rafmacalaba/datause-encoder-data huggingface dataset:
|Does this statement describe you?|Y|es||No|_N_|
|---|---|---|---|---|---|
||_L_|_H_|_L_|_H_||
|I have no trouble making and keeping|0.97|0.97|0.00|0.03|62|
|friends.||||||
|If I need help in carrying of a plan of mine,|0.06|0.13|0.84|0.77|62|
|it’s usually difcult to get others to help.||||||
|I often fnd it hard to get my point of view<br>across to others.|0.03|0.17|0.78|0.77|62|
|In attempting to smooth over a disagree-<br>ment, I usually make it worse.|0.09|0.07|0.84|0.83|62|

Table 5: Responses to the survey on self-efficacy.

# **7 Conclusion**

There is increasing awareness that one should not draw inferences about universals in human behavior from the results of experiments with US college students. This paper provides a new example in behavioral economics in which results do not generalize and in which cultural traits have important economic consequences. It presents results from an experiment in coordination with high-caste and low-caste men in ten villages in northern India. In general, the low-caste men coordinated on the efficient convention, just as experimental results with US college students led us to expect, but the high-caste men did not—either when they were interacting among themselves or with members of low castes, though convergence to efficiency was more likely with members of low castes. Under the efficient convention, the endowment in each period grew by 67 percent (from 6 rupees to 10 rupees), whereas under the inefficient convention, it grew by only 17 percent (from 6 rupees to 7 rupees). The disparity in outcomes may be due to the culture of honor. Because the high castes adhere to the culture of honor much more strongly than the low castes, they may interpret the loser’s payoff and other affronts as challenges to their honor or status. The culture of honor is associated with the mindset, “Cross me and I’ll punish you,” under which accidental miscoordination gives rise to retaliation and more miscoordination and misunderstanding and the unraveling of attempts at collaboration. Almost 90 percent of both high- and low-caste players continued to take the collaborative action, _Stag_ , after reaching the efficient equilibrium in the Stag Hunt. There is thus no evidence that high- or low-caste men seek to “insult” their partners by imposing a loss on them. However, the mental representations that high-caste men create to interpret the loss they incur from miscoordination (the loser’s payoff) appear to lead them to this biased view. Our results are robust to controls for education and wealth, which 27
Appendix 5: Seemingly unrelated system equation estimates of non-farm wage employment and non-farm self-employment participation model

||GHK Simulated<br>estim|bivariate ML<br>ates|Seemingly unrelated<br>estim|bivariate ML Probit<br>ates|
|---|---|---|---|---|
|VARIABLES|Wage<br>employment|Self<br>employment|Wage employment|Self employment|
|Age of the household head|-0.000|-0.000|-0.000|-0.000|
||[0.982]|[0.977]|[0.984]|[0.983]|
|Male-headed household (0/1)|<br>0.423***|<br>0.061|<br>0.423***|<br>0.061|
||[0.003]|[0.599]|[0.003]|[0.596]|
|Highest level of formal education acquired by household head|||||
|PSLC|0.192|0.003|0.192|0.001|
||[0.270]|[0.984]|[0.270]|[0.993]|
|JCE|<br>0.621***|<br>0.158|<br>0.622***|<br>0.156|
||[0.004]|[0.425]|[0.004]|[0.430]|
|MSCE|0.991***|0.117|0.991***|0.118|
||[0.001]|[0.685]|[0.001]|[0.683]|
|Non-University Diploma and above|0.894|0.942+|0.896|0.934+|
||[0.169]|[0.127]|[0.169]|[0.130]|
|Maximum level of formal education acquired in the household|||||
|PSLC|0.097|0.071|0.098|0.072|
||[0.525]|[0.568]|[0.524]|[0.565]|
|JCE|<br>0.102|<br>-0.157|<br>0.102|<br>-0.154|
||[0.579]|[0.332]|[0.580]|[0.341]|
|MSCE|0.408*|-0.211|0.408*|-0.211|
||[0.094]|[0.360]|[0.094]|[0.360]|
|Non-University Diploma and above|<br>1.099*|<br>-1.195**|<br>1.098*|<br>-1.192**|
||[0.066]|[0.037]|[0.067]|[0.038]|
|Number of infant (<5yo) in HH|0.007|0.086*|0.007|0.087*|
||[0.906]|[0.064]|[0.904]|[0.062]|
|Number of children (5-14yo) in the household|0.087**|0.003|0.087**|0.003|

47
**Table 2:  Maximum wind speed, 1877-2016 525 cyclonic storms, Arabian Sea and Bay of Bengal**

|**Period**|**Cyclonic**<br>**Storms**|**Years in**<br>**Period**|**Mean Max**<br>**Wind Speed**<br>**(kt)**|**2-Period**<br>**Mvg. Avg**|
|---|---|---|---|---|
|**1877-1900**|**65**|**24**|**51.3**||
|**1901-1920**|**64**|**20**|**51.6**|**51.4**|
|**1921-1940**|**72**|**20**|**48.9**|**50.2**|
|**1941-1960**|**52**|**20**|**49.4**|**49.1**|
|**1961-1980**|**127**|**20**|**53.3**|**51.4**|
|**1981-2000**|**84**|**20**|**63.3**|**58.3**|
|**2001-2016**|**61**|**16**|**58.6**|**61.0**|

**Sources: BMD, IMD, IBTrACS**

For long-run trend analysis, the most informative view may be a comparison of two-period MA end points in Table 2 (column 5): 51.4 kt in 1901-1920 vs. 61.0 kt in 2001- 2016. From these benchmarks, we infer an increase of 18.5 % in maximum wind speed during the past century.

For overall perspective, we posit a rough composite index of storm power within a period as the product of storm incidence and mean wind speed. During the past century, the incidence and speed components of this index have increased by 35.9% and 18.5%, yielding an increase of 54.4% in the composite index.

# **4.  Spatial Landfall Dynamics in Bangladesh, West Bengal and Odisha**

As we noted in the Introduction, we have constructed the cyclone database to support research on household- and community-level adaptation to cyclone risks. These risks may affect coastal population dynamics in at least two ways. In the long run, the number and size of coastal communities may be affected by the long-run frequency and power of cyclone strikes. Other things equal, we would expect areas that have been largely cyclone-free for many years to be more populous than frequently-impacted areas. Shorter-term, if impact-intensive zones shift over time, 8
the Eastern province, where World Vision is particularly active in service delivery, 49.06 percent of respondents believe that public services in their community have improved over the past year. Of these respondents, 56.52 percent credit World Vision and only 26.09 percent credit the government for the improvement in service delivery. The remaining 17.39 percent of respondents credit other NGOs for the improvement in service delivery. Of the 47.17 percent of respondents who do not believe that services have improved over the past year, 88.0 percent blame the government and 12.0 percent blame the community. Not one respondent blamed an NGO or donor for the lack of improvement in service delivery. Further, respondents were asked if the decision was up to them, to whom would they prefer to pay their taxes. Half of the ward’s respondents said that they would prefer to pay their taxes to donors, NGOs, or other non-state actors and the other half would prefer to pay their taxes directly to the government.

Unlike sectarian and rebel movements that tend to flourish where they are able to provide the goods that citizens are not receiving from their own government (Chen, 2004; Fritsch, October 2, 2001; Iannaccone and Berman, 2006), NGOs and donors do not have aspirations to wrest control from the government and are therefore unlikely to replace the state as a source of authority. Yet, they still pose a threat to states in terms of undermining citizens’ perceptions that the state is not fulfilling its end of the fiscal contract. Several governments including Uganda, Ethiopia, and Zambia have passed bills or taken other concrete actions designed to control and regulate NGOs. Although these actions are primarily directed at limiting the ability of NGOs to oppose government policies, they are also aimed at enhancing the state’s role in service delivery in the eyes of its citizens.

# **4 Can the provision of services by donors and non-state actors the con- strengthen fiscal tract?**

Many scholars argue that the effect of aid is contingent on the institutional environment in place (Burnside and Dollar, 2000, 2004; Kosack, 2003; Morrison, 2009, January 2010). Where accountable government institutions are in place survey was funded by an Opportunity Fund generously provided by the World Justice Project.

7
|latin America and the|
|---|
|Caribbean (>AC) Region|
|(and Northern America) ........<br>45|
|America ....<br>48|
|Borrowers only<br>.........<br>48|
|LAC Department I .........<br>50|
|<br>LAC DepartmentII........<br>52|
|LAC Department IV ........<br>54|
|<br>LAC DepartmentIV<br>.<br>_56_<br>|
|L.AC Department IArgentir.a<br>LAC<br>Depaxrtment I|
|Brazil<br>.<br>.....<br>60|
|LAC Department 11|
|Costa Rica .<br>....<br>...<br>64|
|El Salvador ....<br>...<br>66|
|Guatemala ..<br>....<br>70|
|Monduras ......<br>70|
|**Mexico.72**<br>'72GuaCuba.134|
|Nicaragua................74|
|Panama ...<br>76|
|LAC Department III|
|<br>Antigua and Barbuda<br>80|
|<br>Bahamas<br>....<br>82|
|Barbados ....<br>84|
|Belize<br>'...<br>.86|
|Bolivia<br>.<br>.<br>88|
|Colombia<br>.....<br>90|
|Dominica............<br>92|
|DominicanRep<br>9|
|Grenada .....<br>.....<br>96<br> <br>|
|Guyana.....<br>.....<br>98|

|Haiti ....<br>......<br>100|
|---|
|Jamaica.<br>102|
|<br><br>St.Kitts an Nevis.<br>106|
|St. Lucia .108<br>|
|St. Vincent and the Grenadines 110<br>i|
|Surname .112<br> <br>|
|Trinidad and Tobago.<br>114<br> <br>|
|Venezuela<br>..............<br>116|
|LAC Department IV<br>..............<br>120<br>Chil<br>122|
|e .........<br><br>Ecuador .124|
|Paraguay .126<br> <br>|
|Peru ..................<br>128<br>|
|Uruguay .130|
|Nonborrowers (Latin America<br>and the Caribbean)<br>34<br> <br>|
|Guadeloupe<br>....<br>...<br>136<br>|
|Martinique .138|
|Netherlands Antilles .140<br> <br>|
|Virgin Islands (U.S.) .......<br>142<br> <br>|
|O........<br>ther Latin America and the<br>|
|Caribbean .144|
|Nonborrowers (Northern America)<br>|
|Canata .148<br> <br>|
|Pueno Rico .............<br>150<br> <br>|
|UnitedStates of America....<br>152<br>O..........<br>94<br>ther Northern America<br>....<br>154|
We estimate a probit model that incorporates adjustments for heteroskedasticity. The results, reported in Table 1, provide only weak support for the standard explanatory model of individual exposure. Three individual variables are significant (pesticide amount, high-hazard % and use of protective items), however the first has a perverse sign.<sup>8</sup> Neither training in safe-handling nor use of IPM techniques has the expected effect. However, we find strong provincial effects. Controlling for individual circumstances, the incidence of poisoning in Can Tho and Tien Giang is much lower than in Tra Vinh (excluded) and An Giang.

**Table 1. Determinants of blood cholinesterase inhibition**

||**Model 1**|**Model 2**|
|---|---|---|
|**Variable**|**Coefficient**|**Coefficient**|
|PESTAMT|-0.098**|-0.105**|
||(-2.09)|(-2.24)|
|PWHOIab|0.039***|0.030**|
||(2.51)|(2.08)|
|PWHOII|0.003<br>(0.46)||
|PWHOIII|0.008||
||(1.19)||
|PWHOU|0.006<br>(0.90)||
|PTRAIN|0.262<br>(1.22)||
|NPROTECT|-0.443*|-0.440*|
||(-1.86)|(-1.91)|
|IPMD|0.043||
||(0.19)||
|An Giang|-0.118<br>(-0.33)||
|Can Tho|-0.821**|-0.628**|
||(-2.49)|(-2.39)|
|Long An|-0.449<br>(-1.36)||
|Tien Giang|-0.841***|-0.605**|
||(-2.58)|(-2.41)|
|Observations|187|187|

* - significant at the 1% level; ** - significant at the 5% level *** - significant at the 10% level

> 8  We also estimated a regression for pesticide use, with the following right-hand variables: farm size, IPM and pesticide training, actual use of IPM, farmer’s age, education, and income and provincial dummies. The significant variables were farm size, farmer’s age, and province dummies for An Giang, Long An and Tien Giang.  Farm size has the largest impact (ε =0.93), suggesting that pesticide use is primarily driven by scale.

10
_American Economic Review_ .

Morales, A. E. P., Fuentes, O., Searle, P., & Stewart, F. (2017). Pension funds and the impact of switching regulation on long-term investment. The World Bank.

Michaud, R. O., Bergstrom, G. L., Frashure, R. D., & Wolahan, B. K. (1996). Twenty Years of International Equity Investing. _The Journal of Portfolio Management_ . https://doi.org/10.3905/jpm.1996.409579 Newey, W. K., & West, K. D. (1987). _A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent_ . _Econometrica_ .

Newey, W. K., & West, K. D. (1994). Automatic lag selection in covariance matrix estimation. _Review of Economic Studies_ . https://doi.org/10.2307/2297912 Pedraza, A., 2015. Strategic interactions and portfolio choice in money management: Theory and evidence. Journal of Money, Credit and Banking, 47(8), pp.1531-1569.

Pedraza, A. (2019). Strategic information aggregation and learning from prices. Journal of Corporate Finance, 58: 208-225 Pedraza, A. & Pulga, F. (2019). Asset Price Effects of Peer Benchmarking: Evidence from a Natural Experiment. International Review of Economics & Finance 62: 53-65 Pedraza, A., Pulga, F., and Vasquez, J. (2020). Costly Index Investing in Foreign Markets. Journal of Financial Markets, 51, 100509 Raddatz, C. and Schmukler, S.L. (2013). Deconstructing herding: Evidence from pension fund investment behavior. Journal of Financial Services Research, 43(1), pp.99-126.

Roy, A. D. (1952). Safety First and the Holding of Assets. _Econometrica_ . https://doi.org/10.2307/1907413 Sharpe, W. F., Chen, P., Pinto, J. E., & McLeavey, D. W. (2007). Asset Allocation. En J. L.
**Table 3. Share of Households That Produce and Sell Staple Food Crops (Percent)**

||Productio|n of Food<sup>a</sup>||Sales of F|ood<sup>b</sup>||
|---|---|---|---|---|---|---|
||_Urban_|_Rural_|_All_|_Urban_|_Rural_|_All_|
|Ethiopia|9.34|71.46|55.37|4.70|36.86|28.52|
|Zambia|12.93|69.47|47.48|7.40|42.47|28.83|
|Cambodia|34.50|78.58|72.03|22.65|53.77|49.15|
|Bangladesh|8.75|51.64|42.94|5.38|33.49|27.79|
|Vietnam|11.50|81.30|64.30|8.31|54.33|43.12|
|Madagascar|28.93|75.67|64.36|14.61|41.67|35.10|
|Nicaragua|11.14|58.43|29.42|5.34|54.25|24.24|
|Bolivia|5.12|36.53|16.86|1.65|32.84|13.31|
|Peru|6.63|52.50|20.63|4.45|25.89|10.99|
|Unweighted|||||||
|Average|14.31|63.95|45.93|8.28|41.73|29.01|

_(a) Percent of Households with Positive Food Production_ _(b) Percent of Households with Positive Food Sales_

# **III. B. Vulnerability**

The definition of net food buyers and sellers used here and in other studies does not discriminate between marginal and significant food buyers, i.e. food buyers whose net purchases are a small portion of their incomes (as measured by expenditure) versus buyers whose food purchases constitute a significant share of their incomes. This latter category of households are vulnerable to large price shocks, while marginal food buyers could be rich households for which food purchases constitute a small share of income and not vulnerable, or alternatively they could be households that are close to being selfsufficient and purchase only small amounts of food.

Since we are interested in the poverty impact of food price changes, a more important group to focus on would be households that spend a significant share of their income on food. These groups would be more affected by increases in food prices than households whose food purchases are a small portion of their income. Thus, rather than focusing on the net buyer and seller definition, we can look in greater detail at vulnerable net food buyers, and the income sources of these vulnerable net food buyers and sellers in rural areas.

Since staples crops are used as the basic definition of food, the impact of staple food price increases would depend on the share of the main staples in the food basket and the share of overall food in the consumption basket of the household<sup>19</sup> . So, in countries where staples constitute a major portion of food consumption, the impact of the price change in that commodity will be more significant than the case where households have multiple food sources.

> 19 We thank E. Sadoulet for pointing this out. See the tables (Box 4.7) in World Development Report 2008 (World Bank, 2007), which presents the share of staple foods in food consumption of the poor households in these countries and which were derived using this database.

9
46

**Figure 11: Fixed and variable components of specific tax for diesel in Chile under MEPCO**

<!-- Start of picture text --> 80 0.16<br>70 0.14<br>60 0.12<br>50 0.10<br>40 0.08<br>30 0.06<br>20 0.04<br>10 0.02<br>0 0.00<br>‐10 ‐0.02<br>‐20 ‐0.04<br>‐30 ‐0.06<br>‐40 ‐0.08<br>‐50 ‐0.10<br>Aug‐14 Oct‐14 Dec‐14 Feb‐15 Apr‐15 Jun‐15 Aug‐15 Oct‐15 Dec‐15<br>Fixed component in pesos Variable component in pesos<br>Specific fuel tax in US$<br>US$ per liter<br>Pesos per liter<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on data at http://www.sii.cl/pagina/valores/impto_valores_petro/impuesto_enero2016.htm _._

**Figure 12: Comparison of retail gasoline price with and without smoothing in Chile, Chilean pesos per liter**

<!-- Start of picture text --> 950<br>900 MEPCO →<br>850<br>800<br>750<br>700<br>Price with smoothing<br>650<br>Price without smoothing<br>600<br>Jan‐11 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15<br><!-- End of picture text --> _Source:_ Comisión Nacional de Energía.

To encourage price competition, Resolution No. 60 issued in January 2012 requires that each retail outlet enter price information in a national online price database, which is published on the website of the National Energy Commission (Comisión Nacional de Energía, CNE). The information is available on mobile equipment, and the CNE also provides API (application programming interface) to download price information. The website displays the address of each retail outlet, fuel prices, and the date and time of the last price change. The viewer has the option of ranking prices in order of increasing or decreasing prices. Retailers are required to enter new prices no earlier than 15 minutes before price changes are implemented.
Following the passage of this amendment, Kerala, India’s most literate state, which had a long history of progressive politics, initiated a radical program of participatory decentralization (Isaac and Franke 2000), where the _gram sabha_ played a central role. The program rested on three pillars. It devolved 40 percent of the state’s development budget to village panchayats, gave substantial powers to these councils, and instituted a People’s Campaign – a grassroots program to raise awareness and train citizens about how to exercise their rights and become active participants in the panchayat process, primarily by participating in _gram sabhas_ .

_Gram sabhas_ have become central to Kerala’s village planning process, which is based on a set of nested piecemeal stages (Isaac and Heller 2003). Working committees and “development seminars” are held in conjunction with _gram sabhas_ to make them practical spaces of deliberative decision making and planning. Instead of open deliberation, attendees are divided into resourcethemed groups or committees and the discussions within each group yield consensual decisions regarding the designated resource. This structure is geared towards increasing the efficiency of consensual decision making, and is facilitated by various training programs to instruct both citizens on deliberative planning and local bureaucrats on methods for turning plans into effective public action. Heller, Harilal and Chaudhuri (2007) study the impact of the People’s Campaign in Kerala with qualitative and quantitative data from 72 _gram sabhas_ , and find that the campaign has been effective, with positive effects on the social inclusion of lower-caste groups and women in decision making. Furthermore, Gibson (2012), examining the same data, argues that the key explanation for the effectiveness of _gram sabhas_ in Kerala is the high level of participation by women.

Over the last two decades, all other Indian states have implemented the various tenets of the 73<sup>rd</sup> amendment, but with varying levels of intensity – and none as effectively as Kerala. The effects of several aspects of the amendment (including the strength of electoral democracy, the impact of quotas for women and lower-castes, and the implications of elections for distributive politics and 11
not eligible for the cash award at the 4, 8, and 12-month testing rounds if they tested positive for any of the following: Chlamydia trachomatis, Neisseria gonorrhea, Trichomonas vaginalis. HIV testing was conducted at baseline and month 12, but payments were not conditioned on those results because of local ethical sensitivities. Individuals in the intervention arms testing positive for any of the conditioned curable STIs did not receive the cash award payment, but were eligible to continue as a study participant in subsequent rounds after having been treated. Individuals in the control arm were not eligible for cash award payments, but all other study procedures were identical between the control and intervention arms. Anyone testing positive for an STI (regardless of arm) was offered counseling and free STI treatment (for self and partners) through public health facilities of the Ministry of Health and Social Welfare located in the research communities. Individual pre-test and post-test counseling was provided to study enrollees at each testing interval, following Tanzania national testing guidelines. In addition, monthly group counseling sessions emphasizing relationship-skills training adapted from a sub-set of the Stepping Stones curriculum (Jewkes, 2008), were also made available to all study participants in all villages, but were not mandatory.

_Data Collection, Management and Analysis_ In February-April 2009, 2,399 participants were enrolled in the RESPECT study, and were interviewed using a closed-ended, structured questionnaire. Participants were interviewed before being randomized into one of the three intervention arms, so during the baseline interview they did not know if they would be eligible to receive the cash award. In June and July of 2009, 1,943 of the 2,399 participants returned for the 4-month visit of the study, and were again interviewed using a closed-ended structured questionnaire. This analysis drops a small number of observations due to missing data (56 participants did not respond to the question about intended behavior change).

Data were analyzed using STATA version 10 (StataCorp, 2007). Analyses were conducted exploring associations between demographic and other characteristics and intention to change as well as reported change; then reported change analyses were stratified by baseline change intention. Additional 8