rafmacalaba/datause-encoder-data huggingface dataset:
Three of our data sources have undergone significant revisions in the past year. First, following changes to the OECD development center’s African Economic Outlook publication, we have dropped one variable altogether (“Hardening of the Regime”) and replaced the indicator “Political Troubles” with the new indicator of “Civil Tensions” that the AEO reports going back to 1996. We note that this new variable is highly correlated with their previous “Political Troubles” rating. We use this variable as an ingredient in Political Stability and Absence of Violence/Terrorism going back to 1996.

The second applies to the Gray Area Dynamics ratings previously produced by Merchant International Group (MIG), a commercial business information provider. MIG was acquired in 2008 by Cerberus Corporate Intelligence, and the Gray Area Dynamics ratings were completely revised. This has resulted in a new set of 30 indicators starting in 2008. We find these to be a significant improvement over the previous version, with much more detailed definitions of concepts being measured, and more explicit guidance as to the rating scale. Since the new version of Gray Area Dynamics is not available in previous years, and since it differs substantially from the previous version, we treat it as a new data source beginning in 2008. At the same time, we retain the old version of Gray Area Dynamics as we used it before during 2002-2007, continuing to refer to the latter as MIG. The new and old versions of this source are described in Appendix Table A23. Finally, there has been a significant simplification in the questionnaire for the Business Environment and Enterprise Performance surveys (BPS), with the result that several questions asked in previous years were not asked in the latest round of the BPS. In order to ensure over time comparability for this source, we drop these questions from the previous rounds of the BPS as well. These changes are detailed in Appendix Table A5.

We have also made a few further minor revisions to various sources. We have added a few questions available in the Afrobarometer surveys that we had not used in past years to make the source completely consistent over time. At the request of Global Integrity, we have dropped the 2003 round of the Global Integrity Index from our indicators for 2003, 2004, and 2005, as there were changes in Global Integrity’s methodology between the first and subsequent rounds of this exercise, making the first round not fully comparable with subsequent rounds. We have also dropped a question 11
Economic System Reform Commission, 1992b, Slads for Limite Libility S Companies, May 15, in People's Daily (June 20. 1992).

Fan, Gang, Zhang Shuguang, Wang Limin, 1993, "Dual-Track Transition and Dual Track Regulation and Control--A Study of the Characteristics of Economic Fluctuations in China Since the Beginning of the Reforms (Part I), EconoMic Research, No.10 (October).

FBIS, 1993, "Bank Official Views Upcoming Monetary Reform," FBIS-CHI, Daily Reprt China, (December 13).

Feltenstein, Andrew, and Farhadian Ziba, 1987, "Fiscal Policy, Monetary Target, and the Price Level Determination in a Centrally Planned Economy: An Application to the Case of China," Journal o y, Credit. and Banldng, Vol.19, No.2, pp.137-156.

Findlay, Christopher, Will Martin, and Andrew Watson, 1992, "Policy Reform, Economic Growth and China's Agriculture," University of Adelaide and The World Bank, Mimeo.

Forster, Keith, 1991, "China's Tea War," Chinese Economy Research Unit, The University of Adelaide, Mimeo.

Fujiwara, Toshihiro, 1992, "Intergovermental Fiscal Relations in Japan: Lessons from ppost-War Reform and Evolution Thereafter," Draft, Africa II Department, The World Bank.

Gordon, Robert, 1990, Macroeconomics (5th), Scott'Foresman-Little, Glenview Illinoir.

**Haenry** Sender, 1993, "Party of the First Part," Far Eastern Economic Review, Febnrary 11. Huang, Yashang, 1990, "Web of Interests and Patterns of Behavior of Chinese Local Economic Bureaucracies and Enterprises During Reforms," The China Ouarterly, No. 123 (September).

Hu, Angang, and Shaoguang Wang, 1994, China: Report of Government Capacity (Zhongguo Guojia Nengli Baogao), Liaoning People's Press, Liaoning Province.

Kydland, F. and E. Prescott, 1977, "Rules Rather Than Discretion: The Inconsistency of Optimal Plans," Journal of Political Economy, Vol.85, 473-490.

Labor Department and Economic System Reform Commission, 1992, "Tentative Regulations of Labor and Wages in Trial Stock Companies' Peo s Daily g(Jul 13. l992).

Lee, Kyu-Uck, Shujiro Urata, and Inbom Choi, 1992, "Industrial Organization: Issues and Recent Developments," in Vittorio Corbo and San-Mok Suh (eds.), Structural Adjustment in a Newly Industrialized--Countr, the John Hopkins University Press.

Li, Yingming, 1992, "Local Forces Will Dominate Economic Reform in China,'" Ctral Dily, October 23.

72
|Emotional Stability|0.002|0.038|0.005|-0.034|0.017|-0.008|0.053*|
|---|---|---|---|---|---|---|---|
||(0.03)|(0.04)|(0.03)|(0.04)|(0.03)|(0.03)|(0.03)|
|Grit|0.049*|-0.012|-0.036|-0.067|0.028|0.033|-0.037|
||(0.03)|(0.05)|(0.03)|(0.05)|(0.03)|(0.03)|(0.04)|
|Decision-making|-0.031|-0.028|0.063**|0.004|0.040|-0.019|0.004|
||(0.03)|(0.05)|(0.03)|(0.04)|(0.03)|(0.03)|(0.03)|
|Observations|1,557|847|1190|1464|1454|1370|1948|

_Note_ : Standard errors in parentheses (* _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01). The sample includes wage and self-employed workers. _Schooling_ is measured as completed years of education. Reference categories for _Self-employed_ and _Gender_ variables and for computer complexity are those not self-employed, males, and no computer use, respectively. Scores on the reading proficiency assessment and personality and behavior traits have been standardized to a mean of 0 and standard deviation of 1. The wage model controls for gender, self-employed workers, experience and occupation. The Heckman method is used to correct for selection bias.

30
**Table 4. Model averaging results**

|||Full sample<br>||Below 50|% external deb<br>|t over GNI|Above 50%<br>|external de<br>|bt over GNI|
|---|---|---|---|---|---|---|---|---|---|
||PIP|PM/PSD|MEFF|PIP|PM/PSD|MEFF|PIP<br>P|M/PSD|MEFF|
|External debt as % of reserves|**1.0000**|**7.7958**|**0.0005**|**1.0000**|**5.0431**|**0.0008**|**1.0000**|**4.6676**|**0.0001**|
|CPIA|0.2362|‐0.5058|‐0.0232|**0.9838**|**‐2.9691**|**‐0.1928**|0.0019|0.0232|0.0000|
|GDP growth|0.2335|‐0.5018|‐0.0035|0.0043|‐0.0501|0.0000|0.0020|‐0.0278|0.0000|
|Short term debt as % of total external debt|0.0205|‐0.1263|‐0.0001|0.1642|‐0.3970|‐0.0015|0.0013|0.0189|0.0000|
|Openness|0.0071|‐0.0714|0.0000|0.0013|0.0076|0.0000|0.0038|‐0.0479|0.0000|
|Overvaluation|0.0051|‐0.0602|0.0000|0.0026|0.0049|0.0000|0.2003|‐0.4513|‐0.0004|
|External debt as % of GNI|0.0040|0.0457|0.0000|0.0520|‐0.2091|‐0.0004|0.0043|0.0503|0.0000|
|Inflation rate|0.0037|0.0455|0.0000|0.0008|0.0087|0.0000|**1.0000**|**3.5289**|**0.0029**|
|Effective interest rate|0.0036|‐0.0500|‐0.0071|0.0018|‐0.0185|0.0000|0.0009|‐0.0134|0.0000|
|Short term debt as % of reserves|0.0035|‐0.0424|0.0000|0.0539|‐0.2118|‐0.0001|0.0014|0.0133|0.0000|
|US T‐Bill rate|0.0023|0.0280|0.0000|0.0016|‐0.0043|0.0000|0.0016|0.0243|0.0000|
|Current account as % of reserves|0.0013|‐0.0163|0.0000|0.0012|‐0.0263|0.0000|0.0036|‐0.0453|0.0000|
|Primary deficit|0.0012|0.0183|0.0000|0.0593|‐0.2233|‐0.0016|0.0041|0.0496|0.0000|
|Current account as % of FDI|0.0011|‐0.0190|0.0000|0.1751|‐0.4155|0.0000|0.0006|0.0061|0.0000|
|Current account as % of GNI|0.0010|0.0197|0.0000|0.0018|0.0299|0.0001|0.0020|‐0.0295|0.0000|
|Effective maturity|0.0006|0.0137|0.0000|0.0009|0.0131|0.0000|0.0130|0.0961|0.0000|

Notes: PIP stands for posterior inclusion probability, PM stands for posterior mean (mean of the posterior distribution of the corresponding parameter), PSD stands for posterior standard deviation (standard deviation of the posterior distribution of the corresponding parameter) and MEFF stands for the model‐ averaged marginal effect. Variables ordered by PIP in the full sample. Results obtained from 10,000 replications of the MC3 procedure after a burn‐in phase of 10,000 replications.

14
**Table 3: Heterogeneity in Treatment Effects**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Amount|19.6***|14.53|12.88|10.17*|8.79**|19.91***|
||(6.16)|(15.7)|(11.61)|(6.14)|(3.74)|(6.75)|
|Amount*Manufacturing|-22.1**<br>(6.85)||||||
|Amount*MajorCustomer||-3.28<br>(8.71)|||||
|Amount*MajorSupplier|||-2.66<br>(7.76)||||
|Amount*Female||||-3.19<br>(7.70)|||
|Amount*Years Education|||||1.69<br>(1.08)||
|Parents / children<br>unaffected by tsunami||||||-9.77**<br>(4.39)|
|Observations|1993|1993|1993|1925|1993|1790|
|Number of firms|200|200|200|193|200|172|

Notes: Fixed effects estimation, estimated over 11 waves for firms reporting profits in 3 or more waves.

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 All regressions include wave effects and interactions of waves and variable shown. Trimmed samples delete the top 1% of percentage and absolute changes in profits from one wave to the next.

**Table 4: Treatment and capital stock**

||(1)|
|---|---|
|Amount|10638***<br>(5831)|
|Amount*Manufacturing|-4175*<br>(6910)|
|Observations|1857|
|Number of firms|200|

Notes: Fixed effects estimation, estimated over 11 waves for firms reporting profits in 3 or more waves. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 35
# **INTRODUCTION**

Agronomic-economic and cross-sectional models are two major economic approaches that have been employed to study the interaction between climate, water, and agriculture. The agronomic-economic approach begins with calibrated agronomic models and predicts outcomes, using economic simulations. For example, an economic model and agronomic experimental results have been combined to make numerous simulations of the impact of climate change on US agricultural production (Adams et al 1990; 1999; 2001). The cross-sectional approach compares the choices and performance of existing farms that face different climate, soil, and other relevant production conditions. For example, the Ricardian approach links farm values to climate (Mendelsohn, Nordhaus, and Shaw 1994; 1999; 2001). Both the cross-sectional and agronomic-economic models have provided independent insights into how climate and soils affect farming. The two approaches have also jointly confirmed a number of hypotheses, such as the hill-shaped relationship between crop productivity and temperature. This confirmation should be reassuring to all practitioners because it shows that the results are robust across all the assumptions inherent in each method.

In this paper, we use cross-sectional methods to explore the relative importance of climate normals versus climate variation on agriculture. Earlier Ricardian studies showed that climate normals and climate variance had significant effects on cropland value per hectare (Mendelsohn, Nordhaus, and Shaw 1999; 2001). This paper extends the earlier studies by quantifying the relative importance of climate normals versus climate variance. The paper also explains the percentage of cropland across the landscape. Where crops are grown also depends on climate.

The study examines two key economic measures of agriculture: net revenue per hectare and the fraction of land used for cropland. In all three countries, net area sown divided by total area was used to measure the fraction of land in cropland. These dependent variables are regressed on climate and other important control variables, such as soils and socioeconomic data. Satellites were used to provide consistent climate measures across countries.

# **METHODOLOGY**

The Ricardian method is a cross-sectional approach to study agricultural production. The method was named after Ricardo because of his original observation that land rents would reflect the net productivity of farmland. Net revenue (NR) consequently reflects net productivity and costs: NR = Σ Pi Qi (X, F, Z, G) - Σ RX where PLE is the net revenue per hectare per year, Pi is the market price of crop i, Qi is the output of crop i, F is a vector of climate variables, Z is a set of soil variables, G is a set of economic variables such as market access, X is a vector of purchased inputs (other than land), and R is a vector of input prices (see Mendelsohn, Nordhaus and Shaw 1994). The farmer is assumed to choose X to maximize net revenues given the characteristics of the farm and market prices. The Ricardian model is a reduced form model that examines how a set of exogenous variables, F, Z, and G, affect net revenue.

61
bundle of the poor. Hence, it can be anticipated that relaxation of these import bans would have a positive impact on poverty in Nigeria.

Beyond the prohibition list, Nigeria maintains high tariffs in a number of sectors. The Nigerian tariff schedule was simplified in 2005, and was partially aligned with the ECOWAS Common External Tariff (CET), with the maximum rate lowered, but only to 50 percent, well above the ECOWAS proposed ceiling of 20 percent.<sup>7</sup> A number of products including rice, sugar, cigarettes, plastics, tires, steel, household appliances, and vehicles are currently subject to 50 percent tariffs. If liberalized preferentially with the EU only, these high tariffs are very likely to lead to welfare-reducing trade diversion.

**Table 3.  Distribution of tariffs applied to Imports from EU**

|Tariff<br>category<br>defined by:|Importsfrom E<br>statutory<br>tariff rate|U (mln.US$)<br>applied tariff<br>rate|Share of E<br>statutory<br>tariff rate|Uimports<br>applied tariff<br>rate|
|---|---|---|---|---|
|0|1,725|1,853|20.1%|21.6%|
|0 <=5%|4,079|3,542|47.5%|41.3%|
|5%<= 10%|1,541|1,898|18.0%|22.1%|
|10%<=20%|961|1,116|11.2%|13.0%|
|>20%|273|170|3.2%|2.0%|

Source: Computed by authors based on TRIST model and database Similar to many other countries, Nigeria’s trade regime uses a number of preferences and exemptions that are crucial for determining the incentive structure of trade, but are unfortunately often overlooked by analysts due to constraints of data availability and analytical approach. TRIST, though, makes it possible to work with tariff line data as received from Customs and reflect the prevalence of preferences and exemptions. Once the various duty exemptions and preferential rates are taken into account, Nigeria’s weighted average actually applied tariff rate was 7.5 percent in 2006, according to tariff data collected from Nigerian Customs.<sup>8</sup> Two percent (US$ 170 million) of Nigerian imports from the EU were assessed a tariff rate of more

> 7  Nigeria has recently demanded that ECOWAS institute a 5th tariff band with a rate of 50 per cent as a condition for participating in the CET. Subsequently it has been suggested that the maximum rate could be 30-35%.  More recently (on September 23, 2008), the Nigerian government approved a new more liberal trade and tariff regime. A large number of imports that were previously in the 50 percent tariff bracket was moved to the 0,5,10,20 CET range and the 35 percent fifth band.  Also, there are some indications that the list of import bans might be curtailed. 8  If the various tariff exemptions were not taken into account, the trade weighted average “statutory” rate was 9 percent in 2006.

9
<!-- Start of picture text --> J P N<br>E U 2 7<br>T U R K O R<br>C H L<br>A U S<br>U K R C O LZ A F R U S U S A<br>IN DP H LE G Y M E X A R GB R A IS R N ZL S G P C A N<br>T H AC H N M Y S<br>0 2 0 00 0 4 00 0 0 6 0 00 0<br>gd p ca p<br>sta nd a rd s (av g # ) F itte d va lu e s<br>Figure 2. Average Number of Regulated Pesticides per Product<br>60<br>50<br>40<br>30 BRICS<br>non-BRICS<br>20<br>10<br>0<br>2006 2007 2008 2009 2010 2011<br>6 0 0<br>b e r)<br>4 0 0<br>2 0 0<br>S ta n d a rd s (a v g . n u m<br>0<br><!-- End of picture text --> 20
With respect to the aftermath of the rating changes, the results are more ambiguous. We first examine the responses of bond yields. The results suggest an asymmetric response of bond spreads after upgrades and downgrades. In particular, according to this event study analysis, the effects of downgrades tend to be somewhat more sustained while the effects of upgrades are usually reversed within two days. Typically, after experiencing an upgrade, bond spreads decline about 2 percent but within ten days bond spreads increase by about 4 percent, relative to the value at day -10. A different picture emerges from the analysis of downturns. While the contemporaneous reaction is similar to that of an upgrade (the spread changes by about 2 percent), following downgrades, the bond market does not recover. On the contrary, spreads continue to increase by at least 2 percent. The effects are somewhat stronger when we examine downgrades of foreign-currency denominated bonds only. Spreads widened an extra 5 percent. In contrast, the effects of upgrades seem to be long lasting in the stock market, with domestic stock markets gaining an extra 2 percent return relative to that of the stock index in the United States. This is not the case for downgrades.

Figure 4 displays event studies for foreign events. Instead of using as an event upgrades and downgrades on sovereign ratings from the domestic country, the figure displays the behavior of EMBI spreads and stock spreads around upgrades and downgrades of spreads from other emerging markets. The figures on the left display upgrades, while the figures on the right show downgrades. The top panel uses EMBI spreads, while the bottom one uses stock spreads. The results show that foreign-currency upgrades are followed by large decreased in EMBI spreads and large increases in stock 17
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

- Carruthers, Robin, Ranga Rajan Krishnamani, and Siobhan Murray. 2009. ―Improving Connectivity: Investing in Transport Infrastructure in Sub-Saharan Africa.‖ AICD Background Paper 7, Africa Region, World Bank, Washington, DC.

- Gwilliam, Ken, Vivien Foster, Rodrigo Archondo-Callao, Cecilia Briceño-Garmendia, Alberto Nogales, and Kavita Sethi. 2008. ―The Burden of Maintenance: Roads in Sub-Saharan Africa.‖ AICD Background Paper 14, Africa Region, World Bank, Washington, DC.

- Kumar, Ajay, and Fanny Barrett. 2008. ―Stuck in Traffic: Urban Transport in Africa.‖ AICD Background Paper 1, Africa Region, World Bank, Washington, DC.

- Ocean Shipping Consultants, Inc. 2009. ―Beyond the Bottlenecks: Ports in Africa.‖ AICD Background Paper 8, Africa Region, World Bank, Washington, DC.

- World Bank. 2007. Burkina Faso: The Challenge of Export Diversification for a Landlocked Country, Diagnostic Trade Integration Study for the Integrated Framework Program. Washington, DC: World Bank.

- ———. 2009b. Burkina Faso: Promoting Growth, Competitiveness and Diversification. Country Economic Memorandum. Volume III: Enhancing Growth Factors. Report No. 51815-BF, Washington, DC, November 24.

- World Bank/AfDB (African Development Bank). 2009. ―Railway Concessions in Sub-Saharan Africa: Lessons Learned.‖ Joint presentation. Washington, DC.

- World Bank/Mott Mac Donald. 2009. Evaluation of the Proposed Ouagadougou-Donsin Airport Development, Burkina Faso. Washington, DC: World Bank.

# Water supply and sanitation

Banerjee, Sudeshna, Vivien Foster, Yvonne Ying, Heather Skilling, and Quentin Wodon. 2009. ―Cost Recovery, Equity, and Efficiency in Water Tariffs: Evidence from African Utilities.‖ AICD Working Paper 7, Africa Region, World Bank, Washington, DC.

- Banerjee, Sudeshna, Heather Skilling, Vivien Foster, Cecilia Briceño-Garmendia, Elvira Morella, and Tarik Chfadi. 2008. ―Ebbing Water, Surging Deficits: Urban Water Supply in Sub-Saharan Africa.‖ AICD Background Paper 12, Africa Region, World Bank, Washington, DC.

- Fall, Matar, Phillipe Marin, Alain Locussol, and Richard Verspyck. 2009. ―Reforming Urban Water Utilities in Western and Central Africa: Experiences with Public-Private Partnerships.‖ Water Sector Board Discussion Paper Series, PPIAF Paper No.13, World Bank, Washington DC.

Gulyani, Sumila, Debabrata Talukdar, and Darby Jack. 2009. ―Poverty, Living Conditions, and Infrastructure Access: A Comparison of Slums in Dakar, Johannesburg, and Nairobi.‖ AICD Working Paper 10, Africa Region, World Bank, Washington, DC.

Keener, Sarah, Manuel Luengo, and Sudeshna Banerjee. 2009. ―Provision of Water to the Poor in Africa: Experience with Water Stand posts and the Informal Water Sector.‖ AICD Working Paper 13, World Bank, Washington, DC.

48
- 12 -

indexed on a variable under the borrower's -ontrol (e.g., project return) and those that may be exogenous to the decisions of the borrower. However, there are clearly more problems associated with writing contracts on earnings than on exogenous prices; once again the relevant trade-offs must be carefully analyzed in the context of assumptions on information and control variables. It is precisely these types of costs that are investigated in section 5.

Contingency may also alter default risks in a willingness-to-pay framework. In the extreme, consider the case of a commodity price being the single source of uncertainty; then clearly, default-free commodity-contingent contracts could be designed. In general, the effects that such contracts will have on incentives depends on the range of choices open to the borrower and on assumptions concerning the information structure.

There are further costs to introducing indexation. There may be a cost from the point of view of the lender's portfolio. Contracts that leave mean returns to the lender unaltered may impose a cost by altering the overall risk of the portfolio. It has been argued (Gemmill and Gordon, 1988) that adding commodity risks to a bank's portfolio should not add considerable risk as commodity returns have a low beta in a capital asset pricing model (CAPM) approach. 1/ This cost is discussed more formally in section 4.

_1/_ Statistical work by Priovolos (1988) and others does not always verify the assertion that commodity return betas are lower than one.
# **5 Robustness checks**

# 5.1 Sensitivity to Measurement Error

We assess the robustness of the above estimates to various amounts of measurement error, as inequality measures can be particularly sensitive to this source of bias. Following Chesher and Schluter (2002), we first produce overall inequality estimates correcting for some amount of measurement error. Assuming an error to signal variances ratio of 10% (resp. 20%) would decrease the estimated Gini from 47.1 to 45.3% (resp. 43.6%) and the Theil-T from 42.8 to 39.5 (resp. 36.1).<sup>25</sup> The assumed variance of measurement error needs to reach 40% of that of the signal for the Gini to go down to a level comparable to the uncorrected published statistics (40 instead of a published 40.3). Such level of measurement error is highly unlikely. Bound et al., 2001, give much lower orders of magnitude for measurement error in income, closer to 20%, and this is the range considered by Chesher and Schluter, 2002 in their application to Indonesian data. Hence, it is unlikely that measurement error explains the high observed inequality in the PSF survey. In addition, given ESPS data is as likely to be error-ridden as PSF, measurement errors certainly cannot account for the difference in the inequality estimates.

In the case of the PSF survey, because consumption data are collected at the cell level, measurement error will take place at that level. In such a case, working at the household level would help to average out some of this noise, while it would be maximal when working at the cell level. That would suffice to induce some intra-household inequality even if the true distribution is egalitarian. Although we already know, thanks to the analysis of section 3.2, that this within-household inequality is not pure noise, as it correlates with a number of observable characteristics of the households, it is important to evaluate the sensitivity of our estimates to measurement error at the cell level. We will again resort to simulations. Assuming measurement 23