rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A16:  Gallup World Poll (GWP)**

**Data Provider** The Gallup Organization **Description** Commercial survey firm based in Washington, United States **Website** www.gallup.com **Data Source** Gallup World Poll **Type** Survey **Respondents** Households **Frequency** Annual starting in 2006 **Coverage** Global sample of countries **Public Access** Full dataset is commercially available.  Averages of sub-indicators are publicly available in this spreadsheet.  Country level averages of variables from the core questionnaire are freely available through the Gallup World View website (worldview.gallup.com) **Description** The Gallup World Poll is a new survey polling representative samples of households in a large sample of countries.  The core survey instrument asks a wide range of questions, including some related to governance as indicated below.  Note that in 2007 and 2008 we have carried forward scores for those countries that were covered in previous years but not in current year.

|**Voice and Accountability**|2008|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|---|
|Confidence in honesty of elections|X|X|X|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**|||||||||||
|Satisfaction with public transportation system|X|X|X|..|..|..|..|..|..|..|
|Satisfaction with roads and highways|X|X|X|..|..|..|..|..|..|..|
|Satisfaction with education system|X|X|X|..|..|..|..|..|..|..|
|**Regulatory Quality**|||||||||||
|NA|..|..|..|..|..|..|..|..|..|..|
|**Rule of Law**|||||||||||
|Confidence in the police force|X|X|X|..|..|..|..|..|..|..|
|Confidence in judicial system<br>Have you been a victim of crime?|X<br>X|X<br>X|X<br>X|..|..|..|..|..|..|..|
|**Control of Corruption**|||||||||||
|Is corruption in governmnent widespread?|X|X|X|..|..|..|..|..|..|..|
|**Country coverage ***|**143**|**140**|**122**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|
|**Year of Publication**|**2008**|**2007**|**2006**|**..**|**..**|**..**|**..**|**..**|**..**|**..**|
|* includes countries carried forward from previous years|||||||||||

55
Park, Walter. 2001. “Intellectual Property and Patent Regimes.” Chapter 4 in _Economic Freedom of the World: 2001 Annual Report_ . Freedom House: New York.

OECD (Organization for Economic Co-Operation and Development). 1998. _Technology, Productivity and Job Creation_ : Best Policy Practices. Paris: OECD Publications.

OECD. 2001. _Innovative Clusters: Drivers of National Innovation Systems_ . Paris: OECD Publications.

Reinganum, Jennifer F.. 1984. ”Practical Implications of Game Theoretic Models of R&D.” _American Economic Review_ 74(2): 61-66.

Reinganum, Jennifer F.. 1989. “The Timing of Innovation: Research, Development and Diffusion” Chapter 14, Richard Schmalensee and Robert D. Willig eds. _Handbook of Industrial Organization_ , Volume 1. North Holland New York. 850-908.

Romer, Paul M.. 1987. "Growth Based on Increasing Returns Due to Specialization." _American Economic Review_ Papers and Proceedings 77: 56-62.

Romer, Paul M.. 1990. “Endogenous Technological Change.” _Journal of Political Economy_ 98: 71-102.

Segerstrom, Paul S. 1998. “Endogenous Growth without Scale Effects” _American Economic Review_ 88 (5) 1290-1310.

Segerstrom, Paul S., T.C.A. Anant, and Elias Dinpoulos. 1990. “A Schumpeterian Model of the Product Life Cycle.” _American Economic Review_ 80(5): 1077-92.

Trajtenberg, Manuel. 2001. “Innovation in Israel 1968-1997: A Comparative Analysis Using Patent Data.” _Research Policy_ 30(3): 363-389.

UNESCO. 1980. _Statistical Yearbook._ United Nations, Paris.

Windmeijer, Frank. 2000. "A finite sample correction for the variance of linear two-step GMM estimators." IFS Working Papers W00/19, Institute for Fiscal Studies.

Wooldridge, Jeffrey M..1997. "Multiplicative Panel Data Models without the Strict Exogeneity Assumption," _Econometric Theory_ 13, 667-678.

World Economic Forum. 2000. _World Competitiveness Report._ Lausanne, Switzerland.

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# **SUMMARY**

This paper examines whether the choice of crops is affected by climate in Africa. Using a multinomial logit model, the paper regresses crop choice on climate, soils, and other factors. The model is estimated using a sample of over 7000 farmers across 11 countries in Africa.

The study finds that crop choice is very climate sensitive. For example, farmers select sorghum and maize-millet in the cooler regions of Africa, maize-beans, maize-groundnut, and maize in moderately warm regions, and cowpea, cowpea-sorghum, and millet-groundnut in hot regions. Further, farmers choose sorghum, and millet-groundnut when conditions are dry, cowpea, cowpea-sorghum, maize-millet, and maize when medium wet, and maize-beans and maizegroundnut when wet. As temperatures warm, farmers will shift towards more heat tolerant crops. Depending upon whether precipitation increases or decreases, farmers will also shift towards drought tolerant or water loving crops, respectively.

There are several policy relevant conclusions to draw from this study. First, farmers will adapt to climate change by switching crops. This will inherently reduce the damages from climate change as farmers move away from crops that cannot perform well in the new climate towards crops that can. Governments and farmers should anticipate that new crops will be grown in places that experience climate change.

Second, global warming impact studies cannot assume crop choice is exogenous. For example, agronomic studies or studies that use weather as a proxy for climate, implicitly assume that crop choice will not change as climate changes. Unless these studies treat crop choice as endogenous, they will seriously overestimate the damages from warming.

Third, this study only examines choices across current crops. Future farmers may well have more choices. There is an important role for agronomic research in developing new varieties more suited for higher temperatures. Future farmers may have even better adaptation alternatives with an expanded set of crop choices specifically targeted at higher temperatures.

2
Table 8: Long-term Effect of FPE on Educational Achievement: Without PESP-affected students

|VARIABLES|(1)<br>|(2)<br>|
|---|---|---|
||Treatment group:<br>First year students|Treatment group:<br>16-18 year olds|
|ATT (<sup>ˆ</sup>_δSDID_)|16.7749|-3.5147|
||(3.7257)|(3.1510)|
||[10.5722]|[15.3209]|
|Observations|9,946|3,893|

_Notes_ : The point estimates of the FPE effect on achievement (OWM) and standard errors in parentheses are produced using `asdid` Stata command by Houngbedji (2016). Bootstrapped standard errors, clustered by high school attended, in brackets with 1000 replications, and significance levels are indicated as follows: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All models are estimated using a linear polynomial function of degree 4 to approximate the propensity score. The covariates used in the estimation of the propensity score are APS, gender, district, and age dummies to approximate the cohort and where 1 _{T_ = _t}_ is an indicator function that equals 1 event in the braces is true, _μ_<sup>_rc_</sup> _d,Y_<sup>(</sup><sup>_T,_</sup><sup>**X**</sup><sup>_i_) =</sup> _T · μ_<sup>_rc_</sup> _d,_ 1<sup>(</sup><sup>**X**</sup><sup>_i_) + (1</sup><sup>_−T_)</sup><sup>_· μrc_</sup> _d,_ 0<sup>(</sup><sup>**X**</sup><sup>_i_),and</sup><sup>_μrc_</sup> _d,t_<sup>(</sup><sup>**X**</sup><sup>_i_)istheoutcomeregressioninrepeatedcross-section</sup> settings.

Table 9 presents the results. Panel A reports traditional DR DID estimates that are DR consistent and locally semiparametrically efficient. Panel B reports DR DID estimates that are DR consistent, locally semiparametrically efficient, and DR efficient (i.e. they are efficient under misspecification in either the outcome model, _μ_<sup>_rc_</sup> _d,t_<sup>(</sup><sup>**X**</sup><sup>_i_),orthepropensityscore,</sup><sup>_π_(</sup><sup>**X**</sup><sup>_i_)).</sup> We can see from panel A of the table that DR estimates are positive and statistically significant when the treatment group is defined as the first year students (the upper bound effect). We observe the same in panel B. For example, the results from column (1) of panel A indicate that the FPE program increased students academic performance by between 0.77 percentage points (or 1 percent) and 2.89 percentage points (or 5 percent). In standard deviations, the FPE effect is bounded between 0.03 and 0.15 standard deviations. While these effects are economically smaller than those reported in the main results table, they are consistent in terms of the direction of the 30
the figures corroborate this suggested explanation, because the percentage of firms having internet connection increases substantially with firms’ size (see Table 6). While in OECD countries differences are less than 5% (small is 95% on average and large, 99.6%), large firms in Africa show a percentage that is more than the double than small firms. Foreign, importers and two-way traders exhibit higher shares compared to other firms.

_On average, more than 50% of firms with internet connection purchase or sell online._ Huge differences emerge comparing countries regarding e-commerce. For instance, the percentage of firms doing e- commerce in Ghana is half of what is observed in Kenya. Manufacturing firms not only purchase online more than firms in the service sector but also sell more online, yet the difference is small (less than 5%). The percentage of firms selling online is higher than those purchasing online in all different firm sizes.

_-_ _<u>Table 9 Firms doing E commerce (Percentage in terms of Firms with Internet Connection)</u>_

|Type|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|---|
||**Aggregate**|31.0|47.7|55.6|70.0|51.5|60.1|52.7|
|ine|**Main Sectors**||||||||
|Onl|Manufacturing|33.8|36.0|79.8|75.9|50.4|55.9|55.3|
|ing|Service|28.1|53.0|46.5|68.6|51.9|62.1|51.7|
|has|**Size**||||||||
|urc|Small|25.4|46.5|48.8|66.0|49.1|57.7|48.9|
|P|Medium|35.8|48.1|68.9|70.2|51.7|58.7|55.6|
||Large|39.7|56.5|59.0|93.4|66.3|72.7|64.6|
||**Aggregate**|29.9|39.8|68.4|65.8|53.4|61.2|53.1|
|e|**Main Sectors**||||||||
|nlin|Manufacturing|32.3|37.5|79.6|79.3|51.0|56.6|56.0|
|O|Service|27.6|40.8|64.2|62.5|54.4|63.3|52.1|
|lling|**Size**||||||||
|Se|Small|23.7|34.9|65.1|59.5|52.4|58.6|49.0|
||Medium|29.8|44.5|75.7|75.4|48.6|61.8|55.9|
||Large|60.6|63.6|65.8|90.3|72.8|69.8|70.5|

Source: Author’s own elaboration from enterprise surveys _Table 10 Firms Managing Inventory on the Internet (Percentage in terms of Firms with Internet_ _<u>Connection)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|30.6|54.3|89.2|77.5|50.4|63.2|60.9|
|**Main Sectors**||||||||
|Manufacturing|37.1|53.0|93.4|81.5|53.6|65.3|64.0|
|Service|24.1|54.9|87.5|76.7|49.2|62.2|59.1|
|**Size**||||||||
|Small|23.1|50.4|85.2|72.7|53.4|62.2|57.8|
|Medium|36.6|52.9|97.4|86.2|42.2|63.1|63.0|
|Large|46.1|84.0|85.1|94.0|53.4|66.8|71.6|

Source: Author’s own elaboration from enterprise surveys _Nearly 90% of Tanzanian firms with internet connection manage inventory on the internet_ . On average, 60% of African firms use internet to manage their inventory, Tanzania and Uganda showing the highest 18
exceeds 90 percent or the lagged short-term debt-to-GDP ratio exceeds 21.5 percent. Our corresponding result from the GPSTR model indicates that the threshold of expected public debt is high (low) when the share of foreign investors is low (high). For example, when the share of foreign investors is 15 percent, the threshold of the expected public debt is 91 percent. In comparison, when the share of foreign investors is 30 percent, the threshold is 59 percent. As the marginal impact of expected public debt on interest rates under the GPSTR model increases exponentially, depending on the foreign private holdings ratio, an increase in foreign private holdings of government debt would cause a rise in long-term interest rates, even if the debt level is low. Hence, the result of the GPSTR model differs substantially from that of the IT model.

# **4. Conclusion**

This study examines threshold values to understand the non-linear behavior of long-term interest rates using a novel approach, a panel smooth transition regression with a general logistic model. This paper examines how the interaction between the funding source and the expected public debt level affects interest rates, based on the data of 11 advanced economies and 14 emerging markets.

The main findings presented here are threefold. First, the impact of expected public debt levels on interest rates increases exponentially as the share of foreign private holdings exceeds 20 percent denominated in the local currency, even if strong home bias could mitigate this upward pressure. Second, if the expected public debt-to-GDP ratio exceeds a certain level, an increase in the share of foreign private holdings of government debt would raise long-term interest rates, offsetting the downward pressure on long-term interest rates from expanding market liquidity. Third, the out-of-sample forecasts of the generalized panel smooth transition regression model are more accurate than those of previously used methods.

The composition of government debt has a significant impact on the highly non-linear behavior of the longterm interest rate. Policy makers need to consider how government bond yields react to the level of public debt. In particular, as the share of foreign private investors in a small open economy is easily affected by capital flows, sovereign risk could fluctuate in the short-term. From a long-term perspective, Hoshi and Ito (2014) point out that an increase in local-currency-denominated domestic assets would slow down due to the rapid aging of the population in Japan. As the government cannot indefinitely rely on private domestic investors, foreign investors would have to step in and absorb the government debt at some point. Consequently, sovereign 19
_LF_ <u>(</u> _<u>A</u>_ <u>)</u> 1 _−F_ ( _<u>A</u>_ <u>)</u><sup>unitsof“loans”perproducer,whichisalsotheaveragesizeofloans.</sup> For the average loan, I use $550 _,_ 000, the average size of corporate and industrial loans in the US in 2007.<sup>21</sup> For _L_ , I use $400 _,_ 000 times (1 _− γ_ ). $400 _,_ 000 is the average size of US household wealth in 2007, excluding housing wealth.<sup>22</sup> A fraction 1 _− γ_ of this wealth can be lent. We therefore have: In other words, by equation 16, the estimated consumption gains from eliminating the financial system are 137.5%. This is obviously an unrealistically high number.

Part of the problem is that, in this model, _θ_ captures both the marginal cost of financial intermediation and its average cost. In the presence of a decreasing returns intermediation technology, the average cost of intermediation is lower than its marginal cost, implying some productivity gains from using financial intermediation and some profits associated with intermediation activities. However, adding decreasing returns to the intermediation technology is not a trivial extension.<sup>23</sup> To summarize, while this model highlights a price externality that potentially leads to quantitatively significant welfare losses, it is too soon to conclude that the financial system should be eliminated entirely. A richer quantitative investigation that takes into account decreasing returns (both in intermediation and in production) would be necessary in order to assess the optimal scope of financial intermediation.

> 21Data source: Average Loan Size for All C&I Loans, All Commercial Banks (EAANQ), Federal Reserve Bank of St. Luis (FRED database)

> 22Data source: table 1.B, Wolff [2010].

> 23When profits from intermediation are included in income, the model is no longer linear and becomes harder to solve analytically. I therefore reserve this important quantitative exercise for future work.

27
first firms identified in a 1999 ITU report in a ranking of the main 20 providers according to their turnover. It turns out that 8 of these firms are present in both the fixed and the mobile lines business. For fixed lines, the 9 top firms were then: Bell South Corporation, BT Group, Deutsche Telekom, France Telecom, SBC, Telecom Italia, Telefonica de España, Telstra, and Verizon. For mobile, the firms covered are: Bell South Corporation, Deutsche Telekom, France Telecom, SBC, Telecom Italia, Telefonica de España, Telstra, Verizon and Vodafone. BT is dropped from the mobile list while Vodaphone is not present in the fixed line list. For fixed lines we consider 35 countries (18 OECD and 17 non OECD) while for mobile, we cover 65 countries (20 OECD and 45 non OECD).

A few words of caution on this list may be appropriate. First, there is a fair amount of turnover in ownership in this sector. Some of the firms in the 1999 list have now changed owners (and names). Our choice of operators is thus arbitrarily frozen in 1999 even if the information is collected for these companies in their form as of 2003. Second, there is a clear regional specialization of firms. There are only 4 firms with a strong presence in LDCs: France Telecom, SBC, Telecom Italia, Telefonica de España. This last one is particularly dominating in Latin America, Telstra in Asia and France Telecoms in Eastern Europe. Third, the list does not do justice to the growing presence of regional actors in the various regional markets with strong South African firms present in Africa and strong Mexican and Brazilian operators present in Latin America for instance. However, most of these firms and many of those included in the ITU list do not publish the necessary information on their webpages and hence we cannot make a fair assessment of their effective role in diluting or reinforcing market power in this sector. However, many of these firms are often at least partially owned and controlled by one of the top 12 through cross-ownership arrangements. Overall, the firms covered here are present in 67 countries through 127 operators.

# **a. Telecoms Fixed**

With the market performance indicator adopted here and based on the 35 countries in which the top 9 operators are present, Table 4 shows that the global concentration level is quite high. These operators controlled 39% of the market in 2003 and have both the HH min and HH max above the 1800 threshold level. This very high levels of concentration generally reported is not that surprising. In many countries, fixed telephony is still a monopoly. What is more surprising is to see the low ratio of firms to country across regions. Only Africa and 13
Finally, and perhaps most importantly, adopting IE as routine practice in AFT projects requires the “evaluation community” to work on reducing IE costs. Although experienced IE practitioners like to warn newcomers against “doing IE on a shoestring”, the currently very high cost of IEs acts as a powerful deterrent. In trade policy, there should be scope for better use of existing statistics and, crucially, for more dialogue with governments to ensure the availability of firm-level statistics. That is where the issues of cost and buy-in converge: Governments will be more willing to relinquish semi-confidential data to researchers if they understand the value of the results generated.

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# 7. CASE STUDY REVIEW

# 7.1. INTRODUCTION: TEN AREAS OF ACTION

This chapter reviews the Latin American case studies, as well as examples cited elsewhere, in terms of ten areas of activity. Seven of these are drawn from conventional conservation planning practice, but three are added: organization, compatible resource utilization, impact management. They are added because they are either significant for World Bank project planning or for the lessons they teach in cases where indigenous people become involved in conser-ation.

Organization is stressed because so often most of the groups and agencies involved in conservation already have the advantage of a dedicated organization while the indigenous people may not. Compatible resource development still plays a minor role in conservation planning; to reflect local interests, it is elevated to equivalent status. Impact management is in theory of only occasional significance to protected area management but in planning studies related to Bank-supported development projects it would assume greater significance.

The intention, in adding these three subjects, is to define an area of planning in which conventional conservation practice is adapted to the special case in which large-scale development projects affect areas utilized by indigenous peoples. This planning process may contemplate several categories of reserved area: Indigenous areas, protected areas such as national parks, biosphere reserves or management regimes that are not tied to a reserved area.

The ten areas of activity are placed into three general categories: protection, economics, operations.

In the absence of either adequate laws or their adequate enforcement, protection has become the overwhelming goal of the managers of conservation areas in Latin America. In Canada and the United States, general public sentiment prevents the more overt violation of protected areas which is commonplace in Latin America. Conservation agencies recognize that such support can only be generated over time with a focused effort at public education and propaganda and education is therefore subsumed under protection.

Latin American conservation agencies invariably lack financial resources, even though governments may concede that conservation areas need not be self-sufficient and should be maintained from the public purse. In this regard, the ten case studies are looked at in terms of there typical operating costs and the scope for recovering part of these in the form of revenues from appropriate recreational and resource development.