rafmacalaba/data-use-mentions-extended huggingface dataset:
employment, while women in rural areas reduced hours working in non-paid and informal employment.

The second strand of the literature investigates the impact of migration and remittances on wages and other labor market outcomes such as unemployment using a variety of methodologies. Mishra (2014) surveys the rather limited empirical literature on the impact of emigration on real wages in the source countries, which includes his own work on Mexico. This literature includes single country econometric estimates of the determinants of wages which use an estimate of the fall in the domestic labor force (or a specific section of the labor force) due to emigration as an independent variable in the regressions. All of the studies surveyed find that a reduction in the labor force as a result of emigration raises real wages for workers in the source country, by between 2 and 5.5 percent for a 10 percent supply shock to the labor force.

Acosta et al (2007) construct a two sector dynamic stochastic general equilibrium model of a small open economy and test it with data from El Salvador using a Bayesian Vector Autoregression analysis. An inflow of remittances induces both an increase in consumption and increased demand for leisure, and thus reduced labor supply which leads to increased production costs in the relatively labor intensive non-tradeable sector. Bayangos and Jansen (2011) construct a New Keynesian macroeconomic model of the Philippines and use it to simulate a sustained increase in remittances. They find that the increase in migration and remittances leads to a fall in the labor force and a rise in real wages. Although labor productivity increases, this is not by enough to offset the rise in real wages. Consequently, there is a significant increase in real unit labor costs. Bouton et al (2011) using household survey data, find that a supply shock to the labor force caused by emigration raises wages in Moldova.

What can we conclude from this survey of the literature? Remittance receiving households appear to reduce labor market participation and/or the number of hours worked in formal employment, which together with the direct
# **1 Introduction**

The question of whether the digital revolution has had effects on productivity has been around for a long time. Firms expect to become more efficient as a result of using more Information and Communication Technologies (ICT) and therefore, to obtain positive returns from these investments. Accordingly, ICT has been the most dynamic component of total investment in OECD countries (OECD, 2007). However, empirical studies have not found much evidence on this relationship. Moreover, this lack of evidence led to the so called Solow Paradox during the 1980s in which the computer age was everywhere but in the productivity statistics (Solow, 1987).

Recent evidence points to a reversal of this paradox as ICT is considered one of the factors that can potentially increase firm-level productivity (Syverson, 2011). According to Brynjolfsson and Hitt (1996) previous non-significant results arise from mismeasurement, lags on the effects, as well as the fact that the impact of ICT on variety, quality and other intangibles is more likely to be detected at the firm level than using more aggregated data. Using firm-level data for the U.S., these authors find that computers have significant effects over output, and that their contribution to marginal product is at least as high as the one from other capital investments. Among other studies that obtain similar results are Stiroh (2002), Oliner et al. (2008) and Jorgenson et al. (2008).

Even though some agreement has been reached regarding the positive effects of ICT on productivity, there is still little evidence on the channels or the factors that could enhance these effects. For example, as Jorgenson et al. (2008) argue, in order to successfully leverage ICT investments, firms must undergo complementary investments such as changes in business organization, human resources, workplace practices, training, etc., which Brynjolfsson et al. (2002) refer to as intangible assets. In order to explain the differences in terms of productivity growth after 1995 between the U
_F_  _AF_ * _MP_  33 _Y_  5.0 _Pop_ .007 (8) In order to test whether other variables may also influence the damage function, a set of damage regressions were explored using the international data set. Democratic elections, the literacy of men and women, infant mortality rates, HIV rates, the Gini coefficient, and the percent of people living in poverty were all tested and found to be insignificant. As shown in Table 4, the percent urban is quite significant. As with population density, the percent urban suggests that damages fall rapidly as density rises.

Table 4 Alternative Damage Regression

|Variable|Coefficient<br>(t statistic)|
|---|---|
|Constant|13.76<br>(3.73)|
|Log (Population Density)|-0.17<br>(1.76)|
|Log (Income)|0.95<br>(7.43)|
|Log (urban)|-2.99<br>(7.22)|
|Log(Gini index)|-1.25<br>(1.71)|

The damage function is then used to estimate the impacts of each tropical storm generated by each climate model. The climate models are also used to predict the intensity and frequency of each storm in each ocean basin. By construction, the global expected damages given current climate and current baseline conditions are $26 billion per year (0.043 percent of GWP).

In order to project how damages might increase with economic growth, both population and income are projected to 2100. The population in each country is assumed 14
<!-- Start of picture text --> 8%<br><!-- End of picture text --> <!-- Start of picture text --> 100% Chinese owned<br>8%<br>21%<br>Joint venture with local<br>private firms<br>71%<br>Host government<br>contracts<br><!-- End of picture text --> _Source: Shen and Zhai._ The average project size of Chinese investment is about US $4 million, based on available data from six host countries. There is some notable diversity in project size among the participating host countries, which correlates largely with the different sectoral spreads in those countries. For instance, Nigeria, with a number of large capital-intensive projects in energy and infrastructural sectors, has the largest project size among the participating host countries, with an average $10 million per project. Following Nigeria, Rwanda shows an average project size of $5 million, Ethiopia $2.3 million, Liberia $1.2 million, and Zambia $0.9 million. Ghana, with nearly 60% of the projects concentrated in the service sector, has the smallest project size, averaged at $163,000.

Most responding country investment authorities provided data that allow comparing Chinese investment with the overall FDI they attracted and with FDI from other major source countries. These data show that, in terms of size and sector, Chinese investment is similar to that from other emerging economies, notably India and South Africa, the two other top source countries mentioned by host countries IPAs. The data also show that investment originating from western countries is of relatively large size and more engaged in resource-extracting sectors. Figure 9 compares the project sizes of all major FDI source countries, with China being the smallest of all.

**Figure 9: Average Size of Chinese Investment Projects Compared with that of Projects from Other Origins** (in
<!-- Start of picture text --> FIGURE IV-3: DISTRIBUTION OF THE TOTAL VALUE OF THE UPC<br>25<br>19%<br>20<br>16.9%<br>15<br>10<br>5<br>1.6%<br>0.4%<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Subsidized Contributory<br>Source: World Bank Group staff estimates based on EPNH 2017 and DIAN<br>Notes: The deciles were created using per-capita market income + pensions.<br>UPC values are already re-scaled.<br>% transferred to each decile<br><!-- End of picture text --> The current version of the CEQ does not consider public health resources for vaccination and preventive care since the beneficiaries cannot be identified directly from the survey. Nor does it include resources for the poor, uninsured population. It is worth clarifying that we tried to include the budget for the attention of this population. In this manner, the total value spent for this item in the local administrations was calculated, and a per capita value was taken considering the numbers of the poor population not served by the Ministry of Health. However, inconsistent per capita values were obtained in some cases. For example, in Vaupés, the per capita value of this item was equivalent to 10.7 times the UPC, so the decision was made not to include a value that could overestimate the effects on inequality. Finally, following the methodological framework of the CEQ, health transfers were rescaled.

# ii. The impact on inequality

The combined effect of taxes and social spending helps to reduce inequality in Colombia substantially.<sup>44</sup> Figure IV-4: shows the changes in the Gini coefficient when different taxes and social spending are considered, using the income definitions of Figure III-1 for two CEQ Exercises
6 Figure 1: Picking Performance Indicators <!-- Start of picture text --> Ad g cPerfonance e Indicators<br>M Productivity Indicators t Cost Frontiers,<br>-Labor/output -Deterministic & non parametric (data envelope<br>-Capital /output analysis)-DEA<br>-Stochastic and param ptric (stochastic frontiers)-SF<br>-Quality whe r Disadvantage: o v<br>Advantage Incomplete and Advantage Disadvantage:<br>Easy to can generate Allows require a lot of<br>compute perverse A comparison for good data<br>-Tchoy wincentives more than 1<br>dimension<br>Must be used jointly with indicators that data<br>allow to take into account external factors:-inSFterinist Frontier<br>SFlevels (for various<br>-Qaiy. hnteeaen iiao products) and control<br>-Quallty: when there are no minima or ~~~variables that allow<br>minima differcoprsn<br>-Othercharateristc osts nd on affcting Incoplete ataRoeCompedate: unoisys<br>-Population density in the service area<br>-Technology: when exogenous to the firrn A priori, it is not possible to say which one is bte<br>-Other charasteristic affecting costs and on , I^,<br>whc fim aen oto Incomplete data or I omplete unnoisy<br>noisy data data<br>| tcatc Frontier: | etrinistic Frontier.<br>| SF l lDEAl<br><!-- End of picture text -->
(Tasselled Cap Brightness index, TCB), as measured by NASA’s MODIS system <u>(http://modis.gsfc.nasa.gov/); aridity, as measured by the CGIAR project (http://csi.cgiar.org/Aridity/);</u> brightness of night‐time lights measured by the DMSP OLS project (https://ngdc.noaa.gov/eog/); and a delineation of urban versus rural areas constructed by the Nelson Institute (https://nelson.wisc.edu/sage/data‐and‐models/schneider.php). A final covariate was constructed based on physical access to improved water sources. This covariate was created for the current study by, first, using a ‘friction surface’ that estimates the time required to traverse each 1×1km pixel across Tanzania.<sup>34</sup> This varied according to the type of landcover, topography, and the layout of the road and wider transport network across the country. The friction surface was then used in a least‐cost path algorithm to estimate the likely journey time from the center of each 1×1km pixel to the nearest functioning water point (such as a well, bore hole, or pump). The latitude and longitude, as well as level of functionality, of every such water point in Tanzania were recorded as part of the Water Point Mapping project undertaken by The Ministry of Water and Irrigation, Government of Tanzania.<sup>35</sup>

# _Model‐based Geostatistics framework_

The predictive approach used in this study to generate a fine‐scale map of stunting prevalence across Tanzania was based on a body of statistical theory known as model‐based geostatistics (MBG)<sup>36,37</sup> . MBG models are a class of generalized linear mixed model, with an approximation of a multivariate Normal random field (i.e., a Gaussian
Table B1: ANOVA Decomposition of FDI Concentration at the CountryIndustry-Year Level

||Conce|ntration|Measure,||
|---|---|---|---|---|
|||% Varian|ce||
||CR5<br>(1)|HHI<br>(3)|CR5<br>(3)|HHI<br>(4)|
|Destination-Year:|||||
|Year|1.77|1.98|2.33|2.44|
|Country|79.56|70.12|75.24|66.02|
|Region-Year|10.93|11.70|13.79|14.21|
|Country|66.91|56.99|60.37|51.13|
|Observations|2,349|2,349|2,047|2,047|
|Destination-Industry-Year:|||||
|Sector-Year|32.94|20.07|31.45|15.49|
|Country-Year|36.55|35.40|42.66|43.57|
|Sector-Year|8.93|3.01|7.78|2.72|
|Country-Year|29.80|33.14|37.45|43.27|
|Country-Sector|29.32|12.05|23.67|8.39|
|Observations|9,464|9,464|5,422|5,422|
|Minimum # of|||||
|Foreign Investors||5|1|0|

_Note_ : The unit of observation is at the destination-industry-year level. Industries are grouped into 17 ISIC codes (1-digit). “CR5” stands for the concentration ratio of the top 5 investing firms; “HHI” stands for the HerfindahlHirschman concentration index (computed using the “CR
Figure 11. Lorenz Curve before and after Pension Transfers 2006 2016 <!-- Start of picture text --> 1 1<br>0.9 0.9<br>0.8 0.8<br>0.7 0.7<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>0.1 0.1<br>0 0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Total Family Income Social Pension Total Family Income Social Pension<br>Contributory Pensions Contributory Pensions<br>Accumulated Percentiles Accumulated Percentiles<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1<br><!-- End of picture text --> Source: author elaboration based on ENEMDU In summary, the income protection system for older people has a slight incidence on poverty, though it does not affect income distribution.

# 3.4 Financial situation of the contributory pension scheme

The financing of the contributory pension scheme administered by the IESS has three main sources: (1) current income, composed of statutory contributions made by workers and employers; (2) capital income, which mainly relates to the sale of assets in general managed by the technical reserve fund and, (3) public transfers. The latter source of resources was defined as 40% of total expenditures on pensions and was used until year 2015.

Figure 12 presents expenditures, income level and financial results of
selected EA, all households are listed and then randomly selected from the complete list. Population and housing censuses are used as sampling frames. Once households have been sampled and interviewed, sampling weights are constructed and provided to data users to allow for the calculation of nationally and subnationally representative estimates. These weights reflect a given household’s inverse probability of selection into the sample. For each survey, that is for any country-wave, the household weights approximately sum to the total population of households in the country. Weights were adjusted to account for attrition, inclusion of split-off households (i.e. new households that form by splitting from an existing household) and were post-stratified to ensure that they sum up to known population totals.

The surveys included in the HP are panel surveys that follow different units from round to round across time. Households are tracked through time in Ethiopia, Niger, and Nigeria, individuals are tracked in Malawi, Uganda, and Tanzania. Where individuals are tracked, the households they reside in are also tracked. Enumeration Areas (EAs) are tracked in Mali, with resampling of households within EAs in each wave. Agricultural plots cannot be tracked through waves (or even across seasons, in the case of Uganda), but parcels are tracked across waves in Ethiopia and Malawi, and across seasons within waves in Uganda. Parcels are defined by the World Program for the Census of Agriculture as a “piece of land of one land tenure type entirely surrounded by other land, water (...) or other features not forming part of the holding”, whereas plots are a section of the parcel “on which a specific crop or crop mixture is cultivated”(Food and Agriculture Organization of the United Nations, 2015). In Malawi, we assume that “gardens” are equivalent to parcels. In Tanzania, “plots” have a definition that is akin to parcels, and parcels are not explicitly tracked.

<mark>In some cases, such as in Malawi or Tanzania,
As in Ben Nassar, Martinez and Pineda (2014), equation (1) is estimated using a single-stage estimation procedure that employs an OLS-based Panel-Corrected Standard Errors (PCSE) approach, which is appropriate for time series cross-section data where N < T.<sup>3</sup> In estimating equation (1), it is necessary to settle the complications associated with error terms. First, the error terms for each bank are interdependent over time (autocorrelation). Second, as banks operate in the same industry and country, there is the possibility that the error terms are correlated between banks (contemporaneous correlation). Third, the errors tend to have non-constant variances between banks (heteroskedasticity).<sup>4</sup> For these reasons, the model is estimated using an OLS-based PCSE procedure, which improves parameter efficiency and generates more accurate z-statistics by correcting residual correlation between equations and cross-sectional heteroskedasticity (Beck and Katz 1995).

# **Dependent variable**

Bank interest spreads can be measured using both ex-ante and ex-post spreads.<sup>5</sup> The ex-ante spread is the difference between contractual rates charged to borrowers and those paid to depositors. The ex-post spread is the difference between banks’ actual interest revenue and their actual interest expense, usually divided by loans and deposits, respectively, or earning assets. The ex-post spread differs from the ex-ante spread by the amount of loan defaults. Therefore, ex-post spreads allow for a broader examination of the costs of financial intermediation.

Moreover, ex-post spreads allow for an analysis of the driving factors of financial intermediation efficiency in a general equilibrium setting, taking into account all bank operations. For example, banks may compensate for the high taxation of a certain form of intermediation by charging lower rates on other forms. This effect would not be captured by ex-ante spreads.

For all these reasons, this paper uses ex-post spreads. Specifically, ex-post spreads are narrowly calculated as the difference between interest received on loans divided by (net)
90 percent of the total damage. The 1 percent worst storms in the world account for 58 percent of the damages. With warming, these powerful storms get even more harmful. In a warmer climate, the 10 percent worst storms are predicted to account for 93 percent of the total damages and the 1 percent worst storms are predicted to account for 64 percent of the total damages. Because these large storms explain so much of the story and are very rare, it is very difficult to rely on observations of tropical storms over time to detect a trend. The large storms are so rare so that it may take several centuries of observations to see whether there is a change in their frequency.

27
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sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed, and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent.<sup>3</sup> In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest

> 2 For a list of the economies included, see table A.1 in the 2017 Global Findex Database, “Measuring Financial Inclusion and the Fintech Revolution” (Demirgüç‐Kunt and others 2018). It shows the data collection period, the number of interviews, the approximate design effect, and the margin of error for each economy as well as sampling details, where relevant.

> 3 The Kish grid is a table of numbers used to select an interviewee. First, the interviewer lists the name, gender, and age of all permanent household members age 15 and older, whether or not they are present, in order by age. Second, the interviewer finds the column number of the Kish grid that corresponds to the last digit of the questionnaire and the row number for the number of eligible household members. The number in the cell where the column and row intersect is the person selected for the interview.

31
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