rafmacalaba/datause-encoder-data huggingface dataset:
Baron, D. P., and D., Diermeier. (2001). “Elections, government, and parliaments in proportional representation systems.” _Quarterly Journal of Economics,_ Vol. 116(3): pp. 933-967.

Battaglini, M., and S., Coate. (2006). “A dynamic theory of public spending, taxation, and debt.” _NBER Working Papers Series,_ WP # 12100 Bawn, K., and F., Rosenbluth. (2006). “Coalition parties versus coalitions of parties: How electoral agency shapes the political logic of costs and benefits.” _American Journal of Political Science,_ Forthcoming.

Brender, A., and A., Drazen. (2006). “How do budget deficits and economic growth affect reelection prospects? Evidence from a large cross-section of countries.” Updated working paper based on _NBER Working Papers Series,_ WP # 11862 Brender, A. and A. Drazen. (2005). “Political budget cycles in new versus established democracies.” _Journal of Monetary Economics_ , 52(7): 1271-1295 Brunnermeier, M. K. and J. A. Parker. (2005). “Optimal expectations.” _American Economic Review_ , vol. 95(4): 1092-1118.

Cox, G., and M. McCubbins. 1986. “Electoral politics as a redistributive game.” _The Journal of Politics_ 48: 370-89.

Diermeier, D., H., Eraslan, and Merlo, A. (2003). “A structural model of government formation.” _Econometrica_ , Vol. 71: pp. 27-70.

Dixit, A., and J. Londregan. 1995. “Redistributive politics and economic efficiency.” _American Political Science Review_ 89: 856-66.

Dixit, A., and J. Londregan. 1996. “The determinants of success of special interests in redistributive politics.” _The Journal of Politics_ 58: 1132-55.

Dixit, A., and J. Londregan. 1998. “Ideology, tactics, and efficiency in redistributive politics.” _Quarterly Journal of Economics_ 113: 497-529.

Easterly, W. and R. Levine (1997). “Africa’s Growth Tragedy: Policies and Ethnic Divisions”. _The Quarterly Journal of Economics_ , 112(4), 1203-1250 Eslava, M. (2006). “The Political Economy of Fiscal Policy: Survey,” IADB Research Department Working Paper No. 583, Inter-American Development Bank, Washington DC Fabrizio, S. and A. Mody, 2006. “Can budget institutions counteract political indiscipline?” _Economic Policy_ , October 2006, 691-738, IMF Working Paper WP/06/123, Washington, DC Feddersen, T. J., and Pesendorfer, W. (1996). “The swing voter’s curse.” _American Economic Review_ , Vol. 86(3): pp. 408-424.

Fleck, R., (2001). “Inter-party competition, intra-party competition, and distributive policy: A model and test using New Deal data.” _Public Choice_ , Vol. 108: pp. 77-100.

23
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(Psacharopolous, 1985; 1986; Lauglo, 1985). The criteria of effectiveness of primary importance in these studies were labor market outcomes -- employment rates, earnings and social rates of return. While academic outcomes were, in many cases, comparable with academic secondary schools, higher costs lowered returns. For countries with severely constrained resources for education, investments in diversified schools clearly represented trade-offs with the creation of more places in lower-cost academic secondary schools. Implementation difficulties associated with diversified schools further lessened their attractiveness (Lauglo, 1985).

Supporters of the diversification concept have asserted that these schools were evaluated on the wrong criterion: that they should not be intended to prepare students directly for work, but rather to contribute to social objectives such as broad and stronger social familiarity with crafts and technology, or more favorable attitudes toward manual labor or blue collar work. Indeed, in Thailand, the diversification of much of the national secondary system has proceeded on this rationale, and there is evidence that it has succeeded in turning a greater proportion of secondary graduates towards technical streams in tertiary education.

At the same time, in his review of 20 completed World Bank investments in diversified schools, Haddad (1987) found of the 66 objectives stated for these investments, only 2 related to social goals. Thus a major part of the World Bank lending program was not justified in these terms.
**Table 1:** Background characteristics of IDPs.

||Mean|S.D.|Min.|Max.|<br>N|
|---|---|---|---|---|---|
|**Basic characteristics**||||||
|Age (years)|22.08|18.17|0|97|11854|
|Female|0.50|0.50|0|1|11854|
|Born in El Fasher|0.29|0.45|0|1|11850|
|Is main respondent|0.17|0.37|0|1|11854|
|**Displacement characteristics**||||||
|Years displaced|11.60|4.77|0|18|11850|
|Ever returned to place of origin|0.32|0.47|0|1|11850|
|In contact with people at place of origin|0.65|0.48|0|1|11850|
|Household lost documentation during displacement|0.39|0.49|0|1|11702|
|Number of times household moved|1.77|0.78|1|10|11097|
|Time spent in El Fasher|10.46|5.08|0|18|11850|
|Age when household arrived|11.61|18.75|-15|95|11850|
|Many other people from place of origin nearby|0.32|0.47|0|1|11322|
|Arrival as family|0.51|0.50|0|1|11847|
|**Conflict exposure (indices)**||||||
|Violence (any form)|-0.00|1.00|-1|3|10416|
|One-sided violence|0.00|1.00|-1|3|10416|
|Fatalities|0.00|1.00|-1|4|10416|
|**Pre-displacement**||||||
|Access to land before displacement|0.86|0.35|0|1|11798|
|Farming household|0.87|0.34|0|1|11788|
|Paid work main activity of household|0.02|0.15|0|1|11788|
|Enrolled at time of displacement|0.34|0.47|0|1|9882|
|Employed before displacement|0.46|0.50|0|1|4068|
|Employed in agriculture before displacement|0.41|0.49|0|1|4059|

19
The remainder of this paper is organized as follows. In Section 2 we discuss how we can use data from field experiments such as WTP surveys to estimate uptake of piped water connections. Section 3 provides a brief overview of the data collection in Southwest Sri Lanka and a profile of our sample. Section 4 presents the quantitative findings from regression models of WTP. We then use the model to evaluate uptake of piped water (house) connections under various scenarios. Section 5 concludes.

# **2. Gauging Demand for Improved Piped Water Service**

## **_General Framework for Demand Assessment_**

WTP surveys can be used to gauge household demand for improved network water and sanitation services by presenting consumers with a contingent scenario in which they can obtain the described service. It is a technique originally and most widely used in the area of environmental economics to estimate the public’s WTP for improvements in environmental quality (Cummings, Brookshire, and Shulze, 1986; Mitchell and Carson, 1989). Over the last decade, Contingent Valuation (CV) has been used increasingly in developing countries for improvements in WSS services. However, applying CV in developing countries requires careful adaptation of the method to account for local conditions and cultural differences (Whittington, 1998). Households can be asked to consider attributes of the service, including monthly user‐ charges and one‐time connection costs, and to choose between scenarios that present different combinations of service levels, charges, and costs. WTP data from CV surveys measure the amount of monthly income that the household could give up after obtaining the improved network service and be just as well off as in a situation without an improvement in water supply or sanitation. Thus, it is a measure of the households’ economic value. Maximum WTP and the survey response data define household values, underlie their demand for service improvements, and, therefore, inform tariff design.

Because of these features, WTP surveys represent a type of field experiment where the analyst balances control and relevance. The deliberate decisions employed in the framing of the survey 6
expenditure and CWI given that both axes are normalized to one. In reality we cannot exclude some data generation pattern in the MCA approach that would make the CWI scores exhibit consistently lower or higher inequality than a typical expenditure measure. Intuitively, one would expect a measure built on several indicators to show higher inequality than a measure built on one indicator but this is not the case. The CWI measure shows invariably less inequality than the expenditure measure and this is true for both urban and rural areas. This finding is also robust and visible all along the two distributions. We can also see that rural areas exhibit higher inequality than urban areas for both the CWI and expenditure measures. This is particularly true for the CWI measure suggesting that diversity in non-monetary measures is much more pronounced in rural areas than in urban areas.

[Figures 1 and 2]

# **7. Conclusions**

The paper has used Multiple Correspondence Analysis (MCA) to construct a consistent Composite Welfare Indicator (CWI) and used this indicator to assess poverty in Morocco between 2001 and 2007. This is one of the least explored paths in the measurement of multidimensional poverty and complements existing studies on poverty in Morocco. It is also an approach that has two important computational advantages. It gives more weight to indicators with a small number of hits, hence giving more importance to deprived minorities, and complies with the welfare axiom of reciprocal bi-additivity (see Asselin, 2009).

The MCA procedure was carried out separately for urban and rural areas on the assumption that the MCA models would be different in the two areas. Results of the two-steps procedure showed indeed that the resulting models are different and confirm the known fact about Morocco of a deeply divided society along urban and rural lines. The multidimensional indicators that characterize these two areas for both the poorest households and the least poor are often different, which is an important finding for policy purposes.

Overall, we found that the multidimensional approach largely confirms previous results on monetary poverty. Monetary and non-monetary indicators are positively and significantly correlated but this correlation is by no means unitary suggesting that multidimensional and monetary poverty do not describe the same phenomenon. However, the multidimensional approach corroborates monetary results in various respects. In 2007, poverty is found to be higher in rural areas as compared to urban areas and according to all FGT poverty indicators. And the decline in poverty observed between 2001 and 2007 has 18
Policy ReseaRch WoRking PaPeR 4667

# **Abstract**

This paper compares two datasets designed to measure entrepreneurship. The Global Entrepreneurship Monitor dataset captures early-stage entrepreneurial activity; the World Bank Group Entrepreneurship Survey dataset captures formal business registration. There are a number of important differences when the data are compared. First, GEM data tend to report significantly greater levels of early-stage entrepreneurship in developing economies than do the World Bank data. The World Bank data tend to be greater than GEM data for developed countries. Second, the magnitude of the difference between the datasets across countries is related to the local institutional and environmental conditions for entrepreneurs, after controlling for levels of economic development. A possible explanation for this is that the World Bank data measure rates of entry in the formal economy, whereas GEM data are reflective of entrepreneurial intent and capture informality of entrepreneurship. This is particularly true for developing countries. Therefore, this discrepancy can be interpreted as the spread between individuals who could potentially operate businesses in the formal sector—and those that actually do so: In other words, GEM data may represent the potential supply of entrepreneurs, whereas the World Bank data may represent the actual rate of entrepreneurship. The findings suggest that entrepreneurs in developed countries have greater ease and incentives to incorporate, both for the benefits of greater access to formal financing and labor contracts, as well as for tax and other purposes not directly related to business activities.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study entrepreneurship. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at lklapper@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Atkinson, A. B., and A. Brandolini. “On Analyzing the World Distribution of Income.” _The World Bank Economic Review_ 24 (2010): 1-37.

- Atkinson, A. B., and A. Brandolini. “Unveiling the Ethics behind Inequality Measurement: Dalton's Contribution to Economics.” _Economic Journal_ 125 (2015): 209-234.

- Bandyopadhyay, S., Bishop, J. A., and Rodriguez, J. G. _Research on Economic Inequality: Poverty, Inequality and Welfare Vol: 25_ . London: Emerald Publishers, 2017.

- Borraz, F., N. González, and M. Rossi. “Polarization and the Middle Class in Uruguay.” _Latin American Journal of Economics_ 50 (2013): 289-326.

- Bosmans, K., Decancq, K., and A. Decoster. “The Relativity of Decreasing Inequality between Countries.” _Economica_ 81 (2014): 276-292.

- Bourguignon, F., and C. Morrison. “Inequality Among World Citizens: 1820-1992.” _The American Economic Review_ 92 (2002): 727-744.

- Clementi, F., A. L. Dabalen, V. Molini, and F. Schettino. “When the Centre Cannot Hold: Patterns of Polarization in Nigeria.” _Review of Income and Wealth_ 63 (2017): 608-632.

- Clementi, F., and F. Schettino. “Declining Inequality in Brazil in the 2000s: What is Hidden Behind?” _Journal of International Development_ 27 (2015): 929-952.

- Clementi, F., V. Molini, and F. Schettino. “All That Glitters is Not Gold: Polarization Amid Poverty Reduction in Ghana.” _World Development_ 102 (2018): 275-291.

- Clementi, F., Fabiani, M., and V. Molini. “The Devil is in the Detail: Growth, Inequality and Poverty Reduction in Africa in the Last Two Decades.” _Journal of African Economies_ ejz003 (2019a). <u>https://doi.org/10.1093/jae/ejz003</u>

- Clementi, F., Fabiani, M., Molini, V., and R. Zizzamia. _Worlds Apart: What Polarization Measures Reveal about Sub-Saharan Africa’s Growth and Welfare Distribution in the Last Two Decades_ . Working Paper 1, African Centre of Excellence for Inequality Research, University of Cape Town, Cape Town, 2019b. <u>https://aceir.org.za/publications/</u>

- Cuesta Leiva, J. A., Lara Ibarra, G., Barry, A. G., Maratou-Kolias, L. M., and A. R. El-Laga. _Tunisia—Poverty Assessment_ . Washington, D.C.: World Bank Group, 2016.

- Dang, H.-A. H., and E. Ianchovichina. _Welfare Dynamics with Synthetic Panels: The Case of the Arab World in Transition_ . Policy Research Working Paper 7595, World Bank Group, Washington, D.C., 2016.

35
# **Table 7.A. Model comparison: Bias of lead price forecasts**

(U.S. dollars per metric tonne)

|**Approach**|**Horizon**|**Consensus**|**Futures**|**Bivariate**<br>**correlations**|**BVAR**|**OEM**|**Machine**<br>**learning**|
|---|---|---|---|---|---|---|---|
|Consensus|3|-70.6|...|...|...|...|...|
|Futures|3||-39.3|...|...|...|...|
|Bivariate correlations|3|||-3.1|...|...|...|
|BVAR|3||||-48.7|...|...|
|OEM|3|||*||-77.1|...|
|Machine learning|3||*|*|||-115.2|
|Consensus|6|-74.6|...|...|...|...|...|
|Futures|6||-36.3|...|...|...|...|
|Bivariate correlations|6|||138|...|...|...|
|BVAR|6||||-68.1|...|...|
|OEM|6|||||-84.9|...|
|Machine learning|6||*||||-124.3|
|Consensus|9|-76.3|...|...|...|...|...|
|Futures|9||-34|...|...|...|...|
|Bivariate correlations|9|*|*|211.2|...|...|...|
|BVAR|9||||-81.6|...|...|
|OEM|9|||||-89.1|...|
|Machine learning|9||*||||-125.1|
|Consensus|12|-76.1|...|...|...|...|...|
|Futures|12||-32.3|...|...|...|...|
|Bivariate correlations|12|*|*|221.9|...|...|...|
|BVAR|12|||*|-49.9|...|...|
|OEM|12|||*||-81.3|...|
|Machine learning|12||*||||-119.4|
|Consensus|15|-77.7|...|...|...|...|...|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15|...|...|...|...|...|...|
|BVAR|15||...|...|-67.8||...|
|OEM|15||...|...||-87.7|...|
|Machine learning|15||...|...|||-100.2|
|Consensus|18|-81.0|...|...|...|...|...|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18|...|...|...|...|...|...|
|BVAR|18||...|...|-35|...|...|
|OEM|18||...|...||-75.5|...|
|Machine learning|18||...|...|||-78|
|Consensus|21|-79.2|...|...|...|...|...|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations<br>BVAR|21<br>21|...|...<br>...|...<br>...|...<br>68.6|...<br>...|...<br>...|
|OEM|21||...|...||-67.8|...|
|Machine learning|21||...|...|||-87.5|
|Consensus|24|-78.4|...|...|...|...|...|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24|...|...|...|...|...|...|
|BVAR|24||...|...|103|...|...|
|OEM|24||...|...||-21.4|...|
|Machine learning|24||...|...|||-87.2|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias).

44
# **Introduction**

Conventional approaches to HIV prevention have been important in educating populations about HIV risk factors and risk reduction strategies, and many have been found to be cost-effective, but by themselves they have had limited success in slowing the AIDS epidemic. Over the past decade the annual number of new infections has decreased by about one-fifth, but in 2009 there were still an estimated 2.6 million new HIV infections, and 1.8 million of these were in Sub-Saharan Africa (UNAIDS, 2010). HIV experts have highlighted the need for new combination approaches to complement existing prevention efforts (Coates et al. 2008; Padian et al. 2011), including efforts to stimulate individual demand for prevention.

This paper considers the use of economic incentives to encourage people to engage in behavioral change strategies that reduce risky sexual behaviors. Incentives have been widely used in a variety of related domains, but are only recently being explored as a possible HIV prevention strategy. One type of widely adopted incentive scheme in the health domain is as part of conditional cash transfer (CCT) programs such as the well-known Oportunidades program in Mexico, which provides significant cash welfare support to households that engage in specific positive behaviors including appropriate prenatal and well-child care. Lagarde, Haines, and Palmer (2007) and Fiszbein and Schady (2009) survey such programs in detail. Related programs are being explored in the Sub-Saharan African context, but with conditionality focused on nonhealth behaviors such as schooling (which can then indirectly yield positive health consequences), e.g. Baird et al. (2010). There is also a history of providing positive price incentives for using specific types of preventive health services, most relevant in this context being the use of cash incentives to promote receipt of HIV test results (Thornton, 2008). The aforementioned incentive programs are all conditioned on observable _input_ behaviors, which can be controlled by participants. A parallel set of ―contingency management‖ programs have been designed for situations in which behavior changes are more complex and/or not easily observed, and in which conditionality is instead based on 2
Policy Research Working Paper 7711

# **Abstract**

Deepening real and financial integration of developing countries into the world economy has prompted renewed interest in the contribution of external shocks to their macroeconomic fluctuations. This paper revisits the issue using four decades of annual data for a large sample of developing countries. The paper implements a conditionally-homogeneous panel vector autoregression with exogenous variables to model GDP fluctuations in these countries. It uses sign restrictions to identify four external structural shocks— demand, supply, monetary, and commodity shocks—and analyzes how their impact on growth is shaped by countries’ policy and structural framework. External shocks are found to account for a small share of the forecast error variance of GDP, especially at short horizons. However, their contribution has been on the rise in recent decades. Further, global monetary shocks have become the leading external source of GDP volatility in developing countries. The paper presents a quantitative assessment of the effects of real and financial opening up, as well as those of commodity specialization, on the impact of external shocks on GDP. The results suggest that increasing openness can account for the increasing trend in the volatility attributable to external shocks, as well as the changing roles of different shocks. Moreover, commodity-intensive developing countries are found to be more vulnerable than the rest to all types of external shocks, not just commodity shocks.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lserven@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
certain simple intuitive ideas about pricing. The list of data includes: original issue ILS data, secondary market ILS data, a cyclic price index, ILW price indication sheets and a set of remodeled scenarios allowing evaluation of individual ILS and ILW and portfolios of the same. The quality of the data is uneven but to our knowledge has not been previously brought together in this fashion. The exercise is comforting in that the various relationships tested herein make intuitive sense and, just as important, highlight areas of insufficient knowledge.

Catastrophe risk pricing has for too long been a subject of actuarial scrutiny of statistical costs (expected losses) with little attention paid to market driven prices. The analysis herein shows that market prices follow a cycle, and they vary considerably by peril. Even low premium, low correlation perils appear to follow the same cycle, even if the cycle is amplified for peak perils. Pricing also depends on the risk characteristics of individual transactions.

None of these are surprising in themselves, and yet the analysis shows that each component is significant. It remains, of course, to bring all the elements together in a single model and quantify that relationship. That is for the future. The present study we believe takes the first step in that direction.

20