rafmacalaba/datause-encoder-data huggingface dataset:
https://doi.org/10.1017/CBO9781107415324.004

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Government of Fiji & World Bank. (2017). _Climate vulnerability assessment : making Fiji climate resilient_ .

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https://doi.org/https://doi.org/10.1016/j.jdeveco.2007.08.003

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WEF. (2018). _The Global Competitiveness Report 2017-2018_ . Cologny: World Economic Forum.

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World Bank. (2018a). Logistics Performance Index. Retrieved from https://lpi.worldbank.org/

- World Bank. (2018b). Open Budgets Portal - BOOST initiative. Retrieved February 11, 2018, from http://boost.worldbank.org/

11
**Plc,Research, and Exte'rna f International Trade**

WPS 470 This paper-a product of the International Trade Division, Intemational Economics Departrnent -is part of a larger effort in PRE to provide basic infornaiion for analyzing the presert level and composition of developing countries' exports and projecting future changes in them. Copies are available frec from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Jean Epps, room S8-037, extension 33710 (29 pages, including tables).

Despite their extensive applications in research and policy studics, no product-level comparisons had been made between Bela Balassa's "revealed" comparative advantage (RCA) index and indices associated with the National Bureau of Economic Research (NBER) that reflect the standard Hecksher-Ohlin theory of comparative advantage.

in a broad group of labor-intensive products; for other items, their RCAs are generally below unity.

Within the labor-intensive group, however, developing countries failed to develop a revealed comparative advantage for about half of the items.

Yeats conducted several empirical tests for developing countries' exports of manufactured products, partly to identify factors that often lead to differences between the two indices.

The results show that products in which developing countries have achieved a revealed comparative advantage are highly concentrated A regression model suggests that in the labor-intensive group, revealed comparative advantage falls as the requirements increase for natural resources, for physical capital, and for - human capital including higher per capita wages, and more professional or technical personnel.

The PRE Working Paper Series disseminates thc findings of work under way in the Bank's Policy, Research, and Extemal Affairs Complex. An objective of thc scries is to get these ftndings outquickly, even ifpresentations are less than fullypolished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

IProduced by the PRE Dissemination Centcr
# Figure 1. Global trade

## A. Global trade and output growth

<!-- Start of picture text --> Percent<br>Trade Output<br>8<br>6<br>4<br>2<br>0<br>1970 - 2008 2011 - 2019<br><!-- End of picture text --> <!-- Start of picture text --> B. Composition of global trade, 2010-19<br>Percent Goods Services<br>100<br>75<br>50<br>25<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> Source: World Bank.

Note: EMDEs = emerging market and developing economies.

A. Bars indicate annual average growth. World output growth is real GDP growth computed as a weighted average (at 2010-19 average prices and exchange rates) as reported in the January 2023 Global Economic Prospects report. Trade growth refers to the average growth of import and export volumes.
# _Ordinary Least Squares (OLS) estimates_

Table 9 presents OLS-based wage differentials between the public and private sectors, by age, education level, and occupation for four groups of workers distinguished by gender and type of residence. Similar to the patterns of unadjusted differentials, wages of public sector workers are higher than their wages would have been, had they been employed in the private sector for nearly all groups of workers. Overall, the wage advantage of public sector workers represents 62%; it is 57% among urban men and it reaches 75% among urban females. Wage differential is higher in rural, as compared to urban, areas.

With respect to differences across occupational groups, among urban men, the highest differential is registered among sales workers who receive a 75% wage premium. Professional and technical employees and men working in administrative occupations enjoy a 50% wage advantage. Among rural men, professional and technical public sector employees receive wages twice as high as they would have had they been in the private sector, and have the highest wage advantage across all occupational categories. Among rural women, the highest public-private wage differential of 116% is observed in sales occupations. Professional and technical female employees in urban areas earn wages that are 109% higher than the wages in the same occupation in the private sector. Alternative wages of public sector workers would have been higher only for women working in some occupational categories (administrative and sales occupations).

The wage differential of young and pre-retirement-age workers is lower than the wage differential of workers in the twenty-six to forty-five age range, on average. In urban areas, wage differential declines with age from about 60% for the younger group to 52% for male workers forty-six to fifty-five years old. In rural areas, the wage differential is the highest for the twenty-six to thirty-five age group where it reaches 77%. The wage differential among young urban women is 54%; it rises to 86% for women twenty-six to forty-five age range, and drops for older women.

While the overall wage differential tends to be higher among workers with a middle-school diploma as compared to workers with less and more education, for urban males the public-private wage ratio is higher for workers with a lower level of education. For example, the ratio of public to private sector wages is equal to 1.7 for illiterate workers and 1.5 for employees with a university degree or higher. In rural areas, 20
slightly lower among girls than boys. Child undernutrition is much lower in households in which the father is literate and similarly for the mother. Finally, we note that in both 2013/14 and 2015/16 the prevalence of child undernutrition appears to be somewhat lower in female headed households for both indicators.

**Table 3: Distribution of z-scores in Ethiopia, 2013/14 and 2015/16 (% below -2 S.D) for children between 6 to 59 months of age.**<sup>**20**</sup>

||2013/14||2015/16||
|---|---|---|---|---|
||**Stunting**|**Wasting**|**Stunting**|**Wasting**|
|Overall|40.0|10.9|41.7|10.3|
|Male|40.6|11.7|42.2|12.2|
|Female|39.4|10.0|41.2|8.3|
|Mother illiterate|43.3|12.1|45.5|11.8|
|Mother literate|31.3|7.6|35.9|9.5|
|Father Illiterate|47.7|14.3|45.3|9.9|
|Father literate|33.5|8.1|39.4|11.5|
|Female headed household|35.7|8.3|40.2|9.8|

Table 4 shows the prevalence of child undernutrition for the two indicators according to the age group of the child. Children aged 12 – 24 months have higher rates of stunting compared to other age groups whereas for wasting the youngest group has the highest rate. For stunting, rates declined between the two survey years for children aged 24 to 36 months but increased for all other age groups. While for wasting, children between 24 and 36 and 36 to 48 months showed improvement.

**Table 4: Child nutritional status by age, 2013/14 and 2015/16 (% below -2 S.D)**

||2013/14||2015/16||
|---|---|---|---|---|
|**Description**|**Stunting**|**Wasting**|**Stunting**|**Wasting**|
|_Age_|||||
|6 months - 12 months|28.3|11.6|36.1|16.7|
|12 months - 24 months|46.7|11.0|47.9|13.0|
|24 months - 36 months|43.3|11.3|41.7|10.4|
|36 months - 48 months|38.7|10.9|42.3|8.6|
|48 months-59 months|34.5|9.4|41.0|10.5|

> 20 We ran a t-test to check if the means of nutritional outcomes are statistically different over the two surveys. Our results indicate that the means of nutritional outcomes are not statistically different from each other although there were increases in stunting rates between the two surveys. Similar results were found for child nutritional status by age and by area and region.

10
||233.99|198.02|161.95|146.12|158.55|126.84|84.79|69.49|87.01|86.29|77.44|76.05|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|R Adj sq.|0.262|0.321|0.323|0.339|0.391|0.431|0.434|0.483|0.079|0.159|0.16|0.164|
|N|10906|10906|10906|10906|4922|4922|4922|4922|5984|5984|5984|5984|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

34
Let xj be a vector of characteristics that are commonly observed between the two surveys, where _j_ indicates survey type, with 1 the base survey and 2 the target survey. Let us assume that the welfare indicator is a function of household and individual characteristics ( ): jj xx where is the welfare indicator (consumption per capita per month), is a vector of parameters, is cluster random effects, and jj is the idiosyncratic error term. jj yy ββ ccjj jj μμ εε The framework proposed by Dang et al. (2017) is based on two assumptions. The first assumption (Assumption 1), which is critical for poverty imputation, states that measurement of household characteristics in each sample of data is a consistent measure of the characteristics of the whole population. In other words, it stipulates that the two surveys are representative of the same target population. In our context, the two surveys represent the same population of refugees, and they were conducted approximatively at the same time. Therefore, we do not expect major issues with this first assumption. However, we will conduct means difference tests on the observed overlapping variables between the target data and base data to ensure that this is the case. The second assumption states that changes in between the data collection periods of the two data sets can capture the change in welfare over the period (Assumption 2). Since data collection for jj xx the two data sets we use refers to the same year, there is no need to test Assumption 2. Under these two assumptions, the imputed welfare is Dang et al. (2017) propose different imputation methods for parameter estimation. The first method relies on the assumption of the normal distribution for the two error terms (μcj and εj are uncorrelated and μcj ∕xj μcj) and εj ∕xj εj)). Hereafter, this method is referred to as the normal linear regression model. An alternative method proposed is the empirical error ~NN(0, σ ~NN(0, σ method, which assumes no functional form for these error terms and uses instead the empirical distribution to estimate the parameters. Dang et al. (2017) also propose two other alternative 6
Turkson, D., Addai, N.B., Chowdhury, F. et al. 2021. “Government policies and firm performance in the - COVID-19 pandemic era: a sectoral analysis.” SN Bus Econ 1, 168. https://doi.org/10.1007/s43546 <u>021-00170-6</u> Taylor, J., & Castillo O'Sullivan, A. 2015. “Timely, Targeted, and Temporary?' an Analysis of Government Expansions Over the Past Century.” MERCATUS RESEARCH, Available at SSRN: http://dx.doi.org/10.2139/ssrn.3211609

- Pop, G., & and Amador, A.. 2020. “To Aid and How to Aid: Policy Options to Preserve Markets.” _European State Aid Law Quarterly_ 19 (2): 127–36, https://doi.org/10.21552/estal/2020/2/4.

Wagner, J. 2021. “With a little help from my website, firm survival and web presence in times of COVID19: Evidence from 10 European countries.” Working Paper Series in Economics No. 399, University of Lüneburg, Lower Saxony, Germany, http://hdl.handle.net/10419/234588.

- World Bank. 2021. _Supporting Firms in Restructuring and Recovery_ . EFI Insight: Finance, Competitiveness and Innovation, Washington, DC.

- World Bank. 2021a. “Firm Level productivity estimates.” Methodological Note. World Bank, Washington, DC. Available at: www.enterprisesurveys.org.

13
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 7. Evolution of hidden costs in Cape Verde’s water sector**

<!-- Start of picture text --> 0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>unaccounted losses underpricing collection inefficiencies<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from ELECTRA Annual Reports 2001–09.

# Information and communications technologies

## **Achievements**

Cape Verde has made substantial progress in expanding access to ICT (table 9, top). The percentage of the population with a mobile phone rose from 17 percent in 2005 to 69 percent in 2009. In 2005 just 5 out of 100 people were Internet users; in 2009 this number was 22— the second-highest in Sub-Saharan Africa (after Mauritius).

Significant reform of the ICT sector has taken place since the mid-2000s. A new telecommunications law was introduced in 2005 that ended the fixed and international communications monopoly of Cape Verde Telecom (CVT). The National Agency for Communications ( _Agência Nacional das Comunicações_ , ANAC), the industry regulator, was created in 2006. A second mobile operator, T+ Telecomunicações, was launched in December 2007 as a competitor to CVT.

17
**Table 6: Crop productivity, by agronomic potential**

|agro potential<br>quintiles, cash crop|Crop productivity,<br>tobacco+dry<br>season+rainfed,<br>MKper m<sup>2</sup>|<br>Crop productivity,<br>tobacco,<br>MK per m<sup>2</sup>|Crop productivity,<br>dry season crops,<br>MK per m<sup>2</sup>|<br>Crop productivity,<br>rainfed crops,<br>MK per m<sup>2</sup>|
|---|---|---|---|---|
|1 (worst)|29.0|191.3|59.3|26.8|
|2|30.2|160.4|52.1|29.6|
|3|38.3|198.0|82.3|35.4|
|4|36.3|245.7|113.3|31.5|
|5 (best)|43.4|176.6|78.4|37.7|
|Total|35.4|195.1|77.0|32.2|

Data Source: IFPRI SPAM, and the IHS2

# <u>Market Access</u>

27. We also find that crop production intensity is influenced by market access. Easy access to urban centers can provide incentives for increasing the scale of crop production (market size), as well as provide better access to relevant price information, productivity-improving information, and reduced transport costs for crops and intermediate inputs. To examine if market access matters for agriculture specialization in Malawi, travel times to the nearest urban center (Lilongwe, Blantyre, Mzuzu, and Zomba) were computed using a Spatial Analysis program in ArcGIS. This program finds the route offering the shortest travel time from an origin to a destination location. The computation employed the combined GISHDM4 database along with the GIS files with information on the location of roads. The 564 IHS2 Enumeration Areas are grouped into travel time (to urban centers) quintiles.

**Table 7: Cash crop production, by agronomic potential and travel time to urban centers**

|Cash crop prod<br>share, %|uction,<br>|5<br>(longest)|trav<br>4|el time to nea<br>3|rest 4 major<br>2|cities<br>1<br>(shortest)|subtotal|
|---|---|---|---|---|---|---|---|
|5|(worst)|1.1|3.3|3.5|0.1|0.0|8.0|
||4|3.0|0.7|1.0|2.3|0.8|7.9|
|Agronomic<br>tntil|3|5.4|13.7|1.4|1.0|0.5|22.0|
|poea,<br>cash crop|2|10.3|4.1|9.3|2.1|0.2|26.0|
|<br>1|(best)|1.2|7.6|10.0|15.1|2.2|36.0|
|s|ubtotal|21.0|29.4|25.3|20.6|3.7|100.0|

Data Source: GIS+HDM4 data, IFPRI SPAM, and the IHS2

**Table 8: Tobacco production, by agronomic potential and travel time to urban centers**

|Tobacco production,<br>share, %|5<br>(longest)|trav<br>4|el time to nea<br>3|rest 4 major<br>2|cities<br>1<br>(shortest)|subtotal|
|---|---|---|---|---|---|---|
|5(lowest)|1.0|3.5|2.9|0.0|0.0|7.5|
|<br>4|3.0|0.8|0.8|2.4|0.9|7.9|
|Agronomic<br>ttil<br>3|5.6|13.9|1.4|0.9|0.5|22.3|
|poena,<br>cash crop<br>2|11.1|3.1|9.4|1.7|0.2|25.6|
|<br>1(highest)|1.1|7.3|10.4|16.1|2.0|36.8|
|subtotal|21.8|28.6|24.9|21.1|3.6|100.0|

Data Source: GIS+HDM4 data, IFPRI SPAM, and the IHS2 28. Table 7 and Table 8 combine information on agronomic potential with travel times (to urban centers) for cash crop and tobacco production respectively. Quintiles of agronomic potential are on the rows, and quintiles of travel times to urban centers are in the columns.

- 13 -
impacts, as well as to use the high rate of business registration to measure whether this form of formalization alone has any benefits for firms.

The remainder of the paper is structured as follows: Section 2 describes in detail the business registration process in Malawi, contextualizing these in terms of the procedures in other countries. Section 3 explains the impact evaluation and the data collection methodology and discusses baseline characteristics of our sample. Section 4 presents the short-term impacts of the interventions on formalization and access to financial services. Section 5 concludes and outlines next steps in the research.

# **2. Business Registration in Malawi**

This evaluation takes place in the context of a broader effort by the Government of Malawi to improve the business environment and to streamline the process of business registration. As part of the Business Environment Strengthening Technical Assistance Project (BESTAP) supported by the World Bank, the government introduced a new Business Registration Bill seeking to enforce the registration of informal enterprises; drafted a new Business Licensing Bill; and transitioned to an online-based electronic system of business registration reducing the time to register firms<sup>2</sup> . The goal was to reduce the turnaround time from 14 days to less than five.

The government is also considering combining these reforms with outreach campaigns promoting the potential benefits of business registration, and is committed to experimentally assessing the value of MSMEs becoming formal. Ultimately, the government aims to provide further information to firms about registration in the future (if the impacts of registration are positive) or to identify other bottlenecks that constrain enterprise performance (if the results are negative or zero).

# **2.1 The Formalization Process**

According to the _Doing Business_ report (2011), a limited liability company in Malawi had to go through 10 procedures in order to become formal. These included reserving a unique name, applying for a business registration certificate (BRC), registering at the Malawian Revenue

> 2 The effects of these reforms have not yet been recognized in the 2015 Doing Business Report because some of its details (regulations under the auspices of the Bills to be enacted, launch of automated system) are being finalized.

5
18 Since an _NRA_ cannot be less than -1 if producers are to earn anything, neither can the _RRA_ . This measure is a useful indicator for providing international comparisons over time of the extent to which a country’s policy regime has an anti- or pro-agricultural bias.

# **Putting the theory into practice**

Making the above theory operational in the real world, where data are often scarce especially over a long time period is as much an art as a science.<sup>12</sup> Thankfully one does not have to start from scratch in many countries. Nominal rates of assistance are available from as early as 1955 in some cases, and at least from the mid-1960s, to the early or mid1980s for the 18 countries included in Krueger, Schiff and Valdes (1988, 1992) and Anderson and Hayami (1986). Much has been done to provide detailed estimates since 1986 of direct distortions to farmer (though not food processing) incentives in the highincome countries that are now members of the OECD, and (since the early or mid-1990s) in selected European transition economies and Brazil, China and South Africa (OECD 2007a,b). As well, at least for direct distortions, the K/S/V measures have been updated to the mid-1990s for some Latin American countries (Valdes 1996) and provided also for some East European countries (Valdes 2000); and a new set of estimates of simplified PSEs for a few key farm products for China, India, Indonesia and Vietnam since 1985 are now available from IFPRI (Orden et al. 2007). Each of these studies uses variations on the above methodology, but the basic price data at least, as well as the narratives attached to those estimates, are invaluable springboards for the present study.

# **_Farm product coverage_**

The agricultural commodity coverage includes all the major food items plus other key farm products (e.g., tree crop products, tobacco, cotton, wool), with the aim of reaching 70 percent of each country’s value of agricultural and food production at undistorted farm-gate prices. Priority is given to the most-distorted industries, because then the residual will have not only a low weight but also a low degree of distortion.

> 12 In addition to the methodologies of Krueger, Schiff and Valdes (1988, 1991) and the OECD (2006) for estimating agricultural distortion and producer support indicators, see the recent review of methodologies of other previous studies by Josling and Valdes (2004).