rafmacalaba/datause-encoder-data huggingface dataset:
_the social ranking evaluated at the preference profile_ ( _Ri_ ) _i_ ∈ _N is complete. Then, the truthful voting profile s_<sup>∗</sup> ( _R_ ) _is a Pareto-efficient Nash equilibrium for the associated strategic form game_ Γ _R_ = ( _N,X,U_<sup>_dα_</sup> ) _._ The conditions of Theorem 4 are quite innocuous. In particular requiring the social ranking evaluated at the truthful voting profile to be complete means that the social ranking always produces a ranking between any pair of candidates under truthful voting, although social incomparabilities between candidates may occur if some voters engage in strategic voting. This condition is a bit weaker than the one imposed by Arrow (1951), who requires that the social ranking be strict and transitive. If Arrow’s condition is satisfied, then ours also will be satisfied. Theorem 4 shows that truthful voting is a Pareto-efficient Nash equilibrium when these four conditions are satisfied.

Our next result is a direct consequence of Theorem 4, and illustrates a ranked-choice election in which the completeness condition of the social ranking evaluated at the truthful voting profile is satisfied.

**Corollary 2 :** _Let_ ( _N,X,_ ( _Ri_ ) _i_ ∈ _N ,W,F_ ) _be a ranked-choice election such that voters’ preferences are linear and neutral, W is simple majority rule, and the number of voters is odd. Assume social incomparability is broken by a fair coin toss, then the truthful voting profile s_<sup>∗</sup> ( _R_ ) _is a Pareto-efficient Nash equilibrium for the associated strategic form game_ Γ _R_ = ( _N,X,U_<sup>_dα_</sup> ) _._

In fact, it is well known that majority rule always produces a strict and transitive ordering of the candidates when the number of voters is odd. It therefore follows from Theorem 4 that truthful voting is a Pareto-efficient Nash equilibrium under this rule if the number of voters is odd.

We illustrate our findings with an example taken from Dasgupta and Maskin (2020), which analyzes the 2016 primary elections in the Republican Party in the United States.

**Example 7 :** _Consider the ranked-choice election_ ( _N,X,_ ( _Ri_ ) _i_ ∈ _N ,W,F_ ) _reflecting a pattern in the early 2016 Republican presidential primaries (Dasgupta & Maskin, 2020). Initially, there were about 16 candidates in the race. However, only a handful of candidates, including Donald Trump, Marco Rubio, and John Kasich, survived beyond a couple of weeks. Let us imagine that voters’ preferences broke down into three groups, as shown in the following table:_

23
**Appendix A: Definitions of Variables and Data Sources**

|_Variable_|_Definition_|_Source_|
|---|---|---|
|World Bank<br>funding|Total commitments of investment projects<br>of the World Bank in U.S. dollars split<br>equally (or population weighted) across the<br>subnational first-level administrative areas<br>in which a project has been active; 2005–14|_AidData_<br>World Bank Geocoded Research<br>Release, (database), AidData,<br>College of William and Mary,<br>Williamsburg, VA (accessed<br>November 23, 2016),<br>http://aiddata.org/data/world-<br>bank-geocoded-research-release-<br>|
|||level-1-v1-4-2|
|Share of World<br>Bank funding|Share of World Bank project funding a<br>subnational administrative area receives|_AidData_|
|Share of the<br>bottom 40|Share of the national bottom 40 population<br>living in an administrative area; data from<br>surveys within the 2005–14 period|_Global Poverty Monitoring_<br>Source: Global Monitoring<br>Database (internal database),<br>Poverty and Equity Global<br>Practice, World Bank,<br>Washington, DC (accessed<br>December 2016–January 2017)|
|Ln bottom 40|Number of the national bottom 40<br>population living in an area (in log)|_Global Poverty Monitoring_|
|Ln population<br>Capital area|Number of people living in an area (in log)<br>Dummy variable equal to one (zero) if the<br>capital of the country is (not) located in the<br>area|_Global Poverty Monitoring_<br>_Internet searches_|
|Ln travel time|Estimated road travel time with private<br>transportation from each subnational area<br>to the respective capital in minutes (in log)|_Internet searches_|
|Conflict-related<br>deaths|Number of conflict-related deaths in an<br>area (per 100,000 inhabitants); 2005–14|_UCDP_<br>UCDP (Uppsala Conflict Data<br>Program) (database), Department<br>of Peace and Conflict Research,<br>Uppsala University, Uppsala,<br>Sweden (accessed June 20, 2017),<br>http://ucdp.uu.se/?id=1)<br>http://ucdp.uu.se/downloads/|
|Ln public<br>expenditure|Subnational public expenditure (in log);<br>2005–14 or subperiods|_Boost_<br>Open Budgets Portal: Boost<br>(database), World Bank,<br>Washington, DC (accessed June<br>20, 2017),<br>http://wbi.worldbank.org/boost/c<br>ountry|
|Ln aid of other|Aid of other donors (in log); 2005-2014|<br>AidData|
|donors|(expect in the case of Senegal for which the<br>period covered is 2005-2012)|http://aiddata.org/subnational-<br>geospatial-research-datasets<br>(accessed: September 28<sup>th</sup>,2017)|

16
**Table A.1. The list** **<u>of 62 poorest districts (Resolution 30a/2008/NQ-CP)</u>**

|Provinces|The number of<br>poorest districts<br>in theprovince<br>Name of the poorest districts|
|---|---|
|Hà Giang|6<br>Đồng Văn, Mèo Vạc, Yên Minh, Quản Bạ, Hoàng Su Phì,<br>Xín Mần|
|Cao Bằng|5<br>Bảo Lâm, Bảo Lạc, Thông Nông, Hà Quảng, Hạ Lang|
|Lào Cai|3<br>Si Ma Cai, Mường Khương, Bắc Hà|
|Yên Bái|2<br>Mù Cang Chải, Trạm Tấu|
|Bắc Kạn|2<br>Ba Bể, Pác Nặm|
|Bắc Giang|1<br>Sơn Động|
|Phú Thọ|1<br>Tân Sơn|
|Sơn La|5<br>Sốp Cộp, Phù Yên, Bắc Yên, Mường La, Quỳnh Nhai|
|Lai Châu|5<br>Mường Tè, Phong Thổ, Sìn Hồ, Tân Yên, Than Uyên|
|Điện Biên|4<br>Điện Biên Đông, Mường Nhé, Tủa Chùa, Mường Ảng|
|Thanh Hóa|7<br>Lang Chánh, Thường Xuân, Quan Hóa, Quan Sơn, Mường<br>Lát, Như Xuân, Bá Thước|
|Nghệ An|3<br>Kỳ Sơn, Tương Dương, Quế Phong|
|Quảng Bình|1<br>Minh Hóa|
|Quảng Trị|1<br>Đa Krông|
|Quảng Ngãi|6<br>Sơn Hà, Trà Bồng, Sơn Tây, Minh Long, Tây Trà, Ba Tơ|
|Quảng Nam|3<br>Nam Trà My, Tây Giang, Phước Sơn|
|Bình Định|3<br>An Lão, Vĩnh Thạnh, Vân Canh|
|Ninh Thuận|1<br>Bác Ái|
|Kon Tum|2<br>Tu Mơ Rông, Kon Plông|
|Lâm Đồng<br>Source: Authors’|1<br>Đam Rông<br>preparation using information from Resolution 30a/2008/NQ-CP (The Government of|

<mark>Source: Authors’ preparation using information from Resolution 30a/2008/NQ-CP (The Government of Vietnam, 2008).</mark> 51
A last comment concerns the statistics indicated at the bottom of the table. _Sigma_ 1, and _Sigma_ 2 are the square-roots of the variance of the residuals of equations (8) and (9) and _rho_ 1 and _rho_ 2 are the correlation coefficients.<sup>24</sup> Only _rho_ 1 (the correlation coefficient between occupational choice and the market profit equation) is significant. Since it is positive, a farmer on the market has higher profits than those of a “random” one.

Table 6 provides the mean conditional profits under the two regimes: _E_ [ _πMi|Ii_<sup>_∗≤_0],</sup> _E_ [ _πSi|Ii_<sup>_∗≤_0],</sup><sup>_E_[</sup><sup>_πMi|I_</sup> _i_<sup>_∗>_0]and</sup><sup>_E_[</sup><sup>_πSi|I_</sup> _i_<sup>_∗>_0],fordifferentvaluesof</sup><sup>_λ∗_.Subsistence</sup> farmers’ average agricultural profits is 30% lower than the profit of market farmers. This can be explained by higher productivity, access to better inputs, etc. Controlling for their characteristics, the switch from subsistence to market participation could increase subsistence farmers income by 36 percent. Similarly, those farmers out of subsistence could see their income fall by half if they were to move into subsistence.

In order to compute the entry costs going along with a move from subsistence towards the market (equation (7)), we must approximate _r_ , the interest rate, and _s_ , the probability of exit from the market sector. To be as close as possible to reality, we proxy _r_ with the interest rate on microcredit. Yearly microcredit rates in Madagascar typically hover between 27% and 43%. An interest rate of 30% therefore seems to be a good approximation. As for _s_ , it was calculated using the survey’s two-year panel<sup>25</sup> by calculating the proportion of 1997 market farms that switched to subsistence between 1997 and 1999. This proportion was then adjusted to find a yearly percentage of farmers switching into subsistence. We find a value of _s_ of 4.08 percent. From equation (7), the entry cost was computed by dividing the opportunity cost by the sum of _r_ and _s_ , that is, by 0.3408.

Using these, our estimate of the entry cost is 536’385 Malagasy francs i.e. 139% of subsistence farmers agricultural profits. The lower bound for the entry cost estimated with the 95% confidence interval is 481’496 Malagasy francs, or 124% of subsistence farmers’ profits. The upper-bound estimate amounts to 591’275 Malagasy francs, i.e. 153% of profits.

> 24Since _rho_ 1 and _rho_ 2 are not equal to zero, it means that _σ_ 1 _ε_ and _σ_ 2 _ε_ cannot take the value zero. Recalling the comment of page 5 on these two variables, this means that the switch is indeed endogenous.

> 25The data contain a panel over about 1000 households, surveyed in 1997 and 1999.

20
-7-

# IV. MEASURES OF PRICE COMPETITIVENESS

The price competitiveness is defined in this paper 9a the advantage in price, which may enable a country to secure sales of its producta in foreign markets at the expense of its competitors. The advantage will<sup>not neces-</sup> sarily guarantee the sales but it will represent a favorable incentive provided to foreign importers to purchase the exporting country's products.<sup>The</sup> price competitiveness will be measured in this paper in terms of differences in relative export prices, that is differences between export prices of CPEs and (world market) prices of their competitors.

The choice of definition of price competitiveness is not entirely straightforward. Price competitiveness has been measured in the literature on the basis of price comparisons or comparisons based on costs. Neither<sup>of</sup> these approaches is without shortcomings, and each has some decisive advantages over the other. The concept of price is considered to be more objective and less likely to vary from one exporter to another. Moreover, in addition to flaws dicussed above, cost data are not typically built up for individual commodities but they are given for whole plants, companies or group of commodities (Kravis and Lipsey, 1971, p. 43). In contrast, competiti' ess measured on the basis of price becomes meaningful only under conditic.b of market imperfections arising from product differentiation (Enoch, 1978, p. 181). More recently, an attempt has been made by Hotson and Gardiner (1983) to develop a structural model of the UK trade in manufactures which estimates separately price and volume equations and utilizes both relative prices and relative costs.8/ For reasons discussed in Section 3 above, the choice of relative prices for the treatment of price competitiveness hau decisive advantages over methods based on relative costs, particularly for the analysis of CPEs. This reflects mainly serious distortions in production costs.

# V. TREATMENT OF PRODUCT QUALITY

We shall use in this paper highly disaggregated data to ensure that our comparisons refer to as highly homogeneous products (groups) as possible. Nevertheless, our comparisons of unit values corntinue to be subject to the "index number problem." While the values may ensure concordance with basic definitions of products, they do not capture explicitly differences in product quality.

The theory provides a guidance on the likely impact of product quality of export prices of the CPEs. The theoretical work of Falvey (1979) shows that nontariff barriers to trade (NTBs), orderly market arrangements and voluntary export restraints will raise the relative price of the least-expensive good in the product category subject to NTBs. As a result, importers will be encouraged to increase the quantity of higher-priced products<sup>within</sup>

- 8/ Hotson and Gardiner (1983) should be also consulted for a brief review of the literature.
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# **Opportunities for expanding financial inclusion  58**

## **Who the unbanked are  59**

What are the self-reported barriers to account ownership? Is there voluntary financial exclusion?

## **How account holders use their accounts  62**

How does the intensity of account use vary across regions? How does the intensity of account use vary by individual characteristics?

## **Opportunities for expanding financial inclusion among the unbanked  64**

Moving cash payments into accounts Channeling domestic remittances through accounts Shifting semiformal savings into accounts

## **Opportunities for increasing the use of accounts among the banked  67**

Paying utility bills and school fees through accounts Sending or receiving domestic remittances through accounts Saving formally

# **References  71**

# **Methodology  73 Indicator table  83**

# **Global Findex questionnaire  85**

The 2014 Global Findex database provides more than 100 indicators on such topics as account ownership and use, payments, saving, credit, and financial resilience, including by gender, age group, and household income. The complete economy-level database is available at http://www.worldbank.org/globalfindex. Individual-level data for 2014 will be published in fall 2015.

All regional and global averages presented in this publication are population weighted.

> THE GLOBAL FINDEX DATABASE Iv
Are certain groups more likely to suffer poor mental health as a result of conflict? For the same exposure to conflict, we find that people with more years of schooling had better mental health outcomes, those who migrated in response to the conflict recalled the war experiences less often, and Croats recalled those events more often compared to other ethnic groups (Table 4, Columns 1-3). None of these differential effects of conflict experience persists into 2004, with the exception for the coefficient on Croatian ethnicity (Table 4, Columns 4-6). Finally, we find that receiving aid is associated with faster mental health recovery (results not shown), but wish to emphasize that we are unable to rule out that this association is spurious and captures decreasing higher-order effects of conflict on mental health, since aid might (and should) be targeted towards high conflict areas.

# **4. Conclusions**

We discussed three major obstacles in empirically documenting the mental health effects of conflict: measurement, comparison and interpretation. Most large-scale household surveys do not contain information on mental health. Even if survey evidence is available, constructing a clinically validated mental health measure is often difficult. In terms of comparison, there are conceptual difficulties of respondent-specific internal scales of comparison, which may change over time. There are also difficulties in finding a suitable “control” group to assess the impact of conflict. Given that measuring mental health through surveys is a relatively new trend, there are often no pre-conflict measures to compare with, and the empirical studies therefore suffer from all the usual problems of cross-sectional analysis. In addition, a strict within-country comparison might completely miss an aggregate effect of the conflict on the whole country. Thus, finding that there are no significant differences between areas with high and low conflict (as in our study on Bosnia) cannot rule out the presence of large aggregate effects. The best way to resolve this empirical problem would be to include mental health measures in household surveys on a regular basis, similar to the inclusion of physical health modules in many surveys.

Despite these major caveats, we do see some similar trends in this nascent literature. The first is that time since conflict does seem to lower the differences in mental health outcomes across people who experienced different intensities of conflict. However, the recovery paths of mental health need not follow the same time line as that of the recovery of economic activity or political developments. The second is that certain subgroups do appear to have lingering effects, 17
**Table 13. Welfare Effects in Guinea-Bissau: Baseline**

|**Policy**|**(I) Base**<br>**Scenario - no**<br>**FUNPI**|**(II) = (I) +**<br>**FUNPI**|**(III) = (II) +**<br>**max intn'l**<br>**price**|**(IV) = (II) +**<br>**average**<br>**intn'l price**|
|---|---|---|---|---|
|_All Households_|--|-5.30|34.70|9.30|
|_Urban Households_|--|-1.65|10.78|2.89|
|_Rural Households_|--|-8.46|55.42|14.86|
|_Poor Households_|--|-7.89|51.67|13.85|
|_Non-Poor Households_|--|-4.40|28.86|7.74|
|_Urban Poor Households_|--|-3.24|21.25|5.70|
|_Rural Poor Households_|--|-9.39|61.51|16.49|
|_Urban Non-Poor Households_|--|-1.39|9.12|2.44|
|_Rural Non-Poor Households_|--|-7.92|51.91|13.92|
|_Cashew Producers_|--|-11.03|72.26|19.37|
|_Poor Cashew Producers_|--|-12.28|80.48|21.58|
|_Non-Poor Cashew Producers_|--|-10.38|68.00|18.23|

Source: QUIBB 2010 and price changes from the simulations of Section 4. The numbers are expressed in percent.

In Tables 14 and 15, we compare the current situation with the FUNPI contribution with different scenarios where the tax is complemented with various additional policies/shocks. The tax implies a lower farm-gate price and entails welfare losses for the farmers. The complementarities investigated here are intended to shed some light on potential ways to ameliorate, or revert, the lower cashew prices. As we have seen, all the scenarios with conservative complementarities are preferred to the baseline scenario of the FUNPI and the export tax. This is because farm-gate prices are higher in the equilibrium with complementary policies. But, in all instances, the simulations find lower farm-gate prices than in the baseline which, consequently, lead to welfare losses. For instance, in Scenario V (FUNPI with export tax coupled by a 10 percent reduction in export costs), the welfare loss for the average household is 4.53 percent, which is lower than the loss without the cost reduction simulation (5.30 percent loss in the baseline with FUNPI and export tax). For the average cashew producer, there is loss of 9.44 percent with the complementarities, as opposed to a larger loss of 11.03 percent with the FUNPI. These results are not surprising given our discussion in Section 4. In fact, our simulation analysis explored thresholds for the complementary policies that would leave farmers indifferent and we here refer to the discussion in Tables 7-10 of Section 5.

29
modern utilities. However, within communities that have access to modern utilities, households that _take-up_ a connection are no more likely to be entrepreneurs than those that do not (except in the case of fixed telephones).

**Table 5.6: Cross-tabulation of household enterprise against access to modern utilities**

|(Propo|rtion of ho<br>|useholds)<br>||
|---|---|---|---|
||Has an|enterprise|Wald<br>|
||Yes|No|Test<sup>2</sup>|
|**Availability**||||
|Electricity|.85|.82|**|
|Piped water|.85|.80||
|Fixed phone|.42|.34|***<br>|
|Cellular phone|.42|.37|**|
|**Takeup**<br>||||
|Electricity|.98|.98||
|Piped water|.95|.95||
|Fixed phone|.57|.46|***|
|Cellular phone|.33|.29||
|**Coverage**<br>Electricity|.81<br>|.72<br>|***<br>***|
|<br>Piped water|.76<br>|.67<br>|***|
|Fixed phone|.22<br>|.14<br>|**|
|<br>Cellular phone|.12<br>|.09<br>|***|
|<br>Publicphone|18|23||
|Population size|459,347|1,731,720||

_Notes_ : Refers only to enterprises that operate in dwelling. Null hypothesis (equality of enterprise owners and non-owners) is rejected at:<sup>***</sup> 99%,<sup>**</sup> 95%,<sup>*</sup> 90%. Pubic phone variable refers to minutes to closest public phone in census tract. _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala However, it is not possible to draw any inferences from cross-tabulations of this kind, because they do not control for many other factors that influence the disposition to form a business. In order to address this issue, a probit model is estimated that looks at the correlation between a variety of factors including characteristics of the households and the head of household, availability of modern utilities and geographical variables. Separate models are estimated for urban and rural enterprises (Table 5.7).

An important methodological issue that arises is the potential endogeneity of access to modern utilities. The model is estimated on the statistical assumption that access to modern utilities affects the probability of forming a micro-enterprise, _but not vice versa_ . However, it could equally be argued that the presence of a micro-enterprise influences the choice of whether or not to connect to modern utilities. In order to avoid the resulting bias in the statistical estimates, it is necessary to find an instrumental variable that is related to the variable of interest but which has a greater claim of exogeneity. In this case, the community availability of the basic service is used as an instrument; that is to say, that instead of looking at whether or not the _household_ has access to the service, the model looks at whether or not the household lives in a _neighborhood_<sup>_27_</sup> that has access to the service. It can be argued that local availability is somehow more likely to be exogenous to enterprise formation than household connection.

> 27 As explained earlier, we use the word _community_ in this context to describe clusters of contiguous households on which the survey design was based.

41