rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The difficulty inherent in reducing nominal wages has recently moved into the spotlight as a result of efforts of a number of European countries, especially within the euro area, to adjust to serious negative economic shocks through internal devaluation. Even with the severity of the economic downturn experienced across Europe in recent years, cuts in nominal wages appear to be a last resort for firms, and a series of papers have established that wages tend to be sticky downwards.<sup>1</sup> Evidence from interviews with business owners and firm managers have even suggested that selective layoffs are usually preferred to across-the-board wage reductions (Bewley, 1999). Bertola et al. (2012), using data from the same survey of European firms that this paper will analyse, found that only 2% of firms would use base wage cuts as the main channel of labour cost reduction if faced with a significant cost shock. A considerably higher percentage reported that they would rely on reducing staff numbers or hours worked as their main strategy.<sup>2</sup> So why is it so difficult to cut nominal wages? This paper uses evidence from a firm survey conducted in a number of EU countries to investigate a range of different theories as to why firms appear reluctant to lower wages. The sample covers 14,975 firms from 14 European countries, representing around 47.3 million employees. Although the data collection predates the onset of the European crisis, the survey provides unique and valuable information on the extent and rationale for wage rigidity, and enables us to evaluate the importance of different explanations for avoiding wage cuts _._ An advantage that this study has over earlier work in this area is the use of cross-country data gathered as part of a harmonised survey designed specifically to examine wage setting practices across firms. Other studies have typically been restricted to the analysis of single countries and, with few exceptions, using relatively small samples that focused on very large firms. Given the large institutional heterogeneity of European labour markets, this unified survey for European countries allows us to evaluate the association of different labour market institutions and policies with the rationales for avoiding wage cuts.

> 1 See for example, Kahn, 1997; Altonji and Devereux, 2000; and Lebow and Saks, 2003 for evidence on the US, and Dickens et al., 2007, 2008 and Babecký et al. (2010) for Europe.

> 2 17.5% said they would reduce numbers of temporary employees, 11% would reduce numbers of permanent employees and 7% would reduce hours.  Regarding wages, 9.4% said they would reduce some flexible components of wages such as bonuses. The use of changes in these flexible components of wages is also analysed in Babecký et al. (2012).

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the expanded intervention significantly reduced syndrome-specific absence incidence (absence due to stomachaches or headaches).

To the best of our knowledge, the studies that so far have found handwashing programs to have significant effects on child health have focused on interventions that impose controlled conditions in small populations over short time periods. These studies are akin to efficacy trials in drug development, which evaluate the impact of a specific intervention under ideal conditions. In most published handwashing studies, the participants are visited each week over a period of months. This style of intense promotion can cause important behavior changes that we would not necessarily expect under non-study conditions where interaction with handwashing promoters is less frequent. Thus, although intensive handwashing interventions have proven effective in reducing diarrhea and acute lower respiratory infections (ALRIs), it has not been proven that similar results could be obtained if those interventions were implemented at scale. This paper, however, studies the effectiveness of a national handwashing campaign to learn the impacts of large-scale handwashing interventions in a real-world context.

Thus, this paper is the first to assess the effect of a large-scale handwashing intervention on a wide range of health indicators. Furthermore, to the best of our knowledge, we are also the first to study other intermediate outcomes, such as the campaigns’ effectiveness and behavior change, which provides important insights on the full theoretical causal chain of disease transmission and ill health.<sup>17</sup> The rest of the paper is organized as follows. Section 2 details the program components. In section 3 we explain the experimental design. Section 4 describes relevant issues concerning the data and sampling procedure and presents some descriptive statistics. Section 5 describes the results of the baseline balance checks and provides an analysis of the panel sample attrition. Section 6 describes the methodological framework. In section 7 we show and interpret the main results of the interventions. Section 8 concludes.

> 17See Cattaneo et al. (2009) for another example of a study of the causal path of an intervention on child health. In this case, the authors studied the effect of improving the floor of houses with cement.

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<!-- Start of picture text --> cision is intrinsically an empirical question. In general, our prediction estimator is most useful<br>when there are co ariates that are both inexpensi ely observable (1.e.,lo alueof and offer<br>a good model for prediction (i.e., lo — alue of [MP Q7!MglV,-'). It should be apparent that<br>prior estimates of these parameters are required to calculate the optimal sample sizes under<br>double sampling. When these are not a ailable, data collection can be done in two stages. That<br>is, one can collect a small sample with both Y; and 2; in the first stage, and use this sample to<br>compute the parameters needed to determine the optimal double sampling structure. In practice,<br>this exercise can be done at the time of a pilot surve , in which case there may be practically<br>no additional logistical costs. While the estimate of obtained from the first-stage sample is<br>likely to be noisy, this does not bias the estimate of 4. Moreo er, the resulting estimates are<br>likely to be more accurate than the estimates created with only one sample under the same bud-<br>get constraint. Therefore, the cost-effecti e estimation approach proposed in Section5 is likely<br>to be a helpful alternati e that will benefit arious institutions, including go ernments, hospi-<br>tals, laboratories, and actories, that are financially constrained or wish to minimize the cost of<br>estimation while achieving a gi en le el of accurac .<br>References<br>Ahmed, ., C. Dorji, S. akamatsu, and N. oshida (2013) ‘Conducting a hybrid surve to<br>impro e the reliability and frequenc of po erty statistics in Bangladesh. mimeo, _ orld<br>Bank<br>Bose, C. (1943) ‘Note on the sampling error in the method of double sampling. Sankhya 6, 329-<br>330<br>Cameron, A.C., and P.K. ri edi (2005) Microeconometrics: Methods and Applications (Cam-<br>bridge Uni ersity Press)<br>Christiaensen, L., P. Lanjouw, J. Luoto, and D. Stifel (2012) ‘Small area estimation-based pre-<br>diction methods to track po erty: alidation and applications. ournal of Economic Inequal-<br>ity 10(2), 267-297<br>Cochran, .G. Sampling Techniques, 3rd edition ed. (John — ile Sons)<br><!-- End of picture text -->
relies on the “levelized cost” method<sup>8</sup> . The levelized cost (LCOE) of a power plant is calculated as follows: where OC is the overnight construction cost (or investment without accounting for interest payments during construction); OMC is the series of annualized operation and maintenance (O&M) costs; FC is the series of annualized fuel costs; CRF is the capital recovery factor; CF is the capacity factor; r is the discount rate and T is the economic life of the plant.

In this section, we discuss the economics of grid connected PV and CSP under various scenarios. One of the main challenges to the economic analysis of power generation technologies is the variation in cost data across technology type, size of plant, country and time. Since fuel costs are highly volatile and capital costs of solar technologies are changing every year, an economic analysis carried out in one year might be outdated the next year. Nevertheless, the analysis presented here could help illustrate the cost competitiveness of solar energy technologies with other technologies at present.

We have taken data from various sources including Lazard (2009), NEA/IEA (2005, 2010), EIA (2007, 2009) and CPUC (2009). The data were available for different years, so we adjusted them using the GDP deflator and expressed them in 2008 prices for our analysis. Moreover, the existing calculations of LCOE for a technology vary across studies as they use different economic lives, capacity factors and discount rates. Some studies account for financial costs (e.g., taxes and subsidies) (Lazard, 2009; CPUC, 2009), while others include only economic costs (NEA/IEA, 2005, 2010). Therefore, we have taken the maximum and minimum values of overnight construction costs for each technology considered here from the existing studies to reflect the variations in overnight construction costs, along with the corresponding O&M and fuel costs, and applied a uniform 10% discount rate and 2.5% fuel price and O&M costs escalation rate to cost data from all the studies. Since our focus is on economic analysis, taxes, subsidies or any types of capacity credits are excluded. Please see Table 2 for key data used in the economic analysis.

> 8 The levelized cost of electricity of a power plant represents the per unit value of total costs (i.e., capital, operation and maintenance, fuel) over the economic life of the power plant (Falk et al., 2008; NEA/IEA, 2010).

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from the principal city to the port of export (which is the relevant distance for which transport is calculated in the data). In addition, we include GPS-estimated total travel time. This variable is calculated as the total time it takes to get from the principal city to the port of exit by assuming a speed of 40 km per hour for unpaved roads and 80 km per hour for paved surfaces.<sup>15</sup> Both variables enter the equation in logs. If transit is primarily a geography effect then the GPS variables should pick up the effect.

Table 7 reports the results using the full sample. GPS distance is negative and significant, but does not alter the effect of inland transit time (columns (1) and (2)). GPS travel time is negative, but is not significant and the coefficient is very small. Column (4) shows results with all three variables, only transit time and GPS distance are significant. Columns (5) and (6) show results excluding the transit time variable and the coefficients of the other two variables remain roughly the same. Column (7) shows the results in logs, and only transit time is significant.

The results suggest that the distance from city to port and whether roads are paved are not the main reason for long delays in transit times. There might be other factors such as the quality of the roads and vehicles, accidents, competition in trucking, road blocks or border waiting times which affect the total time for an exporter to get his goods form the factory to the port of exit.

# **VI. Conclusion**

We use detailed data on key components on the time it takes to move containerized products from the factory gate to the ship to estimate whether and how diverse trade costs affect export volumes in Sub-Saharan Africa. An augmented gravity equation is estimated by regressing aggregate bilateral exports on different time delay components such as inland transit, documentation, ports and customs, and other standard gravity variables.

To control for the possibility that more trade leads to improved trade facilitation, we investigate the effects that documentation, inland transport, customs and ports times respectively have on the exports of new products. Exports in these products are unlikely to have an impact on the historical development of infrastructure. As a robustness check we also use an instrumental variables approach to examine the effect of time trade costs in transit countries on the exports of landlocked countries. Finally, we estimate a

> 15  No information on road condition was used in the calculation of travel time. Furthermore, delays at the border (or otherwise) were not included.

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# _4. Prioritizing SDGs_

**Figure 4 Centrality and density for Uganda in Access to electricity and HIV incidence**

<!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text -->

**Figure 5 Centrality and density for Uganda in Access to electricity and Child marriage**

<!-- Start of picture text --> Source: Authors’ calculations<br><!-- End of picture text --> The centrality for a) access to electricity is 255 and for b) HIV incidence is 212. The question is where in the network Uganda is, given it’s current capacities, i.e., its current successful SDGs. Calculating Uganda’s density with regard to each of these SDGs, it turns out that Uganda is somewhat closer in capacities to a) access to electricity, with density 0.594, than b) HIV incidence, 0.590. Hence, the fact that access to electricity has both higher density and centrality, suggests that it should be prioritized over HIV incidence (all else equal and no weights attached to the different goals).

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Table 3: Technology Sophistication and Firm Characteristics

||(1)|(2)<br>MOST|(3)|(4)|(5)<br>EXT|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|ABF|GBF|SSBF|
|Vietnam|-0.40***|-0.55***|-0.11***|-0.46***|-0.58***|-0.20***|
||(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|(0.04)|
|Senegal|-0.93***|-1.07***|-0.62***|-1.18***|-1.27***|-1.17***|
||(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|(0.04)|
|Manufacturing|-0.09**|0.04|-0.36***|0.14**|0.35***|-0.31***|
||(0.04)|(0.04)|(0.04)|(0.05)|(0.06)|(0.07)|
|Services|0.05|0.30***|-0.26***|0.25***|0.63***|-0.39***|
||(0.04)|(0.04)|(0.05)|(0.06)|(0.06)|(0.07)|
|Medium|0.20***|0.22***|0.09***|0.26***|0.26***|0.24***|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|(0.03)|
|Large|0.53***|0.59***|0.32***|0.65***|0.63***|0.77***|
||(0.03)|(0.03)|(0.04)|(0.04)|(0.05)|(0.06)|
|Age 6 to 10|-0.02|-0.03|0.00|-0.06**|-0.04|-0.09**|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|
|Age 11 to 15|-0.01|-0.01|0.01|-0.05|-0.01|-0.10**|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|
|Age 16+|0.02|0.03|0.02|0.00|0.04|-0.02|
||(0.02)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|
|Foreign Owned|0.25***<br>(0.03)|0.27***<br>(0.03)|0.22***<br>(0.05)|0.24***<br>(0.04)|0.26***<br>(0.05)|0.24***<br>(0.07)|
|Exporter|0.14***<br>(0.02)|0.12***<br>(0.02)|0.10***<br>(0.03)|0.34***<br>(0.03)|0.30***<br>(0.03)|0.37***<br>(0.04)|
|Observations|3,896|3,896|3,076|3,893|3,889|3,080|
|R-squared|0.54|0.57|0.28|0.49|0.50|0.38|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Reference group for country, sector, size, and age categories is Brazil, Agriculture, Small, and Age 0 to 5, respectively. Regressions include constant and a dummy for whether a firm has SSBF.

ogy sophistication. Beyond the purely descriptive relevance of this exercise, studying the correlates of within-firm volatility sheds light on the relative importance of heterogeneity in adoption costs and benefits of technology sophistication across business functions as sources of within-firm variation in technology. The third question we study is whether there are stable relationships across firms between the business function-level and the average firm-level technology sophistications. In a manner akin to Engel curves in consumption theory, such relationships can reveal insights about how business-function level technology sophistication indices aggregate into firm-level TFP.

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customs office for clearance. The customs office may be at the border crossing point or it may be inland, depending on the particular route that is taken. For example, there is a terminal in the capital city of Belgrade (which is inland), and most shipments bound for Belgrade would go through clearance procedures at the Belgrade terminal. Congestion or other complications at the terminal may increase clearance times or cause them to vary in ways that are problematic for the importing firm. By clearing shipments at the warehouse rather than at the terminal, IHC firms can avoid these difficulties. In principle, shorter and less variable clearance times may allow firms adopting the IHC program to increase their imports if the program reduces these implicit trade costs.

# **2.2. Take-Up and Use**

The Serbian customs agency implemented the IHC program in 2011, with the first firm to adopt it doing so in July 2011. The program is open to any firm that meets the qualification criteria that are reported in Table 1. In the import customs data we have for the period 2010-2013, 34 importing firms used the program. The first use of the IHC program to clear imports occurred in July 2011. Our data end in December 2013, and evaluation is not possible without a period of time that follows take-up of the program. We therefore limit the sample to 21 importing firms that adopted the program before August 2013 and that import in every month of the 2010-2013 period. These firms are the early adopters, and they tend to be substantially larger than the typical Serbian importing firm, as will be documented in Section 4.<sup>13</sup> Discussions with those familiar with the operation of the Serbian IHC program indicate that many of the initial participants were large multinational firms. Information from World Bank staffers in Belgrade suggest that smaller firms do not find the benefits of the IHC program to be sufficient to cover the costs associated with participation. Anecdotally, the main constraint that limits firm participation is the requirement that firms provide Serbian customs with a bank guarantee. The cost of financing this guarantee can be unaffordable for small firms. Another constraint is that smaller firms’ imports are often consolidated within the same shipment as those of other firms. Consolidated shipments are not eligible for clearance under the IHC program.

> 13 We drop three firms that take up the IHC program within 2013, but not before August.  These firms have too little data to allow an evaluation.  Additionally, 10 firms that adopted IHC during 2011-2013 but did not import continuously after adoption are also excluded.  All of the IHC firms, their control firms and placebo firms import continuously throughout the time period that is relevant for the estimation of the impact on each IHC firm.

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- (3.9) log Y **=0** +<sup>13</sup> 10g(l+hm) + 32109g(1+hF) + 833109(1+h,) + **B4Iog(1 +h,)iog(1 +ht)** + B13 5<sup>og(1+TOTWET)</sup> + B61og(1+TOTDRIED) + B7MAXED + T

where Y = profit of household from self-employment (both farm and nonfarm) h* = total male hours of work in self-employment hF **=** total female hours of work in seHf-employment he = total child hours of work in self-employment TOTWET = total area of watered land TOTDRIED = total area of dry land MAXED = length of schooling of most educated member of the household T = rancom error term - normally distributed.

T Is supposed to account for unobserved choice variables that affect the production technology. The distribution of the technology attribute, T, is assumed to be normal N(O,t), and it Is assumed to be independent of other Input factors. Note that the land variables appear only in agricultural production, not in nonfarm production. The conditlonal profit function for Uma Is specified as

- (3.10) logY **=** + ,log(l +hM) + 0 2109(l+hF) + P3log(l+hM) log (1+hF) + **014** MAXED + T,

Let g. be the fraction of all self-employment positions that are feasible for the household. Let g9 be the fraction of feasibe positions for an individual of sex r, r=F,M, that are nonfarm self-employment positions. Let g **r** and 9r be defined analogously as the corresponding opportunity probabiiitles for farm self-employment and wage work, respectively.

We have parameterized g, as B (3.11) 1 -gy = - . a+8(1-a) where B = [(-9 r )(1-g r AMr r=F,M
# Economic Output at Risk

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|**Table 8**<br>_Sample of 1974-2004_<br>_Dependent Variable: Ratio of Retribution to Labor to Capital Compensation , (wL/rK)_|**Variables**<br>**[1]**<br>**[2]**<br>**( t )**<br>**Retribution to Labor as % Capital Compensation**|int1. Index (t)<br>-3.041*<br>(1.787)<br>int1. Index ( t-1, t-3)<br>-5.872**<br>(2.885)<br>int2. Index (t)<br>-18.943***<br>(6.794)<br>int2. Index ( t-1, t-3)<br>-34.842**<br>(14.011)<br>_Control Variables_<br>∆Log(ToT)<br>-0.209***<br>-0.226***<br>(0.060)<br>(0.059)<br>Population growth<br>2.434<br>2.952*<br>(1.749)<br>(1.685)<br>Financial account to GDP<br>-0.519**<br>-0.807***<br>(0.251)<br>(0.234)<br>Trading partners growth<br>-0.509<br>-0.181<br>(0.719)<br>(0.673)<br>∆%GDPpw (t-1)<br>-0.467**<br>-0.319*<br>(0.204)<br>(0.177)<br>Observations / Countries<br>595 / 51<br>635 / 53<br>R-squared<br>0.787<br>0.783<br>Mean Dep. var.<br>76.708<br>76.911<br>St Dev Dep. var.<br>35.825<br>35.273<br>Note:_y_(t, t+s) corresponds to the average over the period t to t+s of the variable_y_.<br>Except otherwise indicated, all controls are averages for the period over which the dependent variable is measured.<br>All regressions included country fixed effects and Time dummies<br>Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** significant at 1%|
|---|---|---|