rafmacalaba/datause-encoder-data huggingface dataset:
farm sector to the nonfarm informal sector. Equally importantly, this process does not restrict productivity gains as workers move from the farm sector to the nonfarm formal sector. Equation 2 includes another channel through which structural transformation can increase national productivity. Regions that see a rapid pace of structural transformation (i.e., a higher than the national average) and a higher productivity gap between the farm sector and the nonfarm informal sector (rr<sup>NN</sup> L −L ) can experience a large gain in labor ∆θθ productivity. This channel may appear counterintuitive to the first channel, which relies on a rr<sup>N</sup> rr<sup>AA</sup> lower disparity in subnational intersectoral productivity gaps through a more productive informal sector. In countries where the pace of structural transformation varies substantially across subnational areas, regional equality in intersectoral productivity gaps can increase the productivity gain from structural transformation only through the first channel. However, both channels coincide if the pace of structural transformation across subnational areas is constant. Our analysis assumes a constant pace of structural transformation across subnational areas to isolate the effects of a more equal distribution of subnational intersectoral productivity gaps on the gain in national productivity from structural transformation.

The contribution of this study relates to three strands of literature. First, the underlying mechanism connecting subnational differences in intersectoral productivity gaps to national productivity growth relates to studies on structural transformation and convergence in subnational productivity (Imbs and Wacziarg 2003; Caselli and Tenreyro 2005; Hnatkovska and Lahiri 2012). Second, our argument for a more productive informal sector to minimize subnational intersectoral productivity gaps hinges on productivity gains from the reallocation of resources between the informal and formal sectors as documented by an extensive literature (Banerjee and Duflo 2007; La Porta and Shleifer 2014; McCraig and Pavcnik 2015). Finally, by offering a new perspective on the barriers to growth-enhancing structural transformation, this study fits into the emerging class of studies that aims to understand why structural transformation has failed to produce significant productivity growth in many developing countries (Caselli and Tenreyro 2005; Restuccia et al. 2008; McMillan et al. 2014).

We use data from Ghana to examine the role of subnational intersectoral productivity gaps. Ghana is a representative case among developing countries of marked structural transformation in recent decades accompanied by limited national aggregate productivity growth. The country also has the needed spatially disaggregated data for our analysis.

Using nonfarm enterprise census data for the country collected between 2014 and 2016, we find that average productivity in the informal sector is lower than in the formal sector in all 4
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- 65 -

greater flexibility in use of resources imparted to an additional one quarter. The recommendations are: a. There are a number of earmarking arrangements which have no strong benefit rationale and which are prime candidates for elimination; i.e. those for PROEXPO (plus IFI and Caja Agraria); SENA/ICBF/Cajas de Compensacion; and the departmental taxes on alcohol/tobacco/gambling for health, welfare, and sports.

b. Further potential candidates for elimination should be sought in the large number of very small funds which ma<sup>1</sup> le up about 31 of total earmarking (see Statisticv Appendix Table A2, esp., the categories "Special Funds", "Cont butions and Participations", and "Other" at the national level). While this Report has not had the time to review thise cases, only a small number (e.g. the taxes on tourism, cinema tickets, coal) appear to be benefit related and, in all cases, questions should be raised about whether appropriate levels of funds are being raised and whether the activities being financed are indeed a desirable use of public funds.

c. Those parts of the new Organic Law of tha Budget pertaining to earmarking--namely, the limitations on mini-budgets, the prohibition of new earmarking from existing revenue sources, and the claim of the central budget on public enterprises' operating surpluses--should be implemented expeditiously.

d. Existing revenue sharing arrangements between the central government and the department or municipalities need to be strengthened since they are an important part of the decentralization effort.
cost curve is convex. It decreases initially and then increases at higher levels of decentralization before it starts leveling off. The optimal level of decentralization of maintenance for Korea can be determined by looking at the total cost curve. Total costs increase initially and then decrease to reach a minimum at [50-69] percent decentralization; this observation is very much in line with the results of our general analysis.

As for construction, figure 7 shows that resource costs are at their minimum when financing is centralized. They increase at an increasing rate as decentralization progresses, reaching a resource cost maximum at [50-69] degrees of decentralization and declining after that. As for the preference cost curve, consistent with the theoretical expectations, it is downward sloping, implying that road conditions improve as decentralization advances. Overall, in support of the fiscal federalist argument, the optimal level of service provision would lie at [0-10] percent decentralization if the objective is resource cost minimization. But at this point preference costs are at their maximum, indicating essentially that road quality is poor and not enough money is being spent on road works. Increased levels of decentralization, however, lead to betterment of road conditions but this is achieved with increased resource costs. The total cost curve displays the point where total costs are minimized, namely at [70-90] degrees of decentralization.

A look at figure 8 suggests that administrative operations are conducted most cost efficiently when there is [50-69] degrees of decentralization. At point A, resource costs are low but as decentralization advances, resource costs increase at an increasing rate (see point B). This shows that breaking up the already existing administrative units is costly but decentralization pays off once the units are created. See that when we move from point B to C, we observe declining trends in resource costs; in fact, point C represents the minimum for administrative resource costs. Although the preference cost minimum is reached at point B, the total cost curve shows that point C is the most preferred level of decentralization for financing administrative operations for Korea.

The results from the longitudinal analysis in Korea give a better understanding of the optimal ranges of decentralization for the road sector. We find that the position attained by Germany and the USA in the analysis in section 5, is corroborated in the case of Korea which has had switches in its degree of fiscal decentralization in the road sector.

# 7. Case of **Germany**

Germany is the second detailed case study for the measure of the impact of decentralization on road performance. Germany has been selected for two reasons: First, it has a highly decentralized government structure with state and local governments bearing full autonomy over road expenditures. Second, the combination of a horizontal and vertical financing structure raise some interesting issues with regard to the performance of state and local governments in the infrastructure sector. Table 3 gives a 25
# **3 Performance of the Egyptian Exchange**

Like other markets around the world, Egypt’s stock market has suffered from the current financial crisis. In only five months, the CASE 30 Index fell more than 50 percent to below 4,000 points from its all-time high point of 11,936 on May 5<sup>th</sup> , 2008. The analysis however will focus on the period before the crisis.<sup>2</sup>

## **3.1 Market Capitalization**

- _3.1.1 Market capitalization: Egypt experienced extraordinary market capitalization growth, fueled by strong price increases_

As the CASE 30 Index exhibited exceptional growth since early 2002 until May 2008 (by a factor 24), so did stock market capitalization (albeit with a 2006 correction). Exhibit 3.1 shows that with an average yearly growth rate of around 27 percent, Egypt’s capitalization as a fraction of GDP reached 107 percent in 2007, leaving other countries with similar income levels such as Philippines, Colombia, and Peru behind.

## **Exhibit 3.1 – Stock market capitalization**

### _% of GDP for selected emerging markets, 2003-07 (left graph)_

_% of GDP for Egypt actual versus benchmarked values, 1990-2007 (right graph)_ <!-- Start of picture text --> 150 Egypt Argentina Chile Colombia Hungary Israel Jordan Mexico Morocco Pakistan Peru Philippines Turkey<br>130<br>110<br>90<br>70<br>50<br>30<br>10<br>-10 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007 2003 2004 2005 2006 2007<br><!-- End of picture text --> <!-- Start of picture text --> %<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br><!-- End of picture text --> _Source: EGX, S&P, World Federation of Exchanges, World Development Indicators; World Bank staff analysis Note for right graph: Benchmarked values based on a worldwide regression model that takes into account GDP per capita, population size and density, value of fuel exports to GDP, the poverty gap, and whether the country is an offshore financial center. The confidence interval is plus and minus the standard error of the prediction._ Statistical analysis of worldwide data show there is a relationship between market capitalization, GDP per capita, population size and density, a country’s status as a fuel producer, and whether the country is an offshore financial center. Indeed, Exhibit 3.1 shows benchmarking Egypt against these findings, market capitalization surged after 2002 well above expected levels. The main drivers of this stock market boom seem to be stock market regulatory reform, strong economic growth, and growing net portfolio equity inflows that reached $1.8 billion during the market’s stellar performance of 2005, the year that Egypt joined the World Federation of

> 2 th To the extent that we analyze 2008 data, we included all available information up to and including June 20 .

21
||Source: World Development Indicators,<br>World Bank.|
|---|---|
|Region fixed effects|A set of dummy variables indicating the<br>region of the country. There are 6 dummy<br>variables one for each world region. The<br>regions are: Sub-Saharan Africa (SSA), Latin<br>America and Caribbean (LAC), Eastern<br>Europe and Central Asia (ECA), Middle East<br>and North Africa (MENA), East Asia and the<br>Pacific (EAP), and South Asia (SAR). These<br>regional groupings are based on World<br>Bank’s classification.<br>Source: Enterprise Surveys<br>(www.enterprisesurveys.org)and World<br>Development Indicators, World Bank.|
|City-size fixed effects|<br>Dummy variables indicating the size of the<br>city (sampling region) where the firm is<br>located. Based on total population, cities are<br>grouped into the following size categories:<br>more than 1 million population, between 1<br>million and 250,000 population, between<br>50,000 and 250,000 population, and less than<br>50,000 population.<br>Source: Enterprise Surveys.|
||wwwenterprisesurveysorg|
|Firm Purchased Fixed Assets Y:1 N:0|..<br>Dummy variable equal to 1 if the firm<br>purchased fixed assets during the last fiscal<br>year (prior the year ES was conducted) and 0<br>otherwise.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Foreign Ownership (proportion)|Proportion of the firm that is owned by<br>foreign individuals, companies or<br>organizations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Time Tax (Country-level cell average)|Average value of the percentage of firms’<br>senior management’s time that is spent in<br>dealing with business regulations, where the<br>average is taken over all firms in the country<br>other than the firm in question.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Gross Secondary Enrollment Rate|Gross enrollment rate in secondary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross secondary|

59
Hewitt, K., 2005. The Karakoram Anomaly? Glacier Expansion and the’Elevation Effect,’Karakoram Himalaya. Mountain Research and Development 25: 332-340.

Hewitt, K., 2011. Glacier Change, Concentration, and Elevation Effects in the Karakoram Himalaya, Upper Indus Basin. Mountain Research and Development 31: 188-200.

International Center for Integrated Mountain Development (ICIMOD), 2010. Managing Flash Flood Risk in the Himalayas. Information sheet # 1/10. Available at: <u>http://www.preventionweb.net/files/13252_icimodmanagingflshfloodriskinthehim.pdf.</u> Accessed 12 June 2011.

ICIMOD, 2011. Glacial lakes and glacial lake outburst floods in Nepal. Kathmandu.

International Center for Integrated Mountain Development. ISBN 978-92-9115-194-3. Immerzeel, W., 2008. Historical Trends and Future Predictions of Climate Variability in the Brahmaputra Basin. International Journal of Climatology 28: 243-254.

Immerzeel, W.W., P. Droogers, S.M. De Jong, and M.F.P. Bierkens, 2009. Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing. Remote Sensing of Environment 113: 40-49.

Immerzeel, W.W., L.P.H. Van Beek, and M.F.P. Bierkens, 2010. Climate Change Will Affect the Asian Water Towers. Science 328:1382-1385.

Immerzeel, W.W., L.P. van Beek, M. Konz, A.B. Shrestha, and M.F. Bierkens, 2011.

Hydrological response to climate change in a glacierized catchment in the Himalayas. Climatic Change. doi: 10.1007/s10584-011-0143-4.

Indian Network for Climate Change Assessment (INCCA), 2010. Climate Change and India: A 4x4 Assessment. INCCA, Ministry of Environment & Forests, Government of India. p. 34. Inman, M., 2010. Settling the science on Himalayan glaciers. Nature Reports Climate Change. doi:10.1038/climate.2010.19.

International Rice Research Institute (IRRI), 2011. Coping with Climate Change. Available at: <u>http://beta.irri.org/news/images/stories/ricetoday/63/feature_coping%20with%20climate% 20change.pdf. Accessed 14 May 2011.</u> Intergovernmental Panel on Climate Change (IPCC), 2007. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. In ML Parry, OF Canziani, JP Palutikof, PJ van der Linden and CE Hanson (Eds). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

35
than on learning,<sup>130</sup> retention rates and the quality of education imparted is a serious flaw and it is quality that has been a source of GDP growth in the past and it is the quality and depth of skills that will determine whether East Africa benefits from the latest round of technological advances. E-learning could help improve literacy and numeracy but improving the quality and motivation of teachers, an upgrading of the curriculum with an eye to evolving employment opportunities, and the instilling of analytic as well as soft skills, may deserve priority. The State of Education Report for Africa 2015<sup>131</sup> concluded. “More students than ever before in history are enrolled in schools throughout Africa. That’s good reason to cheer, but the pipeline of trained teachers, instructional materials, and infrastructure development have not kept pace with the heavy demand. Rising enrollment rates have drastically outpaced an increase in education funding, resulting in shortages of instructional materials and supplies, poorly stocked libraries and overuse of school facilities. Indeed, while more students are in school classrooms, there is a deeper learning crisis at play: many students are not gaining basic skills while attending school. In fact, some students in school are not much better off than those who missed school. Consequently, the quality of education in Africa is in a perilous state.” A lack of vocational skills means that many jobs remain unfilled. The situation with higher education is equally dismal. Although only between 4 and 8 percent of the relevant age cohort is enrolled in tertiary education (Malawi is at the lower end, Rwanda is close to 8 percent),<sup>132</sup> high demand and few places has resulted in overcrowding of lectures, declining quality of instruction and insufficient emphasis on STEM subjects.

Figure 2: The relationship between GNI and the Human Capital Index 2016 for low and middle-income countries

> 130 As the WDR (2017) has noted with an abundance of supporting facts, years of schooling are uncorrelated with learning. Primary and secondary schooling all too frequently does not impart - functional literacy and numeracy. <u>http://www.worldbank.org/en/news/press release/2017/09/26/world-bank-warns-of-learning-crisis-in-global-education</u>

> 131 <u>http://www.aaionline.org/wp-content/uploads/2015/09/AAI-SOE-report-2015-final.pdf</u>

> 132 <u>http://wdi.worldbank.org/table/2.8</u>

39
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nature and does not investigate changes due to the pandemic, but rather changes occurring during the pandemic. We extend this conversation by estimating causal relationships about coping with the pandemic through livelihood diversification, using both pre- and post-outbreak data.

Finally, our paper contributes to the small but growing body of research that uses pre-analysis plans in observational studies. While pre-analysis plans are generally associated with studies relying on experimental data (Duflo et al., 2020), the first use of a pre-analysis plan in economics was an observational study of the impacts of minimum wage laws (Neumark, 2001). The main argument against using pre-analysis plans in observational studies is the difficulty in credibly committing to a plan prior to data availability (Olken, 2015). But, as Janzen and Michler (2021) argue, there are numerous study settings where research questions can be clearly formulated ahead of the release of data. Democratic elections (Humphreys et al., 2013), policy changes (Neumark, 2001), and the timed release of government data (Chang et al., 2021) are all examples in which researchers combine pre-analysis plans with observational data. In our case, in the month immediately following the outbreak of COVID-19, the World Bank formulated a plan to collect at least 12 rounds of monthly panel data from households that had been surveyed in the year prior to the pandemic. This commitment to future data collection, following a standardized survey instrument, allowed us to formulate hypotheses, develop an empirical approach, and register our plan prior to the collection and public release of all rounds of data (Furbush et al., 2021). In a research setting in which there are numerous ways one could define the variables of interest and model their relationships, a pre-analysis plan lends credibility to our analysis.

# **2 Data**

Our analysis focuses on changes to food insecurity after the outbreak of COVID-19 relative to food insecurity status pre-COVID. The spread of the virus impacted household finances indirectly, largely through the closure of businesses and schools and the interruption of supply chains. Governments in the three countries imposed various restrictions to movement, business interactions, and on educational institutions throughout the course of the pandemic. While these restrictions sought to slow the spread of the virus and protect citizens from infection, they disrupted normal activities, including household income generation.

## **2.1 COVID-19 Shock**

We describe the circumstances in each country, based on government restrictions that were in place during each data round. In Ethiopia, restrictions were largely implemented at the nationallevel. Ethiopia closed schools and suspended public gatherings on 16 March 2020. On 8 April 2020, the country declared a state of emergency which included limiting international and domestic travel. However, Ethiopia never went into a complete national lockdown in the sense of closing 4
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