rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> My % f’ a<br><!-- End of picture text --> Table 1: Local Government Administrative Structure and Size by Region and Income Class of Countries

||# t|iers|av. p|op.|av.|area|
|---|---|---|---|---|---|---|
||mean|sd|mean|sd|mean|sd|
|Total|2.03|(hs|101.06|175.47|2.13|6.95|
|By region:|||||||
|Southern Asia|243|0.98|70.76|75.5|0.32|(0.58|
|Europe and Central Asia|2|0.74|90.49|56.38|0.29|O4|
|Middle East and North Africa|2|0.86|11.79|116.41|5.14|15.68|
|SubSaharan Africa|2.02|0.76|17164|178.56|4.00|&|
|LatinAmericaand Caribbean|1.74|0:63|63.16|51.88|1123|1.73|
|Fast Asia and Pacific|25|i|W714|379.83|1.22|3.53|
|North America|2|0|11.6|6.79|1.32|L723|
|By income:|||||||
|high income|L69|(67|7251|119.35|1.13|2.71|
|middle upper income|L760|O<.72)|«667.506|678.76|4.00|13.95|
|middle lower income|235|(0.76|93.99|246.423|1.12|23.33|
|lowincome|3.96|(0.82|163.295|178.02|2.58|5.45|

Source: Authors’ calculations based upon data sources reported in Annex Table Al. Note The classification of the countries is according to the World Bank.
# **Appendix 3. Spurious Instruments**

As the placebo tests based on randomizing food aid allocations demonstrate, what really seems to drive the NQ findings is the fact that conflict incidence follows nonlinear trends that are not parallel between irregular and regular recipients of aid. US wheat production interacted with regularity of aid receipt from the US identifies an effect of food aid on conflict because US wheat production, US food aid shipments, and conflict in regular aid recipients follow a similar parallel trend that is not shared by conflict incidence in irregular recipients. We therefore expected that any variable that exhibited the same inverted U time series pattern – high in the 1980s and 1990s, lower in the 1970s and 2000s – would “work” as an instrument, no matter how spurious the association with food aid shipments. This exercise illustrates the fragility of the NQ estimation method when identification depends on differentiation between likely endogenous groups (regular and irregular food aid recipients, in the present case) that exhibit heterogeneous trends in the time series whose variation is used to identify the causal parameter of interest.

To show that NQ’s IV approach identifies only spurious correlation and not a causal effect, we replicate their IV strategy exactly, but replace wheat production with the value of music cassette sales globally. The music cassette sales time series data are taken from IFPI (2009). It is hard to imagine any plausible causal mechanism by which an increase in global audio cassette tape sales would drive increased US food aid shipments. In what follows, we demonstrate that using a data series of global audio cassette sales in place of the US wheat production instrument yields estimates of the relationship between food aid deliveries and conflict nearly identical to those estimated by NQ. Furthermore, to show that the cassette sales instrument is identifying only the spurious correlation of food aid shipments and conflict in recipient countries rather than proxying for a causal effect of lagged US wheat production, we also implement a second specification where we use cassette 49
**_Energy demand models for policy_** **_<u>formulation</u>_**

Bhattacharyya, S. C., 1997, Energy taxation and the environment: a developing country perspective, Natural Resources Forum, 21(4), pp. 273-80.

Bhattacharyya, S. C., 2008, Investments to promote electricity supply in India: regulatory and governance challenges and options, Journal of World Energy Law and Business, 1(3), pp. 201-223.

Blackmore, F. B., C. Davies and J. G. Issac, 1994, UK energy market: An analysis of energy demands, Part 1: Disaggregated sectoral approach, Applied Energy, 48, pp. 261-77.

Bohi, D., 1981, Analyzing demand behaviour: A study of energy elasticities, Baltimore, John Hopkins University Press.

Bohi, D. R. and M. B. Zimmerman, 1984, An update on econometric studies of energy demand behaviour, Annual Rev of Energy, Vol. 9, pp. 105-54.

Borges, A. M., and A. M. Pereira, 1992, Energy demand in Portuguese manufacturing: A two-stage model, Energy, 17(1), pp. 61-77.

Bose, R. and M. Shukla, 1999, Elasticities of electricity demand in India, Energy Policy, 27, pp. 137-46.

Bouachera, T. and M. Mazraati, 2007, Fuel demand and car ownership modeling in India, OPEC Review, 31(1), pp. 27-51.

Brendt, E. R. and D. O. Wood, 1975, Technology, prices and the derived demand for energy, The Review of Economics and Statistics, 57(3), pp. 259-68.

Brendt, E. R. and G. Botero, 1985, Energy demand in the transportation sector of Mexico, Journal of Development Economics, 17(3), pp. 219-38.

101
Table 8: The effect of collective actions by SHGs on the type of issues WMs deal with

|||||Probab|ilitythat the|WM deals w|ith:||||
|---|---|---|---|---|---|---|---|---|---|---|
||Alcoho|l issues|School|issues|Forest|issues|Infrast|ructure|Welfare|scheme|
||OLS|IV|OLS|IV|OLS|IV|OLS|IV|OLS|IV|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|SHG active|0.30<sup>_∗∗∗_</sup>|0.29<sup>_∗∗_</sup>|0.19<sup>_∗∗∗_</sup>|0.31<sup>_∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|0.35<sup>_∗∗_</sup>|0.04|0.10|0.14<sup>_∗_</sup>|0.40<sup>_∗_</sup>|
||(0.06)|(0.14)|(0.06)|(0.14)|(0.05)|(0.17)|(0.07)|(0.21)|(0.08)|(0.23)|
|Male|-0.11<sup>_∗∗∗_</sup>|-0.11<sup>_∗∗∗_</sup>|-0.07|-0.08<sup>_∗_</sup>|-0.06|-0.06|0.09<sup>_∗_</sup>|0.09<sup>_∗_</sup>|-0.14<sup>_∗∗∗_</sup>|-0.16<sup>_∗∗∗_</sup>|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.05)|(0.05)|(0.05)|(0.06)|
|Education level (years)|0.01<sup>_∗∗_</sup>|0.01<sup>_∗∗_</sup>|-0.00|-0.00|-0.01|-0.01|0.00|0.00|0.01|0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Land (acres)|-0.01|-0.01|-0.01<sup>_∗∗∗_</sup>|-0.01<sup>_∗∗∗_</sup>|-0.01|-0.01|-0.01|-0.01|-0.00|-0.00|
||(0.01)|(0.01)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Number of children|-0.02<sup>_∗∗_</sup>|-0.02<sup>_∗∗_</sup>|0.01|0.01|-0.00|0.00|-0.03|-0.03|0.01|0.01|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|(0.01)|
|Age|0.00|0.00|0.01|0.01|0.00|0.00|0.02<sup>_∗_</sup>|0.02<sup>_∗_</sup>|0.02<sup>_∗_</sup>|0.02|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Squared age|0.00|0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00<sup>_∗_</sup>|-0.00<sup>_∗_</sup>|-0.00<sup>_∗_</sup>|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Caste category: SC|-0.08|-0.08|0.05|0.06|-0.12|-0.12|-0.10|-0.10|0.11|0.13|
||(0.06)|(0.06)|(0.08)|(0.08)|(0.08)|(0.08)|(0.09)|(0.09)|(0.11)|(0.11)|
|Caste category: OBC/FC|-0.14<sup>_∗∗_</sup>|-0.14<sup>_∗∗_</sup>|0.08<sup>_∗_</sup>|0.08<sup>_∗∗_</sup>|-0.03|-0.03|-0.01|-0.01|0.16<sup>_∗∗_</sup>|0.18<sup>_∗∗_</sup>|
||(0.06)|(0.06)|(0.04)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|(0.07)|(0.07)|
|Ward fxed effects|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Election year dummies|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Observations|441|441|441|441|441|441|441|441|441|441|

An observation in the regression model is a WM. The dependent variable is a binary variable that equals one if the WM deals with the issue during his mandate. The variable “SHG active” is a dummy that takes value one if a SHG started undertaking collective actions in ward _j_ before or during the mandate of WM _i_ . The odd numbered columns provide the results using OLS. In the even numbered columns we instrument for “SHG active”, using the timing of SHG creation in the ward. Standard errors clustered at the ward level are given in parentheses. *** significant at 1 percent, ** significant at 5 percent, * significant at 10 percent. _Source_ : Authors’ analysis based on data described in the text.

41
In the first regime under which projects are proposed, a village head is chosen from village elites. To make our point, we assume village elites have the highest valuations for public projects and occupy the interval [ _xe_ ,1], _xe_  1/ 2 . Other work shows that public good composition changes with electoral reforms in the context of caste and landlessness in India (Foster and Rosenzweig 2005 and Munshi and Rosenzweig 2010). In Aceh there are no castes, and the reform is to have elections, not to shift the balance of power within an election regime. More critically, the range of relevant services involved is narrower, mostly maintenance, cleaning, or renovation of village public facilities and areas including docking facilities and aquaculture channels, which have more appeal to elites who intensively use such facilities. This characterization is based on our extensive fieldwork, which also involved professionals who grew up in Aceh; it is broadly consistent the case studies reported in Evers (2000). We assume that, in the elite regime, elites choose all projects desired by the person with preferences<sup>_x_</sup> _e_ .<sup>Under a democratic</sup> regime, _d_ , we assume the median voter dominates, so<sup>_x_</sup> _d_<sup></sup> 1/ 2 .<sup>7</sup> The decision maker in each regime can only muster volunteer labor if he also volunteers (after all he must lead the volunteer group). He only does that if the total benefits to him of having the project equal or exceed his cost of volunteering, or

[ _N_ (1  _s_ )]  _C xi j_  _b_ , _j_  _e_ , _d_ . This defines a second implicit function defining the lowest valuation project announced by the village head:

> Note for this also that _dC_ / _db s_  0, _dC_ / _dx s_  0 .

> 7. Either volunteer labor is the only election decision, or preferences on other election dimensions are ordered in the same fashion on the unit interval.

13
# **1. Introduction**

Entrepreneurship is essential for the continued dynamism of the modern market economy and a higher entry rate of new businesses can foster competition and innovation (Klapper, et al., 2006; Ciccone and Papaioannou, 2007; Aghion, et al., 2009). To promote private sector growth, many countries have focused on simplifying the registration process, seeking to reduce the costs, days and/or procedures required to formally register a business.

A methodology for measuring the effectiveness of the regulatory framework for firm registration was developed by Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2002). Since 2003, the World Bank’s annual Doing Business report has used this methodology to quantify the registration process in over 170 countries in its “Ease of Starting a Business” section.<sup>1</sup> An outstanding question, however, is whether, and to what degree, there exists an economically meaningful relationship between the costs, days and procedures to start a business and the actual number of new firms that register each year. Given the widespread recognition of the importance of business environment reforms<sup>2</sup> , it is surprising that there is little research, and no crosscountry panel analyses, on the real effect of these regulatory reforms. Our paper aims to address this gap in the literature by examining the impact of institutional reforms on the number of new firms registered.

Understanding the regulatory environment that promotes entrepreneurship is necessary to successfully identify appropriate policies to foster entrepreneurship in local economies. In this paper we empirically investigate the relationship between the regulatory ease of registering a business and actual new business registrations. Next, we explore the magnitude of reform in

> 1 Reports are available on-line at: www.doingbusiness.org.

> 2 For instance, a 2008 report of the Multilateral Donor Committee for Enterprise Development  states, “Reforming the business environment is a priority for development agencies and governments because of the significant influence the business environment has on the development of the private sector and therefore on economic growth and the generation of livelihoods and jobs.” (DCED, 2002, p.3)

2
**Figure 5. Mobile cellular connections per 100 people**

<!-- Start of picture text --> 140<br>120<br>100<br>Hungary<br>80 Romania<br>Ukraine<br>60 Slovak Republic<br>Belarus<br>40 Moldova<br>20<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: World Bank 2012.

10
- . - - - ~~~~~12

director instead.

Metallurgy, managers reported that in 1992 over 80% of sales were direct deliveries to the.ultimate user, in electromachinery only slightly less. Two years earlier ste3l mills had only a few customers, sometimes Dne, who would take care of the whole distribution process. with the producer fully isolated- from the market.In consumer goods sectors such as food processing and light manumfacuring, a simlar process is **mider** way. Over half of output is sold directly to retailers to avoid wholesalers' commissions and contacts with often unviable and small parners. The best firms are building up their own networks of reliable iistributors for wholesaling and storage, sometimes supplemented with facmory-sponsored reail shops..

In 1990, over 90 perent of exports by the sample firms were through state-owned foreign trade ompanies (FTCs). Dissatisfied by high conmmissions and sometimes inadequate service, SOEs have found dternatives. By 1992, FTCs accounted for just over _50_ percent of the exports _of_ the sample firms: 27 percent was sold directly to western clien and 16 percent through new private agents.

# _Ywmnar'_ o _rA4i#=usr_

The preceding evidence shows that SOEs have been willing to take painful measures to mprove hieir long-nm profitability. The most significant among these has been labor-shedding. The data aLsc support the view that efficiency in materials and energy usage is up, while tends in unit labor costs are Mcouraging. The more successful firms have experienced a revival of sales and point to the mportance f product mix changes. As early as 1990, SOEs proved that they could quickly sense and take advantage Xf profit opporunities. captured by the immediate and large diversion of sales from the home and CMEA arkets to the west. The presing problem of social assets has also begun to be addressed in a convicing manner, and marketing, the Achilles' heel of SOEs. is improving. These adjusments fly in the face of :arly predictions regarding SOE behavior, which were based on the view that managers and workers .would sauander stae assets and did not have the incentive or exeriise to restcucture. We now turn to
The UK accounts for 3.8 percent of world exports and 3.9 percent of world imports. In 2011, the UK exported $440 billion of goods and it imported $550 billion.<sup>6</sup> In terms of services, the UK exported $260 billion and it imported $163 billion. Table 3 highlights the importance of the EU as the UK’s trading partner. Trade in goods with the EU accounts for more than half of the UK’s exports and imports. The EU is also an important market destination for UK services, absorbing almost half of UK exports. The importance of the EU as a source of services for the UK is instead much smaller: only one‐third of imports of services come from the EU.

While the share of UK exports to the EU remained constant in the period under consideration, the composition of trade changed substantially. In 1995, more than 80 percent of UK exports were goods. This share declined to around 63 percent in 2011. The reshaping of UK exports towards services is even clearer when looking at trade with the EU. The share of exports of services to the EU more than doubled in 16 years, going from 8 to 18 percent of total exports. The pattern for exports outside the EU is similar, but the increase is more moderate. This evidence suggests a “servicification” of UK exports.

_Table 3:_ UK trade in goods and services

|||Services|**UK Exports**<br>Goods|Total|Services|**UK Imports**<br>Goods|Total|
|---|---|---|---|---|---|---|---|
||**Extra‐EU**|$135,599 M<br>19%|$212,739 M<br>30%|$348,338 M<br>49%|<br>$108,268 M<br>15%|$243,531 M<br>34%|$351,799 M<br>49%|
|**2011**|**EU28**|$124,412 M|$228,725 M|$353,137 M|<br>$55,527 M|$308,208 M|$363,735 M|
|||18%|33%|51%|8%|43%|51%|
||Total|$260,012 M|$441,464 M|$701,476 M|<br>$163,795 M|$551,739 M|$715,534 M|
|||37%|63%|100%|23%|77%|100%|
||**Extra‐EU**|$33,866 M|$121,579 M|$155,445 M|<br>$21,048 M|$95,116 M|$116,164 M|
|||11%|40%|51%|7%|33%|40%|
|**1995**|**EU28**|$24,437 M|$123,086 M|$147,524 M|<br>$23,275 M|$146,607 M|$169,882 M|
|||8%|41%|49%|8%|51%|59%|
||Total|$58,304 M|$244,665 M|$302,969 M|<br>$44,323 M|$241,723 M|$286,045 M|
|||19%|81%|100%|15%|85%|100%|

Germany is both the main destination and source country of goods, accounting for 13 percent of the UK’s exports and 15 percent of the UK’s imports (see Figure 3). The other top 4 destination countries in the EU are France, the Netherlands, Ireland and Spain. As to the source of UK imports, the Netherlands, France, Belgium and Italy are in the top 5. Outside the EU, the US is the main destination country, absorbing 12 percent of UK exports, followed by China, Canada, the Russian Federation and Australia. UK imports from outside the EU come mainly from China (8 percent), US, Japan, India and Canada. The main markets for UK services are US, Ireland, Germany, Luxemburg and the Netherlands, all importing more than 5 percent of UK services. Other top destination markets are Canada, China, Belgium, Turkey and Brazil. The most

> 6 Data from COMTRADE show that UK trade increased between 2011 and 2015, but it remains in the same order of magnitude. The UK exported 466 billion dollars and it imported 629 billion dollars in 2015.

9
# **B. REAL EXCHANGE RATE BEHAVIOR IN THE CEMAC ZONE**

The behavior of the real effective exchange rate for the CEMAC zone follows a similar path to the UEMOA zone, appreciating 7 percent from 1999 to 2004. The evolution of the real effective exchange rate since the introduction of the euro can be divided into two phases. Firstly, from January, 1999 to January, 2001 there was a decrease in the REER index by more than 15 percent as the euro slid against the dollar as well as vis a vis other currencies linked to the dollar. During the second phase, from January, 2001 to December, 2004 there was an appreciation of 25 percent due to the gain in the euro against the dollar and other currencies. As in West Africa, the main reason for this was the twin deficits in the US, which have resulted in a sustained dollar depreciation and greater market confidence in the euro. Also, prudent monetary management at the BEAC resulting in lower inflation than that of many trading partners, reduced misalignment. In particular, low inflation in Chad of -4.6 percent in 2004 dampened the appreciation.

One of the main reasons for the lower appreciation of the CFA in Central Africa as opposed to West Africa, has been the very low inflation in the CEMAC zone of less than 1 percent in 2004 compared to more than 3.5 percent in UEMOA. Furthermore, higher inflation in Central Africa’s trading partners such as East Asian economies, especially in China (9.4 percent in 2004), also reduced the extent of appreciation. While West Africa historically trades with Europe, Cameroon, Gabon, and the Republic of Congo are fast becoming important sources of resources of energy and mineral wealth for burgeoning resource-starved Asian economies like China and Taiwan (China). After the United States, with 21.1 % of trade share in 2000-2001 and France with 18.8 percent, amongst Central Africa’s largest trading partners are Taiwan (China) with 7.6 percent, China with 6.4 percent and Korea with 4 percent. Thus, greater inflation in its trading partners has helped CEMAC zone competitiveness.

**CHART 9: REAL EFFECTIVE EXCHANGE RATE FOR CEMAC USING CPI: 1999-2004**

<!-- Start of picture text --> 110<br>105<br>100<br>95<br>90<br>85<br>80<br>1999 M11999M041999M071999M102000M012000M042000M072000M102001M012001M042001M072001M102002M012002M042002M072002M102003M012003M042003M072003M102004M12004M42004M7 2004M10<br><!-- End of picture text --> 13
Another major weakness highlighted by responding firms concerns language and communication skills of local MBAs. The survey found that 27% of the respondents identify this as the greatest weakness of local MBAs (though most of this finding is due to the negative perception of these skill types by Latin American and East Asian firms).

The breakdown of results by World Bank income categories shows that across all income groups, firms consistently reported that the greatest weakness is the level of practical work experience of local MBAs.<sup>17</sup> Another interesting point is that 18% of responding firms stated that the greatest weakness of local MBAs is their inability to communicate or interact with others. This implies that communication and interaction skills are significant weaknesses of developing and transition country MBAs (even though this goes against the results demonstrated in Question 6B above).

In terms of weaknesses, both the CFO survey and Neelankavil’s survey provide information on the quality of U.S. MBA graduates. The CFO survey found that 60% of new MBA graduates learn too much finance theory and not enough about practical financial management. In addition, 70% of the executives responding to the CFO survey were dissatisfied with the personal communication skills of MBA graduates. In his survey, Neelankavil found that executives thought that U.S. business schools emphasized financial and analytical skills over knowledge of manufacturing processes, and that the greatest weaknesses of MBAs were their ability to handle global challenges, their understanding of the importance of quality, and their written communication skills.

The strengths and weaknesses found in the IFC survey broadly agree with the results found in the two surveys of U.S. firms regarding the quality of U.S. MBAs: The MBA graduates are very well prepared in terms of technical and analytical skills, but they lack a working understanding of international business (because of a lack of practical work experience and real world training), and certain communication skills.

> 17 World Bank income categories are based on the per capita incomes of countries.  The range of the income categories is I-V, with I being the lowest and V being the highest:  Countries in Category I have a per capita income of $746 or less;   those in Category II,  between $746 and $1435;  Category III,  between $1436 and $2975;  Category IV,  between $2976 and $5185;  Category V, over $5185.

20
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