rafmacalaba/datause-encoder-data huggingface dataset:
to be minimal due to the fact that the project under valuation is a real investment plan, and the local residents treat the project very seriously. The multiple bounded discrete choice (MBDC) value elicitation format and the stochastic valuation estimation approach are used to overcome the potential uncertainties involved in the study.

The results show that on average a household is willing to pay (WTP) about 30 yuan per month continuously for 5 years for implementing the project, with a median value of 11 yuan per month. This WTP is equivalent to roughly 2.9 percent of household income. This result is within the range of the results obtained in previous similar studies conducted in China that is between 1 and 4 percent of income. The total cost of the project is estimated to be 34.02 million yuan, providing a high economic internal rate of return of 18%.

The results generally give reasonable coefficients for WTP determinants. Households with higher incomes would be willing to pay more; the estimated elasticity of WTP with respect to income is 0.21. Older people are willing to pay less; people with higher education are willing to pay more. People who donated to public charities before are willing to pay more. Farmers who use the lake for irrigation are willing to pay less, as the improvement in water quality implies less nutrition in the water. People who believe the project will improve the ecosystem also value the project more.

This study demonstrates that people’s previous knowledge about the valuation subject plays a significant role in forming their values of the subject. People who noticed the water quality change before, heard about the project before, or observed press discussions before about the water quality change and the related issues, value the water quality improvement more than those who did not. This may impose a challenge on using WTPs to measure the economic values. The values measured are the perceived ones, and the actual values may be higher, because better are people informed of the subject, more they will be willing to pay. This implies that such WTP studies can significantly underestimate the true values if the issues under valuation are not well presented. A researcher should try best to provide sufficient information to respondents about the subject in survey design and implementation, and should try to calibrate the final estimation after the survey is conducted.

This study also reveals that the interviewer effect can be negative, as the one-on-one individual interviews, where the respondents are aware of the fact that the enumerators know the reported answers of the respondents, provides a lower WTP estimation than those interviews conducted in a setting that the answers are provided anonymously. While the interviewer effect is found generally to be positive in the 16
post-earthquake participation. This casts some doubt on the efficiency of targeting of C/FfW programs, particularly in areas where people were severely hit by the earthquake.

In directly affected areas, regressions are estimated separately for cash-for-work, foodfor-work and both considered jointly. It is shown in Tables 15a to 15c that both the preearthquake asset index and asset losses have no significant effects on participation in either CfW or FfW programs. Agricultural asset losses have a negative and significant impact on CfW participation (but not on FfW participation). This may be an indication that the most agrarian households participated less in CfW programs. Being married and living in a female-headed household have negative and significant impacts on CfW participation. Living in private housing and having savings before the earthquake both have positive and significant effects on CfW participation. In non-directly affected areas, none of these variables are significant. In these areas, participation in Tontine/Sol/Sabotay and the presence of breast-feeding women in the household have positive and significant impacts on CfW participation.

Determinants of FfW are different from CfW ones. In directly affected areas: other social associations, the number of adults between 18-60 and having a bank account have positive and significant effects on FfW participation. The number of workers has a negative and significant effect on FfW participation. In non-directly affected areas: the number of children displaced and hosted in the household, participation in unions or in agricultural associations have positive and significant impacts on FfW program participation.

Finally, even when considering CfW and FfW jointly, there is no significant impact of both the pre-earthquake assets index and assets losses. Therefore, it is clear that these programs are not specifically targeted at people who need it the most, because of their low level of subsistence or losses due to the earthquake.

# **7. Discussion**

The survey used in this paper made an attempt to be representative of the population in the directly affected areas, and of certain non-directly affected areas. The 2003 census was _de facto_ made obsolete in the wake of the earthquake. So, the sampling probabilities used to determine enumeration sections which were proportional to population size in the census were relatively imprecise. Moreover, the situation in Haiti evolved rapidly. Some camps had at that point been set up but had not yet been registered by the IOM. After a peak in mid-2010, the population in camps has decreased drastically. Many people have been displaced from one area 25
higher expense ratios resulted in a much lower annual surplus and a slower build-up of the life fund.

**Table 7: Structure of Long-Term Insurance, 2001**

|(percent)|3Large<br>Companies|5Medium<br>Companies|6Small<br>Companies|All<br>Companies|
|---|---|---|---|---|
|GrossPremiums|79.0|17.7|3.3|100.0|
|CededPremiums/GrossPremiums|3.2|5.7|8.8|3.8|
|NetClaims/NetPremiums|53.1|48.2|60.2|_52.5_|
|NetCommissions/NetPremiums|3.2|6.9|8.4|4.0|
|AdminCosts/NetPremiums|14.4|20.6|32.7|16.8|
|InvestmentIncome/NetPremiums|53.4|49.8|51.8|52.7|
|AnnualSurplus/NetPremiums|82.7|74.1|50.6|79.4|
|<br>ChangeinLifeFund/NetPremiums|82.5|69.2|42.6|75.2|
|<br>ReturnonLifeFund|10.2|10.4|11.1|9.5|
|MultipleofAssetsoverPremiums|6.2|4.8|4.0|5.9|

* percent of gross premiums Source: Estimated on the basis of data collected by the FSC.

# V. General Insurance

18 companies operate in the general insurance market, 11 are composites and 7 specialize in nonlife business. However, one of the composites (British American) is predominantly a life insurer with a very small and selective participation in nonlife insurance. The general insurance market comprises five classes of business, viz. fire, motor, personal accident, transport and miscellaneous. Except for the last named, concentration is much smaller in these classes than in life insurance. For general insurance as a whole, the four largest companies accounted in 2001 for 56 percent of gross premiums, while the Herfindahl index ainounted to 1090 (compared with 2168 for life insurance).

Motor insurance is the largest class of general insurance, accounting for 45 percent of total nonlife premiums in 2001 (Table 8). Its share has been rising, reflecting the growing number of cars in circulation. But the fire and miscellaneous insurance classes are also relatively large, each with about 20 percent of total premiums. These two lines cover large industrial and commercial risks and tend to rely heavily on reinsurance.

Most large industrial and commercial risks are reinsured with nonproportional treaties. This is a very sound policy, because their gross claims ratios show considerable variability, connected to the volatility of large losses caused by low frequency events. Outside motor insurance, where only 13 percent of premiums are ceded to reinsurers, the other four classes operate with an average reinsurance ratio of 75 percent. In line with most developing countries around the world, product liability and malpractice insurance are not developed.

9
Table 3 presents the ranking of economies based on the overall Public Procurement index. The top performing high-income economy is the United States while the bottom is Namibia. The top 10 economies are all high-income economies in Europe or North America, with the exception of the Republic of Korea. The bottom 10 economies are more spread out across Sub-Saharan Africa, East Asia, Middle East and North Africa, as well as the Caribbean. In general, economies with higher incomes tend to have better public procurement regulation. This can be seen in the scatterplot presented in figure 1 between the PP score and GDP per capita income.

# _Dependent Variable_

Our measure of road quality is the quality of trade and transport-related infrastructure measure obtained from the Logistics Performance Index maintained by the World Bank. This data set is based on a survey of logistics professionals who are asked questions about the countries in which they operate.<sup>4</sup> The quality of trade and transport infrastructure is rated from “very low” (1) to “very high” (5). The average road quality score in the sample is 2.8. Germany has the highest (4.44) followed by the Netherlands (4.29) and Sweden (4.27). Haiti has the lowest score (1.47), followed by Equatorial Guinea (1.5) and Mauritania (1.54).

# _Control Variables_

We include several variables that may be correlated with road quality. This includes the level of development (GDP per capita) and the quality of institutions – proxied by the rule of law. Annual rainfall precipitation and the proportion of urban population are likely predictors of road quality and are thus included as covariates in the estimations. The mean precipitation in the sample is

> 4 The Logistical Performance Index does have some limitations given that it is based on a single rating. However, data on road quality are quite difficult to find, especially for developing economies (Russ et al., 2017).

8
# **Table 11.1 Magnitude and Composition of Capital Inflows (% of GDP)**

||_85-88_|_East Asia_<br>_89-92_|_93-96_|_85-88_|_ASEAN-4_<br>_89-92_|_93-96_|
|---|---|---|---|---|---|---|
|Net long-term capital flows|1.4|3.0|6.2|2.0|4.8|6.9|
|- Net official flows|0.4|0.6|0.4|1.2|1.3|0.4|
|- Net private flows|1.0|2.4|5.8|0.8|3.5|6.6|
|Bank/trade lending|0.0|0.7|0.7|-0.3|0.9|0.8|
|Portfolio bond|0.3|0.1|1.0|0.2|-0.1|1.4|
|FDI|0.7|1.3|3.0|0.9|2.3|2.4|
|Portfolio equity|0.0|0.2|1.1|0.1|0.4|2.0|
|IMF credit|-0.1|-0.1|0.0|-0.1|-0.1|0.0|
|Other private flows|-0.4|_-0.5_|-1.9|0.3|2.0|-0.1|
|of which: short-term debt|0.2|0.7|0.9|0.1|2.0|2.3|
|||_South Asia_|||_LAC_||
||_85-88_|_89-92_|_93-96_|_85-88_|_89-92_|_93-96_|
|Net long-term capital flows|2.2|1.9|2.6|1.3|1.7|4.3|
|- Net official flows|0.9|1.1|0.4|_0.5_|0.3|0.0|
|- Net private flows|1.3|0.8|2.1|0.8|1.4|4.4|
|Bank/trade lending|1.1|_0.5_|0.4|0.3|0.0|_0.5_|
|Portfolio bond|0.1|0.2|0.0|-0.2|0.2|1.2|
|FDI|0.1|0.1|0.6|0.7|0.9|1.6|
|Portfolio equity|0.0|0.1|1.1|0.0|0.3|1.1|
|IMF credit|-0.4|0.2|-0.1|0.0|0.0|0.1|
|Other private flows|0.0|0.3|0.6|-0.7|0.7|-1.0|
|<br>of which: short-term debt|0.4|0.1|-0.2|-0.1|0.7|0.6|

4
# **REFERENCES**

Barrett, Christopher B. (1998). “Food Aid: Is It Development Assistance, Trade Promotion, Both or Neither?” _American Journal of Agricultural Economics_ 80(3): 566-571.

Barrett, Christopher B. and Daniel G. Maxwell (2005). _Food Aid After Fifty Years: Recasting Its Role._ New York: Routledge.

Bazzi, Samuel and Michael A. Clemens (2013). “Blunt Instruments: Avoiding Common Pitfalls in Identifying the Causes of Economic Growth.” _American Economic Journal: Macroeconomics_ 5(2): 152-186.

<mark>Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan (2014). "How much should we trust differences-in-differences estimates?"</mark> _<mark>Quarterly Journal of Economics</mark>_ <mark>119(1): 249-275.</mark> <mark>Chu, Chi-Yang, Daniel J. Henderson, and Le Wang (2016). “The Robust</mark> <mark>Relationship Between US Food Aid and Civil Conflict.”</mark> _<mark>Journal of Applied Econometrics</mark>_ <mark>http://dx.doi.org/10.1002/jae.2558.</mark> <mark>Dube, Oeindrila, and Juan F. Vargas (2013). "Commodity price shocks and civil conflict: Evidence from Colombia."</mark> _<mark>Review of Economic Studies</mark>_ <mark>80(4): 13841421.</mark> Farm Service Agency and National Agricultural Statistics Service, USDA (2006). “Appendix table 9--Wheat: Farm prices, support prices, and ending stocks, 1955/56-2005/06.” Accessed 14 May 2015. <u>www.ers.usda.gov/webdocs/DataFiles/</u> Hanna, Rema and Paulina Oliva (2015), “The Effect of Pollution on Labor Supply: Evidence from a Natural Experiment in Mexico City.” _Journal of Public Economics_ 122 (1): 68–79.

Nunn, Nathan, and Nancy Qian (2014). “US Food Aid and Civil Conflict.” _American Economic Review_ 104(6): 1630-66.

24
# **References**

Adolwa, Ivan Solomon, Stefan Schwarze, and Andreas Buerkert. 2019. “Impacts of Integrated Soil Fertility Management on Yield and Household Income: The Case of Tamale (Ghana) and Kakamega (Kenya).” _Ecological Economics_ 161 (July): 186–92. https://doi.org/10.1016/j.ecolecon.2019.03.023.

- Aker, Jenny C, Rachid Boumnijel, Amanda McClelland, and Niall Tierney. 2016. “Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger.” _Economic Development and Cultural Change_ 65 (1): 1–37.

Ambler, Kate, Alan de Brauw, and Susan Godlonton. 2020. “Cash Transfers and Management Advice for Agriculture: Evidence from Senegal.” _World Bank Economic Review_ 34(3): 597– 617. doi: 10.1093/wber/lhz005

- Anderson, Jock R., and Gershon Feder. 2007. “Agricultural Extension.” In _Handbook of Agricultural Economics_ , edited by Robert E. Evenson and Prabhu Pingali, 3:2343–78. Amsterdam and New York: North Holland. https://doi.org/10.1016/S1574-0072(06)03044-1.

- Barrett, Christopher B., and Leah E. M. Bevis. 2015. “The Self-Reinforcing Feedback between Low Soil Fertility and Chronic Poverty.” _Nature Geoscience_ 8 (12): 907–12. https://doi.org/10.1038/ngeo2591.

- Beaman, Lori, Ariel BenYishay, Jeremy Magruder, and Ahmed Mushfiq Mobarak. 2018. “Can Network Theory-Based Targeting Increase Technology Adoption?” NBER Working Paper 24912. Cambridge, MA: National Bureau of Economic Research.

BenYishay, Ariel, and A. Mushfiq Mobarak. 2019. “Social Learning and Incentives for Experimentation and Communication.” _Review of Economic Studies_ 86: 976–1009. https://doi.org/10.1093/restud/rdy039.

38
region (where the monthly earnings level is one of the lowest, similar as the Eastern Region), the individuals who are employed have much higher earnings in Zagreb Region, Adriatic North Region, and Adriatic South Region. Women have a lower probability to get a job than men in every region – they have the hardest condition in the Eastern Region and the most favorable condition in the Zagreb Region; for those who manage to get a job, women workers earn less than men, particularly in the Adriatic North Region and the Adriatic South Region.

# **Table 8 -- Determination of employment and earnings at the national level in Croatia (2002-2004) – using years of school as an indicator of human capital**

|**_Estimation of income equations (dependant varia_**<br>**_kuna))_**|_Regres_<br>**_ble: monthl_**<br>|_sion 1_<br>**_y earnings (_**|_Regre_<br>**_in thousand_**<br>|_ssion 2_|
|---|---|---|---|---|
||Coef.|t-stat.|Coef.|t-stat.|
|age|0.09|12.57|0.09|12.45|
|age*age|0.00|-9.32|0.00|-9.24|
|years of school|0.34|103.1|0.32|94.17|
|sex (male=0, female=1)|-0.64|-34.94|-0.67|-36.66|
|Regional dummies (Central Region as reference)|||||
|Eastern Region|||0.06|2.44|
|Zagreb Region|||0.68|27.25|
|Adriatic North Region|||0.60|20.41|
|Adriatic South Region|||0.50|18.29|
|constant|-2.68|-16.1|-2.69|-16.45|
|**_Estimation of selection equations_**|||||
||Coef.|t-stat.|Coef.|t-stat.|
|age|0.23|85.78|0.23|85.48|
|age*age|0.00|-91.1|0.00|-90.73|
|years of school|0.05|28.14|0.05|29.83|
|sex (male=0, female=1)|-0.24|-21.44|-0.23|-21.26|
|Regional dummies (Central Region as reference)|||||
|Eastern Region|||-0.21|-15.03|
|Zagreb Region|||-0.20|-14.53|
|Adriatic North Region|||-0.10|-5.81|
|Adriatic South Region|||-0.30|-20.35|
|married|0.21|17.38|0.21|17.3|
|household head|0.20|15.54|0.20|15.45|
|constant|-4.58|-91.26|-4.47|-88.11|
|Log likelihood|-121325||-120545||
|Number of observations|78148||78148||
|Note: All coefficients are significant in 1%.|||||

Given the large variation of the labor market structure and performance in different regions, we relax the implicit assumption on the homogeneities of the roles of the individual characteristics in employment and earnings across regions, and estimate the 16
1 .45 Figure 1.C.4 LAND USE UNDER FIXED RESOURCES, 2015-19 (MiLLior of Acres) <!-- Start of picture text --> 4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>0.5<br>120 140 160 180 200<br>Cocoa Price (1987 Ccdib/Kgi<br>a Perennial + Annuol 0 Follow<br><!-- End of picture text -->
As mentioned above, during the last two decades the Western part of Germany, like other market economies, has been undergoing significant adjustments in response to globalization. At the firm level, one observes a relatively sizable increase in the labor turnover rate, which is reflected in a two-thirds increase in the inflow rate into unemployment between 1991 and 2005. The rise in the inflow rate in market economies such as West Germany appears to be driven in part by a decline of some traditional industries and rise of the service sector, with the former having a lower and the latter a higher rate of turnover. These different rates of turnover in turn seem to be caused by lower competition and greater firm-specific human capital in the declining relative to the rising sectors of the economy. Another part of the explanation for the rising inflow rate is growing international competition and greater frequency of shocks that result in permanently higher rates of job destruction and job creation.<sup>6</sup> Models of transition from plan to market assume that the turnover (inflow) rate would rise dramatically as the old state sector sheds workers who go through unemployment into new jobs that are being created in the emerging private sector (e.g., Aghion and Blanchard, 1994, Blanchard, 1997, and Castanheira and Roland, 2000). The models predict that the inflow rate would be temporarily very high and gradually decline and approach the level observed in otherwise similar market economies such as West Germany. Interestingly, data from the five transition economies, presented in Table 1, indicate that the inflow rate trajectories have been very different from the theoretical scenario. First, except for East Germany (to be discussed presently), none of the transition countries that we study had a considerably higher inflow rate than West Germany during the entire 1991-2005 period. In fact, some of the countries had a lower inflow rate than West Germany for extended periods of time -- the Czech Republic in the early-to-mid 1990s being a notable example. Second, by the mid 2000s the inflow rate in all economies except East Germany converges to a similar range (1.1-1.6). Third, by the mid-2000s the West German

> percent in the Czech Republic.

> 6 For more on the issue of shocks and labor market institutions, see Ljungqvist, L. and T. Sargent, (1998), den Haan et al.

− 7 −
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12 averaging period. The longer the period over which reference prices are averaged, the smoother the price but the larger the departure from cost recovery, potentially leading to large financing gaps.

Turning to natural gas, there are three distinct global markets—North America, Europe, and Asia— driven by fundamentally different factors. Natural gas is more expensive to store and transport than oil, making international trade in natural gas costly. As a result, only 30 percent of natural gas consumed globally crossed national borders in 2014, about two‐thirds by pipeline and one‐third as liquefied natural gas (LNG), against 62 percent for oil (BP 2015). This lack of trade explains the persistence of distinct gas markets to this day.

With the exception of North America, natural gas prices have also fallen substantially, especially in Asia where gas prices are indexed to oil prices. Figure 6 shows the evolution of natural gas prices in the three major markets. Since mid‐2009, Asia has had the highest natural gas prices, followed by Europe, and finally North America, although in February 2015, spot prices for LNG in Asia fell to US$6–7 per million British thermal units (mmBtu),<sup>5</sup> lower than the prices in Europe ( _LNG Intelligence_ 2015). North America is the only one of the three markets in which gas prices are delinked from oil prices, there is gas‐to‐gas competition, prices are determined by supply and demand in spot trading, and gas production is rapidly expanding.

**Figure 6: Monthly average natural gas prices**

<!-- Start of picture text --> 20<br>USA Europe Japan<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Jan‐09 Jan‐10 Jan‐11 Jan‐12 Jan‐13 Jan‐14 Jan‐15 Jan‐16<br>US$/mmBtu<br><!-- End of picture text --> _Source:_ World Bank.

_Notes_ : USA = spot price at Henry Hub, Louisiana; Europe = import border price with a spot price component; Japan = import price.

End‐user natural gas prices also span a wide range across countries, differing by more than 50‐fold for gas supplied for power generation, an important consumer of natural gas (Figure 7). Because prices paid by large consumers are often bilaterally negotiated, information is not readily available except where prices are published by the government or utility companies. As such, the prices shown in the figure 5 In this paper, prices are shown per mmBtu (energy unit) or thousand cubic feet (mcf), depending on the pricing in that particular market. In the abbreviation, m stands for 10<sup>3</sup> and mm for 10<sup>6</sup> . Although conversion from a volumetric unit to an energy unit depends on the energy content of the specific gas, for the purposes of this paper, mmBtu and mcf are considered interchangeable. Where prices are per cubic meter (m<sup>3</sup> ) in a market, they are converted to prices per mcf.