.152 Log pseudo-likelihood = -639678.55

||_Ro_|_bust Std._<br>|||||
|---|---|---|---|---|---|---|
||_Coef._|_Err._|_z_|_P>|z|_|_[95% Co_|_nf. Interval]_|
|dprim3|0.782|0.229|3.410|0.001|0.332|1.232|
|dsec1|0.633|0.298|2.120|0.034|0.049|1.218|
|dsec2|0.597|0.324|1.840|0.066|-0.039|1.232|
|exp|0.101|0.023|4.340|0.000|0.055|0.146|
|exp2|-0.002|0.000|-4.270|0.000|-0.003|-0.001|
|gend|-0.759|0.188|-4.030|0.000|-1.127|-0.390|
|preta|-0.500|0.336|-1.490|0.137|-1.159|0.159|
|ddnonrem|-0.849|0.171|-4.960|0.000|-1.185|-0.513|
|ddworkown|-1.731|0.307|-5.640|0.000|-2.332|-1.130|
|dprop|-1.839|0.462|-3.980|0.000|-2.745|-0.934|
|headnagr|1.122|0.167|6.710|0.000|0.794|1.450|
|headmover|2.468|0.374|6.610|0.000|1.736|3.200|
|_cons|-4.369|0.298|-14.640|0.000|-4.954|-3.783|

# **Changes in Predicted Probabilities for Moving out of Agriculture**

||_0->1_|_-+sd/2_|_MargEfct_|
|---|---|---|---|
|dprim3<br>|0.010||0.007|
|dsec1|0.007||0.006|
|dsec2|0.007||0.005|
|exp||0.017|0.001|
|exp2||-0.024|0.000|
|gend|-0.007||-0.007|
|preta|-0.004||-0.004|
|ddnonrem|-0.007||-0.007|
|ddworkown|-0.011||-0.015|
|dprop|-0.008||-0.016|
|headnagr|0.015||0.010|
|headmover|0.084||0.021|

Source: Authors’ calculations. Note: dprim3: 9 years of schooling dsec1: 11 or 11 years of schooling dsec2: 12 years of schooling exp: age minus schooling exp2: experience squared gend: female 1 preta: black ddnonrem: non-remunerated household member ddworkown: own-consumption worker dprop: holding own land headnagr household head in non-agricultural sector headmover: head has moved out of agriculture.

57
||`(0.27)`|`(0.05)`|`(0.02)`|`(0.04)`|
|---|---|---|---|---|
|`judi`|`-0.086`|`-0.196`|`0.070`|`0.055`|
||`(0.53)`|`(1.16)`|`(1.13)`|`(0.86)`|
|`judirate`|`-0.183`|`-0.170`|`-0.031`|`-0.029`|
||`(0.83)`|`(0.75)`|`(0.43)`|`(0.40)`|
|`centgov`|`0.435`|`0.474`|`-0.017`|`0.007`|
||`(1.57)`|`(1.66)`|`(0.20)`|`(0.08)`|
|`parl`|`-0.654`|`-0.644`|`-0.065`|`-0.075`|
||`(2.58)**`|`(2.48)* `|`(0.81)`|`(0.92)`|
|`cbank`|`0.111`|`0.104`|`-0.042`|`-0.053`|
||`(0.48)`|`(0.44)`|`(0.47)`|`(0.58)`|
|`police`|`0.126`|`0.115`|`0.014`|`0.014`|
||`(0.68)`|`(0.61)`|`(0.20)`|`(0.19)`|
|`govine`|`0.392`|`0.315`|`0.080`|`0.026`|
||`(2.37)* `|`(1.84)`|`(1.19)`|`(0.38)`|
|`ginter`|`0.176`|`0.159`|`0.120`|`0.072`|
||`(0.93)`|`(0.80)`|`(1.77)`|`(1.03)`|
|`corobs`|`0.233`|`0.200`|`0.012`|`0.034`|
||`(1.17)`|`(0.98)`|`(0.14)`|`(0.38)`|
|`corry`|`0.276`|`0.290`|`0.090`|`-0.010`|
||`(1.55)`|`(1.58)`|`(1.02)`|`(0.12)`|
|`corqb `|`-0.173`|`-0.245`|`0.078`|`-0.035`|
||`(0.80)`|`(1.13)`|`(0.58)`|`(0.25)`|
|`infr`|`-0.169`|`-0.053`|`0.029`|`-0.017`|
||`(0.75)`|`(0.23)`|`(0.30)`|`(0.18)`|
|`roads`|`-0.055`|`0.015`|`0.131`|`0.117`|
||`(0.34)`|`(0.09)`|`(2.01)* `|`(1.75)`|
|`post`|`0.189`|`0.198`|`0.192`|`0.147`|
||`(0.85)`|`(0.86)`|`(1.50)`|`(1.13)`|
|`elec`|`0.022`|`-0.089`|`-0.121`|`-0.101`|
||`(0.11)`|`(0.42)`|`(1.06)`|`(0.87)`|
|`water`|`-0.006`|`0.034`|`0.014`|`-0.018`|
||`(0.03)`|`(0.16)`|`(0.14)`|`(0.18)`|
|`crimest`|`0.026`|`0.033`|`0.105`|`0.117`|
||`(0.11)`|`(0.14)`|`(1.15)`|`(1.25)`|
|`crimeorg`|`0.145`|`0.186`|`-0.173`|`-0.206`|
||`(0.65)`|`(0.81)`|`(1.77)`|`(2.07)* `|
|`sale1`||`-0.850`||`-0.207`|
|||`(2.61)**`||`(1.88)`|
|`sale2`||`-0.384`||`-0.087`|
|||`(1.12)`||`(0.82)`|
|`owind`||`0.296`||`0.397`|
|||`(1.29)`||`(4.96)**`|
|`owfam`||`0.392`||`0.641`|
|||`(1.40)`||`(5.71)**`|
|`owork`||`-0.456`||`-0.249`|
|||`(1.54)`||`(2.11)* `|
|`owsta`||`-0.240`||`-0.441`|
|||`(0.90)`||`(4.45)**`|
|`Constant`|`0.005`|`0.540`|`0.125`|`0.193`|
||`(0.01)`|`(0.98)`|`(1.12)`|`(1.25)`|
|`Observations`|`431`|`431`|`2094`|`2094`|

``` Source: BEEPS. Absolute value of z statistics in parentheses; * significant at 5%; ** significant at 1% ``` 44
failure, with the long hours of operation leaving owners little time to coordinate, which combined with competition and a lack of trust prevents this from occurring.<sup>4</sup>

# **2.2.Agruppa’s Solution**

Agruppa is a start-up social enterprise founded in 2014 with the goal of leveraging mobile phone technology to aggregate demand for produce from small vendors, creating daily collective orders which can then be purchased in bulk directly from farms (and, by taking advantage of economies of scale, at a discount), and then be delivered to the business places of the small retail shops. This effectively shortens and makes more efficient the supply chain between farms and retailers. Their goals are to reduce the travel time and costs for the owners of these small stores, deliver fresh fruits and vegetables at a price discount of at least 5 percent on what store owners would pay at Corabastos, and potentially lower the costs of food to poor consumers buying from these stores.

Agruppa carried out market research and piloted a minimum viable product version of their operating model with 20 neighborhood retail stores in Bogota in 2014. In 2015 they were then selected as one of the winners of an SME innovative ideas competition run by the World Bank, and additionally attracted funding from social investors including Wayra (the accelerator of Telefonica), Mercy Corps Social Venture Fund and Yunus Social Business. This led them to agree to work with us on an impact evaluation to evaluate the effectiveness of their model as they launched and expanded their business in 2016.

Agruppa intended to develop an app that retail store owners could use to place their daily orders, but technology problems meant that this was not developed during our implementation phase. Instead, Agruppa offered customers three ways to make their orders: (1) ordering directly from the transporters, who would take the next order while delivering the current day’s order – used in 36 percent of transactions; (2) phone orders to a call center, where retail stores could call between 4 and 10pm each evening and place their order for the next day – used in 40 percent of transactions; and (3) WhatsApp orders, where customers could use their mobile phones to place orders directly – used in 24 percent of transactions. Mobile technology thus still facilitated two-thirds of the orders. These orders were then aggregated into a daily collective order per product, and Agruppa would then purchase these products as early in the supply chain as possible – either directly from

> 4 Likewise, since each wholesaler at Corabastos typically sells only one product, no single wholesaler can deliver the bundle of goods a retail store typically buys at the market, and they do not typically offer delivery services.

7
saving so as to make good any shortfall in foreign financing. Furthermore, we also discovered that government borrowing crowds out the business sector much more than the household sector. It should of course be noted that this argument is based on average behavior of the countries in our sample, and there are exceptions in specific countries at specific times. Like all conclusions based on econometric evidence it is subject to revision in the light of the collection of additional information.

# 4.4 Cyclical factors in individual countries

Sectoral surpluses in most of the countries which we examined are quite variable from year to year. Here we briefly mention some features of the experience of four countries for which relatively long time series are available.

Colombia. Figure 2 displays the co-movements of the household surplus with the net position of the foreign plus government sectors, 1970-86. The first half of this period was marked by sharp fluctuations in the series, while the second half displays stability. The underlying reason for the difference between the two halves is evident from figure 3: from 1978 on, movements in the external surplus were sterilized by fiscal action: the external and fiscal surpluses became mirror images of one another. In this respect the second half of the Colombia data set provides an exception to the general rule that government surplus does not respond to shifts in the external sector. In Colombia, government revenue is very dependent on export taxes, and thereby negatively correlated with the current account deficit (i.e. the surplus of the foreign sector) But the government did not, however, allow fluctuations in this revenue source to influence expenditure. Because of the importance of export performance in GDP, the 44
to pre-democracy regimes (equivalent to between 26 and 40 percent of a standard deviation of the vote shares going to these parties). We interpret this finding as evidence that malapportionment can provide political influence to pre-democracy elites after transition to democracy.

Next, we conduct a robustness test in which we run regressions similar to the Equation 3 but using data for elections that took place _during_ non-democratic times. This is the case for the 1978 elections in Brazil, the 1988 plebiscite in Chile, and the 1991 parliamentary elections in Mexico. For these elections, we computed the share of votes supporting the regime in each electoral region<sup>19</sup> . The results in Columns 3 and 4 of Table 4 show a positive correlation between overrepresentation in democratic times and support for the elites in pre-democratic times. The economic significance of these results is similar to the first two columns. A one standard deviation rise in overrepresentation translates into an increase of between 38 and 56 percent of a standard deviation of votes supporting the regime.

Table 5 provides an indication of how large the additional political influence is that predemocracy elites can gain after transition to democracy due to malapportionment. The table shows that the percentage of seats that went to parties associated with pre-democratic elites in the first election after transition to democracy was quite a bit larger than the percentage of votes that went to these same parties, in all countries other than Brazil. On average, parties associated with pre-democratic elites received 33.5 percent of all votes, but due to malapportionment this translated into 39.5 percent of seats in the lower house.

Overall, the evidence in Tables 4 and 5 supports our argument that malapportionment can provide political power to pre-democracy elites since malpportionment tends to give more political representation to parties that are aligned with the pre-democracy elite.

# **4.2 Malapportionment and Democratic Consolidation**

Our finding from the previous section that malapportionment may allow pre-democracy elites to sustain their political power in a democracy could imply that malapportionment may make

> 19 For Brazil, we look at support for the ARENA party, for Chile at the SI option in the 1988 plebiscite, and for Mexico support in favor of the PRI party.

13