rafmacalaba/datause-encoder-data huggingface dataset:
The results, reported in column (1) of Appendix Table A.7, provide no evidence that the relatives of incumbent mayors were more likely to win closely contested elections. Restricting the sample to elections decided by 3 percentage points or fewer does not change this basic conclusion. In columns (3) and (4), closely fought elections involving a relative are added to the main sample. Their inclusion reduces the magnitude of the discontinuity from 0.016 (reported in column (1) of Table 1) to 0.008 and the estimate becomes statistically insignificant. However, the estimate based on a bandwidth of 3 percentage points, reported in column (4), is very similar in both magnitude and significance to the estimate reported in column (3) of Appendix Table A.1. The analysis then relates the narrow victory of relatives to the number of post-election casualties using a sample of narrow incumbent victories which also includes relatives. The results in columns (1) and (2) of Appendix Table A.8 are similar to the results reported in Table 3 and Appendix A.2. This analysis suggests that narrow victories of incumbent relatives are not associated with post-election violence.

# **4 Conclusion**

There is a widespread belief among academics and practitioners that elections perceived as fraudulent can increase support for non-democratic actors and serve as a catalyst for civil conflict (World Bank, 2012; Donno, 2013; Norris, 2014; Hall et al., 2015). Over the past few decades, international donors and the World Bank have intensified their efforts to ensure that elections in fragile democracies are conducted fairly (Daxecker, 2012) and have spent substantial amounts on election monitoring and other related programs (Kelley, 2008).

This study examines the association between narrow incumbent victories and civil conflict using data from the 2007 mayoral elections held in the Philippines. With a forensic ap49 municipalities in which one of the candidates is a relative of the incumbent mayor. Limiting the sample to a bandwidth of 3 percentage points reduces the number of such municipalities to 38.

37
_-_ 26 _Structural Factors._ Four internal factors related to fiscal and monetary policy. The role of government fiscal policies; the role of monetary expansion,; whether institutional or stock market-related; the role of regulatory authorities; and the behavior of the dealers, banks, investment companies, and Gulf and Kuwaiti shareholding companies.

To understand the role of the government, it is important to look at the structure of the Kuwaiti economy in the period preceding the crisis. Kuwait is essentially an oil-based economy, but the oil sector provides relatively little direct benefit to the population. In 1985 while this sector produced 52 percent of GDP it employed only about 1 percent of the total labor force .1/ . Since oil is a government monopoly, the real benefits to the economy come from government expenditures in other sectors.

Two patterns in GDP are worth noting from 1974 to 1987 (see figure 5). First, nonoil GDP increased from 20 percent to 55 percent of GDP; and second, while the oil sector and total GDP began declining in 198O, nonoil GDP started to decline in 1982, but not by the same extent, primarily as a result of government expenditures. Real GDP also declined from 1979 to 1982, falling 43 percent. It is not surprising that there was a shift from real to financial assets and speculation in real estate and stocks by investors searching for substitute income sources.

r/ Kuwait Ministry of Planning, Annual Statistical Abstract. 1987.
# **<u>Table C.6 First-stage regressions, Tunisia</u>**

||**2005-**|**2010**|**2010-**|**2015**|**2015-**|**2021**|
|---|---|---|---|---|---|---|
||**2005**|**2010**|**2010**|**2015**|**2015**|**2021**|
|Head`s age|0.004***|0.008***|0.005***|0.008***|0.006***|0.014***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Head is female|0.143***|0.053**|0.006|0.197***|0.184***|0.113***|
||(0.03)|(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|
|Highest education level is primary|0.312***|0.305***|0.300***|0.210***|0.227***|0.142***|
||(0.04)|(0.04)|(0.04)|(0.01)|(0.02)|(0.02)|
|Highest education level is secondary|0.386***|0.293***|0.266***|0.443***|0.460***|0.354***|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Highest education level is tertiary|0.945***|0.764***|0.754***|0.904***|0.911***|0.704***|
||(0.03)|(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|
|Urban|0.495***|0.497***|0.499***|0.355***|0.370***|0.287***|
||(0.02)|(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|
|_cons|6.547***|6.747***|6.881***|7.006***|7.084***|7.183***|
||(0.05)|(0.04)|(0.05)|(0.03)|(0.03)|(0.04)|
|adjusted R2|0.30|0.29|0.29|0.29|0.29|0.21|
|N|6769|7507|6425|16456|13635|10520|

**Note:** ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. Regression is estimated using Weighted Ordinary Least Squares. Household heads’ ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second round. The reference groups are household with no primary education and living in rural areas.

84
where _Rk_ , _t_  1 is the gross real return on any asset, including the portfolio itself.

When investment opportunities are constant, portfolio shares are also constant, implying that _Rp,t+1_ is time-invariant. Thus, the optimal consumption policy, characterized by equation (11), implies a constant consumption-wealth ratio. It also entails a constant portfolio share in all available assets. In other words, agents behave as short-term investors and optimally choose a “myopic” portfolio allocation. To obtain dynamic portfolio choices, we relax the hypothesis of constant investment opportunities over time. In our setup, if asset returns are non-i.i.d., a relative risk aversion parameter greater than one is a sufficient condition for the optimal portfolio allocation to be dynamic (non-myopic).<sup>9</sup> Thus, we assume a relative risk aversion parameter greater than one.

Therefore, to model time-varying investment opportunities, we explore the empirical evidence that financial asset returns are predictable to some extent.<sup>10</sup> For example, Amromin and Sharpe (2006) provide empirical evidence based on survey data suggesting, for example, that expected stock returns are extrapolated from actual returns. We thus assume that asset returns follow a first-order vector autoregression (VAR).<sup>11</sup> This assumption captures the historydependence of expected returns. The empirical finance literature has identified several predictive variables, besides the historical values of asset returns themselves. Nominal yield on short-term bonds, the term spread, and earnings-to-price ratio have been documented to forecast asset returns for many asset classes. Thus, we also use these variables ( _st_ ) to estimate expected returns.

> 9 See Campbell and Viceira (2002) for an extensive study on strategic asset allocation.

> 10 See Campbell (1987), Campbell and Shiller (1988), Fama and French (1988, 1992), and more recently, Campbell and Yogo (2006) and Watcher and Warusawitharana (2007), among many others.

> 11 A similar specification can be found in Campbell, Chan, and Viceira (2003) and Barberis (2000), for example.

12
mortgage.<sup>21</sup> We focus on the results for 2007 in the main text, but we report estimates for 2005 and 2006 in Appendix Tables A9 and A10, respectively.

To avoid confounding the relationship between mortgage holding and financial vulnerability with the impact of other household characteristics on financial vulnerability, we control for the same household characteristics as in the previous section. We expect larger families with more dependents to be more likely to report a financial burden and incur arrears, households with lower income and whose head is not full-time employed or draws on a stable retirement income are expected to have higher financial burden and higher likelihood of arrears. The location and type of the dwelling, as well as other characteristics of the household head (age, gender, and education) do not have an a-priori positive or negative impact on financial burden and arrears.22 The regressions results in Table 6 show similarities but also differences between old and new EU countries. First we find substantial variations across countries in the relationship between tenure status and financial burden. In Belgium, Estonia, France, Ireland, Lithuania, Netherlands, Poland and Sweden, we find that mortgage holders report a lower financial burden than outright owners, with the reverse relationship in Austria, Cyprus, Finland, Greece, Hungary, Italy, Latvia, Portugal, Slovenia, Spain and the UK. When comparing mortgage holders with renters, we find that in Hungary, Greece, Ireland, Luxembourg, and Poland mortgage holders report, on average, a lower financial burden, than renters, while they report, on average, a higher financial burden than renters in Austria, Cyprus, Italy, Lithuania, Portugal, Slovenia, and Spain. Only in Austria, Cyprus, Italy, Portugal, Slovenia, and Spain, do mortgage holders report the highest financial burden compared to the other two groups, but in none of the new EU countries.

> 21 Since it is not possible to compute such an effect in the 2SLS regressions – as there we work with continuous predicted variables rather than dummies - we report marginal effects on the coefficients.

> 22 See Anderloni and Vandone (2008) for a literature review of studies of household indebtedness and different variables that are typically included.

28
# **Measuring the Impact of Minimum Wages:**

# **Evidence from Latin America'**

**William F. Maloney** _The World Bank_ **Jairo Nuniez** _Universidad de los Andes Bogota, Colombia With_

**Wendy Cunningham, Norbert Fiess Claudio Montenegro, Edmundo Murrugarra, Mauricio Santamaria, and Claudia Sepulveda**

**_The_** _World Bank_

The individual country kernel density analysis was done by Wendy Cunningham (Argentina, Mexico), Norbert Fiess (Brazil), Claudio Montenegro and Claudia Sepulveda (Chile), Edmundo Murrugarra (Uruguay), Mauricio Santamaria (Colombia) and Corinne Siaens (Bolivia, Honduras). Our thanks for helpful comments to Carmen Pages.
|330610|Dentifrices|Nepal's Exports|2147.7|12627.1|24047.2|12117.8|
|---|---|---|---|---|---|---|
|||World Trade|300047.0|960194.0|1300000.0|2300000.0|
|480210|Hand-made paper and<br>paperboard|Nepal's Exports|32.6|197.2|566.4|641.1|
|||World Trade|46551.7|88279.3|113977.0|118094.0|
|570110|Carpets and other textile<br>floor coverings, of wool|Nepal's Exports|166121.6|160137.7|104527.9|86510.4|
|||World Trade|1400000.0|1600000.0|1300000.0|1000000.0|
||Carpets and other textile||||||
|570190|floor coverings, of other<br>materials|Nepal's Exports|104.2|329.9|1284.3|6612.5|
|||World Trade|248704.0|260952.0|206499.0|235607.0|
|570210|Kelem, Schumacks,<br>Karamanie and others|Nepal's Exports|230.9|797.4|1133.1|1235.2|
|||World Trade|86604.9|133416.0|115009.0|122975.0|
|570220|Floor coverings of coconut<br>fibres (coir)|Nepal's Exports|31.9|1.9|9.6|4.5|
|||World Trade|31109.3|74299.7|79397.6|95784.8|
|570500|Other carpets and other<br>textile floor coverings|Nepal's Exports|22.0|82.6|698.4|514.0|
|||World Trade|208672.0|383319.0|460653.0|823343.0|
|621410|Shawls, Scarves, Mufflers,<br>Mantillas, Veils, of Silk or Silk<br>Waste|Nepal's Exports|11.9|873.1|4781.3|2612.6|
|||World Trade|286419.0|437781.0|303276.0|428502.0|
|621420|Shawls, Scarves, Mufflers,<br>Mantillas, Veils, of Wool or<br>Fine Animal Hair|Nepal's Exports<br>World Trade|80.3<br>122233.0|5315.7<br>203986.0|24127.5<br>362202.0|13456.5<br>491360.0|
|621430|Shawls, Scarves, Mufflers,<br>Mantillas, Veils, of Synthetic<br>Fibres|Nepal's Exports|10.7|62.7|506.4|745.1|
|||World Trade|183118.0|340903.0|358044.0|540824.0|
|621440|Shawls, Scarves, Mufflers,<br>Mantillas, Veils, of Artificial<br>Fibres|Nepal's Exports|1.2|47.6|159.2|213.6|
|||World Trade|53801.5|109363.0|126250.0|284493.0|
|621490|Shawls, Scarves, Mufflers,<br>Mantillas, Veils, of Other<br>Textile Materials|Nepal's Exports<br>World Trade|25.9<br>38456.7|241.6<br>75832.0|977.7<br>114970.0|1082.2<br>360809.0|
|711311|Art. of jewellery and pts<br>thereof of silver w/n|Nepal's Exports|1180.8|2373.1|4989.8|5974.7|
|||World Trade|471548.0|1000000.0|1900000.0|4200000.0|
|711319|Art. of jewellery and pts<br>thereof of/o prec mtl|Nepal's Exports<br>World Trade|56.9<br>6500000.0|1305.5<br>12000000.0|2650.9<br>15000000.0|656.5<br>30000000.0|

8
# Figure 4: PV of post-reform/pre-reform lifetime benefits, normalized*

<!-- Start of picture text --> Low earng nunled wommn g largest liMrm gain frnm dfmn<br><!-- End of picture text --> <!-- Start of picture text --> as<br>N ~ ~<br>2 3 -0-A4e5br-avYam<br>0f; .1* Base Aguoeaywac rrjia<br>normalized posnnsi t rf o e t c<br><!-- End of picture text -->

*Based onjointa **duiatyion mencandpt pincudeso jo~eoint** nutyrcivdbwmn

normalized by setting new/old ratio for man in top education category =1, separately for each country.

68
Table 1 shows the baseline difference between the in-camp and out-of-camp refugees' characteristics before the crisis. The average age of the household head for the total sample is 40. However, the household heads living out of camps on average are slightly older than the household heads living in camps. The difference is about 1.5 years. About 14 percent of the total sample are households headed by women. Female-headed households are more likely to live in camps than out of camps. Thus, 16 percent of the in-camp refugee households are female-headed compared to 13 percent living out of camps. Linking with the literature on gender and refugee studies, I assume the reasons female-headed households are more likely to live in camps in contrast to male-headed households are disproportionate gender inherent deprivations and vulnerability women face in the cities. For instance, the greater difficulties for women to secure jobs or accommodations compared to men.

Furthermore, households with bigger household sizes are more likely to live out-of-camp (n = 6) than households with smaller household sizes (n = 5). This result may be linked to the limited space per household available in camps. A bigger household size means a greater likelihood of having working-age members, implying more capability to pay rent.

Although there is no significant difference in the economic situation between in-camp and out-of-camp households before the crisis, the in-camp households tend to have a larger asset base than out-of-camp households in Syria before the crisis. This asset disparity in favor of camp refugees may seem paradoxical, considering that assets may proxy for wealth and expenditure or the ability to afford rent (Harttgen & Vollmer, 2013). However, considering that many of these assets were either destroyed in the crisis or have become low liquidity value, households who stock assets may become less wealthy during a crisis, leading to lower ability to afford rent in cities.

One concern is whether the estimation should also capture refugee time of arrival in Jordan based on the fact that early arriving refugees are more likely to establish networks, integrate, and navigate daily living experiences, thus to move out of camps or achieve higher QoL. Thus, I added arrival time (month of stay) as part of the baseline control characteristics. Since I restrict my analysis to the region of common-support, thanks to the propensity score, there will be no significant difference between groups in whether they arrive early or later in Jordan.

17
# **d. Pricing**

**With the exception of St. Vincent, the OECS ports are expensive.** The Doing Business indicators contain a measure of costs for importing a container for nearly every country in the world, broken down by costs for document preparation, customs, port handling and terminal charges, and inland transport charges. On average the port and terminal charges alone in the OECS are 86 percent higher than in the Organisation for Economic Co-operation and Development (OECD) countries, and 34 percent higher than in Latin America and the Caribbean (LAC) (figure 5.2). Removing St. Vincent, an outlier, from the sample, the average price for the remaining five islands jumps to $574 per container—more than double the OECD average and 50 percent higher than the LAC average.

**Figure 5.2 Port and Handling Charges, 2013**

<!-- Start of picture text --> 1,000<br>800<br>600<br>400<br>200<br>0<br>Antigua Dominica Grenada St. Kitts St. Lucia St. Vincent<br>Ports and terminal handling OECS Avg OECD Avg LAC Avg<br>US $<br><!-- End of picture text --> _Source:_ Doing Business Indicators 2014, World Bank _. Note:_ Port charges have been collected on-site in the OECS by the authors, and they are reasonably close to those found in the Doing Business indicators. The Doing Business indicators are presented here because they allow for regional comparisons. Avg = average; OECD = Organisation for Economic Co-operation and Development; OECS = Organization of Eastern Caribbean States; LAC= Latin America and the Caribbean.

## **Grenada’s handling charges (reported at $745 per 20’ container and $1,192 per 40’ container) are**

**among the highest in the region (table 5.4).** With the introduction of the free along side (FAS) port charging scheme in 2001, Dominica reduced tariffs by 20 percent. Methods of setting container-handling charges vary across the OECS. St. Lucia does not charge per container, for example, but per tonne (the number per container presented in table 5.4 is an industry-estimated average). In addition, at many ports other charges, spread over each container (such as the usage of cranes, by hour), place the overall per container charge higher than the handling charges. In general, transshipment containers are charged at a significantly lower rate than imports and exports, with the exception of St. Vincent. General cargo and bulk dry cargo fees, charged by the tonne, also vary widely.

38
<!-- Start of picture text --> cu<br>HIC<br>MENA<br>ECA<br>SAR<br>EAP<br>LAC<br>SSA<br>fe) 2 4 6 8<br>Rate of motor (pervehicle 100,000) air pollution deaths<br><!-- End of picture text --> <!-- Start of picture text --> Low motor vehicle/passenger car per 1000<br>High motor vehicle/passenger car per 1000<br>4<br>35<br>3<br>25<br>2<br>1.5<br>1<br>0.5<br>0<br>Death air. pollution. rate’ax Death air. pollution. rate EK<br><!-- End of picture text --> <!-- Start of picture text --> MDALY MYLL BYLD<br>MENA<br>SAR<br>SSA<br>LAC<br>HIC<br>EU<br>ECA<br>EAP<br>0 5 10 15<br>Ranking of health loss due to road tranport injuries<br>and air pollution compared with leading risk factors<br><!-- End of picture text -->
The main advantage of collecting innovation data as part of the ES is that data are fully comparable across countries, allowing benchmarking and comparison of indicators. Moreover, innovation indicators can be related to a broader set of firm‐level indicators covering firm’s performance, job creation and experience of the business environment. The ES are conducted by using a global methodology, which includes the same universe of inference, same sampling methodology – stratified random sampling –along with a common questionnaire. The sample for each country is stratified by firm‐size, sector of activity and location within the country. Weights are provided in the survey to ensure that the sample is representative of the formal (registered) non‐agricultural, non‐mining private sector of the economy.<sup>6</sup> The objective of the project was to test the reliability of the answers provided in the short questionnaire in order to determine if a short set of questions on innovation can be incorporated into standard firm‐ level surveys. As suggested above a challenge in measuring innovation, using CIS‐type of surveys is the great subjectivity of what constitutes an innovation. In the case of the short questionnaire this problem is exacerbated by a lack of follow up control questions that can provide some details to guide the veracity and accuracy of the answers. Additional methodological issues, like respondents’ reactions to combined questionnaires vis‐à‐vis to single topic questionnaires, should be considered when choosing the most appropriate instrument for innovation surveys. The project also incorporated an additional test to determine the extent to which cognitive problems play a role in biasing respondents’ answers. Half of the respondents in the short questionnaire were shown explanatory cards with examples or definitions to improve the comprehension of the innovation questions.<sup>7</sup> Four types of innovation outcomes ‐ product, process, marketing and organization ‐ and one innovation input – R&D ‐ were considered in both surveys. Following the Oslo manual, product innovation is defined as the introduction of new or significantly improved product (goods and/or services) and process innovation is disentangled into three components: methods of manufacturing goods or offering service (“methods” from now onwards); logistics, delivery, or distribution methods for inputs, products, or services (“logistics” from now onwards); supporting activities such as maintenance systems or operations for purchasing, accounting, or computing (“auxiliary” from now onwards). For product and process

> follow‐up questions, however, is slightly different as innovations questions are administered only to firms identified as innovators based on the ES.

> 6 We note that Enterprise Surveys cover manufacturing as well as services sectors but certain services such as education and health are not covered. Moreover, the primary sector, which encompasses agriculture, mining, forestry, etc., is also excluded from the survey.

> 7 The experiment of using show cards on half of the sample was conducted in Ghana, Kenya, Tanzania, Uganda, and Zambia.

10
types of businesses: 1) female.-only firms (possibly with children), 2) male-only firms (perhaps with children), and 3) mixed firms. Most retail firms employ only one or two family workers and do not use hired laborers. (See Table 3) We use the follr.wing variables in the empirical analysis. The dependent variable is the logarithm of monthly gross revenues, which is the value of output. The set of regressors can be grouped into two categories -- those that describe customer arrivals and those chat affect the probability of a purchase. The former group includes the age of the enterprise in years, which can be interpreted as a reflection of the reputation of the firm and perhaps as a predictor of learning by doing in attracting clientq. Also included is the place - of operation as a proxy for ease of access by customers. Two dummy variables - 'in the home' and 'at a fixed location' (a kiosk or stall) -- incorporate the site information. Itinerant operations with no fixed location (such as peddling and street hawking) are excluded.

The following variables are deemed to affect the probability of a purchase. First, the value of capital, which includes land, buildings, furniture, tools, machinery, equipment and vehicles. Second, the value of inventory stocks and third, monthly operating expenses which measure the cost of goods purchased for resale, raw materials, and such items as repairs, utilities, and fuel. The timeframe of this variable corresponds to that of the dependent variable. The fourth variable is labor input, measured by the logarithm of monthly hours devoted by all family workers in the ent._rprise. The annual hours of farming labor are divided by the months of operation. Other aspects of managerial or sales skills are described by two proxies: 1) total work experience (in years) of the most experienced adult family worker in the firm and 2) the level of educational attainment of the most educated adult family worker in the firm. Work 23