rafmacalaba/datause-encoder-data huggingface dataset:
Yemen, and by about 12 percentage points in Pakistan. They do not find any significant effect in African countries, though. Interestingly, in their study the impact of better water access on school attendance is similar for boys and girls.

Nankhuni and Findeis (2004), using a cross-sectional survey of 10,698 households in Malawi, investigated whether time spent by children (aged 6-14) to collect water and fuelwood impacted on the likelihood of attending school. Each child in the survey was assigned the median value of hours spent on fuelwood and water collection for the area of residence (the survey covers 136 areas), which is likely to induce some measurement error. They found evidence that the probability that a given child is involved in fuelwood and water collection is reduced in households with more women, and more members beyond school age. They also found a significant relationship between time spent collecting fuelwood and school attendance so that “children in districts with severe fuel wood deficits (all of the south and most of the central region) are about 9% less likely to attend school than those from fuel wood surplus districts.” They however provided no direct measure of the impact of the time to collect water on school participation.

Ilahi and Grimard (2000) measured how the quality and quantity of water-supply infrastructure (calculated from the distance to the source of water averaged over all households in the community that report collection but excluding the household in question) affect the allocation of time among Pakistani women to various activities, using a crosssectional survey from 1991 of Pakistani women above 15 years of age. Improvements in the water-supply infrastructure were here found to increase the time that women allocate to income-generating activities (although no magnitudes are given). Ilahi and Grimard did not study impacts on children’s school enrollment.

Lokshin and Yemtsov (2005) used community-level data from Georgia to assess the welfare impact of infrastructure rehabilitation projects (school rehabilitation, improvements in road infrastructure, and water system rehabilitation projects) in rural areas between 1998 and 2000. Using propensity score-matching and difference-in-difference methods, they measured the impact of water projects (including installing new or repairing existing communal water tanks, installing water treatment equipment, fitting new pumps, repairing or installing pipes, and rehabilitating wastewater management networks) on various outcomes including female wage employment and incidence of water-borne diseases. Their results indicate that water rehabilitation projects significantly reduce the incidence of water-borne diseases. The effect on female employment was however found not to be significant (which, they argue, could be explained by the low number of such projects in the sample).

4
LSDVC model to other popular models designed to address dynamic panel data bias, including the popular system GMM model developed by Blundell and Bond (1998). They find that even in the case of moderate endogeneity and serial correlation, the LSDVC emerges as the most accurate methodology. We estimate dynamic model (1) separately for the resource and non-resource sectors and perform a Chow test (Chow, 1960) in order to test whether the coefficients in the resource and non-resource FDI estimations are statistically different from each other. The Chow test is designed to test whether the coefficients of a model estimated over one group are similar to those estimated in another group.

In addition to analyzing the short-term effect of political violence on greenfield FDI inflows, we are also interested in the long-term effect. The dynamic panel model (1) makes it possible to identify the long-term equilibrium effect of political violence on greenfield FDI as follows:

# **Results**

Table 1 shows the baseline results estimated using the Bun and Kiviet LSDVC estimator for three different specifications in which the dependent variable represents total greenfield FDI (columns 1 and 2), resource greenfield FDI (columns 3 and 4), and all other greenfield FDI, also referred to as nonresource FDI (columns 5 and 6). Battle-related deaths are negatively associated with total FDI flows, and the variable is significant at the 10% level. A 10% increase in the number of battle-related deaths decreases total greenfield FDI flows by approximately 0.95%, ceteris paribus. This effect is in addition to the decline in FDI stemming from worsening macroeconomic conditions and restrictions in investment policies; these additional effects are captured by the controls for changes in GDP, exchange rates, inflation, and the regulatory quality <mark>index. The long-term effect of BRD on total</mark> greenfield FDI flows, shown in Table 1, is slightly greater than the short-term effect. We therefore find support for the first hypothesis (H1), namely that political conflict has a negative effect on total greenfield FDI.

In the second specification, we take into account the geographic scope of conflict by adding a moderator for scope of conflict to the model. The coefficient of the moderator is positive and eliminates the negative effect of the main effect. However, the moderator is not significantly different from zero.

21
Table 7: Gender Differences in General Coping Strategies - Both Channels

|||Househ|old Interna|l||External - Fa|mily/Network|
|---|---|---|---|---|---|---|---|
||Reduce Meal<br>(0/1)|Sell Asset<br>(0/1)|ln<br>Asset|Loan<br>(0/1)|ln<br>Loan|Family<br>Transfer (0/1)|Gift/Assistance<br>(0/1)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Female Headed Household (0/1)|0.059<sup>_∗∗∗_</sup>|-0.030<sup>_∗_</sup>|-0.102|-0.018|-0.091|0.034<sup>_∗∗_</sup>|0.016|
||(0.016)|(0.017)|(0.136)|(0.018)|(0.141)|(0.016)|(0.013)|
|Female Head (0/1)_×_Network (0/1)|-0.142<sup>_∗∗∗_</sup>|-0.015|-0.329|-0.056|0.175|-0.003|-0.053|
||(0.046)|(0.046)|(0.383)|(0.062)|(0.243)|(0.046)|(0.044)|
|Network (0/1)|0.089<sup>_∗∗∗_</sup>|0.031|0.186|0.072|0.054|0.161<sup>_∗∗∗_</sup>|0.159<sup>_∗∗∗_</sup>|
||(0.028)|(0.028)|(0.148)|(0.045)|(0.181)|(0.030)|(0.030)|
|Female Head (0/1)_×_Dirt Floor (0/1)|-0.060<sup>_∗∗_</sup>|-0.003|0.017|-0.011|0.227|0.008|-0.004|
||(0.027)|(0.025)|(0.257)|(0.026)|(0.285)|(0.028)|(0.024)|
|Dirt Floor (0/1)|0.109<sup>_∗∗∗_</sup>|0.009|-0.144|-0.051<sup>_∗∗_</sup>|-0.345<sup>_∗_</sup>|0.038<sup>_∗_</sup>|0.038<sup>_∗_</sup>|
||(0.020)|(0.019)|(0.131)|(0.022)|(0.199)|(0.020)|(0.021)|
|Household Head Age|0.002<sup>_∗∗∗_</sup>|-0.000|0.001|-0.002<sup>_∗∗∗_</sup>|0.009|0.002<sup>_∗∗∗_</sup>|0.001<sup>_∗∗_</sup>|
||(0.001)|(0.000)|(0.004)|(0.001)|(0.006)|(0.001)|(0.001)|
|ln Household Size|0.004|0.022|0.192|-0.009|0.183|-0.092<sup>_∗∗∗_</sup>|-0.039<sup>_∗_</sup>|
||(0.028)|(0.015)|(0.125)|(0.020)|(0.172)|(0.020)|(0.023)|
|Child-adult Ratio|0.068<sup>_∗∗∗_</sup>|0.010|-0.211<sup>_∗∗_</sup>|0.010|-0.046|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.082)|(0.015)|(0.109)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.109|-0.013|0.136|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.130)|(0.013)|(0.010)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.076|0.027|0.275|0.043|0.180|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

15
reports the regression of the natural logarithm of hourly earnings<sup>9</sup> on a range of HE operators‟ characteristics (using this specification means that the coefficients can be interpreted as the percentage effect on earnings).

**Figure 3.3:   Normalized Earnings by Employment Type and Gender, 2006**

<!-- Start of picture text -->
1.5<br>1.25<br>1<br>0.75<br>Male<br>0.5 Female<br>0.25<br>0<br>Public wage non- Employer Private wage  Household  Wage agriculture<br>agr. non-agr. enterprise non<br>agr.<br><!-- End of picture text -->

_Note: Reference category = public wage employment, non-agriculture_ _Source: Calculations based on the ILFS 2006 data._ 31. Not surprisingly, education is found to have a strong effect on earnings, although there are clear diminishing returns. HE operators without complete primary education are clearly disadvantaged.<sup>10</sup> For example, for a male HE operator, completing primary education increases earnings by 23 percent at the mean, but completing ordinary secondary education adds only one percent more. This is not surprising, as few HE operators have reached this level of education. The returns to highest levels of education are significantly greater, but very few HE operators have education beyond primary school (less than one percent has gone beyond ordinary secondary). Age (a proxy for experience) adds much more to the earnings of male HE operators than of female HE operators. Migration status, however, has no effect on earnings.

> 9 Hourly earnings are obtained by taking the net earnings of enterprises divided by the hours reported by all those who worked in these enterprises.  One concern regarding the measure of hourly earnings for the self-employed is that earnings are qualitatively different from wages, i.e., they include remuneration for capital and for entrepreneurial risk. The data does not allow us to address the latter concern. With regard to earnings, the potential bias is likely to be minor.

> 10 Obviously, the coefficient on “complete primary” could be picking up other factors than education;  in other words, there may be a selectivity issue. Clearly, there is a selectivity issue into operating an HE, but we were unable to pick up anything other than education as a selection variable. Education is by far the strongest predictor of occupational choice in Tanzania, but it also exerts a strong impact on earnings.

17
goods and services to an economy also may have dire consequences on private s3ctor performance.

Increasing concern, especially at the World Bank, over deteriorating government performance from the level of individual projects to that of national public management, has prompted interest in the relationship between government pay and employment policies and government performance. In keeping with the focus of the 1988 WDR on government as a direct economic agent, the remainder of this paper primarily considers this relationship.

# B. RESOURCE MISALLOCATION IN GOVERNMENT PRODUCTION

Throughout the developing world, governments are accused of gross inefficiencies in prolucing a broad range of goods and services. Examples include teachers who have no textbooks or chalk to teach with, public health workers with insufficient supplies of vaccines, and agricultural extension workers without fuel for their vehicles. Gross redundancies in civil service staffing are aiso frequently reported. Examples include corridors full of seemingly unproductive messengers outside the offices of overworked senior administrators. In still other settings, high vacancy rates for professional positions, widespread absenteeism of government workers, or pursuit of other than official business during working hours may be commonplace.

What these examples share in common is the potential for an inefficient mix of inputs in the production of government output. It may be that too much or too little labor is employed relative to the availability of non-labor inputs. Or, the combination of labor skills hired for a particular task may be more costly than some other mix of labor skills capable of producing the 7
**Appendix E. Variable Descriptions and Definitions of Data from the Brazil Electoral Study**

|**Variable**|**Question**|**Scale**|
|---|---|---|
|Bolsonaro vote round 2|Voted for Jair Bolsonaro in the second round of the<br>presidential elections|0=No<br>1=Yes|
|Bolsonaro-PT switch|Switched from voting for Dilma in 2014 to Bolsonaro<br>in 2018||
|Age|Four age categories: <30 years, 30-44 years, 45-59<br>years,60years or older|Age in years|
|Gender|Gender of the respondent<br>|Male/female|
|Has a partner|Dummy taking the value of 1 if the respondent is<br>married or has a domesticpartner,and 0 otherwise.|1=yes; 0=no|
|Other people in<br>household|Dummy taking the value of 1 if the respondent has<br>other people than their partner living in the same<br>household|1=yes; 0=no|
|Evangelical|Religious denomination: evangelical<br>|1=yes;0=no|
|White|Ethnicity: Preto, Pardo, Branco, Amarelo, Índio|1=White (Branco)<br>2=Other|
|Immigrant|Wereyou born in Brazil,or not?|1=no;0=yes|
|Lives in a rural area|The respondent’s self-reported type of settlement: (1)<br>Rural; (2)urban|1=rural; 0=urban|
|Region|Region in which the respondent is living|1= North<br>2= Northeast<br>3= Central-West<br>4= Southeast<br>5= South|
|Education level|Highest level of attained education|1=Elementary<br>2=Secondary<br>3=Tertiary|
|Employment status|Current employment status|1=Paid<br>employment<br>2=Self-<br>employed/Boss<br>3=Unemployed<br>4=Out of labor<br>force|
|Income|Approximate monthly household income|1= Lowest<br>quintile-<br>5=Highest<br>quintile;<br>|
|||6=Unknown|
|Economic situation<br>country|Do you think that the current economic situation in<br>the country is much better, a little better, the same, a<br>little worse or much worse than in the last twelve<br>months?|1=much/little<br>better; 2=the<br>same;<br>3=little/much<br>worse|
|Personal economic<br>progress|In recent years, it has been argued that many people<br>have experienced a change in their social class.<br>•<br>8 years ago, you and his family belonged to<br>the class: (1=Highest, 6=Lowest)<br>•<br>Today they belong to class: (1=Highest,<br>6=Lowest)|1= No change;<br>2= Upward<br>3= Downward|
|Perceived corruption|How widespread do you think corruption is in Brazil,<br>such as bribes among politicians?|1(hardly happens)<br>-4(very<br>widespread)|

46
# 3.2. The Model

The data are a balanced panel spanning 28 years for the 6 countries in Central America (Guatemala, Honduras, Costa Rica, Panama, Nicaragua, and El Salvador). As discussed in the previous section, wind speed data was aggregated by year. When more than one event occurred in a calendar year in the same country, only the strongest event was taken into account. To evaluate the existence of other relationships between multiple hurricanes in a year and per capita GDP growth, we included controls for the number of hurricanes per year with no substantial change in the results.<sup>17</sup> During the 30 years of the study, only four hurricanes reached SS scale equal to or greater than 3,<sup>18</sup> while 19 events reached SS scale equal to or greater than 1. It is important to highlight that in this period, Panama and Costa Rica did not suffer any major events (i.e., events with SS ≥ 1). Nicaragua accounted for nearly half of the events (9 events in total) followed by Honduras (5 events in total). Storms under category SS 1 represented 63 percent of events (12 in total), while category SS 2 and SS 3 accounted equally for nearly 33 percent of total events.

Our empirical strategy exploit the natural random variation in the formation, path and intensity of hurricanes and tropical storms as a source of exogenous within‐country variation in disaster exposure to identify the causal effect of hurricanes in per capita GDP. It follows an ordinary least squares (OLS) models with fixed effects by year and country. Hence our identification strategy exploits the variation over time within countries following equation 4.

��� � ������ ����� ������ ��� ����� �4� Where:

- ��� is per capita GDP growth for country _i_ in year _t;_

- ��� is the hurricane damage index for major events for country _i_ in year _t_ (SS ≥ 1 or 3);

- ��� is a matrix of control variables;

�� are year fixed effects; �� are country fixed effects;

- is time trend.

For each country, the dependent variable is per capita GPD growth. As we discussed in the previous section, we adopted three different measures of the intensity of the damage ( ��� ): MSWS, IHD and WEI. MSWS corresponds to the unweighted wind speed aggregated at the country level per year, IHD relates to the population‐weighted wind speed, while WEI relates to the area‐weighted wind speed. Each index has its strengths and weaknesses highlighting the trade‐off between exogeneity and measurement error.

The control variables ( ��� ) mirror those in Strobl (2012) and include: investment as a percent of GDP, population growth, openness (the sum of exports and imports as a percentage of GDP), and the log of GDP per capita. All these variables are lagged one period to capture the contemporary economic impact of hurricanes in the region. Additionally, a regional time trend ( � ) was included to account for common factors to all countries.

> 17 Results available upon request.

> 18 Nicaragua: Joan (1998), Cesar (1996), Felix (2007). Honduras: Mitch (1998).

11
Development (OECD) (Figure A.1). The ratio in Turkey was 0.74 compared with the OECD average of 0.53. As result, a large share of workers formally employed in Turkey are paid around the minimum wage. In the last quarter of 2015, shortly before the minimum wage rose, over half the registered workers in Turkey were paid at around the minimum wage. This implies that any sizable increase in the minimum wage would have a substantial impact on labor costs among formal-sector firms that employ workers.

While the minimum wage to median wage ratio in Turkey is on the high end of the distribution of minimum wages in advanced economies, high Kaitz ratios are quite common in developing economies. For example, the ratio of the minimum wage to median wages was 0.68 in Peru, 0.78 in Brazil, 0.98 and Philippines and 1.05 in Indonesia (Rani et al., 2013). Therefore, the relative level of the minimum wage in Turkey is similar to that observed in many other developing economies that have a mandatory minimum wage in place.

Firms in Turkey strictly comply with the minimum wage when their workers are registered with social security.<sup>9</sup> Informality at the firm level is rare due to low cost of firm registration and a high probability of detection. Formality at the worker level within firms, on the other hand, is more widespread as it is costly while the probability of detection of unregistered employment by authorities is fairly low (Taymaz, 2009). Employers must pay between 22 percent and 27 percent of registered workers’ monthly gross wage in a security premium, which is high by international standards. As a result, informal employment – that is, not registering workers with social security institutions – remains common at the worker level, even though it has declined in recent years (Figure 1). As of 2015, about 22 percent of wage workers in non-agricultural employment (the population of interest in this paper) were not registered with social security institutions. This proportion rises to 30 percent when all workers in non-agricultural worker, included the selfemployed, are considered, and to 40 percent when the agricultural sector is included.

# 2.2. The 2016 minimum-wage increase

In December 2015, Turkey drastically increased the national minimum wage compared to previous years. The nominal minimum wage was raised by 33 percent, from an average of 975 TL (about

> 9 According to social security records, virtually 100 percent of workers who are registered with social security institutions are paid at least the minimum wage.

7
Figure 2.4: TANZANIA, evaluation of performance of the ICA method I. Kernel<sup>1</sup> estimates of output and input densities in the complete case and in the sample after imputing missing values by the ICA method <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>5 10 15 20 5 10 15 20<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>C. Capital stock (log) D. Employment (log)<br>5 10 15 20 8 10 12 14 16<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>.2 .15<br>.15<br>.1<br>.1<br>Density Density<br>.05<br>.05<br>0 0<br>.15 .4<br>.3<br>.1<br>.2<br>Density Density<br>.05<br>.1<br>0 0<br><!-- End of picture text --> II. Table of descriptive statistics and tests of equality of distributions of output and inputs in the complete case and in the sample with imputation by the ICA method

|||**# Obs.**<br>**(# imputed)**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**One-sample K-S**<br>**Test (p-value)**|
|---|---|---|---|---|---|---|---|
|Sales (log)|Complete case|1497|14.24|2.10|7.78|19.40|0.012|
||ICA meth.|1821 (324)|14.30|1.99|7.78|19.40|0.001|
|Materials (log)|Complete case|1293|13.19|2.31|4.33|18.65|0.169|
||ICA meth.|1822 (529)|13.37|2.13|4.34|18.65|0.093|
|Capital (log)|Complete case|1289|11.39|2.26|0.63|19.65|0.053|
||ICA meth.|1816 (527)|11.32|2.05|1.05|19.65|0.027|
|Empl (log)|Complete case|2529|11.63|1.45|7.64|15.42|0.006|
||ICA meth.|2548 (19)|11.63|1.45|7.64|15.42|0.002|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values.

The null hypothesis of the one-sample Kolmogorov-Smirnov Test is that the cumulative distribution differs from the hypothesized theoretical normal distribution.

Source: Authors’ estimations with ICSs data.

From a more detailed analysis of Figure 2.1, which illustrates the case of India, it is clear that there are not significant differences in the distributions of any of the PF variables in the complete case and in the sample with replacement by the ICA method, which is supported by the KolmogorovSmirnov tests. Furthermore, both the sample mean and the standard deviation do not change significantly before and after the imputation process (especially important is the fact that the standard 24