rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.39|0.42|0.54|0.54|0.54|0.58|
|Household Characteristics||X|X|X|X|X|X|X|X|
|Urban/Rural|||X|||||||
|Survey Blocks||||X|||||X|
|Food Products|||||X|||||
|COICOP 2-dig||||||X||||
|COICOP 3-dig|||||||X|||
|COICOP 4-dig||||||||X|X|

This table shows the average slope of the Informal Engel curve across countries for different specifications under the probabilsitic assignment of places of purchase to formality. The Probabilistic scenario uses the observed probability of VAT registration by store type in the Mexican census as the formality probability of each store across countries (see Figure 1). The slopes are estimated from: _Share In f ormali_ = _β_ . _ln_ ( _expenditure pc_ ) _i_ + Γ _Xi_ + _εi_ . The dependent variable is informal expenditure share and the main explanatory variable is log expenditure per capita. Controls include household characteristics (household size, age, gender, education of head), location indicators (urban/rural, survey enumeration blocks), and product codes for food vs all other purchases, 2nd, 3rd and 4th level of the COICOP classification.

68
# **_The Impact of Reform_**

The elimination of barriers to entry in services provoked a dramatic response from foreign and domestic providers (Gordon and Gupta, 2004). FDI inflows into services following liberalization by far exceeded those into other sectors. Ten percent of FDI inflows during 1990-2005 went into the transport sector, 9.6 percent of the inflows were into the telecommunications sector, and 9.6 percent of the inflows were into the financial and other services sector (Ministry of Commerce and Industry, 2008). At the same time, the services sector grew by an average of 11 percent per year, with the more liberalized sectors generally growing at relatively faster rates (Chart 1, and Eichengreen and Gupta, 2010). The share of services in overall value added rose from 39 percent in 1993 to 50 percent in 2004 (National Accounts Statistics, 2005, constant 1993 Rs).

Growth has been particularly strong in the services sectors on which we focus in this paper: communication services displayed average annual growth rates of 13.6 percent in the 1990s, while banking grew by 12.7 percent on average, transport grew at an average rate of 6.9 percent and insurance grew at a rate of 6.7 percent (Gordon and Gupta, 2004). Output per worker in the services sectors in India has increased by over 7.5 percent per year during the 1990s, clearly outpacing the agricultural or industrial sectors (Bosworth and Collins, 2008, p.56). Other evidence suggests that strong total factor productivity growth was at the root of this remarkable performance, not capital deepening or higher markups (Bosworth, Collins, and Virmani, 2006; Gordon and Gupta, 2004). Indeed, services prices decreased relative to manufacturing prices, as indicated by a slower pace of growth in the services deflator than the overall GDP deflator.

The reforms produced striking improvements in sectoral performance. In 1990, the average turn-around time for a container at major ports in India was 8 days
In this paper, we investigate how increased market access stemming from road network improvement in Sub-Sub-Saharan Africa increases the amount of cultivated land over a long period (1970-2005). This period covers a significant improvement in the nascent road network in the region as well as important demographic spatial changes associated with population increases and urbanization. Although agricultural impacts can occur at both the extensive and the intensive margins, due to data constraints, we focus on the former only (cropland area). In the context of Sub-Saharan Africa, this approach is reasonable given stagnating agricultural yields and the patterns of extensive rather than intensive margin growth (Deininger et al., 2011, Rakotoarisoa et al., 2011). To explore the relationship between roads and cropland area, we bring together four geo-referenced panel datasets on roads, cropland, ‘local GDP’ and urban population, all defined at a small geographic level.

The closest papers in the literature are Jedwab and Storeygard (2016) and Blankespoor et al. (2016), which similarly study the impact of market access using the same panel road data. Jedwab and Storeygard (2016) estimate the impact of a change in market access on urbanization in 39 countries. Blankespoor et al. (2016) focus on the specific case of Mali to investigate the impact of changes in market access on the dynamics of population and sectors of employment. In the present paper, we focus on the impact on agricultural land use for the whole of Sub-Saharan Africa.

The remainder of this paper is organised as follows. In Section 2, we present our empirical approach, introducing our measure of market access and presenting our identification strategy. This is then followed by a section in which we describe our data sources and construction of 4
Policy Research Working Paper 10334

# **Abstract**

Analyzing data from four waves of the Nigerian General Household Survey and the Nigerian Multiple Indicator Cluster Survey, covering the period from 2010 through 2019, this study provides evidence that poverty levels of children exceed those of adults. Overall, rural children throughout the country and children in the North face higher poverty and chronic poverty rates than urban children and those living in the South without clear trends of a closing of those gaps. These findings hold for monetary poverty as well as, for severe health, education, food, shelter, water, information deprivation and improved sanitation deprivation across Nigeria’s six regions. One exception is severe sanitation deprivation, for which especially rural areas in the Southwest stand out with higher levels of severe sanitation deprivation than in rural areas in the north and any other region. Large inter-state heterogeneity of estimates within regions, ranging up to 50 percentage points, for all except severe food deprivation however highlight the importance of looking beyond regional poverty estimates and regional differences. Only state specific, but no systematic evidence has been found for a gender difference in severe educational deprivation and school enrollment rates. Existing gender gaps though seem negligible compared to the overall level of deprivation and urban-rural and northsouth gaps. Moreover, the parents’ literacy and more so the educational level is highly correlated with the probability of being poor or deprived in any dimension, in particular in rural and northern areas. Interestingly, up to about half of the monetary non-poor children at the top of the consumption distribution still face at least one severe deprivation.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at at mazad@worldbank.org and cschimanski@worldbank.
the presence of restrictions and that NPIs may often function more like a coordinating device among increasingly predisposed individuals than repressive measures per se. We also confirm a more direct link of this voluntary effect using data on restaurant reservations in the United States and movie releases and revenues in Sweden and show that these, too, experience most of their fall before any imposition of NPIs. Overall, the

> 4 There are three margins upon which societies can work to reduce the death toll.  1.  Detect and quarantine so the disease never gets a foothold.  2.  Once established, reduce social mobility to mitigate the spread (reduce the R factor).  3. Increase the capability to treat the sick. On the third, Favero (2020) notes that limitations on ICU beds led to the extremely high death rate in Lombardy.  In practice, developing countries have far less capability to treat- 10 African countries have no respirators.https://www.nytimes.com/2020/04/18/world/africa/africa-coronavirus- <u>ventilators.html?referringSource=articleShare</u> If northern Italy could not ramp up sufficiently enough along this dimension, it is highly unlikely that most poor countries can. On the first, many advanced countries have missed the window to detect and quarantine and again, this may be more challenging in the developing world.

> 5 See Chen and Qiu (2020), Gonzalez-Eira and Niepelt (2020) for conceptual treatments of optimal shutdown policies. Hartl et al (2020) find for Germany that growth rates of Covid-19 cases fell 50% as a result of German restrictions to shut down schools, stadiums and eventually many restaurants and shops. See Baldwin and Weder de Mauro (2020) for a compilation of recent thinking on Covid Economics.

> 6 <u>https://www.bloomberg.com/news/articles/2020-04-15/wuhan-s-life-after-lockdown-isn-t-business-as-usual?</u>

> 7 The NBC News-Wall Street Journal poll was conducted between April 13 and April
China's (uneven) progress against poverty.” _Journal of Development Economics_ 82(1): 1-42.

Rosenberg, M. (1965). Society and the adolescent self-image. Princeton, NJ: Princeton University Press.

<mark>Tucker, J. (2008) Women, Family, and Gender in Islamic Law. Cambridge: Cambridge University Press.</mark> U.S. Agency for International Development and CARE (2008). The Coping Strategies Index.

Veit, C. T. and J.E. Ware (1983). “The structure of psychological distress and well-being in general populations.” _Journal of Consulting and Clinical Psychology_ 51(5): 730-742.

Verme, P. (2015). “Economic development and female labor participation in the Middle East and North Africa: a test of the U-shape hypothesis.” _IZA Journal of Labor & Development_ 4(1): 1-21.

World Bank (2018). _Economic Transformation in Djibouti: Systematic Country Diagnostic_ . World Bank.

World Bank (2019). _Challenges of Inclusive Growth: A Poverty and Equity Assessment of Djibouti_ . World Bank.

World Bank East Asia and Pacific Gender Innovation Lab (2020). “Impact Evaluation 31
**Table A.2. E-commerce development and various categories of household expenditure per capita by region (Extended Model 1)**

|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Urban|Rural|East|Central|West|
|Total household expenditure|0.257***|0.194***|0.695***|0.248***|0.247*|0.538***|
||(0.067)|(0.058)|(0.103)|(0.080)|(0.126)|(0.157)|
|Observations|10,872|5,072|5,800|4,359|3,428|3,163|
|Adjusted R square|0.343|0.320|0.379|0.346|0.320|0.338|
|Cosmetics and beauty|0.404***|0.339***|0.945***|0.462***|0.289*|0.996***|
||(0.115)|(0.112)|(0.235)|(0.151)|(0.154)|(0.334)|
|Observations|10,764|5,023|5,741|4,312|3,399|3,053|
|Adjusted R square|0.344|0.367|0.326|0.330|0.361|0.361|
|Food|0.311***|
usually coverage is difficult to define, particularly in the case of broad-based PHC programes. To relate impact to inputs into<sup>an</sup> intervention usually requires consideration of the situation before and after that intervention. Country health studies could be repeated after several years but how long is an appropriate interval? A shorter period (2 - 5 years) of evaluation tends to favour<sup>those</sup> programmes which have an immediat **e** ffect (eg. immunisation, ORT etc.). Improvements in health services or sanitation may take a much longer period to reach maximum effect and over that longer period the effectiveness of the other types of interventions may decline. Can investment be delayed until long-term evaluations have been undertaken?

Ideally, meas. ement of health impact should be judged with<sup>reference</sup> to a control group, to determine whether changes observed are really due to a newly-introduced intervention or whether they would have occurred anyway eg. as a result of changing climate or improvements in agricultural extension. Where the health intervention is directed at the community, the samples for these control and intervention groups should be samples of communities and not of individuals, because individuals in a community receive the same health services/programmes, and the size of the samples is measured in numbers of communities rather than individuals. With<sup>the use of</sup> sample communities there is a serious problem of confounding variables - for example communities that have better health services are also likely to have better literacy, greater wealth, better leadership etc. In the 4 sence of randomised intervention studies very large samples would be required to overcome these confounding factors statistically.

Clearly, providing all the information required to<sup>measure</sup> consequences is a major task often beyond the efforts and time of evaluators. Instead recourse is often made to measures of intermediate output, such as the numbers of immunised children, or<sup>other</sup
regulations. In the same way, Hungary shows a similar level of low restrictions compared to Ireland and Denmark. Moreover, Figure 1 also points out that between 2006 and 2013, some countries actually moved away from the diagonal axis to a greater extent than others. This means that several countries decreased their level of services regulations such as Greece, Slovenia or Italy, while others reformed their services markets at a much lower pace such as in the case of Finland or Luxembourg which has actually increased its level or services regulations somewhat.<sup>1</sup> Naturally, this should have an effect on how these countries have overall performed in terms of productivity over time.

The link between services reforms and economy‐wide productivity effects has gained increasing attention from researchers and has been a focal point in various previous studies. Earlier papers such as Nielson and Taglioni (2003) highlight the fact that services trade liberalization and regulatory reform are likely to lead to larger gains from increasing efficiency and competitiveness rather than increased market access abroad. In another study, based on using a general equilibrium model, Robinson _et al._ (2002) come to similar conclusions in that services liberalization has a large indirect effect (next to a direct effect) on downstream sectors using services as inputs. They state that notably for developing countries TFP would rise in sectors using services as efficiency gains run through upstream to downstream inter‐ industry linkages. Triplett and Bosworth (2004) investigate this issue by focusing on the role of TFP in services sectors themselves and conclude that in particular greater productivity in the distribution sector as well as the financial services sector has helped to a significant extent the US productivity surge after 1995.

This study is closely related to the more recent works that empirically investigate the role of services reforms on downstream or economy‐wide productivity performance, in particular those that use firm‐ level data.<sup>2</sup> Several studies stand out in this regard. One is by Arnold _et al_ . (2011
|
|30|Latin America and Caribbean|VEN|Venezuela, RB|88%|yes|||
|31|South, East Asia and Pacific|VNM|Vietnam|88%|||yes (telephone sets)|
|32|Middle East and North Africa|YEM|Yemen, Rep.|100%|yes|||

Notes: The repartition is based on the principal exportation according to OEC (http://atlas.media.mit.edu/en/visualize/tree_map/hs07/export/fra/all/show/2014/ ; dataset HS07) for the last year available (2014).

51
of more than $80 trillion.

# **Summary and conclusions**

We estimate the impact of COVID-19 on student reading on standardized tests over time for EU and ECA+ countries. We model the effect of closures on achievement by predicting the deviation of the most recent results from a linear trend in reading achievement using data from all rounds. Scores declined from 2016 to 2021 by an average of 20 percent of a standard deviation, just less than a year’s worth of schooling. This validates the scenarios projected by the World Bank (Azevedo et al. 2021) early in the pandemic. Losses are significantly larger for students in schools that faced relatively longer closures. While there are no significant differences by sex, it is shown that lower-achieving students experienced much larger losses.

13
‘slum’ by the UN Habitat/Accra Metropolitan Assembly <u>(UNAMA).</u> <!-- Start of picture text --> Nairobi Land Cover c. 2003<br>Nairobi Land Use 2003<br>(semi‐automated classification)<br>(manual classfication)<br>60%<br>60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Distance from CBD (km) Distance from CBD (km)<br>Residential (Irregular)<br>Residential (Irregular)<br>Residential (Regular) Residential (Regular)<br>Commercial/Industiral Commercial/Industiral<br><!-- End of picture text -->

> 38 2005. Nairobi Land Use Memo. Download from Nairobi GIS Maps.

> 39 Engstrom et al (2015).Mapping Slums Using Spatial Features in Accra, Ghana. IEEE.

19
|---|---|
|**Countries With Fiscal Rule**|2.78%|1.01%|
|**Countries Without Fiscal Rule**|3.41%|0.81%|

Source: Davoodi et al. (2022a) and World Economic Outlook Database (April 2022 Vintage) Note(s): Sample size of 41 middle and low-income countries.

Perhaps the most notable development in fiscal rule usage recently has been the unprecedented spike in escape clause usage and fiscal rule suspension witnessed during the COVID-19 pandemic. In 2020 and 2021, 39% and 36% of low- and middle-income countries with fiscal rules, respectively, either temporarily suspended a rule or used an escape clause.<sup>13</sup> The previous peak occurred in 2010, when suspensions and escape clause usage stood at a 7% rate, as only three countries abandoned their fiscal rules (see Figure 2). With the growing importance and relevance of fiscal rules in the years leading up to the pandemic, such a sudden breakdown in fiscal discipline raises the question of how long it will take for fiscal balances to return to their pre-pandemic trend, thus allowing

> 13 See Appendix Table A1 for a list of all low- and middle-income countries that either enacted an escape clause or temporarily suspended a fiscal rule during 2020-2021.

7
2|201.7|0.575|0.619|0.663|1.474|
|2004|393.9|210.0|0.564|0.591|0.644|1.618|

Note: Incomes are monthly household incomes per capita, measured in September 2004 Reais. Source: Authors' calculations from the PNADs.

Two main features of the data jump out from Table 1. The first is the difference between mean and median income: the median-to-mean ratio ranges from 0.446 in 1989 to 0.539 in 1992. This

> 10. The Gini coefficient is only perfectly decomposable when sub-groups of the population do not overlap in the space of incomes.

> 11 While households with total incomes equal to zero are included in the distributions used to calculate mean and median incomes, as well as poverty measures, they are excluded from the calculations of inequality measures. Such households range from 0.5% to 2.0% of the sample. The Gini coefficient and E(2), which can also be computed including the zero values, are not much affected by this exclusion, as can be seen from the comparison in Appendix 3. Trends are entirely unaffected.
products, except machinery and equipment; 31 = Manufacture of electrical machinery and apparatus n.e.c.; 34 = Manufacture of motor vehicles, trailers and semi-trailers; 36 = Manufacture of furniture; manufacturing n.e.c. Source: Authors’ elaboration using data from the Enterprise Survey for Egypt and UN COMTRADE.

> 13 We focus on total imports from Egypt rather than total exports by Egypt because imports tend to be better measured.

37
Over the years NCHRD conducted systematic national assessment studies and produced and disseminated several reports. These reports were circulated heavily in the country. NCHRD also guided Jordan’s participation in international exams to supplement their efforts in the area of assessment of student learning. In recent years, NCHRD has been in charge of developing and executing a comprehensive evaluation framework for Jordan’s largest education reforms. It is based on a mixed-method approach. It uses continuous and systematic assessment of students’ performance based on national assessments and international studies incorporating the Trends in International Mathematics and Science Study (TIMSS) and the Program of International Student Assessment (PISA). It has also supplemented the national assessment program with new assessment tools that have been conducted biannually since 2008 with a focus on skills needed for the knowledge economy, known as the National Assessment for Knowledge Economy Skills (NAfKE). The approach also includes regular observations and evaluation of what is happening at schools and in the classroom. These are in addition to evaluations of different designs and experiments (such as the Jordan Education Initiative’s Discovery Schools that utilize technology-rich instruction).

Initial analysis of TIMSS 1999 (Abdul-Hamid 2001) indicated that socioeconomic and family characteristics related to education continue to have the biggest influence on student achievement. Between-schools differences in achievement were associated with school authority (public versus private, with private superior), school location (urban versus rural, with urban locations producing better results), and school climate (including teacher morale). Gender is also a significant factor in achievement to the advantage of girls. School resources and teacher qualifications were also investigated and tended to have a positive influence on achievement.

Jordan also used the international results to compare itself with the world’s best achievers. Jordan reviewed systems such as those in Japan, Singapore, Chinese Taipei, and organized study tours to Korea, Japan and Singapore. It used these benchmarking activities to guide the educational reforms within Jordan.

Following up on the analyses, the Ministry of Education, in collaboration with NCHRD
adult households. A smaller proportion of female adult-only households were less resilient at only 20 percent compared to 53 percent for male adult households, and 23 percent for both male and female adult households (Table S3). Thus, female-headed and female adultonly households are more vulnerable and less resilient. Households that used CSA, as defined in this paper, were more resilient and less vulnerable, but the differences are small.

Figure 2 presents transitions in and out of poverty over the three survey waves in 2012, 2015, and 2019. At the national level, 46 percent of smallholder farmers were poor in all three survey waves in 2012, 2015, and 2015, while 2 percent were never poor (Figure 2). The rest of the farmers transitioned from being poor to non-poor status, or vice versa, over the three survey waves. For example, 13 percent of smallholders that were not poor in 2012 fell into poverty in 2015 and 2019, and about 14 percent of smallholders that were poor in 2012 and 15