rafmacalaba/datause-encoder-data huggingface dataset:
in 2002 to less than 10% and 5% in 2016 and 2020, respectively. Notably, the first goal of the United Nations’ Millennium Development Goals was to reduce extreme poverty rates by half between 1990 and 2015 but Viet Nam appears to have well exceeded the target.<sup>7</sup> More summary statistics are provided in Appendix A, Table A.2.

# **2.3. Regional distribution of poverty**

The country-level trends in economic growth, poverty and inequality do not reflect the regional variation in these indicators. In Table A.1 in the Appendix, we present the estimates for each of these indicators in 2020, for all 63 provinces. In the last two decades, although overall poverty declined rapidly in Viet Nam, poverty rates varied significantly across provinces. Poverty rates were lowest in Ho Chi Minh City (1.8% average over time) and neighboring Binh Duong province (3.2% average). Ho Chi Minh City is the largest city and the prime economic center in Viet Nam. The city has numerous export processing zones, industrial parks, colleges, and universities, as well as the largest international airport in the country. After Ho Chi Minh City, Binh Duong is the second highest recipient of foreign direct investment. Both Ho Chi Minh City and Binh Duong province are in the southeast region. Poverty was also lower in the Red River Delta, for instance in the capital city of Hanoi (5.4% average) and the port city of Hai Phong (5% average).<sup>8</sup> On the other hand, poverty rates were very high in northwest provinces of Son La (50% average), Dien Bien (62% average), Lai Chau (60% average) and Ha Giang (56% average) in the northeast. These

> 7 The poverty and inequality estimates in Table 1 can differ from those published by the World Bank because of different data sources used. The country’s performance is even more impressive if we consider that the poverty rate was 58% in 1992-1993 (World Bank, 1999).

> 8 These results concur with those in earlier studies. In particular, McCaig (2011) finds that provinces that were more exposed to the U.S. tariff cuts experienced faster decreases in poverty in the early 2000s. Pham and Mukhopadhaya (2018) also found that poverty rates were lower in the regions of Southeast and Red River Delta.

9
Policy Research Working Paper 10952

# **Abstract**

This paper provides an overview of the data sources and aggregation methodology for the Worldwide Governance Indicators (WGI). The WGI report six aggregate governance indicators measuring Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption in a sample of 214 economies over the period 1996–2023. The aggregate indicators combine information from 35 different existing data sources, capturing subjective perceptions of the quality of various dimensions of governance reported by experts and survey respondents worldwide. The paper briefly discusses how to use reported margins of error when interpreting cross-country and over-time differences in the aggregate indicators. The paper also updates and extends earlier analysis on three key issues relating to the WGI methodology: (a) the effect of correlated perception errors, (b) the robustness of the aggregate indicators to alternative weighting schemes, and (c) the existence on trends in global averages of governance.

This paper is a product of the Office of the Chief Economist, Prosperity Practice Group. 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 akraay@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
ADM, with a rate roughly equal to the overall average (Table 3). Using categories based on the initial employment, in fact, the net growth rate for large establishments is 0.4%, while the rate among SMEs is 4.8%. The net growth rate for SMEs decreases (as the rate among large establishments increases) when using the period average, confirming the upward (downward) bias of using initial size for SMEs (large establishments) as pointed out by Davis et al (1996). Panel b additionally shows gross job creation rates, indicating again that these rates are highest among SMEs, by all metrics. The gross job creation rates are highest among mature establishments, at a rate of 3.4%, followed by young establishments; however taken together establishments that have been operating for ten years or less (young and mid-age establishments) demonstrate a gross job creation rates slightly above the rate for mature establishments (5% for establishments in operation for 10 years or less compared to 3.4% for mature establishments).

**Table 3: Job Growth Rates**

# (a) Net Employment Growth Rate

||Based o<br>|n Initial<br>|||Based on<br>|Average<br>|<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|||||||||Y|Mid|M|
||||||||||-||
|SME|Large|SME|Large|SME|Large|SME|Large|g<br><=5yrs.|g<br>6-10yrs.|<br>11+yrs.|
|4.8|0.4|5.1|0.1|4.0|1.3|4.7|0.5|2.9|1.1|1.2|
|(b) Gross|Job Crea<br>Based o|tion Rate<br>n Initial|||Based on|Average|||||
|100 em|p. cutoff|250 emp|. cutoff|100 em|p. cutoff|250 emp|. cutoff||||
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|6.5|2.1|7.5|1.1|5.9|2.7|7.3|1.4|3.3|1.7|3.4|

# (c) Job Contraction Rate

|100 em|Based o<br>p. cutoff|n Initial<br>250 em|p. cutoff|100 em|Based on<br> p. cutoff|Average<br>250 emp|. cutoff||||
|---|---|---|---|---|---|---|---|---|---|---|
|SME|Large|SME|Large|SME|Large|SME|Large|Young<br><=5yrs.|Mid-age<br>6-10yrs.|Mature<br>11+yrs.|
|1.7|1.7|2.4|1.0|1.9|1.5|2.6|0.8|0.5|0.6|2.2|

_Age and initial size defined by the re-call period prior to the survey. Average-period categories defined by average establishment size between t and t-d._ Gross job growth rates are more instructive when also compared with job contraction rates<sup>27</sup> as shown in panel c of Table 3. Taken together, a fuller picture emerges: while SMEs show higher gross job creation rates, they also have higher job contraction rates (in all but the SME100, categorization based on initial employment, in which case SMEs and large firms share the same rates of contraction), indicating greater employment churning for these enterprises. Moreover, the differential between SMEs and larger establishments (by all

> 27 We refer to these rates as “contraction”, instead of destruction, as the ES data do not contain information on exiting establishments and thus may effect a survival bias. Contraction rates are thus only calculated for surviving establishments that have reduced their employment numbers.

16
where _T_ is the number of days in the heating season preceding the election year, and are the number of recorded daily instances of temperature switches and precipitation in day ττ,ii _t_ in municipality ττ,ii ss pp _i_ , and and are the indicator variables, which take the value of one if ] and<sup>mm</sup> ], and zero otherwise.<sup>12</sup> ττ,ii 11<sup>SS</sup> 11<sup>PPmm</sup> ss ∈SS(0, ss<sup>m</sup> ττ,ii We calculate the duration of the heating season each year based on the rule posted on the pp ∈PP(0, pp<sup>m</sup> We calculate the duration of the heating season each year based on the rule posted on the official website of the Moscow mayor<sup>13</sup> , which foresees that, _In Moscow, heating is turned on by order of the City Government if the daily average temperature is below 8°C for 5 days, and the weather forecast expects a further decrease. Heating is turned off according to the same principle - if the average daily temperature for 5 days keeps above 8 °C, and according to the weather forecast, it is expected to increase further._ To calculate the duration of the heating season, we use the archived daily temperature of Moscow VDNKh weather station, posted on the website of _Raspisaniye Pogodi Ltd_ , a weather forecast company based in Saint-Petersburg, Russia.<sup>14</sup> We use the same weather archive to obtain the recorded daily instances of temperature switches and precipitation for 13 weather stations located in the city of Moscow and Moscow oblast in close proximity to the city of Moscow. We apply the kriging procedure to interpolate these weather data and compute the switch and precipitation instances at the municipal level. The details are available in the Appendix.

Using the calculated instrumental variables, we specify the following first-stage equation: ∑ ∑ ∑ I . (3) MM MM−KK The first-stage equation (3) also estimates the SAR model for panel data, as we cannot rule out the mm=MM−KK ii,tt,mm XX ii≠jj ii,jj mm=MM−KK−3 ii,tt,mm ii,tt ii tt ii,tt XX = ρρ ww XX + ZZ ββ + αα + γγ + ωω spatial independence of pothole complaints. The notation in equation (3) closely parallels the one in equation (1), where indicates the spatial lag of pothole complaints, I is the parameter measuring the marginal effect of the instrumental variable, and XX is the error term. If our ρρ ββ instrumental variables are correctly specified, we expect the I parameter to be positive and ii,tt ωω statistically significant. ββ

> 12 For robustness purposes we also calculate an additional instrumental variable, which accounts only for the total instances of daily temperature switch, ignoring precipitation effects. This variable was highly correlated with our preferred instrument, and the first-stage estimates using this variable were little changed.

> 13 <u>https://www.mos.ru/otvet-dom-i-dvor/kogda-vklyuchayut-i-otklyuchayut-otoplenie-v-moskve/</u> [last accessed: September 2020].

> 14 <u>https://rp5.ru/docs/about/en</u> [last accessed: September 2020].

16
# **Table 4a: Tests of the Association Between Governance Structures and a Variety Of Variables, Relations With Suppliers**

||||FDR|||FWER||
|---|---|---|---|---|---|---|---|
|Description<br>|p-value|<br> 1%|5%|10%<br>|<br> 1%|5%|10%|
|Country|0.001***|0.0008|0.0040**|0.0080*<br>|0.0004|0.0021**|0.0042*|
|_Attitudes towards courts_||||<br>||||
|"the court system is fair..."|0.004***|0.0012|0.0060**|0.0120*<br>|0.0004|0.0022|0.0044*|
|Courts as an obstacle|0.016**|0.0020|0.0100|0.0200*<br>|0.0005|0.0024|0.0048|
|_Relations with business associations_||||<br>||||
|Belong to a business association?|0.240|0.0056|0.0280|0.0560<br>|0.0008|0.0042|0.0083|
|Regularly interact with a business association?|0.390|0.0064|0.0320|0.0640<br>|0.0010|0.0050|0.0100|
|_Sector of operation_||||<br>||||
|Manufacturing, retail, or other services|0.180|0.0052|0.0260|0.0520<br>|0.0008|0.0039|0.0077|
|Disaggregated sector|0.000***|0.0004***|0.0020**|0.0040*<br>|0.0004***|0.0020**|0.0040*|
|_Management practices_||||<br>||||
|Management practices (higher means better)|0.078*|0.0036|0.0180|0.0360<br>|0.0006|0.0029|0.0059|
|Top manager’s experience working in sector|0.470|0.0068|0.0340|0.0680<br>|0.0011|0.0056|0.0111|
|_Firm characteristics_||||<br>||||
|Size|0.082*|0.0040|0.0200|0.0400<br>|0.0006|0.0031|0.0063|
|Age|0.770|0.0092|0.0460|0.0920<br>|0.0033|0.0167|0.0333|
|Proportion domestic private ownership|0.008***|0.0016|0.0080**|0.0160*<br>|0.0005|0.0023|0.0046|
|At least 10% foreign owned|0.073*|0.0032|0.0160|0.0320<br>|0.0006|0.0028|0.0056|
|Exporting directly at least 10% of sales|0.070*|0.0028|0.0140|0.0280<br>|0.0005|0.0026|0.0053|
|Proportion owned by females|0.820|0.0096|0.0480|0.0960<br>|0.0050|0.0250|0.0500|
|Female top manager|0.530|0.0076|0.0380|0.0760<br>|0.0014|0.0071|0.0143|
|Main Market – local, national, international|0.350|0.0060|0.0300|0.0600<br>|0.0009|0.0046|0.0091|
|Multi-establishment|0.740|0.0084|0.0420|0.0840<br>|0.0020|0.0100|0.0200|
|Legal form|0.130|0.0048|0.0240|0.0480<br>|0.0007|0.0036|0.0071|
|Sole Proprietorship|0.033**|0.0024|0.0120|0.0240<br>|0.0005|0.0025|0.0050|
|Shareholding company|0.760|0.0088|0.0440|0.0880<br>|0.0025|0.0125|0.0250|
|Share of transactions with suppliers that were||||||||
|fulfilled smoothly|0.130|0.0044|0.0220|0.0440|0.0007|0.0033|0.0067|
|_Corruption and security_||||<br>||||
|Bribery depth|0.680|0.0080|0.0400|0.0800<br>|0.0017|0.0083|0.0167|
|Bribery incidence|0.510|0.0072|0.0360|0.0720<br>|0.0013|0.0063|0.0125|
|Paying for security|0.870|0.0100|0.0500|0.1000<br>|0.0100|0.0500|0.1000|

* significance at 10%, ** at 5%, *** at 1%.

33
border delays related to border compliance come from the “trading across borders” section in the World Bank’s Doing Business Database.<sup>15</sup> Border compliance data capture the time associated with compliance with the customs clearance and mandatory inspections regulations. To account for the very high share of maritime shipping in international trade due to price differentials, the algorithm opts for maritime if the shipping time is lower than four times the shipping time incurred using rail links. In the improved scenario of the network simulation, when a project involves building a new port or upgrading an old port, the associated “processing time” is assumed to decrease to 50 percent of the port delay in the region or to the lowest worldwide processing time, whichever is higher.

Finally, the population weighted time distance between country-pairs is transformed into ad-valorem equivalents using estimates from Hummels and Schaur (2013) on the “daily value of time” at the sector level. These estimates are added to transport costs and data on tariffs from GTAP to obtain country pair-sector values of trade costs. Table 2 presents the results for two scenarios, referred to as the “lower-bound” and the “upper-bound”. The “upper-bound” scenario allows for changes in transportation mode due to the new infrastructure while the “lower-bound” scenario assumes that switching mode of transportation is difficult -allowing for modal changes lower than 5 percent with respect to the pre-BRI modes of transport. The decrease in total trade costs associated with the new BRI projects ranges between 1.05 and 2.19 percent. For some country‐pairs this decline is zero, while the maximum change ranges between 61.52 and 65.16 percent.

_Table 2:Percentage decrease in trade costs due to the BRI_

|% decrease in<br>trade cost<br>|Min<br>|Max<br>**Wo**<br>|Mean<br>**rld**<br>|Std. Dev<br>|
|---|---|---|---|---|
|Lower Bound|0.00%|61.52%|1.05%|2.43%|
|Upper Bound|0.00%|65.16%|2.19%|3.40%|
|||**BRI Co**|**untries**||
|Lower Bound|0.00%|61.52%|1.50%|3.07%|
|Upper Bound|0.00%|65.16%|2.81%|4.18%|

_Note: Summary statistics across all country‐pairs and sectors in the world._ 15 For any border, we use the data on “Border Compliance” and the total delay is assumed to be the sum of export time from the exporting country and the import time from the importing country. We do not include documentary compliance, as it does not relate to travel time. All data are available at: <u>http://www.doingbusiness.org/data/exploretopics/trading-across-borders.</u> 16
**Figure 4. Grants**

<!-- Start of picture text --> Japan<br>Brazil<br>Peru<br>Mexico<br>Belgium (French)<br>Italy<br>Belgium (Flemish)<br>Germany<br>LAC Average<br>Ireland<br>Austria<br>United Kingdom<br>Canada<br>Average<br>Australia<br>High Income<br>France<br>Colombia<br>New Zealand<br>Finland<br>Sweden<br>United States<br>Netherlands<br>0% 2% 4% 6% 8% 10% 12% 14%<br>Grant/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Grants are available in the selected LAC countries, but not wide-spread. The typical LAC student receives a grant equally to two percent of GDP per capita. This is approximately half of the average grant to students in high-income countries. Anglo-Saxon and Northern Europe stand out as two country groups with significant grant programs (6 percent for Anglo-Saxon countries and 10 percent for Northern Europe).

Within LAC countries, Colombia stands out with a scholarship program amounting to six percent. Further, several federal and state programs in Mexico offer grants that in total amounts to one percent of GDP per capita per student. In 2003, the base year for this study, Brazil and Peru did not have any large scale grant programs available for tertiary students.<sup>19</sup> For example, Peru offered US$8 (PPP value) of grants per student. The highest among the LAC countries is Colombia with US$380 (PPP value) per student.

<u>Net Cost</u> is total education costs net of grants (Figure 5).

> 19 In Brazil, the PROUNI program was initiated in 2005. It finances free access to low-income students in non-governmental institutions that in turn are compensated by tax-deductions. Although this program is expected to impact tertiary education affordability, it is not reflected in the analysis because the base year of our study is 2003.

16
**Table 7:  Reasonable Benefit Limits**

|**Income Year**|**Lump Sum (a)(c)**|**Pension (b)(c)**|
|---|---|---|
|2005-06|$648,946|$1,297,886|
|2004-05|$619,223|$1,238,440|
|2003-04|$588,056|$1,176,106|
|2002-03|$562,195|$1,124,384|
|2001-02|$529,373|$1,058,742|
|2000-01|$506,092|$1,012,181|

**Source: Australian Taxation Office Fact Sheet: Reasonable Benefits Limits**

(a) The lump sum RBL usually applies when benefits are taken as a lump sums (ETPs), or as pensions and annuities that do not meet the pension and annuity standards.

(b) To meet the pension RBL a pension or annuity must meet be payable for life or life expectancy, be paid at least annually, and must have no residual capital value. Allocated pensions and allocated annuities generally do not meet the pension and annuity standards as they have flexible payment amounts and terms.

(c) For people who take a mixture of lump sum and pension at least half of all benefits must be taken as benefits subject to the pensions and annuities standard to be eligible for the pension RBL.

Pension and annuity payments received by a taxpayer are included in their assessable income and are subject to taxation at the general marginal taxation rates. However, annuities or pensions paid from a taxed fund to a taxpayer aged 55 or over generally attracted a taxation rebate of 15 percent. This favorable taxation treatment was expected to have a major impact in encouraging use of these products but this expectation was never fulfilled. Because the balances within the superannuation system are still building up and those reaching retirement at the present time have tended to take lump sums, the cost of concessions has been noted in government budget statements as being “indeterminate, but likely to be insignificant”<sup>18</sup> .

As mentioned previously a number of common retirement products were also treated favorably when calculating a person’s entitlement to the public aged pension. Any funds used to acquire a complying pension or annuity or growth pension income streams are partly exempt from the assets test as indicated in Table 8. Similarly, for the income test special rules apply for most income streams because the income stream payments will generally include a return of capital. The treatment of income streams under the income and asset tests will differ depending on the characteristics of the income stream, the duration over which the income and capital is paid, and the age of the person when commencing the income stream.

**Table 8:  Income and Asset Test Comparison**

|**Features**|**Market**<br>**linked income**<br>**streams**|**Allocated**<br>**income**<br>**streams**|**Lifetime income**<br>**streams**|**Life**<br>**expectancy**<br>**income streams**|
|---|---|---|---|---|
|Assets test concession<br>•<br>pre-20 September<br>2004|n/a|0%|100%|100%|
|•<br>from 20 September<br>2004|50%|0%|50%|50%|
|Income tested|Yes|Yes|Yes|Yes|

**Source: Department of Families, Community Services, and Indigenous Affairs, “Understanding, Retirement Income Streams”.**

18 Australian Government Tax Expenditures Statement 2005, Table B1: Concessional taxation of funded superannuation.

21
the coefficients of Chinese standards variables with, a priori, a larger impact of mandatory standards compared to voluntary standards as compliance with the latter is optional.

Internationally harmonized standards applied by Chinese producers are expected to have a positive impact on exports for two reasons. First, compliance with international standards can be a strong signal that Chinese producers meet safety and quality criteria that are widely recognized. Second, compared to national standards, the informational and signaling benefits of complying with internationally harmonized standards are expected to be larger than complying with national standards.

The coefficient of tariffs is expected to be negative. As a proxy of demand for each product group in each importing country, we include the importing countries‟ total domestic consumption and its sign is expected to be positive as higher domestic demand is expected to increase the demand for imports from China. To control for unobserved characteristics specific to an importer, such as distance with China or historical linkages, and specific to a product, we include fixed effects for importers and products.

# **5. Results**

The estimation is carried out in a panel covering Chinese agricultural exports to 132 countries over the period 1992-2008. Table 3 summarizes descriptive statistics. Notice that there are fewer observations for consumption and tariff, as data for these variables are sometimes not reported for some goods in an importing country. Yet, coefficient estimates for standards do not change significantly when these variables are excluded from regressions, as shown below.

## [Insert Table 3 here]

Table 4 reports OLS estimates for our specification. Column 1 reports the results using the total number of standards as explanatory variable. The coefficient has the expected positive sign and is statistically significant. Column 2 reports estimates from the baseline model (equation 1). Standard errors are clustered by product groups. All coefficients have the expected signs. Regarding the relative impact of standards, internationally harmonized mandatory standards seem to have the largest impact on Chinese exports. As consumption and tariff data have missing observations for some sectors in some countries, we exclude these variables from the baseline specification. As shown in column 3, estimates of standards coefficients do not change significantly as a result of the omission of tariffs and consumption variables. Importer dummies control for fixed importer-specific characteristics such as distance and historical linkages with China. Yet, some importer-characteristics evolve over time. In order to provide some temporal variation to importer-specific effects, we define 4-year time dummies and interact them with importer dummies, as well as with 10