rafmacalaba/datause-encoder-data huggingface dataset:
Portugal (EDP), ENDESA, ENTERGY, Union Fenosa, Hong Kong Electric Group (HEG), Public Service Enterprise Group (PSEG) and Southern Energy Company.

||**Tab**<br>**GLOBAL**|**le 3: Regio**<br>**Africa**|**nal HHI for**<br>**Asia &**<br>**Oceania**|**Electricity D**<br>**Latin**<br>**America**|**istribution**<br>**North**<br>**America**|**Eastern**<br>**Europe**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|
|**Market indicator**<br>||N<br>|umberofcus<br>|tomers perco<br>|untry|||
|**HH min**|**1498**|663|1075|1433|-|2316|2000|
|**HH max**|**2933**|1618|2654|3169|-|3769|3455|
|**Aggregate market**<br>**share**|**31%**|16%|33%|41%|12%|38%|43%|
|**Number of firms**|**12**|2|3|6|5|3|5|
|**Number of countries**|**31**|3|3|11|1|6|7|
|**Cases**|**51**|3|4|20|5|6|13|
|**Main actors**|**AEP, AES, CLP, E.ON,**<br>**EDF, EDP, ENDESA,**<br>**ENTERGY, UF, HEG,**<br>**PSEG, Southeast**|AES,<br>EDF,<br>EDP|CLP, UF,<br>HEG|AES, EDF,<br>EDP,<br>ENDESA,<br>UF,PSEG|AEP, AES,<br>E.ON,<br>ENTERGY,<br>Southest|<br>AES,<br>E.ON,<br>UF|E.ON, EDF,<br>EDP,<br>ENDESA, UF|

The decision rule proposed suggests that globally this sector is probably subject to a market power problem. EDF, Endesa and E.ON seem to be strong leaders at the global level with respectively 38.1%, 15.2% and 14.8% served by the 12 top operators of the business. Even if HH min does not reach the 1800 threshold, it is high enough to raise some concern. This impression is strongly enhanced by the HH max which is well above the threshold and this for a fairly large sample size. All of Europe is clearly a concern since in both Eastern and Western Europe, all of the HH are above the 1800 threshold. As mentioned earlier, the liberalization of the sector has not yet gone very far. In Eastern Europe, the issue is that few countries have actually privatized their distribution and E.ON enjoys a significant share of the market by itself. Latin America and Asia & Oceania are both inconclusive but are in the range in which it may make sense to take a closer look at the situation. For Latin America, this is once more somewhat surprising. For all of its reforms, the dominating presence of Endesa, EDF and AES in several of the national markets of the region explain the high HH max.

# **3.2 Telecoms**

The market indicator is the number of lines in working conditions controlled by an operator and the information is collected from the 2003 annual report of each firm. We consider fixed and mobile technologies separately.<sup>12</sup> For each sub sector, we consider the 10

> 12 Even if an obvious question in the “real world” would be to consider that the extent to which a product market that combines fixed and mobile should not also be considered since most of the top firms are the same in the two markets.

12
# **1. Introduction**

The urbanization process is efficient when land can be assembled for urban development at no cost and both capital and labor can move freely across local jurisdictions. This well-known proposition from the literature on city formation stems from the assumption that entrepreneurial local governments can compete to attract the number of firms and workers that maximizes the local surplus. Through land zoning and incentives, they overcome the coordination failures that would arise if firms and workers gathered spontaneously across locations (Henderson 1974, Henderson and Venables 2009).

However, the necessary conditions for the efficiency of the urbanization process may not be met in developing countries. For starters, the assembly of relatively unencumbered land with clear titles can be challenging. Property rights are not always well defined, and they may involve a high degree of state ownership. Cadastral records and titling are incomplete, and regulations cumbersome.

Local governments in these countries are also less capable to maximize local surplus than their counterparts in advanced economies. In the words of Henderson and Becker (2000): “not all agglomerations have single pro-active autonomous local governments. Local autonomy may not be part of the national constitution; many local governments may not or cannot be pro-active; and not all agglomerations are governed by a single local government or by a set of township governments that coordinate well.” With the availability of urban land and the capacity of local governments being constrained, there may not be enough cities. And a few of those that emerge – capital cities in particular – may become oversized. Consistent with this distorted spatial equilibrium, a majority of the megacities of the world can by now be found in developing countries (United Nations 2018). Instead of efficiency, there tends to be excessive primacy combined with overall under-urbanization.

Not surprisingly, cities in developing countries are often described as messy, crowded and disconnected. In South Asia, the widespread existence of slums and sprawl constrains the potential of agglomeration forces to bring about faster improvements in prosperity (Ellis and Roberts 2016). In Sub-Saharan Africa, investments in infrastructure have not kept pace with the concentration of people, with cities developing as collections of small and fragmented neighborhoods that limit job opportunities and prevent reaping agglomeration benefits (Lall et al. 2017). In Latin America and the Caribbean, the productivity of cities seems to be driven by the attraction of more qualified households rather than by agglomeration effects 2
**Chart 12. Ratios describing the liquidity position of the banking sector**

<!-- Start of picture text --> Per cent Per cent<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>FX swaps as percentage of total assets Foreign funds as percentage of total assets<br>Liquid assets as percentage of total assets<br>Feb-03 May-03 Aug-03 Nov-03 Feb-04 May-04 Aug-04 Nov-04 Feb-05 May-05 Aug-05 Nov-05 Feb-06 May-06 Aug-06 Nov-06 Feb-07 May-07 Aug-07 Nov-07 Feb-08 May-08 Aug-08 Nov-08<br><!-- End of picture text --> Source: MNB.

# **_II. 4. Regulations: Why not?_**

As noted above, before the onset of the crisis, lending to households had carried serious risks both in terms of structure and volume, and had given rise to the accumulation of significant funding liquidity risks. This notwithstanding, Hungary did not adopt any regulatory measures aimed at the prevention of these negative developments. Below we attempt to find an explanation for this ―inertia.‖ Why did the regulatory authorities fail to react despite the MNB‘s nearly continuous warnings about the risks of foreign currency lending since the 2004 issue of its _Report on Financial Stability_ ? (For the legal relationship of institutions responsible for the financial stability, see Box 2.) We attempt to determine the pros and cons that may have been weighed as potential regulatory measures were contemplated. First, we examine the general consequences of regulations taken in response to rapid FX loan expansion from the perspective of macroeconomic and financial stability. We proceed to examine the advantages and disadvantages of individual regulatory measures. We structure the potential regulatory tools in line with Hilbers et al. (2005), also taking into account country-specific features.

# **_Macroeconomic impact_**

Over the short term, restricting foreign currency lending or making it more expensive temporarily reduces the household sector demand, depending on the substitutability between forint-denominated loans and foreign currency loans. The weakening of household demand reduces the external financing requirement, decelerating economic growth somewhat. In the short run, the deceleration leads to the deterioration of profitability in certain segments of the corporate sector. As such, the adoption of a regulatory measure during the credit boom would have been an extremely sensitive issue politically. From 2006, on the back of the fiscal adjustments, the income position of households deteriorated, which households tried to offset by borrowing. Nevertheless, economic growth took a significant downturn. Had lending to households been restricted by regulatory measures, this downturn would probably have been 14
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39
# **Estimating Capital Formation and Capital Stock by Economic Sector in China:**

# **The Implications for Productivity Growth**

# Richard Herd

Consultant, World Bank This paper has been produced as background for the Development Research Center of the State Council and the World Bank’s 2019 report _“Innovative China: New Drivers of Growth”_ . The dataset is available in World Bank’s Development Data Hub (https://datacatalog.worldbank.org/).

_JEL Codes: E010, E220, O110, O530._ _Keywords: China, capital stock, incremental capital-output ratio, fixed asset investment_
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<mark>Blattman, Christoper, Gustavo Duncan, Benjamin Lessing, and Santiago Tobon, 2021, “Gang Rule: Understanding and Countering Criminal Governance,” mimeo</mark> Blattman, Christopher, Julian Jamison, Tricia Koroknay-Palicz, Katherine Rodrigues, Margaret Sheridan, 2016, “Measuring the measurement error: A method to qualitatively validate survey data,” _Journal of Development Economics_ , Volume 120, Pp 99-112, Bliss, Christopher J and Nicholas H Stern, 1982, _Palanpur: The Economy of an Indian Village_ , Clarendon Press, Oxford <mark>Bloch, Francis, and Vijayendra Rao, 2</mark> 002, "Terror as a Bargaining Instrument: A Case Study of Dowry Violence in Rural India," _American Economic Review_ , 92 (4): 1029-1043.

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# Table of Contents

|||Page No.|
|---|---|---|
|SUN|MARY.....**.....**..............................|i|
|1.|INTRODUCTION.|I|
|[I.|PAKISTAN'S DEVELOPMENT PERFORMANCE OVER THE PAST<br>TWO DECADES--AN INTERNATIONAL COMPARISON .1||
||Economic Growth .1||
||Social Development.|2|
||Macro-baances..................................|...4|
||Saving and Investment ..|_5_|
|III.|EXPLAINING PAKISTAN'S PAST GROWTH PERFORMANCE.|8|
||Overview<br>.8||
||Statistical Analysis .8||
||<br>Interpretation of the Regression Results.|9|
|IV.|CAN PAST HIGH GROWTH RATES BE SUSTAINED IN THE<br>MEDIUM-TO-LONGER TERM? .13||
||Fiscal Deficits and their Inplications for Sustained Growth .13||
||Fiscal Deficits andExernalBalance .......................|13|
||Fiscal Deficits and Internal Balance .21||
||Sustinable Current Account and Fiscal Deficits-<br>mplications<br>for the 1990s .-------------------------|26|
||Financing High Growth: The Saving-Investnent Challenge .31<br>Human Capital Formation and Implications for Pakistan's||
||Growth Performance .34||
|V.|SUMMARY AND CONCLUSIONS .37||
|REFE|RENCES .41||
|ANN|EX TABLES .44||

!
# **7. Conclusions**

We have provided a new model of firm innovation, which includes a role for both firm and owner characteristics in the innovation process. The model has several new predictions which we verify in the data. The first is that firm size plays a larger role in process and organizational innovations (which spread cost savings over all products), than in product and marketing innovations (which typically apply to only a single product). In contrast, having a diversified portfolio of products matters more for product and marketing innovations than process and organizational innovations. Third, we confirm the general view in the literature that heavy competition is negatively associated with innovation, and show this is more the case for process and organizational innovations than for product and marketing innovations. Finally, we show that owner characteristics matter a lot for innovation, even conditioning on firm size and a host of firm characteristics.

In related work we have shown that attributes of a firm owner such as his or her socioeconomic background, performance on ability tests, and personality traits, differ in the cross-section between owners of smaller and larger firms. This paper shows that many of these same owner characteristics also predict innovation. Since these are measurable characteristics of the firm owner, it may be possible to use these characteristics to predict which small businesses are likely to innovate and grow, and which could be used to help better target policies such as microcredit and business training programs.<sup>4</sup>

# **References**

Aghion, Philippe and Peter Howitt (1992) “A model of growth through creative destruction”, _Econometrica_ 60(2): 323-51.

Aghion, Philippe, Christopher Harris, Peter Howitt and John Vickers (2001), “Competition, Imitation and Growth with Step-by-Step Innovation”, _Review of Economic Studies_ , 68(3): 467-92.

- Almeida, Rita and Ana Fernandes (2006) “Openness and Technological Innovation in Developing Countries: Evidence from Firm-level Data”, _World Bank Policy Research Working Paper No. 3985._

> 4 The Entrepreneurial Finance Lab at Harvard has recently been established to explore such possibilities. See http://www.cid.harvard.edu/efl/.

- 23 -
_Appendix Table_ _<u>9: Disaggregated food consumption by beneficiary and nonbeneficiary households with 6-60-month-old children</u>_

||_Be_<br>|_neficiary Househo_<br>|_lds_<br>|_Non_<br>|_beneficiary House_<br>|_holds_<br>|
|---|---|---|---|---|---|---|
||Log Per Capita<br>Dairy and Egg<br>Consumption|Log Per Capita<br>Cereal<br>Consumption|Log Per Capita<br>Other Food<br>Consumption|Log Per Capita<br>Dairy and Egg<br>Consumption|Log Per Capita<br>Cereal<br>Consumption|Log Per Capita<br>Other Food<br>Consumption|
|_Panel A: Program Impact_|||||||
|Program village|0.019|-0.002|0.020|-0.042|0.018|0.032|
||(0.029)|(0.066)|(0.113)|(0.046)|(0.065)|(0.125)|
|_Panel B: Interaction with Satu_|_ration_||||||
|Program village|0.152|0.141|0.295|0.003|0.160|0.139|
||(0.107)|(0.215)|(0.373)|(0.207)|(0.304)|(0.450)|
|Saturation|-0.138|0.098|0.091|-0.533***|-0.348|-0.747|
||(0.138)|(0.305)|(0.535)|(0.182)|(0.293)|(0.520)|
|Program village*saturation|-0.221|-0.234|-0.451|-0.062|-0.230|-0.160|
||(0.173)|(0.354)|(0.577)|(0.322)|(0.475)|(0.701)|
|_PanelC: Interactionwith abo_|_ve median saturati_|_on_|||||
|Program impact|0.056|0.051|0.119|-0.018|0.090|0.156|
||(0.036)|(0.076)|(0.145)|(0.069)|(0.089)|(0.172)|
|Above median saturation|0.027|0.087|0.184|-0.039|0.055|0.154|
||(0.048)|(0.106)|(0.193)|(0.069)|(0.099)|(0.202)|
|Program Village*Above<br>median saturation|-0.082|-0.112|-0.207|-0.058|-0.168|-0.290|
||(0.057)|(0.134)|(0.226)|(0.089)|(0.130)|(0.240)|
|_Panel D: Interactionwith top _|_quartile of saturat_|_ion_|||||
|Program impact|0.036|-0.003|0.037|-0.022|0.046|0.093|
||(0.029)|(0.069)|(0.129)|(0.050)|(0.076)|(0.141)|
|Fourth quartile of saturation|0.069|0.204|0.322|0.092|0.135|0.356*|
||(0.061)|(0.147)|(0.247)|(0.085)|(0.100)|(0.201)|
|Program Village* fourth<br>quartile of saturation|-0.059|0.024|-0.035|-0.105|-0.147|-0.320|
||(0.075)|(0.163)|(0.231)|(0.112)|(0.141)|(0.277)|
|Control Observations|328|328|328|214|214|214|
|Treated Observations|334|335|334|230|230|230|
|Control Mean|0.214|1.007|5.227|0.333|0.886|5.209|

note: *** p<0.01, ** p<0.05, * p<0.1. All specifications include municipality fixed effects. Standard errors are clustered by village; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 58
banking in Africa. Section 5 concludes and looks forward.

# **2. STOCK-TAKING: WHERE DOES AFRICA STAND?**

Global data collection efforts on the depth, outreach, stability, and efficiency of financial systems have been successful in collecting data on African financial systems for a wide sample of countries,<sup>2</sup> much wider than those used in the earliest studies of African financial development (see, e.g., Honohan and Beck, 2007). Those data are now routinely collected by the World Bank and housed in the Global Financial Development Database (GFDD), which was last updated in 2022.<sup>3</sup> In addition, aggregate data have been complemented with a number of surveys of enterprises, both informal and formally registered with an authority, and surveys for some countries now have a panel dimension (i.e., firms having been surveyed at several points in time).<sup>4</sup> Similarly, household surveys focused on financial services, such as the Finscope and Finaccess surveys in several African countries, have provided important insights into individual and household access to and use of formal and informal financial services, while the Global Findex dataset includes most countries in Sub-Saharan Africa.<sup>5</sup> The following analysis therefore uses an array of databases and other sources to document the development and structure of banking systems across the region.

> 2 Beck, Demirgüç-Kunt, and Levine (2000, 2010), Demirgüç-Kunt and Klapper (2012), Laeven and Valencia (2013). 3 See Cihák, Demirgüç-Kunt, Feyen, and Levine (2012) for description of the GFDD. The GFDD can be accessed at: <u>https://www.worldbank.org/en/publication/gfdr/data/global-financial-development-database.</u>

> 4 For example, <mark>the World Bank Enterprise Survey is a firm-level survey covering a broad range of business environment topics. As of March 2023, data has been collected from more than 191,000 firms in 154 countries, including information on 44 Sub-Saharan African countries. For a detailed overview of the survey, please consult</mark> <u><mark>https://www.enterprisesurveys.org/en/enterprisesurveys.</mark></u>

> 5 See Demirgüç-Kunt, Klapper, Singer, and Ansar (2022) for description of the latest round of the Global Findex Database. Findex data were also collected in 2011, 2014, and 2017.

6