rafmacalaba/datause-encoder-data huggingface dataset:
# Table 1: Testing generators

|_Program:_|||||St|ata||||||||MA|TLAB|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Generator:_||`unif`|`orm()`|||`no`|`rmal(r`|`normal`|`())`|||`’`|`v4’`|`’v5un`|`iform’`|`’def`|`ault’`|
|_Date:_<br>|||||_Jun 2007 -_|_Mar 2_|_011_|_A_|_pr 201_|_1 - prese_|_nt_|||||||
|_Re-initialization method:_<sup>(</sup><sup>_a_)</sup>|0|20|1|A|0<br>20|1|A|0|20|1|A|0|1|0|1|0|1|
||(1)|(2)|(3)|(4)|(5)<br>(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|(17)|(18)|
|`sknuth_Collision`<br>_N_ = 1, _n_ = 10<sup>3</sup>, _d_ = 2<sup>9</sup>, _t_ = 2|ok|ok|(***)|ok|ok<br>ok|(***)|ok|ok|ok|(***)|ok|ok|(***)|ok|(***)|ok|ok|
|`sknuth_Gap`<br>_N_ = 1, _n_ = 10<sup>3</sup>, _α_ = 0_._0, _β_ = 0_._125|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(***)|ok|ok|(***)|ok|(***)|ok|ok|
|`svaria_WeightDistrib`<br>_N_ = 1, _n_ = 10<sup>3</sup>, _k_ = 20, _α_ = 0_._0, _β_ = 0_._125|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|ok|ok|ok|(***)|ok|(***)|ok|ok|
|`smarsa_MatrixRank`<br>_N_ = 1, _n_ = 10<sup>3</sup>, _s_ = 2, _L_ = _K_ = 20|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(***)|ok|ok|(***)|ok|ok|ok|ok|
|`smarsa_RandomWalk1`<br>_N_ = 1, _n_ = 10<sup>3</sup>, _s_ = 2, _L_0 = _L_1 = 100|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(*)|ok|ok|(***)|ok|(*)|ok|ok|
|`smarsa_RandomWalk1`<br>_N_ = 1, _n_ = 10<sup>4</sup>, _s_ = 10, _L_0 = _L_1 = 160|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(*)|ok|ok|(***)|ok|(*)|ok|ok|
|`smarsa_RandomWalk1`<br>_N_ = 1, _n_ = 10<sup>5</sup>, _s_ = 20, _L_0 = _L_1 = 160|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(*)|ok|ok|(***)|ok|(***)|ok|ok|
|`svaria_SampleMean`<br>_N_ = 10<sup>3</sup>, _n_ = 20|ok|ok|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(***)|ok|ok|(***)|ok|(***)|ok|ok|
|`sknuth_CouponCollector`<br>_N_ = 1, _n_ = 10<sup>4</sup>, _d_ = 20|ok|(***)|(***)|ok|ok<br>(***)|(***)|ok|ok|ok|(***)|ok|ok|(***)|ok|(***)|ok|ok|

Test results are coded as three asterisks (***) if all tests performed by the specified routine returned a p-value less than 0 _._ 001; if the routine returns some p-values less than 0.001 but others that are higher, the result is coded as one asterisk (*); if all p-values exceed 0.001, the result is coded as “ok.” MATLAB version R2011b was used for columns (13)-(19); An fully updated version of Stata 11.2 was used in columns (1)-(4) and (9)-(12); Stata 10.1 was used in columns (5)-(8). Tests were performed using TestU01 version 1.2.3; parameter value _r_ = 0 was used for all tests. ( _a_ ) Re-initialization method descriptions: 0=one initialization at start; 20=increment seed and re-initialize after every twenty draws; 1=increment seed and re-initialize after every draw; A=one initialization at start, but advance 10,000 draws between draws.
working papers and journal articles – typically recorded as Z-codes – are underrepresented in the regressions. Whether results are valid for the entire category of analytical products considered depends on the similarity – or lack thereof – between included and excluded observations.

# 6. Main results

The large number of regressions that results from combining two explained variables, six specifications and three samples may somewhat blur the findings. To handle this complexity, summary indicators across all specifications can be constructed for each of the regressions, resulting in a heatmap where color indicates sign and intensity the robustness of the result. Thus, dark green (red) means that the estimate is significantly positive (negative) and of the same sign in at least half of the specifications. A lighter color is used when less than half of the estimates meet the criterion above, and no color at all when all estimates are statistically insignificant, or some of them are significant but with opposite signs. The significance threshold is set at 10 percent, which is relatively low but yields a more granular heatmap (table 4).

**Table 4. A summary of results from the regression analyses**

|||**Ratings**<br>|||**Downloads**<br>||
|---|---|---|---|---|---|---|
|**Explanatory variables**|**All knowledge**<br>**products**|**Flagships**<br>**and debates**|**Country**<br>**products**|**All knowledge**<br>**products**|**Flagships**<br>**and debates**|**Country**<br>**products**|
|Total cost (K$)|||||||
|Trust funds (percent of cost)<br>Time to completion (months)|||||||
|Top TTL|||||||
|Good TTL|||||||
|DEC|||||||
|Global Practice|||||||
|Regional CE office|||||||
|FY17|||||||
|FY18|||||||
|FY19|||||||
|F-test|||||||

_Note_ : Dark green (red) indicates that the estimate is significantly positive (negative) and of the same sign in at least half of the specifications. A light color indicates that this criterion is met by less than half. No color is reported when all estimates are statistically insignificant, or some are significant but with opposite sign. The significance threshold is 10 percent.

23
_Table 2: KPI derived from tracking data in the LPI 2023_

|**Data provider**|**KPI name**|**Definition and significance**|**Period**|**Unit**|**Why it matters**|
|---|---|---|---|---|---|
|Cargo iQ|Number of part-<br>ners|Count of distinct origin country partners per destina-<br>tion country|Q1-2 2022|Number of<br>countries|Air cargo connectivity metric|
|Cargo iQ|Aviation dwell<br>time|Time difference between event NFD (Notification of<br>Readiness for Delivery of Cargo) and DLV (Cargo Deliv-<br>ered to Consignee) at destination country<br>Median and quartiles are provided|<br>Q1-2 2022|days|Efficiency of handling and clearance<br>and notification to consignee|
|UPU|Number of part-<br>ners|Count of distinct number of country partners|2019|Number of<br>countries|Postal connectivity|
|UPU|Postal delivery<br>time|Median time difference between event EMD (Arrival<br>at inwards office of exchange) and events EMH (Un-<br>successful delivery) or EMI (Final delivery) at destina-<br>tion country<br>Median andquartiles areprovided|2019|Days|Efficiency of clearance and postal lo-<br>gistics at destination|
|MDST|Number of ser-<br>vices|Total number of maritime services (operated through<br>liner shipping companies on a predefined rotation) be<br>tween the two countries|-<br>Q1-2 2022|Number of ser-<br>vices|Availability of services and fre-<br>quency of connections|
|MDST|Number of alli-<br>ances|Count of number of alliances per destination country|Q1-2 2022|Number of alli-<br>ances|Competition between services|
|MDST|Number of part-<br>ners|Count of distinct number of country partners per<br>country|Q1-2 2022|Number of<br>countries|Shipping connectivity metric|
|MarineTraffic|Turnaround<br>time|Time difference between first instance of arrival and<br>last instance of departure for consecutive repeated|Q1-2 2022|Days|Proxy of the performance of the<br>ship to shore interface (inc. handling<br>by terminal operator)|

14
<!-- Start of picture text --> GDP<br>PIT<br>TTC<br>PIT =TTC<br>PIT<br>PIT (Stressed) time<br><!-- End of picture text --> <!-- Start of picture text --> Probability (%)<br>EL<br>— PIT=TTC<br>e<br>e<br>| e PIT PIT(stressed)<br>PIT<br>e<br>Losses ($)<br><!-- End of picture text -->
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# <u>Notes</u>

1 The following studies have analyzed the impact of remittances on poverty and inequality in Latin America and Asia: Acosta et al (2006) on Latin America, Lokshin et al (2007) on Nepal, Adams on Guatemala (2004), Taylor, Mora and Adams on Mexico (2005) and Yang and Martinez on the Philippines (2005).

2Studies on remittances and poverty in Sub-Saharan Africa include: Litchfield and Waddington on Ghana (2003) and Gustafsson and Makonnen on Lesotho (1993).

3 According to the Ghana Statistical Service (2000: Table 2), in 1998/99 the poverty headcount index in Ghana was 39.5 percent. This index measures the share of the population living below the poverty line.

4 According to the 2005/06 Ghana GLSS 5 Survey (sub-sample), roughly five times as many households in Ghana receive internal as opposed to international remittances (1159 versus 210 households, respectively). However, the average value received of internal remittances is only about 30 percent that of international remittances: 982,239 versus 3,488,352 Ghanaian cedis/person/year, respectively. See text.

5 For other attempts to treat remittances as a substitute for home earnings and to predict (estimate) the incomes of households with and without migration, see Barham and Boucher (1998) and Adams (1991).

6 This migration and remittances module included about 45 questions on the socio-economic characteristics of current migrants, including their age, educational status, occupation and amount of remittances (cash, food and non-food goods) sent home.

7 Non-food goods include such items as household appliances (stoves, refrigerators), vehicles and equipment.

8 In the 2005/06 Ghana GLSS 5 Survey (sub-sample) only 49 percent of internal migrants (within Ghana) and 68 percent of international migrants (to African and other countries) remit. These figures are similar to those observed in other countries. For example, in their study in the Dominican Republic, de la Briere, Sadoulet, de Janvry and Lambert (2002) find that only one- half of all international migrants remit.

9 In the 2005/06 Ghana GLSS 5 Survey (sub-sample) 56 percent of households receiving internal remittances (from Ghana) and 50 percent of households receiving international remittances (from African or other countries) do not have a migrant. On average, non-migrant households that receive remittances receive less in per capita remittances than migrant households that receive remittances.

10 This paper will follow the convention of using expenditure rather than income data to examine poverty in Ghana for the following reasons. First, since households tend to use savings to smooth fluctuations in income, many economists believe that expenditures provide a more accurate measure of household welfare over time. Second, in developing country situations like Ghana, expenditures are typically easier to measure than income because of the many problems inherent in defining and measuring income for the self-employed in agriculture, who represent such a large proportion of the labor force.

11 See Schmertmann (1994) for a more formal and detailed explanation of this multinomial logit selection model.
**Figure 3. Livestock loss estimate - Exceedance probability curve**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 20 40 60 80 100 120 140 160 180 200<br>loss (USD, million)<br>Exceedance probability<br><!-- End of picture text --> Source: authors.

# **_3.3. Livestock Mortality Rates_**

The Government of Mongolia has been conducting an annual census of adult animals (cattle, sheep, goats, horses and camels) and the reporting of animal mortality for more than 50 years. This process is regulated by several laws and by-laws. Mortality rates, defined as the ratio of losses of adult animals in a given year to the census of adult animals the previous year, can be calculated for each species at the _soum_ level over the period 1971-2004. It is noteworthy that the losses of adult animals reflect all causes of loss, including diseases.

These mortality rates capture the heterogeneity of losses among the species and the geographic areas. Based on the 33 years and 324 soums, Table 3 presents some insights about the frequency of mortality rates (MR) by species. Cattle are more frequently exposed to minor losses (MR lower than 5%) and catastrophic losses (MR higher than 20%) than the other species.

**Table 3. Frequency of annual mortality rate, by species**

|Morality rate|Cattle|Sheep|Goat|Horse|Camel|
|---|---|---|---|---|---|
|0% to 5%|72.9%|70.1%|71.7%|72.2%|64.0%|
|5% to 7.5%|10.6%|14.3%|12.8%|12.6%|17.3%|
|7.5% to 10%|4.9%|6.2%|5.8%|6.1%|8.4%|
|10% to 20%|6.5%|6.9%|7.3%|6.3%|9.2%|
|20% and more|5.2%|2.6%|2.5%|2.8%|1.1%|

Source: authors.

The mortality rates also differ significantly by regions, as shown on Figure 4. They are higher in the southern regions of Mongolia, near the Gobi desert, and in north-west part of the country.

12
Woolcock, M., Narayan, D. (2000) Social capital: Implications for development theory, research, and policy, World Bank Research Observer 15(2), 223-49 Wilson, C.M., Oswald, A.J. (2005) How does marriage affect physical and psychological health?, IZA DP No. 1619 Ram R (2010) Social capital and happiness, Journal of Happiness Studies, 11(4), 409-418 Winkelmann R (2009) Unemployment, social capital, and subjective well-being, Journal of Happiness Studies, 10(4), 421-430 Zak, P.J., Knack, S. (2001) Trust and Growth. Economic Journal 111:295-331.

25
POLICY RESEARCH WORKING PAPER 2194

# Summary findings

Dethier, Ghanem, and Zoli analyze whether political freedom and civil liberties help or hinder economic liberalization, using panel data from 25 postcommunist countries of Central and Eastern Europe and the former Soviet Union between 1992 and 1997.

Building on arguments and counterarguments put forth in recent literature, they identify the channels through which political freedom affects economic liberalization during the transition. Then they test the arguments empirically with an econometric framework that takes into account possible problems with simultaneity between the economic and political transitions.

Their empirical findings clearly reveal that democracy has facilitated economic liberalization in countries of Central and Eastern Europe and the former Soviet Union.

This conclusion is confirmed under various model specifications, for both ordinary and two-stage least squares procedures and using two different measures of liberalization.

The econometric results reveal that the existence of a vibrant civil society at the start of the transition has the most explanatory power in the authors' regressions.

This paper -a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region - was presented at a seminar at the World Bank in April 1999. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hafez Ghanem, room H4-201, telephone _202-458-5557,_ Internet address hghanem@worldbank.org. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/html/dec/Publications/Workpapers/home.html. The other authors may be contacted at jdethier @worldbank.org or ezoli@worldbank.org. October 1999. (30 pages) _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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
WPS3700

# **Costs of Taxation and the Benefits of Public Goods: The Role of Income Effects**

Will Martin James E. Anderson World Bank Boston College

## _Abstract_

The fact that raising taxes can increase taxed labor supply through income effects is frequently used to justify very much lower measures of the marginal welfare cost of taxes and greater public good provision than indicated by traditional, compensated analyses. We confirm that this difference remains substantial with newer elasticity estimates, but show that either compensated or uncompensated measures of the marginal cost of funds can be used to evaluate the costs of taxation– and will provide the same result– as long as the income effects of both taxes and public good provision are incorporated in a consistent manner.

JEL Code: D61, F11, H21, H43.

Keywords: fiscal policy; second best; public goods; distortions; costs of taxation, marginal cost of funds; marginal excess burden, thought experiment.

World Bank Policy Research Working Paper 3700, September 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Correspondence to: Will Martin, Development Research Group, World Bank, MSN3-303, 1818 H St NW, Washington DC 20433. Tel: 202 473 3853; Fax 202 522 1159; wmartin1@worldbank.org or Jim Anderson, Boston College, james.anderson.1@bc.edu.
Policy Research Working Paper 9826

# **Abstract**

Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multidimensional poverty among them, and how it might differ from that of host communities. Relying on household survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Using the MPI, the paper presents multi-country descriptive analysis to explore the relationships between multidimensional poverty, displacement status, and gender of the household head. The results reveal significant differences across displaced and host communities in all countries except Nigeria. In Ethiopia, South Sudan, and Sudan, female-headed households have higher MPIs, while in Somalia, those living in male-headed households are more likely to be identified as multidimensionally poor. Lastly, the paper examines mismatches and overlaps in the identification of the poor by the MPI and the $1.90/ day poverty line, confirming the need for complementary measures when assessing deprivations among people in contexts of displacement.

This paper is a product of the Gender Global Theme. 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 atybogale@worldbank.org, sabina.alkire@qeh.ox.ac.uk, uekhator@worldbank.org, fanni.kovesdi@qeh.ox.ac.uk, juliethsa@iadb.org, and sophie. scharlin-pettee@qeh.ox.ac.uk.

_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
# **A4. Crop/tree classifications**

**Table A.1.** Crop/tree classification by type of tree in LSMS-ISA data

|**CROP**|**Type of Tree**|**CROP**|**Type of Tree**|
|---|---|---|---|
|Agbono (Oro Seed)|Fruit Tree|Black Pepper|Tree Cash Crops|
|Apple<br>|Fruit Tree<br>|Cashew<br>|Tree Cash Crops<br>|
|Avocado|Fruit Tree|Cashew Fruit|Tree Cash Crops|
|Bilimbi<br>|Fruit Tree<br>|Cashew Nut|Tree Cash Crops<br>|
|Bitter Kola<br>|Fruit Tree<br>|Castor Beans<br>|Tree Cash Crops<br>|
|Bread Fruit|Fruit Tree|Chat|Tree Cash Crops|
|Buya<br>|Fruit Tree<br>|Clove<br>|Tree Cash Crops<br>|
|Cherry (Agbalumo)|Fruit Tree|Cocoa|Tree Cash Crops|
|Cinnamon|Fruit Tree|Cocoa Beans|Tree Cash Crops|
|Coconut<br>|Fruit Tree<br>|Cocoa Pod<br>|Tree Cash Crops<br>|
|Custard Apple|Fruit Tree|Coffee All|Tree Cash Crops|
|Date Palm|Fruit Tree|Dry Leaves (Kuka)|Tree Cash Crops|
|Durian|Fruit Tree|Gomme Arabique|Tree Cash Crops|
|Fig|Fruit Tree|Gum Arabic|Tree Cash Crops|
|Gishita<br>|Fruit Tree<br>|Iyere|Tree Cash Crops<br>|
|God Fruit|Fruit Tree|Locust Bean|Tree Cash Crops|
|Grape Fruit|Fruit Tree|Macadamia|Tree Cash Crops|
|Guava|Fruit Tree|Monkeybread|Tree Cash Crops|
|Jackfruit|Fruit Tree|Moringa|Tree Cash Crops|
|Kolanut|Fruit Tree|Oil Palm|Tree Cash Crops|
|Kolanut Shelled|Fruit Tree|Palm Kernel|Tree Cash Crops|
|Kolanut Unshelled|Fruit Tree|Ronier|Tree Cash Crops|
|Lemon|Fruit Tree|Rubber|Tree Cash Crops|
|Lime|Fruit Tree|Rubber Lump|Tree Cash Crops|
|Malay Apple<br>|Fruit Tree<br>|Rubber Sheet<br>|Tree Cash Crops<br>|
|Mandarin/Tangerine|Fruit Tree|Shea Nuts|Tree Cash Crops|
|Mango|Fruit Tree<br>|Tea<br>|Tree Cash Crops<br>|
|Masau|Fruit Tree|Three Leave Yam|Tree Cash Crops|
|Oranges|Fruit Tree|Bamboo|Trees for timber and fuelwood|
|Paw<br>|Fruit Tree<br>|Black Wattle<br>|Trees for timber and fuelwood<br>|
|Peaches|Fruit Tree|Fence Tree|Trees for timber and fuelwood|
|Pear|Fruit Tree|Firewood/Fodder|Trees for timber and fuelwood|
|Plum|Fruit Tree|Kapok|Trees for timber and fuelwood|
|Pomegranate|Fruit Tree|Mahogany|Trees for timber and fuelwood|
|<br>Pomelo|Fruit Tree|<br>Natural Forest Trees|Trees for timber and fuelwood|
|Pomme Du Sahel|Fruit Tree|Other Forest Trees|Trees for timber and fuelwood|
|Rambutan|Fruit Tree|Plantation Trees|Trees for timber and fuelwood|
|Star Fruit|Fruit Tree|Timber|Trees for timber and fuelwood|
|Tamarind|Fruit Tree|||
|Walnut|Fruit Tree|||

Data source: World Bank 2015.

35