rafmacalaba/datause-encoder-data huggingface dataset:
# _Spend More and Do More_

This approach espouses the view that the government’s lack of adequate revenues contributes to less than adequate service delivery. Government performance could be improved by allowing a government greater access to revenues to promote higher public spending. Mounting evidence on developing country experiences does not substantiate the view that higher spending leads to improved and better quality of public services. In fact, the record shows that there is no one-to-one relationship between spending and service delivery in developing countries. Instead, in many countries, higher spending led to reduced access to basic public services due to dysfunctional governance (see World Development Report 2004 and Huther, Roberts and Shah 1998).

# _Spend Less and Do Less_

This represents a leviathan view of government where government bureaucracies may be too large and therefore downsizing and outsourcing government functions may improve government efficiency (Gangl 2007). This approach may be helpful up to a limit, but there are some critical functions, such as unemployment insurance, social welfare assistance and environmental protection, that the private sector may not be able to perform, or at least not adequately without government oversight (Kitchen 2005).

# _Run Government Like a Private Business_

There is some merit in applying business principles in managing government operations (Dickenson 1996), however, in government, lack of a bottom-line makes the application of such an approach difficult. This is because the government has the power of extortion and can carry out taxation in perpetuity to finance its deficits. Government managers therefore may have the luxury of consistently making bad decisions without facing the consequences of these decisions. In the private sector, on the other hand, poor managerial 7
37

|**Country**|**V**<br>**1999**|**V**<br>**2008**|∆**V**|∆**P**|∆**C**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Uganda|0.9|3.3|2.4|0.0|2.4|EIA|
|Ukraine|3.8|4.3|0.5|-0.1|0.6|IEA|
|United Arab Emirates|-15.7|-28.2|-12.6|-16.0|3.4|EIA|
|United Kingdom|-0.5|0.2|0.7|-0.8|1.5|IEA|
|United States|0.8|3.0|2.2|-1.3|3.4|EIA|
|Uruguay|1.3|6.9|5.6|0.0|5.6|EIA|
|Vanuatu|1.1|4.0|3.0|0.0|3.0|EIA|
|Venezuela, RB|-18.5|-27.6|-9.1|-14.3|5.1|IEA|
|Vietnam|-3.8|-0.6|3.2|-5.1|8.3|IEA|
|Yemen, Rep.|-26.9|-20.9|6.0|-6.6|12.6|IEA|
|Zambia|2.1|2.9|0.8|0.0|0.8|IEA|

_Sources:_ EIA 2012, IEA 2012, WDI 2012, and author’s calculations.

_Note_ : Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three significant figures.

Table A1.4: Decomposition analysis for 1999–2008, continued

|**Country**|**Price**|∆**P**<br>**Output**|**GDP**|**Price**|**Oil**|∆**C**<br>**Energy**|**GDP**|**Exch.**|
|---|---|---|---|---|---|---|---|---|
|Albania|-4.3|-1.7|4.1|10.7|0.7|-2.4|-2.2|-3.5|
|Algeria|-62.0|-9.2|52.8|8.8|-0.1|0.0|-5.3|-0.2|
|Angola|-292.8|-172.5|464.6|74.0|18.9|-30.1|-175.5|112.4|
|Antigua and Barbuda|0.0|0.0|0.0|12.4|0.0|-1.1|-2.6|0.0|
|Argentina|-7.7|0.6|0.8|6.5|-0.4|-0.3|-5.1|4.8|
|Armenia|0.0|0.0|0.0|4.3|1.2|-1.3|-1.0|-1.6|
|Australia|-3.0|0.2|2.0|4.3|-0.2|-0.4|-1.0|-1.0|
|Austria|-0.2|0.0|0.1|2.7|0.0|-0.2|-0.3|-0.6|
|Azerbaijan|-173.4|-127.6|275.3|29.0|-6.3|-17.1|-14.9|0.0|
|Bahamas, The|0.0|0.0|0.0|11.3|0.2|1.9|-1.4|0.0|
|Bahrain|-55.0|0.0|42.9|8.7|0.0|-0.4|-3.9|0.0|
|Bangladesh|-0.2|-0.1|0.1|3.9|-0.9|0.3|-1.2|1.0|
|Barbados|-1.5|0.6|0.4|8.6|-0.1|-1.6|-1.9|0.0|
|Belarus|-5.1|0.1|4.9|52.0|-2.7|-19.7|-91.8|63.3|
|Belgium|0.0|0.0|0.0|5.4|0.4|-0.3|-0.7|-1.2|
|Belize|-4.8|-7.2|2.8|14.3|0.3|3.2|-1.5|0.0|
|Benin|0.0|0.0|0.0|12.8|5.8|-0.2|-3.2|-3.1|
|Bhutan|0.0|0.0|0.0|5.5|-2.5|1.4|-1.6|0.0|
|Bosnia and Herzegovina|0.0|0.0|0.0|8.2|-1.0|-0.2|-3.1|-1.7|
|Botswana|0.0|0.0|0.0|5.1|1.2|-0.9|-3.0|1.3|
|Brazil|-6.0|-2.2|4.6|7.1|-0.5|-0.5|-3.5|0.1|
|Brunei Darussalam|-81.6|-1.6|61.7|5.6|-0.7|0.9|-3.1|-0.7|
|Bulgaria|-0.1|0.0|0.1|12.5|0.2|-3.6|-4.3|-2.5|
|Burkina Faso|0.0|0.0|0.0|4.8|-0.1|-0.7|-0.7|-1.0|
|Burundi|0.0|0.0|0.0|6.8|-0.9|-3.2|-4.0|3.2|
|Cambodia|0.0|0.0|0.0|11.8|3.7|-4.4|-3.0|0.5|
|Cameroon|-18.2|2.5|9.9|4.8|0.2|-0.1|-0.8|-0.9|
|Canada|-9.0|-1.5|5.3|6.4|0.1|-0.7|-1.1|-1.4|
# **1. Introduction**

There is a growing body of literature that investigates trends in the skill content of jobs in developed and developing countries. One of the main findings of this literature is that jobs are becoming less intensive in routine tasks across the world. This phenomenon is associated with a host of negative outcomes, including lower earnings and job opportunities for workers with routine skills and increasing wage inequality (Autor et al., 2006; Goos, Manning, and Salomons, 2009; Bussolo, Torre, and Winkler, 2018).

To estimate the task content of jobs, most studies rely on measures tailored for the US economy, where occupations are ranked by the tasks they typically require. These occupation-level measures are then applied to other countries under the assumption that the task content of occupations is the same as in the United States (see, for example, Apella and Zunino, 2018; Arias et al., 2014). This is a strong assumption, considering that jobs may require different skill sets across countries. For instance, manufacturing jobs in developed countries may be more intensive in routine manual tasks if the production technology is more capital intensive than in developing countries, where such jobs may be more intensive in non-routine manual tasks.

This paper is not based on this strong assumption. We use skill surveys from developing countries – i.e. the _Skills Toward Employability and Productivity_ (STEP) surveys - to create indicators of the task content of jobs comparable to those based on O*NET for the US.<sup>1</sup> We find that both sets of measures are consistent regarding the relative non-routine cognitive (i.e. non-routine analytical and non-routine interpersonal tasks) and routine manual task content of jobs across countries and over time. However, occupations relatively more intensive in routine cognitive and non-routine manual tasks are not necessarily the same according to O*NET and STEP. This implies that the estimated trends in the task content of jobs will depend on whether the O*NET or STEP measures are used. In fact, while according to the O*NET indicators only one developing country in our sample shows evidence of job de-routinization, all the countries with sufficiently long-time coverage experience such phenomenon according to the STEP indicators.

> 1 We also construct indexes comparable to those that Autor and Handel (2013) created for the US using the PDII survey. Since the results are very similar to those that mimic the O*NET, we do not report them, but they are available from the authors upon request.

2
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE ———. 2009b. Benin Constraints to Growth and Potential for Diversification and Innovation: Country Economic Memorandum. Washington DC

# Transport

Bofinger, Heinrich C. 2009. ―An Unsteady Course: Growth and Challenges in Africa’s Air Transport Industry.‖ AICD Background Paper 16, Africa Region, World Bank, Washington, DC.

- Bullock, Richard. 2009. ―Off Track: Sub-Saharan African Railways.‖ AICD Background Paper 17, Africa Region, World Bank, Washington, DC.

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- Teravaninthorn, Supee, and Gaël Raballand. 2009. ―Transport Prices and Costs in Africa: A Review of the Main International Corridors.‖ AICD Working Paper 14, Africa Region, World Bank, Washington, DC.

- World Bank. 2007a. Analyse des Strategies de Transport et de Reduction de la Pauvrete (PRTSR). Republic of Benin. Sub-Saharan Africa Transport Policy Program (SSATP).

- ———. 2007b. Republic of Benin Transport Assessment Note for Roads, Airports and Port Sectors. Africa, Transport Sector (AFTTR) Report 40178-BJ. Washington, DC. June.

- ———. 2007c. Memorandum Economique du Benin: Mission d’évaluation du secteur portuaire. Cotonou, October 31–November 4, 2007. Aide Mémoire.

- ———. 2010. Project Appraisal Document. First Phase of the Abidjan-Lagos Trade and Transport Facilitation Program. February 25, 2010. Transport Sector. Country Department AFCRI. Africa Region. Report 49368-AFR.

World Health Organization. 2009. Global Status Report on Road Safety: Time for Action. Geneva.

World Road Statistics. 2007. Data 2000–2005. International Road Federation. 2007.

47
Table 8: Exposure to Coffee Mills and Child Health

||HAZ|Score|Stunte|d (0/1)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|CatchmentArea X Post|0.0967*|0.1021**|-0.0464***|-0.0456***|
||(0.0511)|(0.0500)|(0.0171)|(0.0173)|
|CatchmentArea|-0.0108|-0.0634|0.0313|0.0443**|
||(0.0590)|(0.0560)|(0.0192)|(0.0187)|
|Municipality FE|Yes|Yes|Yes|Yes|
|YOB FE|Yes|Yes|Yes|Yes|
|Province X Year FE|Yes|Yes|Yes|Yes|
|R-squared|0.2030|0.2475|0.1428|0.1745|
|Observations|22794|22794|22794|22794|

Notes: CatchmentArea X Post is an indicator variable equal to 1 for a child living in a community within the vicinity of an operational coffee mill and 0 if otherwise. CatchmentArea is an indicator variable equal to 1 for a child living in a community within the vicinity of a coffee mill. Controls include gender of the child, twin status, kid birth order, age of mother, rural/urban, household wealth, and time trends interacted with coffee suitability and road density. Standard errors are clustered at the enumeration area level. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 38
# Figure 7.  Country correlations and exchange rate regime

<!-- Start of picture text --> CE with Fixed ER CE with floating ER<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>CE with fixed ER regimes = 0.46***<br>-0.2<br>CE with floating ER regimes = 0.36***<br>-0.4<br>-0.6<br>-0.8<br>AzerbaijanYemenCôte d'IvoireUkraine<br>Rwanda Oman Congo, Rep.Trinidad and TobagoNicaraguaEcuador UruguayGuyanaLiberiaArgentina<br>BrazilNamibiaEquatorial GuineaNigerBolivia KenyaZimbabweFijiAngolaQatar<br>SudanSenegalMaliBurkina Faso<br>GhanaBurundiSaudi ArabiaBeninTurkmenistanMozambiqueChad ComorosThe GambiaColombiaTanzaniaLibyaCentral African RepublicGabonIcelandEthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Rep. of Madagascar<br>Kyrgyz RepublicRussian FederationPapua New GuineaLao P.D.R.Cabo VerdeUgandaCameroonIndonesiaHondurasSuriname TogoSeychelles<br>Bahrain Congo, Dem. Rep.Belize<br>Solomon Islands Iraq<br>NigeriaPeru<br>Armenia<br>Sierra LeoneKazakhstanGuinea-BissauMauritaniaCosta Rica Botswana<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>AustraliaGuatemala<br>Brunei Darussalam United Arab Emirates<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br>Canada<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries operating under fixed exchange rates or floating exchange rate regimes during the sample period, according to the classification in Ilzetski _et al_ (2021). The difference between the two averages is not significant (p-value is 0.17). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on Ilzetzki _et al._ (2021) and WEO (IMF) data.
graduates from tertiary education obtain negative net economic returns. These findings, complemented with data for income which show that the gap between the cost and the benefits of tertiary education depend on the quality of tertiary education (World Bank 2011), suggest that the quality of certain types of tertiary education has decreased the skill premium in Chile.

In the case of secondary education, the study by Bassi et al. (2012) for Argentina, Brazil and Chile finds that the decline in the skill premium for secondary education is due to a mismatch between the skills acquired by workers that go directly from secondary education to the labor market and the skills required by the labor market that hire these workers.

# **Summing Up**

During the 2000s, inequality in Latin America declined in the majority of countries for which a comparison can be made. The decline was statistically significant and significant in terms of the order of magnitude. On average, the decline in inequality accounted for about a third of the decline of the decline in extreme poverty —the remaining two-thirds were accounted for by economic growth.

The two main explanations for the decline in inequality are a reduction in hourly labor income inequality, and higher and more progressive government transfers. The fall in hourly labor income inequality, in turn, is explained by the reduction in the returns to education. Whether the latter is predominantly the result of an increase in the supply of workers with more educational attainment or a decline in the demand of workers with higher skills or a degradation of secondary and tertiary education, has not been 12 unequivocally established.

> 12 While not discussed in this paper, some authors have linked the rise of pro-poor government transfers to the process of democratization and Latin America’s turn to the left.See, for example, Robinson (2010), Cornia (2010) and Huber and Stephens (2012).

13
Table 1: Financial fragility and informality

||P|robabilityof fa|llinginto arrea|rs|Weeks to|continue oper|atingwith cas|h available|
|---|---|---|---|---|---|---|---|---|
||(1)<br>Conditional|(2)<br>Cont. initial<br>productivity|(3)<br>Conditional|(4)<br>Cont. initial<br>productivity|(5)<br>Conditional|(6)<br>Cont. initial<br>productivity|(7)<br>Conditional|(8)<br>Cont. initial<br>productivity|
||||**Panel A. B**|**aseline estima**|**tions**||||
|Informality|-0.0388*|-0.0552**|||-10.35**|-9.803**|||
||(0.0230)|(0.0278)|||(1.209)|(1.461)|||
||||**Panel B. By**|**type of inform**|**ality**||||
|Fully formal|||_Baseline_|_Baseline_|||_Baselne_|_Baseline_|
|Necessity|||0.00240|-0.0296|||-5.381**|-5.011**|
||||(0.0175)|(0.0193)|||(0.920)|(1.049)|
|De Soto|||0.0222|-0.00555|||-4.178**|-4.342**|
||||(0.0156)|(0.0170)|||(0.856)|(0.933)|
|Parasite|||-0.00502|-0.0125|||-4.155**|-3.314**|
||||(0.0161)|(0.0227)|||(0.873)|(1.206)|
||||**Panel C. By **|**margin of infor**|**mality**||||
|_PCA 1_|-0.0351*|-0.0466**|||-6.451**|-6.677**|||
||(0.0190)|(0.0229)|||(1.029)|(1.198)|||
|_PCA 2_|-0.000449|-0.00302|||-3.834**|-2.953**|||
||(0.0200)|(0.0235)|||(1.049)|(1.222)|||
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initialproductivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|14,999|11,814|15,306|11,950|7,945|6,074|8,150|6,161|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Weeks to continue operating with cash available was only included as a question in the first two rounds of data collection.

Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-No statistically significant differences across types of informality in Panel B, cols. 3 and 4.

-Differences between necessity firms and the other two types are statistically significant at the 10% level in Panel B, cols. 7 and 8.

-The coefficients of PCA 1 and PCA 2 are statistically different at the 10% level in Panel C, cols. 1 and 2; and at the 5% level in panel C, cols. 5 and 6. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
The Korean statistics display several features which are typical of many other countries. First, the individuals or h2ushold sector is the largest domestic saving sector, in this case with gross saving totalling 8133 billion won6, or over 12% of GNP. To this is added 122 of capital transfers (evidently from the government) giving 8256 to be allocated to the acquisition of assets. In this particular year the household sector sold land and intangible assets in the net amount of 1092, which further augmented to 9348 the funds available for this sector's investment in capital and financial assets. The household sector's accumulation of real capital, at 4702, is well below this, leaving a financial surplus (of 4646, or 7% of GNP) to be lent to other sectors.

The pattern for the corporations or business sector (which here includes both public-owned and private enterprises) is the opposite to that of the individual sector. While its capit&l consumption provisions are sufficient to cover almost two-fifths of gross capital formation, remaining savings fall well short of covering the business sector's remaining needs for investment, and the sector has a net accumulation of indebtedness of 7031, or 10.6% of GNP.

As will become evident, the net position of the government sector varies widely: in Korea in 1984 the general government ran a surplus, not only on current account (positive net savings) but overall, with capital formation falling short of savings allowing net accumulation of financial assets in the amount of 1129 or 1.7% of GNP.

5 The practice is to include unincorporated business with the household sector, while corporate and quasi-corporate enterprises are included in the business sector. It is not always possible, however to adhere strictly to these categories.

6 The unit will be omitted for the remainder of the section.

7
Table 7: Correlations across countries and methods

|||In sa<br>|mple<br>|||Out of <br>|sample<br>||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.799|0.778|0.876|0.698|0.638|0.649|0.771|0.692|
|Madagascar|0.895|0.917|0.921|0.919|0.862|0.900|0.902|0.898|
|Malawi|0.712|0.846|0.903|0.818|0.649|0.747|0.830|0.624|
|Mozambique|0.950|0.960|0.958|0.955|0.846|0.883|0.876|0.888|
|Sri Lanka|0.931|0.944|0.942|0.921|0.832|0.880|0.875|0.883|
|Tanzania|0.899|0.926|0.917|0.913|0.762|0.867|0.837|0.829|
|Vietnam|0.878|0.893|0.906|0.868|0.614|0.595|0.741|0.698|
|Average|0.866|0.895|0.918|0.870|0.743|0.789|0.833|0.787|
|**Panel B: Poverty**|||||||||
|Malawi|0.817|0.924|0.872|0.883|0.782|0.840|0.843|0.668|
|Tanzania|0.899|0.921|0.907|0.840|0.669|0.727|0.697|0.663|
|Average|0.858|0.922|0.890|0.861|0.725|0.783|0.770|0.665|

Note: The first four columns present the simple mean of pearson correlations for in-sample areas across simulations for each country. The last four columns present the simple mean of pearson correlations for out-of-sample areas across simulations for each country. An area is defined as in-sample if at least one of that area’s enumeration areas is in the sample on a given simulation.
because of the uneven coverage of policy uncertainty and other indicators across countries. Our panel sample includes 18 of the 22 countries for which the policy uncertainty index is available.<sup>3</sup>

**Table 1.** **_Data sources_**

|Indicator|Sources and computation|
|---|---|
|Real GDP|IMF WEO|
|Real effective exchange rates(REER)|IMF IFS|
|Importprice deflator|WTO Merch. Indices|
|GDP deflator|IMF WEO|
|Goods and services import volume|IMF WEO|
|Goods import volume|IMF WEO|
|Goods import volume, by main end use (BEC<br>classification)|ratio of UN Comtrade (WITS) values to "import price<br>deflator"|
|Backward GVC participation: intermediate<br>imports embodied in exports|ratio of WIOD ‐ derived values to "import price<br>deflator"|

_Notes:_ IMF WEO: International Monetary Fund World Economic Outlook, Oct. 2016; WB GEM: World Bank Global Economic Monitor database, Feb 2017 download; IMF IFS: International Monetary Fund International Financial Statistics; WTO Merch. Indices: World Trade Organization merchandise values annual dataset; WTO Services values: World Trade Organization services annual dataset; UN Comtrade (WITS): United Nations Commodity Trade Statistics Database – data downloaded via World Integrated Trade Solutions (WITS).

In Section 5, we extend the analysis using the World Trade Uncertainty (WTU) index (Ahir et al. 2019) as an alternative to the EPU index. The WTU index captures uncertainty related to trade policy for a set of 143 developed and developing countries from 1996 onwards and is constructed based on the Economic Intelligence Unit (EIU) country reports, by counting the number of times “uncertainty” is mentioned in proximity to a word related to trade. In principle, the fact that the WTU index is specifically designed to capture trade related uncertainty could give it an advantage over the EPU index for studies focusing on the trade policy uncertainty / trade link. Nevertheless, the WTU index is characterized by reduced variability over time compared to the EPU index, which is extreme for some countries. Germany’s WTU, for example, is zero throughout the whole period we are studying. Positive values of the index are concentrated in 2018 and 2019, likely reflecting lack of attention given to trade policy uncertainty in media reports in the preceding period.

# **4. Economic policy uncertainty, trade, and GVCs**

Table 2 presents the results of regressions using yearly data. Two alternative dependent variables are used: growth in volumes of goods and services imports and growth in volumes of goods imports. Specifications 1a and 2a include the contemporaneous policy uncertainty measure (computed for each year as an average of the EPU index from January through December), while 1b and 2b include policy uncertainty that is lagged by 2 months (computed for each year as an

> 3 The Russian Federation and Mexico behave as outliers; therefore, we exclude them from all specifications. We also exclude India and the Republic of Korea, for which we do not have data on the REER.

8