rafmacalaba/datause-encoder-data huggingface dataset:
laboratories destroyed as reported by different governmental institutions in producer countries.<sup>13</sup> ONDCP’s data on coca cultivation are prepared by the US Director of Central Intelligence, Crime and Narcotics Center (CNC), and are published each March in the _International Narcotics Control Strategy Report_ as part of the US President’s determination of whether to provide assistance to drug producer and transit countries. In preparing its estimates of coca cultivation, CNC analyzes black and white, highresolution satellite imagery and aerial photographs. These are taken only between November and January of each year, weather permitting. The satellite images and aerial photographs cover a representative area of the producer countries’ known or suspected drug growing areas. The technique for analyzing the satellite images and aerial photographs is similar to the one used to estimate agricultural crops throughout the United States (see GAO, 2003 and ONDCP, 2005). However, according to a study conducted by ONDCP in 2002, the CNC’s methodology had not adopted a “statistically rigorous accuracy assessment, commonly known as an error rate” in its methodology to measure coca cultivation. Also, the technology used by CNC was inappropriate as it did not account for image distortions or variations in the terrain and the atmosphere, such as cloud cover. Following the recommendations made by the ONDCP’s study, CNC expected to have many of the recommended changes in place for its 2002 analysis (see GAO, 2003).

> 13 Thoumi (2005a) argues that UNODC does not have enough personnel and claims that “it simply does not have the capability to conduct significant critical studies and to evaluate in detail the quality of the data it collects” (Thoumi, 2005a, p. 189). This claim, however, is backed up only by a specific criticism on a figure of the size of the illegal drug business ($500 billion, which was a clear overestimation) produced by UNODC back in 1997 when this organization was first established. The author also asserts that for the production of UNODC’s main substantial product, _The World Drug Report_ , the organization relies on several consultants who are hired to write chapters and sections for the report, which, in some sense, contradicts the claim that UNODC lacks the human resources to produce significant quality statistics and analysis.

7
# Table 2: **Summary Statistics** – Bank Data

This table reports summary statistics for all accounts in the sample frame based on bank data. “Total overdue” is the total amount of outstanding and overdue agricultural credit as of December 31, 2007. “Eligible debt relief” is the total amount of debt eligible to be waived under the program, which is 100% of outstanding debt for households with _≤_ 2 hectares of pledged collateral and 25% conditional relief otherwise. Treatment effects at the program cutoff _τ_ ˆ _RD_ are estimated using the preferred specification with a local linear control function. Robust standard errors, in brackets, are clustered by bank and district. +p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01

||N|Mean|Median|StDev|Min|Max|ˆ_τRD_|SE|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Total overdue|5,524|52,915|41,427|48,538|0|882,806|127.12|[4149.9]|
|Principal overdue|5,514|40,627|30,000|43,351|0|830,000|533.7|[3802.1]|
|Interest overdue|5,414|12,595|7,193|17,009|0|319,810|1,607|[1333.5]|
|Loan type=crop loan|5,554|0.77|1.00|0.42|0|1.00|0.05|[0.031]|
|Landholding (hectares)|5,554|1.97|1.96|0.29|1.50|2.52|0.00|[0.001]|
|Eligible debt relief|5,554|33,498|23,801|36,823|0|751,594|41,255|[4,850]***|

33
succeeded in developing undernutrition models for SAEs of undernutrition with R-squares as high as 0.6 and 0.7 [16]. However, some express concern for overfitting with R-squares above 0.35 [21]. The full regional regression models are enclosed in appendix Table A2.

As a check on model accuracy, Table 1 shows the model-imputed undernutrition rates and those measured in EMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates from census are similar, and none of the estimates are outside the 95 percent confidence interval of the measured levels.

**Table 1. Measured and imputed undernutrition rates, 2014**

||Region|Imputed<br>undernutrition rate|Survey<br>undernutrition rate|95 % confidence<br>surveyesti|interval for<br>mates|Observations<br>in survey|
|---|---|---|---|---|---|---|
||Tigray|0.440|0.459|0.380|0.537|435|
||Afar|0.404|0.465|0.396|0.533|565|
||Amhara|0.413|0.432|0.376|0.488|518|
|Stun|Oromia|0.410|0.385|0.334|0.435|635|
|ting|Somali|0.420|0.360|0.292|0.427|565|
||B. Gumuz|0.412|0.406|0.353|0.459|426|
||SNNP|0.421|0.443|0.391|0.496|673|
||Gambela|0.275|0.219|0.149|0.288|403|
||Tigray|0.351|0.312|0.262|0.364|445|
||Afar|0.496|0.455|0.408|0.514|608|
|Un|Amhara|0.341|0.291|0.248|0.343|523|
|der|Oromia|0.271|0.229|0.196|0.286|656|
|weig|Somali|0.403|0.386|0.331|0.460|591|
|ht|B. Gumuz|0.327|0.282|0.212|0.364|438|
||SNNP|0.289|0.260|0.213|0.325|699|
||Gambela|0.224|0.199|0.126|0.273|412|

Source: DHS 2014 and imputed values from models.

SAE has great potential to guide policy decisions to address undernutrition, as they provide a much- improved information base, allowing better targeting. However, as also illustrated in Table 1, both measures from surveys and the estimates from our models come with standard errors. The estimated standard errors of undernutrition levels across woredas are on average similar and a little larger than those observed at regional levels from the survey.

Woredas with smaller populations of children generally come with larger standard errors. Figure 1 illustrates the 8
||Box 1: A Good Economic Analysis Answers the Questions|
|---|---|
|1.<br>A cl<br>proje|What is the objective of the project? This helps identify tools for the analysis.<br>early defined objective also helps in identifying the possible alternatives to the<br>ct.|
|2.<br>coun<br>cruci<br>3.<br>of th<br>4.|What will be the impact of the project? This question concerns a<br>terfactual as the difference between the situation with or without the project is<br>al for assessing the incremental costs and benefits of the project.<br>Are there any alternatives to the project? If so how would costs and benefits<br>e alternatives to achieve the same goal compare to the project in question?<br>Is there economic justification of each separable component of the project?|
|5.<br>mak<br>6.|Who gains and who loses if the project is implemented? The analysis has to<br>e sure that the most benefit accrues to the poor.<br>What is the fiscal impact of the project?|
|7.|Is the project financially sustainable and what are the risks involved?|
|8.<br>proje|Are there any other externalities? What is the environmental impact of the<br>ct?<br>Source: Belli 1996|

Even if one cannot do a CBA that produces an IRR for an entire project, it is often very useful to do such calculations on a subset of project components where quantification is more feasible. This type of CBA, limited though it may be, can still produce very useful practical guidance, as shown in the Ethiopia example below. In other cases, it may be better to simply limit oneself to cost-effective analysis, as shown in the example from the Philippines which follows the discussion on Ethiopia.

# **CBA on a project component: Ethiopia**

The World Bank’s Ethiopia Education Sector Development Program (ESDP) is noteworthy for its complexity. The ESDP is an expenditure program to restructure and expand Ethiopia's educational system, aims to improve overall educational attainment while achieving greater social equity. Its longer-term objective is to achieve universal basic education by 2015. The project to support it has many components, including the building, upgrading and renovation 27
# <u>Table A1.4: Japan; Numbers of tariff headings subject to SSG Actions, 1995-2007</u>

|<br>Fiscal<br>Year<sup>a</sup>|<br>Volum|<br>e-based<sup>b</sup>|<br>Price-based|
|---|---|---|---|
||(5-digit<br>headings)|(additional<br>8-digit lines)|(8-digit lines)|
|1995||5|3|
|1996||71|0|
|1997||5|0|
|1998||3|2|
|1999|1|2|8|
|2000|1|6|4|
|2001||10|0|
|2002||0|8|
|2003|6|1|13|
|2004||0|9|
|2005||4|6|
|2006||2|16|
|2007||5|10|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website.

# <u>Notes</u>

a The fiscal year runs from 1 April of the year indicated until 31 March of the following calendar year; e.g. FY 1995 covers 1 April 1995 – 31 March 1996. b In 1996, the large number of tariff lines were for live swine and swine meat. 1999 and 2000: the 5-digit heading was Inulin. 2003: the 5-digit headings were for butter and other dairy fats and for Maize (corn) starch and other vegetable starches and products.

The principal products covered were Milk and products, rice, beans, wheat and rice flours and starches, swine and swine meat, raw silk.

54
- 17 -

## **Table** **<u>3. Summary statistics</u>**

||Abb.|Obs|Mean<br>|Std. Dev.|Min<br>|Max|
|---|---|---|---|---|---|---|
|Share of firms deciding to be located at district_j_|_Sj_|118|0.000027|0.000046 0|.000000|0.000372|
|Number of firms newly establisehd in district_j_in<br>2010||118|93.6|162.2|0.0|1307.0|
|Number of firms that existed in district_j_in 2005|_NUM_|118|137.9|350.4|0.0|2411.0|
|Road density (km/1,000 km2)<sup>1</sup>|_RDDN_|118|0.53|1.08|0.02|8.50|
|Transport cost to the city with more than 100,000<br>population (US$/ton)<sup>1</sup>|_TCITY_|118|8.2|15.2|0.0|157.1|
|Transport cost to the port (US$/ton)<sup>1</sup>|_TPORT_|118|135.8|21.7|96.4|177.4|
|District population in 2002|_POP_|118|283,575|168,541|40,557|1,083,913|
|Average monthly wage rate (TSh)<sup>2</sup>|_WAGE_|118|206,600|270,639|40,333|2,521,750|
|Share of people who completed at least primary<br>education<sup>2</sup>|_EDU1_|118|0.46|0.13|0.14|0.76|
|Share of people who completed at least secondary<br>education<sup>2</sup>|_EDU2_|118|0.05|0.05|0.00|0.24|
|Land rent (estimated land replacement cost) (TSh<br>million per acre)<sup>2</sup>|_RENT_|118|8.71|48.60|0.07|508.00|
|Agriculture production value (US$ million)<sup>3</sup>|_AGV_|118|24.0|38.1|0.0|332.6|
|Local tax (TSh per capita)<sup>4</sup>|_LTAX_|118|844.9|664.9|212.1|4299.4|
|Economic electricity cost (US$ per kWh)<sup>5</sup>|_POWER_|118|310.3|67.2|96.9|366.5|

1/ World Bank calculations based on the Government's road data.

2/ Data are calculated based on the 2010 LSMS.

3/ Based on the National Sample Census of Agriculture in 2002/03, evaluated at the regional commodity prices from FAO statistics.

4/ Based on local tax revenue in 2004. The regional data are available in "Local Government Finance" by President's Office, Regional Administration and Local Government, Ministry of Finance, The United Republic of Tanzania.

5/ World Bank calculations based on the 2010 LSMS data.

# **IV. MAIN ESTIMATION RESULTS**

Instrumental variable spatial autoregressive estimation is performed; the results are shown in Table 4. First of all, agglomeration economies are found to be significant, but the significance seems weak. The elasticity is estimated at 0.14, which is broadly consistent with the existing literature (e.g., 0.18 for French firms in Europe (Procher, 2011); 0.37 for Korean manufactures in the U.S. (Lee et al., 2012); 0.05 for firms in New Zealand (Mare and Graham, 2013)). The policy implication is straightforward: It is important to take advantage
**Table 5:  The determinants of comparative advantage in intermediates and final goods trade**

||Total<br>trade|Total<br>trade|Total<br>trade|Intermediate|Intermediate|Intermediate|Consumption|Consumption|Consumption|
|---|---|---|---|---|---|---|---|---|---|
||A.1|A.2|A.3|B.1|B.2|B.3|C.1|C.2|C.3|
|qxQ||||||||||
|(institutions)|0.187***||0.163***|0.247***||0.207***|0.226***||0.236***|
||(14.318)||(12.081)|(15.820)||(12.621)|(9.878)||(10.036)|
|txT||||||||||
|(infrastructure)||0.225***|0.184***||0.303***|0.244***||0.077***|0.047**|
|||(16.186)|(12.910)||(16.947)|(12.443)||(3.274)|(1.998)|
|kxK (capital)|0.247***|0.102*|0.372***|0.245***|-0.002|0.371***|0.180*|-0.106|0.223**|
||(4.136)|(1.842)|(5.984)|(3.341)|(-0.033)|(4.887)|(1.871)|(-1.156)|(2.161)|
|hxH<br>(human<br>capital)|0.096***|0.057***|0.048***|0.144***|0.089***|0.077***|0.016|0.041*|0.017|
||(9.036)|(4.819)|(4.052)|(10.120)|(5.904)|(4.853)|(0.770)|(1.888)|(0.753)|
|Country fixed||||||||||
|effects<br>Industry fixed|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.752|0.752|0.754|0.748|0.75|0.75|0.722|0.716|0.72|
|Number<br>o|f|||||||||
|observations<br>Number<br>of|29,126|28,343|27,153|18,993|19,600|17,838|11,252|10,971|10,414|
|countries|103|95|95|103|95|95|103|95|95|
|Number<br>of||||||||||
|industries|343|359|342|241|265|241|136|142|135|

Notes: The dependent variable is the natural log of exports in industry k by country i to the World. Coefficients are beta standardized and t-values for robust standard errors are reported in brackets. * Significant at 10%; ** at 5%; *** at 1%.

Next, we test whether there are significant differences in the sources of comparative advantage between intermediate and final goods by estimating equation (2). Table 6 reports the results of the OLS regressions. Columns A.1-A.3 report the results when intermediate exports and all other types of exports<sup>14</sup> are pooled, while columns B.1-B.3 show the results when intermediate exports are pooled with only consumption exports.

Interestingly, we find that while the interaction term between the intermediate good dummy and the quality of institutions is not significant, the one between the intermediate good dummy and the quality of infrastructure is higly significant. In other words, our results indicate that institutions matter in the same manner for both trade in intermediate and consumption goods. In contrast, quality of infrastructure is more important as a factor of comparative advantage in trade of intermediate goods than in trade of final goods.

> 14 This is the sum of consumption goods exports, capital goods exports and exports of goods which are not classified by BEC.

11
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE In per capita terms, East Africa had the second-most aircraft seats in all of Africa in 2007 after SADC. This is striking for a region that has one of the lowest per capita incomes in Africa. Two of the three strongest carriers in Sub-Saharan Africa—Kenya Airways and Ethiopian Airlines— are very active in the market. The routes between Kenya and Tanzania are frequently traveled, reflecting tourist travel for the game parks and Mount Kilimanjaro, as are the routes between Kenya and landlocked Uganda

**Figure 2.12 . Seats for intraregional travel within East Africa**

<!-- Start of picture text --> a. East Africa<br>14,000,000<br>12,000,000<br>10,000,000<br>8,000,000<br>6,000,000<br>4,000,000<br>2,000,000<br>0<br>2001 2004 2007<br><!-- End of picture text --> <!-- Start of picture text --> b. EAC<br>10,000,000<br>9,000,000<br>8,000,000<br>7,000,000<br>6,000,000<br>5,000,000<br>4,000,000<br>3,000,000<br>2,000,000<br>1,000,000<br>0<br>2001 2004 2007<br><!-- End of picture text --> Source: Bofinger 2009.

Air transport connectivity for countries within East Africa is rather mixed. All countries except Burundi and Rwanda have relatively good international connectivity. Burundi is connected through five international city pairs, and Rwanda through seven. The other countries have a much higher number of international city pairs and are well connected with the regional hub in Kenya. It is because of its hub status— both in East Africa and beyond—that Kenya has exceptionally high international connectivity. Domestic connectivity within Ethiopia is the highest in the region. Kenya, on the other hand, has low domestic connectivity, which reflects the limited purchasing power of its domestic market. Burundi and Rwanda have no domestic connectivity (table 2.9).

**Table 2.9 . International and domestic connectivity for 2007 (number of city pairs served)**

|Domestic city<br>pairs|International city<br>pairs|
|---|---|
|Burundi<br>0|5|
|Ethiopia<br>45|52|
|Kenya<br>15|77|
|Rwanda<br>0|7|
|Sudan<br>13|32|
|Tanzania<br>19|38|
|Uganda<br>4|19|
|Source: Derived from Bofinger 2009.||

Not all countries within EAC and the broader East Africa region are connected with one another. They do, however, have direct flights to either Kenya or Ethiopia, which then have frequent direct flights to all of the EAC and East African countries (table 2.10). Most countries in the region have at least seven flights per week to one of the main regional hubs. The route between Tanzania and Kenya is the most frequently travelled, followed by the route between Kenya and Uganda.

29
We focus on LAC to keep the sample relatively homogeneous and because the region has one of the highest levels of informality in the world. As Figure 1 shows, output originating in the informal sector as a percentage of GDP is much higher in LAC and Sub-Saharan Africa (over 33 percent) than in the other regions (between 11.2 and 27.1 percent).<sup>2</sup> Nevertheless, we show below that our main result of a significant positive relationship between informal competition and formal manufacturing SMEs’ likelihood of acquiring a quality certificate holds in other regions for which we have data. However, these results should be treated with caution as they do not pass some of the endogeneity tests (details below).

Our study makes several important contributions to the small but growing literature on the effects of informal competition on formal firms and to the literature on certification. First, we extend the literature on informal competition to another important aspect of formal firms’ functioning, namely obtaining, and using quality certificates. To the best of our knowledge, this is the first study to do so. Concomitantly, we contribute to the literature on the drivers of certification by highlighting informal competition and several nuances affecting firms’ decisions on certification. Second, we explore how the impact of informal competition varies with the level of rule of law and the regulatory burden on formal firms. The analysis yields rich insights that can help design future policies for the informal sector and quality certification. Third, we pay due attention to endogeneity concerns. Our measure of informal competition is based on the average of all manufacturing SMEs in the country-year, which is relatively exogenous to any given firm. We also use the level of informal competition faced by the service firms in the country as a relatively exogenous proxy for the informal competition faced by the manufacturing SMEs. All our regressions account for time-invariant country-specific factors, which eliminates several

> 2 The data source is the World Bank’s Informal Economy Database mentioned in footnote 1.

6
<!-- Start of picture text --> 30%<br>@<br>4<br>£& 25% 9<br>g<br>& 20%<br>re<br>°<br>X 15%<br>@<br>mo]<br>©<br>= 10%<br>c<br>‘aS 5% Se ee er hae eee e LO Peano ereg sevoeer<br>2<br>©<br>_ nowonr wWOandcdanmnsrtnwonRrewOoHO AN<br>DADADRSDDDOKdDCDTECODCoEDAAADAADADPaAaag a Sa<br>Ae TRH TP TN NNNAe ACSECACSESKCSHNNN NNN N S AS<br>ee ASEAN 9 ommmmme MERCOSUR) 0 0 0 @ @ SAARC<br><!-- End of picture text -->
method to compute each country’s “basic scorecard”. They are: tariff and non-tariff barriers, regulatory quality, rule of law, adult literacy rate (% age 15 and above), secondary enrollment, tertiary enrollment, researchers in R&D, patent applications granted by the USPTO, scientific and technical journal articles, telephones (mainlines + mobile phones), computers, and internet users. The KAM website (see fn 1) indicates the variety of sources from which the data are drawn. In addition to this unscaled data, we will also perform factor analysis on these variables, but now a subset of the variables will be scaled so that country size does not influence the analysis. The scaled set of variables are: tariff and non-tariff barriers, regulatory quality, rule of law, adult literacy rate (% age 15 and above), secondary enrollment, tertiary enrollment, researchers in R&D (per million population), patent applications granted by the USPTO (per million population), scientific and technical journal articles (per million population) telephones per 1000 persons (telephone mainlines + mobile phones), computers per 1000 persons, and internet users per 10,000 persons.

Data on the 12 variables are available at two points in time, one measured in 1995 and another during a more recent period, between 2002-04. We will use the term “2002” to indicate the recent data. Table 1 describes the variables and reports descriptive statistics for the 12 variable-pairs.

# **3.1 Missing Data Imputation**

The factor analysis restricts the sample to one which has complete data on all included variables. Hence, a crucial pre-estimation step is to impute missing data in order to have as broad a coverage of countries as possible. The imputations are carried out using a simple regression of the variable with missing data, using as the independent variable a conceptually closely related variable. For example, (unscaled) research95 has data for only 86 countries. However, the closely related research03 has data for 95 countries. Therefore, nine observa15
of as green.<sup>32</sup> Figure 7: Share employed in green occupations by Industry <!-- Start of picture text --> Green strict Green broad All employed<br>100 100 100<br>15 16<br>31<br>80 80 80<br>16 17<br>5<br>60 60 9 60 14<br>24 9 6<br>40 40 15 40 21<br>7 2<br>4<br>13 8<br>20 20 20 5 20 1<br>11<br>4 14 3<br>0 5 0 0 5<br>Agriculture Mining Manufacturing Utilities Construction<br>Trade Transport Finance Services CSP<br>Distribution of employment by main industry<br><!-- End of picture text --> Source: authors’ calculations using PALMS

# 4 Regression Results

Conditional correlations allow us to understand the nuance in the patterns observed in the comparison of means.

# 4.1 Factors Associated with Being in a Green Job

Regardless of the definition used (strictly or broadly green jobs) women are less likely to be in green jobs (Table 4). Women are 5 percent less likely than men to be in strictly green jobs and 25 percent to 31 percent less likely than men to hold broadly green jobs. The conditional results reflect the clustering of broadly green jobs in typically male dominated occupations.

Workers aged 25-44 are the most likely to be in strictly and broadly green jobs. The conditional correlates for the age dummy variables are not stable for strictly green jobs, with statistical significance changing based on the set of control variables. The estimated correlation for the two oldest categories is negative, though with a very small value, reflecting the unconditional correlations. The estimates for the broadly green jobs are consistent across age groups, with all age cohorts being more likely to work in a green job as compared to the youngest age group. This result is consistent with the unconditional estimates in the descriptive section.

More educated workers are more likely to be in strictly green jobs while high school dropouts are most likely to hold broadly green jobs. This reflects the unconditional correlations, where workers with more than a high school (matric) education were over-represented among strictly green workers. The results for broadly green jobs also follow the conditional correlations since the category includes several populous mid-skilled and elementary

> 32 Doan et al. (2022) find that green jobs in Viet Nam are concentrated in utilities (electricity, gas, water), mining and quarrying, and market services.

19