rafmacalaba/datause-encoder-data huggingface dataset:
# **National Trends in Sectoral Employment and Value-Added Shares**

Figure 2 depicts trends in each sector’s share of employment and value-added, which reflect structural transformation, based on ETD statistics. Ghana did not exhibit any strong signs of structural transformation until 2000. Between 2000 and 2010, structural transformation gained momentum, but the changes were mostly limited to agriculture’s share of employment declining and services’ share of employment increasing. Industry’s share of employment grew from 16 percent in 2010 to 20 percent in 2018, driven primarily by manufacturing. Meanwhile, agriculture’s share of employment declined from 55 percent to 33 percent, and services’ share of employment grew from 30 percent to 47 percent over the same period.

The sectoral shares of value-added followed somewhat different trends. Between 1990 and 2018, services’ share of value-added hovered at about 50 percent. Both agriculture’s and industry’s shares of value-added remained close to 25 percent until 2010. Since then, the two sectors’ shares of value-added diverged, with industry’s share rising above that of agriculture. Within industry, mining’s share of value-added increased from 3 percent in 2000 to 12 percent in 2018 (9 percentage points). Construction’s share of industry value-added has increased moderately since 2005, whereas manufacturing’s share has been declining since the mid-2000s.

# **Regional Trends in Industry and Services in the North and the South**

Trends in employment and value-added shares at a more detailed sectoral level are not available in the ETD. As an alternative, we use 2014 IBES summary statistics from GSS (2016) on the number of firms in industry and services, categorized by district and year of establishment, to describe the evolution of sectoral activities at the regional level. We follow the classification of Ghanaian regions prior to the 2018 referendum, which expanded the number of regions from 10 to the 16 in effect today. We define the north as comprising the Brong Ahafo, Northern, Upper East, Upper West, and Volta regions, and the south as comprising the Ashanti, Central, Eastern, Greater Accra, and Western regions (figure 3, panel a). We retain this definition of the north and south of Ghana throughout the paper.

From approximately 1975 to the early 2010s, the numbers of firms in industry and in services increased steadily in both the north and the south, but the total number of firms (for both sectors) in the south has far exceeded that in the north (appendix figure A2, panel a). The growth rate in terms of the number of firms (referred to herein as “growth in firms”) in industry has always been higher in the north than in the south (appendix figure A2, panel b).

8
funneling of trade to small cities through nearby secondary cities, though no direct data on intercity trade are available.

The majority of Africa’s population growth is expected to be in cities over the next few decades (National Research Council, 2003). Indeed, of the approximately 2.5 billion net gain in global population expected by 2050, over 30 percent is expected to be in African cities (United Nations, 2008). Much less is understood, however, about which cities will experience the bulk of that growth. This is the first paper that has the data to systematically address the economic growth of cities in Sub-Saharan Africa. Which cities grow and which do not will have major implications for the future of the region. If growth is concentrated in large coastal cities, agglomeration economies may improve, but urban infrastructure needs, congestion, and the risks associated with sea level rise will all increase. Many countries have pursued decentralization policies at least in part because of these concerns. If growth is more balanced across a large group of cities, intercity infrastructure may be more important. If cities near international borders grow more than others, they may affect international migration and trade.<sup>2</sup> Regional cities that grow enough may become political power bases constituted along social or industrial lines.

Despite a great deal of attention paid to the largest cities like Lagos, Kinshasa and Nairobi, 61 percent of African urban residents lived in cities with a population of less than 500,000 as of 2000, and this percentage is expected to remain high over the next couple of decades. Only 15 percent are expected to live in cities of more than one million by 2015 (National Research Council, 2003). There are only 5 out of 43 Sub-Saharan countries (Liberia, South Africa, Togo, and both Congos) in which the majority of 2005 urban residents were in urban agglomerations with a 2007 population of more than 750,000.<sup>3</sup> In most countries, smaller cities are also growing faster (United Nations, 2008).

These large cities may however have greater importance to national economies than their populations alone suggest. In many countries, a large city or core region, often a port, plays a very important role in the economy, as the largest domestic market, the chief manufacturing center, the primary trading connection with the rest of the world, and the seat of elites and often of the government. Ports have a special role because

> 2The center of some ten Sub-Saharan African capital cities is within 20 km by land of an international border. Such proximity is extremely rare in other regions, except among countries less than 40 km in diameter. In six of these cases, the built-up area of the city directly abuts the border or border-river. More than half the countries in the region have capitals within 100 km of a border by air.

> 3All country counts were conducted prior to the independence of South Sudan.

2
# **<mark>Use of Catastrophe Risk Models in Assessing Sovereign Food Security for Risk Transfer</mark>**<sup>**1**</sup>

Mohan Sharma<sup>2</sup> , Ph.D., Roman Hohl<sup>3</sup> , Ph.D.

## **JEL Classification** – G11,Q18, Q19, Q54

**Keywords** – Agricultural production, Natural disasters, Risk Analysis, Food security, Sovereign risk transfer

> 1 This research has been completed as part of a series of papers developed under the Sovereign Disaster Risk Financing and Insurance Impact Appraisal project. The project is led by the World Bank Disaster Risk Financing and Insurance Program and funded through a cooperation between the UK Department for International Development (DFID) and the Global Facility for Disaster Reduction and Recovery (GFDRR).

> 2 Director Modelling, Asia Risk Centre, 11 A, Stanley St, Singapore 068730

> 3 CEO, Asia Risk Centre, and Deputy Director, Institute of Catastrophe Risk Management, Nanyang Technological University, Singapore
# PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

of installed generation capacities.<sup>8</sup> It can be seen that WEPP dataset does not take into account the fraction of firms that have the smallest generation capacity (less than 50kW), which are very difficult to detect. On the contrary, the enterprise survey dataset underrepresents the fraction of firms with mediumsize and large installed capacities (greater than 5MW), because of the random sampling procedure used to select participating enterprises. The distributions of installed generating capacities fully overlap in the range between 50kW and 1MW, which accounts for 35 percent of participating firms in WEPP dataset and 39 percent of participating firms in enterprise survey dataset.

Figures 5 and 6 show the distributions in observed industrial structure of firms with installed generation capacities between 50kW and 5MW. There are considerable similarities between two datasets. Both comprise a significant share of agricultural industry (23 percent and 29 percent of firms, respectively), metals and mining (14 percent and 26 percent of firms), and chemical products and petroleum (12 percent and 8 percent of firms). Both also have relatively small and significant shares of hotels and restaurants (4 percent and 3 percent of firms), construction and cement (6 percent and 4 percent of firms), and wood and pulp (8 percent and 2 percent of firms). The major differences include a large share of textiles industry in the enterprise survey dataset<sup>9</sup> (20 percent of firms), and a large share of unidentified plants (20 percent of firms) in the WEPP dataset.<sup>10</sup>

**Figure 3 Distribution of electrical generating capacity in Africa according to Enterprise Survey Database, 2002–06**

**Figure 4 Distribution of electrical generating capacity in Africa according to UDI World Electric Power Plants Data Base (WEPP), 2006**

<!-- Start of picture text --> 35<br>% of Firms<br>30 35.0%<br>25 30.0%<br>25.0%<br>20<br>20.0%<br>15<br>15.0%<br>10 10.0%<br>5 5.0%<br>0.0%<br>0 <100KVa 100- 0.5-1MW 1-5 MW 5-10 MW 10-100 100-500<br>Less than 10-50kVA 50-100kVA 100- 500- More than 500KVa MW MW<br>10kVA 500kVA 1000kVA 1000kVA<br>% of Firms<br><!-- End of picture text --> Sources: World Bank, Enterprise Survey Database; UDI World Electric Power Plants Data Base (WEPP).

> 8 The enterprise survey does not ask for information on generating capacity, which is estimated at firm level by taking the ratio of the deflated generator’s acquisition cost to the average capital cost per kW of the generator’s capacity (assuming thermal generation). The data for the capital cost per kW of installed capacity came from the World Bank’s Energy and Water Department (2005). The GDP deflator data came from the World Bank’s World Development Indicators database.

> 9 The textiles industry is oversampled in the enterprise survey dataset for the purpose of international productivity comparisons.

> 10 Unidentified industries in the WEPP dataset may comprise multiproduct corporations, and smaller firms that choose not to reveal their commercial activities.

5
# Table II.1: Methodologies to analWze the impacts of adjustment on education

|Methodology|Nu<br>Source<br>Cou|mber oEc<br>ntries|onmic<br>Indico|Education<br>Indicators|Conclusion|
|---|---|---|---|---|---|
|Before and after<br>adjustment|Sahn|16|Public budget (real<br>and % of GDP)|Education spenading (real<br>and share of discretionary<br>public budget)|No clear impacts<br>of adjustment|
|Fiscal duress|Gallagher|35|GDP growth|Education spending (share<br>of total and discretionary<br>public budgets)<br>Gross enrollment rates<br>(primary and secondary)|No clear impacts<br>of adjustment|
|Adjusting versus<br>Nonadjusting<br>Countries|Kakwani,<br>Makonnen,<br>Van der Gaag|86<br>|Public budget (total<br>and discretionary<br>as % of GDP)<br>Per capita private<br>consumption|Education spending (share<br>of total and discretionary<br>public budgets, GDP)<br>Per capita spending<br>Primary enrollment rates<br>(gross and net)<br>Growth of primary school teac<br>Student/teacher ratios|Intense adjusment<br>related to declines<br>in all education<br>indicators<br>hers|
|Adjusting versus<br>Nonadjusting<br>Countries|World Bank<br>(1990a)|78|GDP growth<br>Public debt<br>Public deficit<br>Savings, investment<br>rates<br>Per capita private<br>consumption|Education spending (%<br>of discretionary<br>public budget and GDP)<br>Per capita expenditures<br>(annual change)<br>Gross primary enrollment rate|Intense adjustment<br>related to declines<br>in all education<br>indicators|
Croke et al. (2016) than the cost.<sup>29</sup> This implies that analyses which divide up the set of available studies will likely be underpowered, limiting the scope for further subsample tests. While the results here suggest that overall, multiple-dose MDA increases child weight, they also suggest that if MDA had similar impacts on weight across drug type or worm species, a meta-analysis focusing on any one species of worm or drug may well be underpowered. We therefore think it would be appropriate for any future studies designed to explore heterogeneity, across worm species or drug type, for example, to report a test of the hypothesis that the average effect of MDA for each worm species or each drug is the same, rather than to simply test the hypothesis that the effect of any one individual drug or MDA against any one species has zero impact on weight. Beyond its relevance for health research, greater awareness of the limitations of under-powered meta-analyses will become increasingly important as more social scientists conduct meta-analyses (Vivalt, 2015).

A further methodological lesson is that it is appropriate to explicitly test the null hypothesis of a common zero effect. Finally, in evaluating policy, it is appropriate to focus on all mass-drug administration studies conducted in environments where mass deworming is recommended under WHO guidelines rather than to mix in studies from environments in which worm prevalence is sufficiently low that the WHO does not recommend mass treatment, or to selectively exclude studies of mass drug administration conducted in highprevalence environments.

> 29 The estimated effect in the full sample is about 50% greater than the estimate in the TMSDG sample, though well within the confidence interval of (-0.11 kg, 0.27 kg) found by TMSDG in their meta-analysis of the impacts of multiple-dose MDA on weight. However, the confidence interval shrinks by 47% to (0.03 kg, 0.24 kg) with the full data set. Thus, incorporation of additional information into the analysis helps address the problem of insufficient power in TMSDG.

29
- <mark>2) Investment climate reform can aim both at visibility and impact. If visibility is the goal, reforms should be undertaken that will result in the largest gains on the overall DB ranking. If impact is the goal, the following additional advice can be given:</mark>

- <mark>a. The DBIs with the largest impact on economic growth are ‘Getting Credit’ and ‘Enforcing Contracts’.</mark>

- <mark>b. Other DBIs with a statistically detectable impact on economic growth are ‘Starting a Business’, ‘Registering Property’, and ‘Protecting Investors’.</mark>

- <mark>c. Sub-indicators focusing on cost, and to an extent time, have the largest impact on economic growth across all nine DBIs examined.</mark>

<mark>It should be borne in mind that some of this advice is based on the statistical analysis of a large crosssection of countries. Naturally, this paints an aggregate picture at the expense of country-specific detail. The results from the analysis do not provide a magic wand for fostering economic growth. They only provide additional guidance which should always complement an in-depth analysis of the situation on the ground (which should involve information from the domestic business community through, for</mark> example, the ‘Enterprise Surveys’) in each country embarking on investment climate reform.

<mark>Whilst the analysis is comprehensive and includes recent data in the study of the business climate and economic growth, there is scope for further investigation. The results in this paper are based on a time period that was overshadowed by the most dramatic financial and economic crisis since the 1930s. Future research should investigate the robustness of these results in a post-crisis world. Moreover, the DBIs are constantly updated, with the ‘Getting Electricity’ indicator included in the latest DB Report. Its mettle should be tested empirically, as should any other indicator that may be added to the current</mark> battery of DBIs.

17
Policy ReseaRch WoRking PaPeR 4566

# **Abstract**

The Poverty Reduction Strategy of the Government of Rwanda seeks to unlock the growth and poverty reduction potential of the tea sector through the privatization of tea estates. This paper uses the logic of causal inference and data from the 2004 Quantitative Baseline Survey of the tea sector to assess the potential impact of the privatization program. This entails a normalized comparison of productivity outcomes to account for household heterogeneity in terms of observable and non-observable determinants of these outcomes. The paper also compares living standards between tea and non-tea households. Three main findings emerge from the analysis. Productivity outcomes are generally better in the private sector than in the public sector. Male-headed households outperform femaleheaded households along all dimensions considered here. And tea households tend to be better off than non-tea households.

This paper—a product of the Poverty Reduction and Economic Management 3 Division, Eastern Africa 2 Country Department—is part of the series of analytical work feeding into the Poverty and Social Impact Analysis of Tea Sector Privatization in Rwanda that has also informed the Country Economic Memorandum, "Rwanda-Toward Sustained Growth and Competitiveness." Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Bessamanssah@worldbank.org, Kezemenari@worldbank.org, and Vkorman@worldbank.org.

_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
# **Table 9A - Robustness Checks: Effect of Equality of Opportunity on Institutional Quality Human Opportunity Index - Average Mathematics, Reading and Science Outcomesfor Level 2 - PISA 2009** **<u>Controlling for Ethnic Fractionalization</u>**

|Regression<br>|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|---|---|---|---|
|Instrument: Logof Wheat Sugar Sustainability<br>Index of Ethnic Fractionalization (0-Low to 1-hih)|OLS<br>-44247***|OLS<br>0073|IV<br>-9338|
|g|.<br>(6.76)|.<br>(8.06)|.<br>(11.02)|
|Human Opportunity Index*||0.783***<br>|0.740***<br>|
|||(0.09)|(0.17)|
|constant|67.794***|18.444*|23.131|
|R-sqr|(3.44)<br>0188|(7.60)<br>0658|(14.37)|
|Number of Observations|.<br>187|.<br>60|48|
|Wald F-statistics from first stage|||13.591|
|**Controlling for Legal Origin**||||
|Regression<br>|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|Human Opportunity Index*||0.762***|0.957***|
|||(0.08)|(0.12)|
|British Legal System|2.821|9.493*|7.547|
||(5.75)|(4.29)|(4.46)|
|French Legal System|-6.042|9.046*|11.288*|
||(5.14)|(3.54)|(4.92)|
|Germany Legal System|42.788***|10.966|7.406|
||(11.44)|(5.54)|(8.22)|
|Scandinavian Legal System|51.321***|19.569***|16.394**|
||(11.44)|(5.53)|(4.95)|
|constant|44.444***|11.598*|-1.385|
||(4.21)|(5.37)|(9.17)|
|R-sqr|0.239|0.79||
|Number of Observations|144|55|48|
|Wald F-statistics from first stage|||21.485|
|**Controlling for Share of Tropical Land**<br>Ri|Dd|Vibl IiiA|WBGI 2009|
|egresson|epene|t arae: nsttutons,|verage|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|% land area in geographical tropics|-26.318***|1.676|12.03|
||(3.92)|(5.77)|(9.74)|
|Human Opportunity Index*||0.794***|1.005***|
|||(0.09)|(0.23)|
|constant|58.603***|17.599**|1.051|
||(2.65)|(6.33)|(16.23)|
|R-sqr|0.219|0.655||
|Number of Observations|163|59|49|
|Wald F-statistics from first stage|||3.306|
|**Controlling for Latitud**||||
|<br>Regression|Depende|nt Variable: Institutions,A|verage WBGI 2009|
|Instrument: Logof Wheat Sugar Sustainability|OLS|OLS|IV|
|latitude of country centroid|0.376***|-0.09|-0.119|
||(0.08)|(0.07)|(0.10)|
|Human Opportunity Index*||0.830***|0.878***|
|||(0.08)|(0.15)|
|constant|37.923***|18.646***|15.343|
|R|(2.63)<br>012|(5.13)<br>0665|(7.67)|
|-sqr<br>Number of Observations|.<br>163|.<br>59|49|
|Wald F-statistics from first stage|||17.839|
|**Controlling for Longitude**<br>|d|bl||
|Regression<br>Instrument: Logof Wheat Sugar Sustainability|OLS<br>Depene|OLS<br>t Variae: Institutions,A|IV<br>verage WBGI 2009|
|longitude of country centroid|-0.043|-0.055*|-0.082*|
||(0.04)|(0.03)|(0.03)|
|Human Oortunit Index*||0803***|0821***|
|ppy||.<br>(0.07)|.<br>(0.11)|
|constant|46.779***|18.086***|15.866*|
||(2.21)|(5.04)|(7.11)|
|R-sqr|0.008|0.678||
|Number of Observations<br>ld ii f fi|163|59|49<br>|
|Wa F-statstcs rom rst stage<br>* p<0.05, ** p<0.01, *** p<0.001|||21.201|

*Average Mathematics, Reading and Science Outcomes Level 2 - PISA 2009

30
power, whereas they reduced market power in the labor market, although the effect was not statistically significant. The data on industry import does not differentiate between intermediate and final goods imports. One possible explanation for the results is that the importation of final goods can increase the variety of products on the market; thus, product differentiation through imports can increase market power.

Table 5: Robustness Check: Identification Assumptions

||Expectat|ion Efect|Trade|Volume|
|---|---|---|---|---|
|VARIABLES|_μ_<sup>_m_</sup><br>_ijt_<br>(1)|_γijt_<br>(2)|_μ_<sup>_m_</sup><br>_ijt_<br>(3)|_γijt_<br>(4)|
|_τ_1991_× Post_1995|-0.0160*|0.0109***|-0.0228**|0.0109***|
||(0.00797)|(0.00329)|(0.00881)|(0.00241)|
|_τ_1991_×_ One year before WTO accession|-0.00992<br>(0.00826)|-0.000606<br>(0.00293)|||
|Total Industry Exports (log)|||-0.0299|-0.0211|
||||(0.0482)|(0.0278)|
|Total Industry Imports (log)|||0.203**|-0.0187|
||||(0.0757)|(0.0446)|
|_ωit_|1.571***|0.0836**|1.574***|0.0844**|
||(0.171)|(0.0286)|(0.168)|(0.0282)|
|Skill Ratio|0.0652|0.145|0.0530|0.118|
||(0.155)|(0.165)|(0.154)|(0.181)|
|Small size frms|0.103|0.288***|0.0832|0.290***|
||(0.0836)|(0.0442)|(0.0879)|(0.0443)|
|Medium size frms|0.0554|0.0906**|0.0460|0.0929**|
||(0.0669)|(0.0357)|(0.0680)|(0.0364)|
|Inverse Mills Ratio||0.142*||0.146*|
|||(0.0633)||(0.0673)|
|Constant|-11.41***|-1.107**|-14.60***|-0.773*|
||(1.357)|(0.468)|(1.511)|(0.487)|
|Observations|1,574|601|1,555|593|
|_R_<sup>2</sup>|0.484|0.12|0.483|0.124|
|Number of frm|223|152|220|149|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. The data on tariffs comes from the _Centre d’Estudes Prospectives et d’Informations Internationales_ (CEPII).

**_Note:_** Table estimate whether results are driven by anticipation and total trade volume. Robust standard errors clustered at three digit industry level in parentheses. _∗∗∗_ Significant at 1%. _∗∗_ Significant at 5%. _∗_ Significant at 10%.

The last robustness check test is for misallocation effects on how surviving firms and entrants/exiters respond to trade openness. To this end, the sample was divided into two groups: surviving firms (i.e., firms active in all the 12 years of the survey and hence active 25