rafmacalaba/data-use-mentions-extended huggingface dataset:
integration. Nemoto and Goto (2004) estimate that the cost efficiency gain from vertical integration in the case of 9 Japanese utilities varies from 0.2% to about 3%. Jara-Diaz et al (2004) based on the sample of Spanish electricity utilities conclude that about 7% of costs can be saved from joint generation and distribution. Despite providing useful insights, these studies are not really relevant to developing countries, however, because of the large differences in economic conditions between advanced and developing countries.

**_Two recent studies expand the available empirical analysis of vertical unbundling_** .  Arocena et al (2009) studied the economies of integration (economies of scale and of scope) for a group of 116 investor owned power utilities in the United States based on data for the year 2001.  The results provided a reference for the expected cost of unbundling.  They concluded that vertical integration provided a substantial and significant cost saving relative to vertical unbundling, and also that horizontal integration in the generation sector provided a further – but smaller – cost saving relative to horizontal unbundling.  Taken together, global savings from integration amount to as much as 12.5% of costs for the sample average firm.  This level indicates a level of anticipated gains needed to justify unbundling.  Meyer (2010) applied a frontier benchmarking approach to measure the economies of scope between the vertical stages of electricity supply in the U.S. electric industry.  The study concluded that the costs of separating the generation stage from networks and retail stages compared to integration is strongly related to the size of the utility.  For the larger utilities in the sample, this type of separation lowers their overall costs because the gains exceed synergy losses, whereas for the smaller utilities this type of separation increases their overall costs.  The threshold size that divides utilities whose costs decrease from those that increase is about 10,000 to 12,000 GWh of annual generation and distribution.

**_There is also some published evidence about the links between integrated power systems and country-level
informed), followed by Soghd (60 percent) and

> 12 Albania, Armenia, Azerbaijan, Belarus, Bosnia & Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Former Yugoslav Republic of Macedonia, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, and Uzbekistan.

> 13 _Europeans’ attitude towards climate change_ Special Eurobarometer 300, Wave 69.2  September 2008. The survey was requested by the European Commission and the European Parliament, and coordinated by the Directorate General for Communication of the European Commission.

8
tends to grow with progress in structural reforms and transition to competitive markets. The absence of efficient courts suppresses investments and business transactions, as it limits investor confidence in the ability of the courts to enforce contracts, in ownership and shareholders rights (see below) and intellectual property rights.

Closely linked to the contract enforcement, the protection of investor rights is the last regulatory area that is well below international standards. The index captures seven different aspects of disclosure of financial information and ownership protecting investors and shareholders against fraud. The absence of regulations compelling disclosure has several negative consequences: investment are lower as potential investors fear of expropriation; the stock market is undercapitalized; economic growth is lower than under full disclosure;<sup>13</sup> and returns from investment are lower (WB 2005a, p. 56-57). With the value of the disclosure index of 2 with its value ranging between 0 and 7, Bulgaria together with Romania was at the very bottom of CEEC-11. For other countries the values of this index ranged between 4 (Croatia, Estonia, Poland and Slovenia), 5 (Hungary and Latvia) and 6 (Czech Republic, Lithuania and Slovakia). Bulgaria also lags behind some SEE-countries, for instance, the index for Macedonia was 4.

There are also other factors compounding the overall hassle cost of doing business. Foreign investors often complain about government bureaucracy, poor infrastructure and frequent changes in the legal framework. This view seems to be shared by local entrepreneurs. FIAS survey points to firms’ complaints about dealings with tax administration, discrepancies in legal interpretations of procedures across municipalities and fear by enterprises of disputing decisions taken by the administration (FIAS 2004). As for complaints about tax administration, uncertainty over tax rates and rules outweigh other issues, with 65 percent of firms surveyed identifying it as a major burden (Figure 6). Needless to add that lack of stability in tax rates and rules make business planning extremely difficult. So do other deficiencies in dealings with the tax administration identified
market structure and institutions**

## **_Stylized Facts_**

Over the past two decades, evidence has mounted suggesting sizable heterogeneity of firms across different interrelated dimensions, size, growth, market shares, life cycle etc. In particular, some regularities have been found in the growing empirical literature, including (see e.g. Sutton, 1997; Pakes and Ericson, 1998, Geroski, 1995 for surveys):<sup>3</sup> _1. Size and growth:_ The probability of survival tends to increase with firm (or plant) size; but, conditional on survival, the proportional rate of growth of a firm is decreasing in size (see Evans 1987a, 1987b; Dunne et al. 1988, 1989).

_2. The firm life cycle:_ For any given size of firm, the proportional rate of growth is smaller the older the firm, but its survival probability is greater (see Foster _et al._ 2001; and the survey of post entry performance of firms in the _International Journal of Industrial Organization, 1995_ ).

3. _Shakeouts:_ The number of producers in a given market tends first to rise to a peak, and later to fall to some lower level. Entry rates tend to be higher for more recent industries but tend to decline as the industry matures (Klepper and Graddy, 1990; Klepper and Simons, 1993; Geroski, 1995).

3. Amongst others, see Aghion and Howitt (1992) and Caballero and Hammour (1994, 1996). Foster, Haltiwanger and Krizan (2001), Caves (1998) and Bartelsman and Doms (2000) offer further discussion of this literature.

3
<!-- Start of picture text --> 1400 500<br>All<br>1200<br>South Sudan 1500<br>400<br>1000<br>800<br>South Sudanese 1000 300<br>Civil War<br>600<br>200<br>400 500<br>200<br>100<br>0 0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2014 2016 2018 2020<br>Year Year Year<br>Total refugee aid (millions)<br>Number of refugees (thousands)<br>Newspaper articles about refugees<br><!-- End of picture text --> Figure 1: : Impact of the South Sudanese civil war on the number of refugees in Uganda (left), the salience of the refugee issue in Uganda (center), and refugee aid levels from international organizations (right). The red line indicates the onset of the war in December 2013. Salience of the refugee issue is measured as the number of local newspaper articles that mention refugees or migration. Data sources: UNHCR, Lexis-Nexis, Factiva.

from other East African countries, mainly the DRC (27 percent) and to a lesser extent Burundi, Somalia, Rwanda, Eritrea, and Ethiopia (World Bank, 2019).

Uganda’s liberal refugee and asylum policies—as codified in its 2006 National Refugees Act— partly explain why it attracts so many refugees (Blair, Grossman and Weinstein, 2021 _b_ ). Based on data collected on refugee and asylum policies in a large sample of 128 developing countries, Blair, Grossman and Weinstein (2021 _a_ ) ranked Uganda as having the second-most liberal refugee and asylum policies in their sample. It is also a party to the 1951 Convention Relating to the Status of Refugees and its 1977 protocol, and a signatory of the 1969 Organization of African Unity Convention Governing the Specific Aspects of Refugee Problems in Africa.

# **Policy
their own money to buy housing. They were qite prepared to pay to move into larger apartments or to join cooperatives, and this created high demand _for_ cooperative housing. Recent studies show that as soon as the oppohrunity to buy and sell housing appeared, many households began to display the intention of solving their housing problems through market methods.

## **2.2.2 Factors influencing household attitudes** to housing market **strategies**

The following factors were taken into account in the analysis of households' prospects of dealing with the housing problem: how pressing the problem is (i.e., the level of the household's provision with housing); the income level; and the demographic structure of the household.

# **a.** **_Level of provision with housing_**

Sociological surveys commnonly show that the main reason why households do not like their living conditions is the small size of their apartments. The urgency of the housing problem strongly influences households' plans. For instance, in the quintile with the lowest general space per person the share of households that have plans to improve their housing conditions in the near future reaches 48.2%/ in Moscow, 41% in Vladimidr and 3 1.7% in Gorodets (Fig. _2.5)._
proposed options include the use of average yield data from comparable districts unaffected by any shocks or yield forecasts based on meteorological and remote-sensed data (FAO, 2020).

As the accuracy of different approaches for micro-level measurement of the attainable yield through surveys likely varies, their empirical assessment constitutes the first goal of this paper. We call this the “quantification of losses”. Concretely, the paper explores a number of surveybased ways to measure or proxy for the attainable harvest using plot-level data<sup>17</sup> including

- (i) explicitly measuring planted area losses and plant productivity losses based on farmers’ self-reports;

- (ii) expert assessments of the share of the attainable harvest that was lost;

- (iii) obtaining attainable yields from past data or comparable but disaster-unaffected plots;

- (iv) using farmers’ post-planting harvest expectations to proxy for the attainable yield.

Importantly, these approaches need not be mutually exclusive, and the paper will aim to highlight the respective strengths and weaknesses of each approach in an aim to combine them for best results.

In addition to the _quantification_ of losses, the second crucial focus of this paper is the _attribution_ of losses to specific disasters. One possible approach relies on self-reported shock identification, either from the farmer, a community congregation, or an ‘expert’. Here, the paper analyzes the consistency of shock identification on plots of the same crop in close proximity and thus assesses the suitability of self-reports to confidently attribute losses to disasters. Alternatively, the paper compares self-reported drought exposure to geospatial rainfall data to obtain a similar “objective measure” as in the agricultural index insurance literature. Through this, it aims to disentangle variation in self-reports due to different shock exposure from variation due to differences in farmers’ perceptions. Notably again, the paper also explores the scope for integrated approaches that combine self-reported and sensor data to harness the strength of each.

For our analysis, we draw on a diverse set of data sources. Our main data source is rich plot-level
14 Next, we present the model’s theoretical underpinnings. In a first version, following Maddala and Nelson (1974), we assume that both residuals ε1,t and ε2,t are stationary processes, independently and normally distributed with variance σ<sup>2</sup> 1<sup>and σ2</sup> 2<sup>respectively.</sup> Under these regularity assumptions, the transformed variable ε1,t - ε2,t is normally distributed with a variance equal to σ<sup>2</sup> = σ<sup>2</sup> 1<sup>+σ2</sup> 2<sup>.</sup> Hence, the reduced variable (ε1,t - ε2,t)/σ follows a _N(0,1)_ . The probability that the observation _qt_ belongs to the demand regime, denoted πt(d), can then be computed as the corresponding _N(0,1)_ fractile: _' '_ where _ht_ =  _x_ 2, _t_  2  _x_ 1, _t_  1  /  , and  _x_ denotes the cumulative distribution function of the (s) _N(0,1)_ . Symmetrically, the probability of obtaining the supply regime, denoted πt , is defined by _P_  _St_ < _Dt_  = 1   _ht_ 8

# **3.3 Data**

The quarterly dataset from 1998 to 2011 was compiled from different sources. Data regarding credit to private sector came from the banking survey published in CBE’s monthly report. Total deposits, banks’ reserves with the CBE (that were used to construct the lending capacity) and bank lending rates also come from the same source. Industrial production comes from the Central Agency for Public Mobilization and Statistics (CAPMAS) deflated by the CPI; and the stock market index ( _EGX30_ ) as reported by the Egyptian Stock Market. EGX 30
gap to urban areas increasing but the gap between richer and poorer segments of the population in the rural areas is also growing. Finally, the gap between rich and poor regions is still large, such as between the North and South regions.

This paper attempts to analyze rural monetary poverty. Two definitions of rural areas are used: _first,_ localities with more than 2,500 and less than15,000 inhabitants refers to semi-urban areas; and, _second_ , dispersed rural areas refers to localities with less than 2,500 in habitants (the official definition of rural in Mexico). The analyses are based on national household surveys ( _Encuesta nacional de Ingresos y Gastos de los Hogares-_ ENIGH) from 1992 to 2002.

The paper is organized in 6 sections. Section 2 presents demographics and their changes during 1970-2000 in terms of rural and urban population size. Furthermore, it shows the recent growth pattern for Mexico and its regions. Section 3 analyzes rural poverty and its depth during 1992-2002 and Section 4 shows the rural Mexican poverty profile. Section 5 presents analyses of determinants of poverty, important factors to

> 2 The _Secretaria de Desarrollo Social_ (SEDESOL) uses three poverty lines: a “food-based” poverty line (income required to acquire enough food to cover nutritional needs); a “human needs” poverty line which includes also the income required to acquire basic education, health, housing, dress, footwear, and transportation; and an “assets-based” poverty line, which also includes other needs. The latter corresponds to the usual broad definition of “poverty”, which we call “moderate poverty’, while the former corresponds to the usual definition of “extreme poverty”.

1
one_ , 13(1):e0190902.

- Maffioli, E. M. (2019). Relying solely on mobile phone technology: Sampling and gathering survey data in challenging settings.

- Mahfoud, Z., Ghandour, L., Ghandour, B., Mokdad, A. H., and Sibai, A. M. (2015). Cell phone and face-toface interview responses in population-based surveys: how do they compare? _Field methods_ , 27(1):39–54.

- Morse, B., Gr ́epin, K. A., Blair, R. A., and Tsai, L. (2016). Patterns of demand for non-ebola health services during and after the ebola outbreak: panel survey evidence from monrovia, liberia. _BMJ global health_ , 1(1):e000007.

-  ̈Ozler, B., C ̧ elik, C ̧ ., Cunningham, S., Cuevas, P. F., and Parisotto, L. (2021). Children on the move: Progressive redistribution of humanitarian cash transfers among refugees. _Journal of Development Economics_ , 153:102733.

27
Out of School and Out of Work: A Diagnostic of _Ninis_ in Latin America<sup>1</sup> _Rafael de Hoyos, Anna Popova, and Halsey Rogers_ The World Bank JEL classification: J13, O15, R23 Keywords: Latin America, Ninis, dropouts, youth unemployment

> 1 The present study is a background paper for the regional study “Out of school and out of work: Risk and opportunities for Latin America’s ninis.” (De Hoyos, Rogers, and Székely 2016). Earlier versions of this paper benefited substantially from comments and suggestions by Julian Messina, Reema Nayar, Kathleen Beegle, Michael Crawford, Harry Patrinos, Wendy Cunningham and participants of two workshops held at the World Bank in Washington DC in October 2013 and March 2014. The authors are especially indebted to Augusto de la Torre and Daniel Lederman for suggesting the introduction of global comparisons and overall guidance. We thank Israel Osorio for granting us access to the GIDD data and to Andrés Pérez for providing able research assistance.
**Figure 2. GDP growth versus service value added growth, 2000-07.**

<!-- Start of picture text --> 0 5 10<br>Service output growth<br>Note: black - all countries, red - EU-15, blue - NMS, green - Accession<br>6<br>4<br>2<br>0<br>-2<br>-4<br><!-- End of picture text --> _Source_ : World Development Indicators, World Bank, 2010 and authors’ calculations.

To better understand the link between services and growth in Europe we will look closer into the following questions: (1) Is there a growing importance of services in growth? (2) Is productivity of services increasing? (3) Is there an increasing tradability of services? (4) Is Europe‘s service exports evolving from traditional to modern? (5) Does Europe have specialization in service exports? (6) What are the trends in terms of direction of Europe‘s service exports?

# **2.1 The growing importance of services for general growth**

The sectoral contribution of services to GDP growth has risen for most countries in the world and the services share in global production has increased considerably. Looking specifically at EU-15, we see that it has a very high level of service domination in recent growth (82 percent), similar to USA and other advanced countries (see Figure 3, and Figures A7-9 for specific countries). As shown earlier, growth in services is somewhat lower than expected, even when taking the relatively low GDP growth in EU-15 into account, but the share of services in GDP is so significant that it is still the dominating sector for overall growth. Only with the exception of Finland and Germany, growth in EU-15 is increasingly service dominated when comparing the two periods, 1995-99 and 2000-08. Expectedly, growth in NMS and ACC is less service driven but again
# **IV. Further Considerations**

In this section, we provide further sensitivity checks, and then investigate how the effects of the business environment differ in several key dimensions. We want to understand whether African firms have a distinct conditional performance advantage depending on their industries and technologies.

# **Manufacturing versus services**

So far we have focused on how African manufacturing firms fare relative to those in other regions. However, do the results differ if we look at services? Different sectors may rely to a greater degree on the political and business environment. Since WBES has no good capital data for services, we cannot estimate TFP for services. We thus focus on comparing the other five measures. The results, in Table 8, indicate significant sector-level differences, and suggest that most of the business environment variables have stronger effects in manufacturing than in services.

Perhaps the most interesting finding is that the conditional Africa advantage is large in manufacturing but non-existent in services. Similarly, party monopoly significantly reduces growth in manufacturing but not in services. Since in developing countries manufacturing tends to out-perform services in productivity (McMillan and Rodrik 2011), one possible implication is that party monopoly might shift a country’s sector structure to less productive sectors, and lower a country’s overall allocation efficiency. Interestingly, foreign ownership has no positive spillovers on exports in services but has significant spillovers in manufacturing. The strong positive effects of ethnic fractionalization in manufacturing is much more muted in services.

28
all education financing was captured in this paper and the accompanying dataset, ‘education identifiers’<sup>3</sup> were used to detect potential misallocations. Each potential misallocation was then reviewed in detail, with only financing allocated entirely for education purposes categorized as 'education' financing.<sup>4</sup> _situations_ in LICs and MICs. It thus provides estimates of DAC members’ and non-DAC providers’ ODA contributions to both refugees and host communities in the refugee-hosting recipient countries. As such the ODA recorded in this dataset is likely to inflate financing for refugee

> 1 For countries where data on refugee populations by age were not available, it was assumed that the number of primary and secondary school-age refugees in these countries were equal to 19 percent and 13 percent, respectively, of the total refugee populations of these countries, in line with the averages in all other LICs and MICs.

> 2 countries), 6 multilateral development banks and 14 UN agencies (core contributions) for the financial year 2020-21. Recipients of official assistance are recorded as the first implementing partner only to avoid duplication.

> 3 OECD dataset for the key words ‘education’, ‘school’, ‘teach’, and ‘classroom’. If any of these key words were identified, then each allocation’s descriptives were reviewed to determine whether financing was for refugee education, with only financing that was allocated entirely for education purposes included as such. Categories of spending captured include direct expenditure on education provision, such as teacher salaries and textbooks, as well as indirect sources of financing for education, such as School Feeding Programmes and Cash Transfers for Education.

> 4 ODA for refugee education analyzed in this paper with no disaggregation by education level possible.

5
also employ a first difference specification in equation (2) relying on the panel dimension of the data. The dependent variable is a first-difference in dummy variable indicators for being employed between 2018 and 2012, while the independent variable of interest thus becomes the firstdifference in dummy variable indicators for using the Internet for job search between the years 2018 and 2012 controlling for the change in home Internet access. The regression also includes the same set of pre-determined controls in equation (1), first differenced between 2018 and 2012. Relying on this first-difference specification is equivalent to the inclusion of individual fixed effects, thus differences out all time-invariant unobservable characteristics. The main estimated coefficient of interest 1 captures the effect of the change in the use of Internet for job search on the change in the probability of being employed between the two years. A positive coefficient αα would therefore mean that an increase in Internet job search is associated with an increase in the probability of employment.

ΔE E E E 1 2 (2) ii ii ii ii = αα ΔIIUUIIEEIIUUEEII jjEEjj ssEEssIIssh + αα ∆XX + νν Finally, I estimate a fixed effects model relying on equation (3). The dependent variable in equation (3) is a dummy variable indicator equal to one if the individual is employed in the corresponding year relying on the market definition of labor force participation. The main independent variable of interest is a dummy variable indicator equal to one if the individual reports using the Internet for job search in the corresponding year. The regressions also include i , which is a vector of time-varying control variables and it includes age, a dummy variable for being XX 9