rafmacalaba/datause-encoder-data huggingface dataset:
administration: to each according to his cultivable area. De facto allocations, however, are determined by interactions between groups of farmers, organized by channel outlet, and the provincial irrigation department. As we will argue, this interaction is characterized by both corruption _and_ rent-seeking. Corruption is of the “with theft” variety (Shleifer and Vishny, 1993), in that the farmers who pay the bribe and the local irrigation official who demands it both benefit; the losers are the farmers at the downstream outlets who receive less water than they are entitled to. Rent-seeking arises as coalitions of headenders and tailenders lobby the higher irrigation department office to intercede on their behalf, e.g., by replacing (or not replacing) the local official. In choosing how hard to lobby, we assume that the head coalition takes into account the bribes its members must pay, while the rational local official internalizes the rent-seeking induced by his own corruption.

Our theoretical model allows a role for political influence in the rent-seeking contest. Of critical importance for the contest outcome is the distribution of lobbying power (or efficacy) along a channel. We establish that, insofar as such influence is relatively greater at the head reaches than at the tail, “theft” will be greater, which is to say that more water will be diverted toward the head and away from the tail. Our model also has implications for the value of farmland, which is assumed to reflect not only the productive value of the canal water delivered but also the bribes that farmers may have to pay to ensure these deliveries. Even though the bribe amount is increasing in the relative influence of headend landowners, thus depressing their land values, we show that the productive value of the greater off-take at the head more than makes up for this; thus, on balance, the head-tail land value differential increases with relative head influence. Finally, the assumption of competitive rent-seeking delivers a distinctive symmetry result: a one unit increase in lobbying influence of the head coalition has an equivalent impact on the head-tail differential in both water availability and land values as a one unit decrease in political influence of the tail coalition.

To test these implications, we have collected a truly unique data set on both landown2
# **<u>Appendix Table 4: Impact of treatments on household size, transfers and fixed deposit</u>**

|<br> <br>**Regressions with stratification cell**|<br>**fixed effects but wit**<br>(1)|<br>**hout additional bas**<br>(2)|<br>**eline controls**<br>(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|**Dependent variable:**|Household size|Total transfers<br>made [MK]|Total transfers<br>received [MK]|Total net transfers<br>made [MK]|Transf. made,<br>biggest gift bf.<br>Oct09 [MK]|Transf. received,<br>biggest gift bf.<br>Oct09 [MK]|Net transf. made,<br>biggest gift bf.<br>Oct09 [MK]|Has fixed deposit<br>account|
|Ordinary Account|‐0.04|650.96*|‐193.97|778.80*|17.33|‐80.05|113.07|0.03**|
||(0.19)|(344.48)|(320.99)|(418.47)|(86.74)|(123.41)|(137.51)|(0.02)|
|Ordinary x Private Raffle|0.23|‐460.58|‐33.77|‐370.38|70.19|‐49.91|135.62|‐0.04**|
||(0.19)|(334.95)|(320.88)|(438.87)|(86.58)|(127.77)|(121.81)|(0.02)|
|Ordinary x Public Raffle|0.24|‐544.64|‐210.67|‐323.01|123.12|‐98.51|206.69*|‐0.02|
||(0.18)|(371.55)|(297.05)|(402.58)|(97.86)|(115.89)|(119.35)|(0.02)|
|Commitment Account|‐0.01|470.16|‐459.24|875.05**|71.05|‐270.77***|347.49***|0.06**|
||(0.16)|(335.84)|(283.53)|(419.60)|(80.33)|(104.13)|(128.29)|(0.02)|
|Commitment x Private Raffle|0.34**|120.40|‐5.04|156.38|40.74|54.79|‐24.20|‐0.02|
||(0.17)|(416.42)|(241.61)|(459.38)|(96.18)|(99.24)|(125.87)|(0.03)|
|Commitment x Public Raffle|‐0.10|‐250.47|424.84|‐733.23|27.47|76.44|‐44.14|‐0.01|
||(0.16)|(378.40)|(258.92)|(454.57)|(102.47)|(101.38)|(148.70)|(0.03)|
|Mean Dep Var in Control|5.72|2871.70|2492.13|417.89|358.53|641.98|‐297.32|0.04|
|Number of observations|2,835|2,835|2,835|2,835|2,835|2,835|2,835|2,835|
|P‐values of F‐tests:|||||||||
|Ordinary, No Raffle =|||||||||
|Commitment, No Raffle|0.849|0.631|0.340|0.830|0.539|0.083|0.068|0.292|
|Ordinary, Private Raffle = 0|0.300|0.505|0.478|0.319|0.265|0.292|0.042|0.950|
|Ordinary, Public Raffle = 0|0.278|0.743|0.177|0.215|0.130|0.105|0.008|0.246|
|Commit., Private Raffle = 0|0.077|0.132|0.106|0.019|0.242|0.061|0.017|0.018|
|Commit., Public Raffle = 0|0.539|0.534|0.909|0.739|0.328|0.096|0.051|0.015|

Notes: Stars indicate significance at 10% (*), 5% (**), and 1% (***) levels. Standard errors are clustered at the club level. USD 1 is ca. MK 145. All regressions include stratification cell fixed effects. F‐tests: "Ordinary, No Raffle = Commitment, No Raffle" tests the equality of means in ordinary and commitment treatment groups each without additional raffle treatments; "Ordinary, Private Raffle = 0" tests if the sum of the coefficient on "Ordinary" and the coefficient on "Ordinary x Private Raffle" is different from zero; "Ordinary, Public Raffle", "Commitment, Private Raffle"; "Commitment, Public Raffle" tests the same sums of the different combinations of savings and raflle treatments; "Private Raffle = 0, Ordinary or Commitment (jointly)" tests whether the coefficient on the interaction of "Ordinary" and "Commitment" with "Private Raffle" are jointly different from zero; "Public Raffle = 0, Ordinary or Commitment (jointly)" peforms the same test but for the public raffle treatment condition.
Vietnam before the BTA, the share of the U.S. in Vietnam’s imports has remained relatively unchanged before and after the BTA at around 2.5 percent.<sup>14</sup> The composition of Vietnam’s imports is characterized by a high proportion of three VSIC sub-categories, namely, “basic manufacturing”, “chemical, rubber, and plastic products”, and “electronics, machinery, and transport equipment” which accounted for more than three quarters of Vietnam’s imports in 2004. If the latter products are capital and skilled-labor intensive, Vietnam’s trade pattern is consistent with the prediction of the H-O model.

# (c) Evolution of Vietnam’s industrial employment

Vietnam’s “formal”<sup>15</sup> industrial employment recorded in the _Enterprise Survey_ data (General Statistics Office (GSO)) grew substantially from 1.8 million workers in 2000 to 3.2 million in 2004 and further increased to 4.1 million in 2007. Figure 5 and Appendix Table A.5 show the actual number of workers employed by industrial enterprises for the period 2000-2007. It appears that employment growth is highly influenced by the expansion and the factor intensity of exports. The number of workers employed by export-oriented labor-intensive manufacturing, and in particular, those in the sectors whose exports to the U.S. expanded (namely apparel/textiles, footwear/leather and furniture and miscellaneous manufacturing) increased substantially. Employment growth in the electronics, machinery and transport equipment sector was also strong, perhaps helped by the rise in exports (e.g., of electronics goods). However, despite the high value of Vietnam’s crude oil exports, enterprise employment generation has been slowest in the mining sector due to the low labor intensity of crude oil production. At the two-digit VSIC

> 14 The rise of the U.S. share in Vietnam’s total imports to 4.5 percent in 2003 is attributable to Vietnam’s purchase of U.S. aircraft (Boeing 777s) (Parker, Riedel, & Quang, 2007).

> 15 Throughout the paper, jobs in “enterprises” as well as those in government are referred to as “formal” employment. The term “enterprise” is defined as “an economic unit that independently keeps business account and acquires its own legal status” under the relevant laws (GSO). “Informal” employment is defined to include wage workers who work in a variety of informal wage sectors as well as those who are self-employed.

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# **5.1 Impacts of policies on time**

In order to demonstrate the discrete time transition model without the added complications of multiple imputation, we first estimate the model using independent variables that are complete for our country sample. We apply the model to both the import and the export days in customs data, using the independent variables described above on the right hand side. First we consider, alone, a set of basic country-level variables that reflect underlying propensities to trade. We then add an additional set of variables that measure aspects of institutional quality, the WGI variables. The results of this specification are reported in Table 3.

Coefficient estimates in the discrete time transition model illustrate the effect of each variable on the probability that the expected duration (time spent in customs) will end on a given day. Positive coefficients indicate that countries with higher values of the variable tend to require fewer days in customs, while negative coefficients indicate that higher values of a variable are associated with more expected days in customs. The results in the first column suggest that OECD and WTO member countries have shorter expected time in customs for imports and for exports, while countries with larger land areas tend to require more time in import customs. These coefficients all have the expected signs. WTO and OECD members are likely to have better performing customs. Countries with larger land areas may trade less and therefore have less incentive to operate customs efficiently. Estimates that are statistically significant at only the 10% level suggest that countries with larger economies and island countries are likely to have faster customs clearance.

In the second column we add the WGI variables as controls. Of these, only the indicator of regulatory quality is found to be statistically significant. The sign on the parameter estimate is as expected; TAB respondents indicate that goods clear customs in less time in countries with higher levels of regulatory quality. The inclusion of the governance indicators affects the estimated levels of significance of some the basic control variables. The landlocked variable becomes significant in this specification, taking the expected negative sign. Landlocked countries have larger reported days in customs on average.<sup>29</sup> When the WGI variables are included the coefficients on the other basic control variables become less significant.

On the export side fewer right hand side variables are significant in either specification. The WTO and OECD dummy variables are significant and of the expected sign in column 3. These effects

> 29Note that this result may be mechanical because TAB asks respondents to report total time in customs for goods moving to a seaport. Freight movements involving landlocked countries require interaction with customs officials in more than one country. They might have higher reported time in customs as a result.

19
Educación Superior ICFES), which administers standardized tests in Colombia. The C600 and the C100 surveys provide general data on an important array of school characteristics and have an identification code that allows researchers to link these datasets with the ICFES test scores. The Ministry of Education collects the data directly via a questionnaire to all the schools in the country. Data are at the school and “schedule” level.

The school level data present information on the number of administrative personnel, the number and level of education of teachers, the number of teachers by subject areas, the number of physicians in the school, the total number of students in the school by grade and by age, the number of students who failed a grade and the number of students who dropped out. These data are available for both 1999 and 2003.

For 1999, there is also information on the physical characteristics of schools. The main variables are: furniture in the school (chairs and desks), support materials such as computers, the number of computers exclusively for teaching purposes, the total number of laboratories, the number of laboratories for specific subjects (physics, chemistry, biology, construction and farming), the total number of rooms, the number of classrooms, the number of libraries, the number of food facilities and dorms, the number of other types of rooms and the number of sports facilities (soccer fields and basketball courts).

The surveys also breakdown the data at the schedule level, the time at which the school offers services. There are four main schedules during which students can attend school: in the morning (usually from 7:00 am to 12:00 pm), in the afternoon (from 12:00 pm to 4:00 pm), in the evening (from 2:00 pm to 7:00 pm); or they can attend a school with a “complete” schedule (from 8:00 am to 3:00 pm). The schedule mechanism was implemented throughout the country in the 1960s to maximize the use of school infrastructure. In this way, some schools that previously operated only during the morning were subsequently open for two schedules (e.g., morning and afternoon), thus allowing them to enroll twice the number of students. It is important to note that the data discriminate between the two schedules as if they were two different schools.

13
**Table 6. European Union, Japanese and United States Uruguay Round Tariff Concessions on Non-Oil Products of Export Interest to Afrka.**

||||-IT<br>l|Tariff Rate(%<br>l<br>|)<br> <br>i<br>|<br>~~~~~~~~~Prefcerw<br>|
|---|---|---|---|---|---|---|
|Exporter|Value ($ million)|Applied 1992|<br>MFN 1992|<br> <br>Applied 1999|<br> <br>MFN 1999|<br><br> Margin Reduction|
|Angola|246.9|0.01|2.48|0.01|1.41|1.06|
|Benin|36.5|0.10|1.80|0.10|1.27|0.53|
|Bumndi|137.0|0.00|2.41|0.00|0.14|2.37|
|Burkina Faso|97.4|0.01|1.08|0.01|0.80|0.29|
|Cameroon|890.9|0.00|3.81|0.00|1.62|2.19|
|Cape Verde|5.7|0.00|19.60|0.00|9.30|10.30|
|Central African Rep.|98.5|0.00|1.63|0.00|0.39|1.24|
|<br>Chad|55.8|0.01|0.39|0.00|0.25|0.14|
|Comoros|20.9|0.00|3.45|0.00|2.07|1.38|
|Congo|231.7|0.00|0.92|0.00|0.49|0.43|
|Cote d' Ivoire|2,062.4|0.01|5.51|0.00|2.94|2.56|
|Djibouti|13.2|0.18|1.13|0.14|0.89|0.20|
|<br>Ethiopia|293.3|0.02|2.49|0.02|0.73|1.77|
|<br>Equatorial Guinea|48.4|0.00|2.90|0.00|1.26|1.64|
|Gabon|624.7|0.00|1.11|0.00|0.71|0.40|
|Gambia|90.3|0.59|7.05|0.37|4.21|2.62|
|Ghana|764.4|0.08|2.99|0.06|2.15|0.82|
|Guinea|419.4|0.01|0.77|0.01|0.04|0.32|
|Guinea Bissau|7.6|0.07|6.52|0.05|4.59|1.91|
|Kenya|667.1|0.11|4.88|0.10|2.88|1.99|
|Liberia|935.6|0.00|0.09|0.00|0.02|0.07|
|Madagascar|268.8|0.31|4.98|0.01|2.45|2.34|
|<br>Malawi|197.5|0.39|9.05|0.34|7.14|1.87|
|Mali|74.9|0.01|0.92|0.01|0.42|0.50|
|Mauritania|402.6|0.24|6.47|0.16|3.90|2.49|
|Mauritius|857.9|3.94|13.29|3.59|11.70|1.25|
|Mozambique|113.1|0.60|8.02|0.23|5.13|2.52|
|<br>Niger|70.5|0.07|2.79|0.06|1.94|0.84|
|Nigeria|676.2|0.07|2.36|0.06|0.83|1.52|
|Reunion|157.9|4.09|4.29|2.09|2.34|_0.05_|
of cumulative discovery costs divided by the cumulative quantity of oil discovered over these years).

**Table 2: Depletion values for oil adjusted for expected undiscovered resources, 2010**

||**_Depletion_**<br>**_of proven re_**|**_as function_**<br>**_source stock_**<br>**Resource**|**_Depletion_**<br>**_of total res_**|**_as function_**<br>**_ource stock_**<br>**Resource**|
|---|---|---|---|---|
||**Depletion**<br>**$m**<br>**(% GNI)**|**stocks**<br>**$m**<br>**(****_S_/****_R_)**|**Depletion**<br>**$m**<br>**(% GNI)**|**stocks**<br>**$m**<br>**(****_S_**<sup>**_*_**</sup>**/****_R_)**|
|Former Soviet Union|$249,554|$5,571,338|$237,348|$6,004,281|
||(13.8%)|(22.3)|(13.1%)|(25.3)|
|Middle East & North Africa|$197,736|$15,966,091|$179,175|$16,174,524|
||(11.7%)|(80.7)|(10.6%)|(90.3)|
|Asia Pacific|$119,319|$1,674,577|$86,771|$2,850,715|
||(0.8%)|(14.0)|(0.6%)|(32.9)|
|Europe|$70,833|$624,858|$62,864|$962,386|
||(0.4%)|(8.8)|(0.4%)|(15.3)|
|North America (excl. USA)|$91,544|$1,775,696|$60,734|$2,784,314|
||(3.6%)|(19.2)|(2.4%)|(45.8)|
|South & Central America|$43,179|$4,029,611|$31,501|$4,105,911|
||(1.1%)|(93.3)|(0.8%)|(130.3)|
|Sub-Saharan Africa|$73,722|$2,424,395|$40,461|$3,169,246|
||(6.9%)|(32.9)|(3.8%)|(78.3)|
|South Asia|$15,848|$388,789|$11,847|$511,206|
||(0.7%)|(24.5)|(0.5%)|(43.2)|

Source: authors’ own calculations, USGS (2012), BP (2011), World Bank (2011a) Table 2 describes our findings across regions. For Sub-Saharan Africa, the extent of undiscovered resources is such that the depletion value for this region falls by nearly half. In the case of Asia Pacific, the difference between depletion values is notable in dollar terms but less significant in terms of percentage of GNI. For most other regions, the differences in depletion value are less significant reflecting in part that there is less likelihood that future discoveries will be extensive relative to existing proven reserves (Gelb _et al_ . 2012).

Table 2 also provides an indication of the asset value of S and S*. In some instances, the physical extent of expected discoveries might not be great (at least compared to proven resources) as is the case, for example, for the Former Soviet Union and Middle East & North Africa. However, for other regions, this physical stock of S* is considerably larger than for S alone. Examples here include Asia Pacific, Sub-Saharan Africa, South & Central America and North America (excl. USA). Table 2 indicates the magnitudes of these differences by showing the respective expected lifetimes of resource in parentheses in columns 2 and 4 (i.e. S/R and S*/R).

17
shown in Table 2), like the Philippines, imposes only specific rates on cigarettes, while Thailand, Cambodia, and Vietnam levy ad valorem rates and Malaysia has a hybrid system.

**Table 2**

**Characteristics of Excise Tax Systems in some ASEAN Countries, 2012**

|**Country**|**Excise on**<br>**‘Representive’**<br>**Cigarettes**<br>**(% of Philippine Rate)**|**Cigarette**<br>**Tax**|**Excise**<br>**on**<br>**‘Representative’**<br>**Beer**<br>**(%**<br>**of**<br>**Philippine**<br>**Rate)**|**Beer**<br>**Tax**|
|---|---|---|---|---|
|**Cambodia**|17|10%|195|25%|
|**Indonesia**|109‐167<br>(depending<br>on production<br>Capacity}|Specific rate depending on<br>production<br>capacityand retail salesprice|490|Specific|
|**Lao PDR**|53<br>(but<br>382<br>for<br>imports)|Specific rates varying with<br>production cost (domestic) or<br>import|390|50%|
|**Malaysia**|267|20% + specific tax|1122|15%<br>+<br>specific|
|**Thailand**|191|85%|460|Specific or<br>60%|
|**Vietnam**|109|65%|347|25%|

_Notes: ‘_ Representative’ products are for purposes of comparison only and not necessarily representative of the products actually consumed in the different countries. _Source:_ Preece (2015) The level and structure of taxes on alcohol and tobacco continues to vary widely across the region, reflecting both different national traditions and the complex factors discussed in the present paper that shape taxation of these products. In addition to the recent Philippine reform (Box 1), Thailand, in addition to introducing ‘surcharges’ on beverages with higher alcohol strength, restructured its taxes on alcohol in 2013, moving from 'greater of' an ex‐factory _ad valorem_ calculation and specific rate calculation to a mixed system in which an _ad valorem_ component is calculated against a 'Last Whole Price' and a specific rate comprising the greater of ‘per litre’ or ‘per litre of alcohol’ rate. Indonesia increased its alcohol taxes significantly in 2009 and raised its rates on tobacco in 2012 and again on alcohol in 2014.

Some examples of earmarking, not always for health purposes, may be found in the region. For example, Indonesia allocates a 2% excise for health to local governments and since 2014 has also earmarked a 10% tax for regional governments. Thailand allocates some alcohol and tobacco taxes not only to health but to local provinces and even for public TV broadcasting. Cambodia even earmarks some excise taxes for public lighting.

26
The result would be that enforcing formalization may cause these firms to shut down, since they cannot afford to operate formally.

Table 4 examines this possibility by looking at the impact of the different treatments on firm closure. Firm closure is measured by whether the firm is observed to be closed at the time of the followup survey, coupled with information obtained from neighboring businesses. Between 14 and 16 percent of the control group is verified as closed at follow-up, with none of the treatments having any sizeable or significant impact on this closure rate. In particular, it is not the case that firms which received enforcement through the inspector treatment are more likely to have closed down.

# **5.4. Impact on Formalization**

Table 7 turns to the main outcome of interest, whether or not the treatments succeeded in getting firms to formalize. We use the administrative data to measure formalization, since this offers impacts without attrition, and offers substantially larger samples to measure impacts, offering the most power.<sup>12</sup> The first few columns compare the control firms to the free cost and communication firms. We see that assigning firms to the free cost treatment have a strongly significant negative impact on the likelihood of MEI registration (p=0.008 for definite matches, p=0.010 for probable matches). One possible explanation for this is that the free cost and communication interventions explained how to register for SIMPLES, but not how to register as a MEI. The free cost treatment also emphasized the need to have an accountant if you formalize, which is not required for MEIs. The unexpected policy change right after we launched this intervention increased the revenue thresholds under which firms could register as MEIs. It is plausible that firms receiving the communication and free cost intervention were less aware of this policy change given the information about the need to register for SIMPLES and get an accountant that they were given in person, causing them to decide not to register at all. The point estimates are also negative for the communication treatment, and testing for equality of treatment effects, we cannot reject that the communication only treatment has the same negative impact as the free cost – but neither can we reject a zero treatment effect for communication only.

> 12 A last minute change in question placement by the survey firm led to a skip pattern skipping the detailed formalization questions for many firms in the follow-up survey. An attempt to re-contact these firms to obtain this extra information only obtained this data for 71 percent of the follow-up survey sample, with this response unbalanced by treatment status. Since the follow-up survey already had relatively high attrition, the end result is that we only have survey measures of formalization for 35 to 50 percent of the assigned sample, depending on treatment group. We used the data collected to cross-check the administrative matching process, but otherwise do not use this data.

21