rafmacalaba/datause-encoder-data huggingface dataset:
# **7. References**

Ambrosi, Ph., Hourcade, J.C., Hallegatte, S., Lecocq, F., Dumas, P., Ha Duong, M., 2003. Optimal control models and elicitation of attitudes towards climate damages. Environmental Modeling and Assessment 8 (3), 133 – 147.

Benson, C., and E. Clay, (2004), “Understanding the Economic and Financial Impact of Natural Disasters”, The International Bank for Reconstruction and Development, The World Bank, Washington D.C.

Dessai, S., and M. Hulme. 2007. Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England. Global Environmental Change 17:59-72.

Dumas, P., S. Hallegatte, 2009: Climate Change Economic Damages: Influence of Inertia Heterogeneity and Ripple Effects in a Two-Sector Model, Environmental Modeling and Assessment, submitted.

Groves, D. G., Knopman, D., Lempert, R., Berry, S., and Wainfan, L. (2007). "Presenting Uncertainty About Climate Change to Water Resource Managers - Summary of Workshops with the Inland Empire Utilities Agency." RAND, Santa Monica, CA.

Hall, J.W., 2007. Probabilistic climate scenarios may misrepresent uncertainty and lead to bad adaptation decisions. Hydrological Processes, 21(8), 1127-1129.

Hallegatte, S., 2005, The time scales of the climate-economy feedback and the climatic cost of growth, Environmental Modeling and Assessment, 10 (4), pp. 277-289 Hallegatte, S., 2006, A Cost-Benefit Analysis of the New Orleans Flood Protection System. Regulatory Analysis 06-02. AEI-Brookings Joint Center, Mar 2006.

Hallegatte, S., 2007. Do current assessments underestimate future damages from climate change? World Economics, 8, 131–146.

Hallegatte, S., 2008. An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina, Risk Analysis 28(3), 779-799 Hallegatte, S., 2009: Strategies to adapt to an uncertain climate change, Global Environmental Change 19, 240-247 Hallegatte, S., J.-C. Hourcade, P. Ambrosi, 2007a. Using Climate Analogues for Assessing Climate Change Economic Impacts in Urban Areas, Climatic Change 82 (1-2), 47-60 Hallegatte, S., J.-C. Hourcade, P. Dumas, 2007b. Why economic dynamics matter in assessing climate change damages: illustration on extreme events, Ecological Economics, 62(2), 330-340 Hallegatte, S., P. Dumas, 2008. Can natural disasters have positive consequences? Investigating the role of embodied technical change, Ecological Economics, 68(3), 777-786 Hallegatte, S., M. Ghil, 2008: Natural disasters impacting a Macroeconomic Model with Endogenous Dynamics, Ecological Economics, 68(1-2), pp. 582-592 16
**Figure 2. Cumulative welfare changes in 2010**

<!-- Start of picture text --> 600<br>400<br>200<br>c -200<br>-400-80 - L-A _<br>0)<br>-600-<br>-800<br>-1000<br>-1200<br>-1400<br>Vietnam Thailand Indonesia Malaysia Philippines Korea<br>I U Standard 0 Services El Manufactures O All Sectors<br><!-- End of picture text -->

*Source: Authors' simulations with GTAP-Dyn (2002).

**Figure 3. Export shares for** 1997

<!-- Start of picture text --> 80<br>teo<br>60<br>Korea Indonesia Vietnam Malaysia Philippines Thailand<br>| Agriculture El Light manufactures s Other rnanufactures E] Conmmrcial services 0 Other services<br><!-- End of picture text -->

*Source: Version 5 GTAP data base (Dimaranan and McDougall, 2002).

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# Table IV.8

## Factury Automnation Sample:<sup>Choice of Export Channels</sup>

||Early|Exporters (B<br>_N =6_|efore 1984)<br>|||.<br>|Late Exp<br>|orters (1984 or<br>_N-10_<br>|Later)<br>Sb|
|---|---|---|---|---|---|---|---|---|---|
||Direct<br>Effort<br>of Firm|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|Sub-<br>Contracting||Direct<br>Effort<br>of Firm|Foreign<br>Buyers|Non-Profit<br>Agency<br>Support|u-<br>contracting|
|Relative Importance<br>At First Export||||||||||
|All|3.2|2.7|1.5|2.8|All<br>|2.3<br>|3.6<br>|2.0<br>|2.2<br>|
|Small (< 90 workers)'|3.3|1.7|2.0|3.3<br>|< 90<br>|2.2<br>|3.6<br>|1.4<br>|1.4<br>|
|Large**(.2**90 workers)'|3.0|3.7|1.0|2.3|90 +|2.4|3.6|2.6|3.0|
|Relative Importance<br>Current"||||||||||
|All|4.4|3.6|2.0|2.0|All<br>|3.1<br>|3.1<br>|1.9<br>|1.9<br>|
|Small Sizet. 90)'|5.0|4.0|3.0|1.5|• 90<br>|224<br>|2.8<br>|1.6<br>|1.0<br>|
|Large Size (.90)'|4.0|3.3|1.3|2.7|90 +|3.8|3.4|2.2|2.8|
|Relative Importance<br>For All Exporters|Ex|ports Unimp|ortant t5. 10%|<sup>of sales)</sup>||Exp|orts Importa<br>|nt (> 10%<sup>of</sup><br>|<sup>sales)</sup><br>|
|At First Export|2.5|3.4|1.4|2.3||3.2<br>|2.8<br>|3.0<br>|2.8<br>|
|Current|3.0|3.2|1.5|1.7||5.0|3.5|3.0|2.0|
|Size at first export.<br>The time elapsed since start-up a|verages<sup>11.3 years</sup>|<sup>for early expo</sup>|<sup>rters and 4 year</sup>|<sup>s for late exporters.</sup>||||||

Note: 'Current' period is 1991.

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**<u>Figure 3. Imports and the real exchange rate</u>**

<!-- Start of picture text --> Dominican Republic Ecuador<br>60 4.80 34 4.80<br>55 4.75 32 4.70<br>4.70<br>50 4.65 30 4.60<br>28<br>45 4.60 4.50<br>40 4.55 26 4.40<br>4.50 24<br>35 4.45 22 4.30<br>30 4.40<br>20 4.20<br>Imports REER Imports REER<br>El Salvador Guatemala<br>30 4.90<br>44 4.90 28 4.80<br>42 4.80<br>4.70<br>40 4.70 26<br>38 4.60<br>36 4.60 24 4.50<br>4.50<br>34<br>32 4.40 22 4.40<br>30 4.30 20 4.30<br>Imports REER Imports REER<br>Haiti Honduras<br>50 5.20 60 5.10<br>45 5.00 55 5.00<br>40<br>4.90<br>35 4.80 50<br>30 4.80<br>25 4.60 45 4.70<br>20 4.40 40 4.60<br>15<br>35 4.50<br>10 4.20<br>Imports REER Imports REER<br>Jamaica Nicaragua<br>65 5.10 60 4.8<br>5.00 4.7<br>55<br>60 4.90 4.6<br>50 4.5<br>4.80<br>55 4.70 45 4.44.3<br>50 4.60 40 4.2<br>4.1<br>4.50 35<br>4<br>45 4.40 30 3.9<br>Imports REER Imports REER<br>Imports/GDP Log of GDP Imports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Imports/GDP Log of REER<br>Imports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Imports/GDP Log of REER Imports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Imports/GDP Log of REER Imports/GDP Log of REER<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Note: increases in the REER index indicate a real appreciation. Source: Author’s calculations.

12
accommodates order randomization as well as a set of other features identified as good practice for MPL design from the authors’ own research and the literature. These features include practice MPL rounds, constant maximal values in each binary choice within MPL, focusing WTP intervals on points of interest for hypotheses tests, and others. Many of these can also be implemented in pen and paper or verbally elicited MPL.

Our paper contributes to the methodological literature on WTP elicitation in field applications (e.g., Berry et al. (2020); Burchardi et al. (2021)). We focus on multiple price lists, an increasingly common elicitation approach in development economics. MPL are popular because they hold the promise of both straightforward implementation for researchers and ease of comprehension for subjects. However, we show that choice error is an important feature of MPL data, and likely any WTP data. When combined with specific features of data collection or analysis, this error has the potential to introduce bias in measured WTP. While others have discussed framing effects in MPL data (e.g., Andersen et al. (2007)), order effects have been largely overlooked to date. We draw three main conclusions: first, in populations where choice error is frequent, we caution against the use of some of the most common methods used in the design and analysis of MPL. Second, researchers using MPL or interpreting MPL data should pay special attention to the potential for order bias. Third, with the right MPL design and estimation approach, we believe that MPL continue to be a valuable tool. Our proposed two-pronged method of order randomization and latent utility estimation provides researchers and practitioners with a unified approach to diagnosing and mitigating bias while correctly accounting for choice error. Our Stata and SurveyCTO templates facilitate the measurement of WTP using MPL.

The next section describes WTP elicitation via MPL. It discusses common patterns in MPL data, presents a framework to think about error in such data, and summarizes how the existing literature has addressed MSB, NSB and interval identification. Section 3 proposes to combine order randomization with a random utility model for MPL choices and uses data from an experiment in Cape Town, South Africa to show how this approach affects measured WTP. We demonstrate in particular that order biases can significantly affect elicited WTP. We also describe how researchers might apply our framework to investigate other sources of bias that arise through MPL design decisions. In section 4, we discuss features of MPLs for WTP elicitation that we identify as useful for improving data quality. These features are implemented in an accompanying template for implementing an MPL in SurveyCTO and estimating WTP from the data, described in detail in a technical appendix (Appendix S2).

4
commuting and telephone services over total household expenditure ( _Social_exp_ ) to measure the efforts for social network expansion. Although the data on cooperation level when facing fraud schemes is not available, we use the survey interviewer’s assessment about households’ compliance to proxy the general cooperation level ( _Cooperation_ ), which is a dummy variable that equals to 1 if the household respondent was assessed as very compliant during the interview.

Both the OLS and the 2SLS estimation results are reported in columns (4) to (6) in Table 9. The results are consistent with our conjecture. Credit-constrained households are more likely to use informal credit. Perhaps to facilitate the necessary social connections needed in informal borrowing, credit constrained households spend more on commuting and telephone services, and are more likely to behave cooperatively. The finding here that credit-constrained households tend to behave more cooperatively is consistent with a calculation that we made that a significant proportion (70%) of fraud victims attribute their victimization to trust and compliance.<sup>19</sup>

# **9. Conclusion**

Using a novel and comprehensive data set on fraud victimization from CHFS, we investigate the extent to which urban households’ financial conditions affect fraud victimization, and then explore potential mechanisms. The baseline regression results indicate that household credit constraints are associated with 2.3 percentage points higher probability of becoming a fraud victim and 20.4 percent higher economic losses among those being approached. To deal with endogeneity issues, we employ the IV approach, and use as instrumental variables the exposure to an unexpected nation-wide private property reform and the local bank density. To address potential sample selection bias, we further use the Heckman selection model where e-commerce coverage at the community level is used as the source of exogenous variations. The findings suggest that household credit constraints lead to both a higher probability of fraud victimization and higher subsequent economic losses upon being approached. Further analyses on potential mechanisms show that the personal discount rate (impatience) and the need for social network expansion are likely pathways via which credit constraints affect fraud victimization. Our paper thus

> 19 Authors’ calculation based on CHFS data.

25
Equation (27) corresponds to a modified version of the Beveridge curve, a negative relation between labor market tightness and unemployment, or alternatively, between vacancies and unemployment. When _θ_ increases, _m_ ( _θ_ ) increases as well, encouraging job creation and reducing _u_ ( _y_ ) (‘job creation’ effect). Stochastic job matching and endogenous job destruction give us an additional counteracting effect (‘reservation productivity’ effect): an increase in _θ_ also increases _rU_ ( _y_ ) (more outside opportunities), which increases _RUF_ ( _y_ ), _RUI_ ( _y_ ), and _RFU_ ( _y_ ). The first two discourage job creation and the latter encourages job destruction, both of which increase unemployment.<sup>49</sup> Aggregate unemployment can be obtained by aggregating across types: Intuitively, in equilibrium there will be some imperfect sorting of workers among sectors based on their types, _y_ . ‘High’ type workers are more likely to take formal-sector jobs, while ‘low type’ workers are more likely to take informal-sector jobs, and medium type workers will take both.<sup>50</sup> In addition to compositional effects, changes in policy parameters affect the steady state distributions of productivities among sectors, affecting also the distributions of wages across formal and informal employment.

# **3.7 Determination of the Value of Unemployment**

The only thing needed to solve the model is to determine _U_ ( _y_ ) for all values of _y_ , as a function of reservation productivities and labor market tightness parameters.

Equation (1), together with equations (26) and (32), imply:

> 49I assume that the first effect dominates the second effect (since empirical evidence supports a downward-sloping Beveridge curve). Stochastic job matching means that changes in labor taxes, subsidized health or prices may shift the relationship between vacancies and unemployment, and that the effect of a change in the relevant policy parameter on _u_ via job offers is mitigated, since there are additional effects that offset this demand-side effect.

> 50This is more or less consistent with the empirical fact that the formal sector is skilled labor intensive while the informal sector is unskilled labor intensive in Colombia: average years of schooling for a FS worker is 12.14 , while for an IS worker it is 9.12 years (Source: Author’s calculations based on ECH, second quarter of 2003).

21
inequality.

We also use the National Health and Family Surveys (NFHS) to obtain estimates of changes in consumer durable assets and access to public services, such as electricity, water and toilet on household premises. We follow Somanchi (2021) and use the publicly released state-level aggregates of 14 states from the NFHS’ 2019 round to validate our reweighting strategy (see section 3).

Finally, we use changes in real rural wages reported in Kundu (2019) to validate estimated changes in the consumption distribution for rural India observed after 2011.

_Non-official surveys_ : We rely on two private survey data sources to further our understanding of household consumption since 2014. The first is the India Human Development Survey (IHDS) subsample round, comprising of a sample of 4,828 households from three states of Rajasthan, Bihar, Uttarakhand and fielded during February to July 2017. The first two rounds of IHDS are nationally representative household panels with waves conducted in 2004 and 2011. Households interviewed in the third subsample round of 2017 are part of IHDS’ original panel (Desai, 2020). Consumption aggregates from IHDS are based on a basket of 52 items. Average national consumption growth between 2004 and 2011 based on IHDS is 3.8 percent compared to compared to 3.5 percent growth reported in NSS. Historically, the mean consumption growth from the two surveys have closely tracked each other.

We also use publicly reported quarterly growth estimates of fast-moving consumer goods (FMCG) from Nielsen to track consumption trends. These estimates are based on Nielsen’s extensive network tracking sales, stock and prices of FMCG goods across brick-and-mortar shops and online channels in rural as well as urban centers.

_National accounts and remote sensing data._ We use growth in private final consumption expenditure (PFCE) per capita based on national accounts and night-time lights data from 2014 to 2020 from Beyer, et al. (2021) to validate our main results. Nighttime light data are aggregated to the district level and measured in Nanowatts/cm2/steradian.

# **2.3 Differences between CPHS and NSS consumption surveys**

In this section, we systematically document differences between CPHS and NSS consumption surveys that hamper direct comparisons of consumption levels between the two surveys.

_Sampling differences._ First, the rural and urban substrata in the two surveys constitute different geographical units. The rural FSUs in the NSS’ 2011-12 survey were drawn based on 2001 population census village boundaries, whereas the rural FSUs in the CPHS are based on the 2011 round of the census. The number of statutory towns 8
are discarded from the NSS consumption survey. On average, the harmonized basket of goods accounts for about 96 percent of per capita consumption expenditure in the NSS-2011. Fifth, we standardize CPHS’ custom industry codes by constructing a concordance with the national industrial classification (NIC, 2008). Sixth, we discard the longitudinal properties of the CPHS by randomly selecting one wave out of a possible three waves in a year.<sup>13</sup> We adjust individual level sampling weights for non-response using an adjustment factor provided in the CPHS. This non-response adjusted weight, by design, addsup to the Census’ population projections for a given year. We choose not to rely on these individual weights as due to the passage of time -- the last available census is now a decade old -- population projections are likely to become imperfect. One of these imperfections stems from faster than expected fall in fertility rates in 2019 reported in the recent National Family and Health Survey round of 2019-21<sup>14</sup> . Instead, we reconstruct individual level survey weights by multiplying household level weights (provided in the CPHS survey) and the household size (observed in the household roster) for each round.<sup>15</sup> This approach allocates the same sampling weight to each household member and relies on the population distribution observed in the survey rather than the Census’ estimated population distribution.<sup>16</sup> Henceforward, we refer to these reconstructed weights as reported CPHS weights and implement a reweighting procedure (that produced adjusted weights) to achieve national representativeness.

# **2.5 Addressing differences in sampling design**

Comparisons of selected statistics obtained with the CPHS with those obtained with several nationally representative surveys identify key biases that raises concern about measurement of poverty and inequality using CPHS data with reported weights. For this reason, we undertake a systematic reweighting exercise with the objective to transform the CPHS into a nationally representative survey (and thereby correct for these biases). Following recent literature (Wittenberg, 2009; Tack and Ubilava, 2013), we adopt the 13Not all households are interviewed in all three waves in a year, due to households being unavailable, locked at the time of survey or other reasons. For households that are visited more than once a year, we choose one visit at random.

> 14If the fertility rate falls to below replacement level, it signals that the population is stabilizing.https://indianexpress.com/article/india/fertility-rate-falls-to-below-replacementlevel-signals-population-is-stabilising-7639986/

> 15The individual level weights that are bundled in CPHS survey dataset are based on population projections from the Census. As these projections can become dated overtime, we observe the household size captured in CPHS’ survey roster and calculate individual weights as the product of CPHS’ reported household weight and its size.

> 16Note that the non-response adjusted household weights are still based on census’ household level projections.

11
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE of Congo, hidden costs reach 600 percent of sector revenues, but for most other countries in the region, hidden costs rarely exceed 200 percent of sector revenues.

**Figure 7. The hidden costs of Angola’s power sector are among the worst on the continent**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>DRC Congo Angola Malawi Tanzania Zimbabwe Botswana Zambia Mozambique<br>(2009)<br>Losses Underpricing Collection Inefficiencies<br>revenue<br>Hidden costs as a share of<br><!-- End of picture text --> _Source:_ Briceño-Garmendia, Smits, and Foster 2009. _Note:_ DRC = Democratic Republic of Congo.

Although Angola’s power costs can be expected to fall in the medium term, today’s tariffs will nonetheless need to increase if the sector is to reach financial equilibrium. Angola faces relatively high power costs as of today, but there is reason to believe these costs will fall over time. Angola still has vast unexploited hydropower potential as well as abundant gas reserves estimated at 10 trillion cubic feet (World Bank 2010a). Both of these primary sources of energy could be harnessed to produce power at a much lower cost than today. Backing up Angola’s largely hydro-based system with gas-fired generation, as opposed to the current oil-fired generation, would prove to be a great deal more cost effective. In addition, as Angola develops its national power grid it will be in a position to interconnect with the Southern Africa Power Pool, opening up access to a range of other cost-effective sources of power, most notably the Inga power scheme in the Democratic Republic of Congo. Even taking all of these factors into account, Angola’s long-run marginal cost of power could still be expected to amount to around $0.11 per kWh, which is almost three times today’s tariffs. This points to the importance of moving tariffs on to a more sustainable trajectory.

**Figure 8. Cost recovery will remain a questionable prospect even in the future**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>-<br>Historical total cost Long-run cost of Long-run cost of Tariff to consumers Average revenue Bulk tariff to EDEL<br>imported power (trade imported power (trade<br>stagnation scenario) scenario)<br><!-- End of picture text --> _Source:_ AICD calculations based on Rosnes and Vennemo (2009); IFC staff estimates; World Bank (2010a).

17