rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix 2. Data sources and definitions**

## A.2.1 Data used for the estimation of eq. (5) in paragraph 3

Data for exports and imports grouped according to Pavitt (1984) in nominal terms were partly provided by the Italian Institute for Foreign Trade (ICE) and partly elaborated by the author on the NIMEXE data base (Eurostat). Bilateral trade flows were elaborated by the author starting from IMF Direction of Trade Statistics data. The "world" aggregate includes: United States, Japan, Germany, France, Italy, United Kingdom. A detailed classification is available on request from the author. They have been transformed in constant dollar values at 1985 GDP price indices and 1985 PPP dollar exchange rates.

The stock of domestic knowledge T is the fractional patent count taken from the US Patent and Trademark Office cumulated on a benchmark initial value.

The stock of foreign knowledge Tw is defined as _T_ but it includes the sum of patent counts for the countries entering the group defined above less the patent counts for the domestic economy. When the European stock of knowledge is used data for the US and Japan are excluded.

_P_ is the real effective exchange rate (source: Bank of Italy) _F_ is the amount of private R&D expenditure transformed in constant dollar values at 1985 GDP price indices and 1985 PPP dollar exchange rates, cumulated on a benchmark initial value and depreciated at the rate of 15 percent a year. (source CNR 1992) The sample period (annual observations) covers 1970-1991. All stocks were measured at the end of period while prices are period averages . All series measured at the end of period were adjusted in order to be consistent with flow data (Gandolfo 1981, equations (30) and (31) of chapter 3). This allows to consider variables which contain both stocks and flows in their definition. The approximate discrete analogue to the continuous model used for the estimation carried out in paragraph 3 was obtained as expounded in Gandolfo
The natural log of the odds of the household to be MPI poor is the logit variable _Dj_ . The maximum likelihood method used for the estimation of equation (7) does not impose any assumption on the normality or homoskedasticity of errors in the predictors.

where βk is the _kth_ element of β _._

# 3.1.2 The general instrumental variable (IV) strategy

From the relationship in equation (6) it is difficult to draw inference regarding the causal effect of forced displacement on the probability of a household being in multidimensional poverty. Indeed, one could argue that the causality runs in the other directions, i.e., poor households are more likely to be displaced. Also, there may be unobservable factors that affect household deprivation while at the same time serving as the push factors for the household’s movement. For instance, restrictive policies by the state towards certain regions could create uprisings and sectarian violence in these regions and simultaneously contribute to spreading vulnerability and deprivation. These policies might then be indirectly correlated with forced displacement through their strong correlation with conflict and violence. Hence, these issues must be accounted for to avoid attributing any effect of these unobservable factors on household poverty to their forced displacement status. To account for potential endogeneity, we add dummy variables for the interaction between origin and destination governorate fixed effect.<sup>5</sup> Besides, we would need an instrument that explains household forced displacement without being correlated with household deprivation status.

Given that Iraq has a long history of conflict, it is a challenging exercise to find a credible instrument. Nevertheless, we use the Iraq Body Count (IBC), a unique publicly available fatality

> 5 The results are robust to including only destination FE – see Appendix D.

10
five, the incidence of diarrhea was 10 percent lower and the incidence of malaria 5 percent lower in households that gained access to electricity relative to households that did not.

Electrification of households also improved education outcomes for children and employment opportunities for adults, although the effects were not widespread. Gaining access to electricity increased boys’ enrollment in schools by up to 3.1 percentage points and boys’ completed years of schooling by 0.32 years; it had no effect on girls’ enrollment or years of schooling.

On average electrification increased labor force participation for women by 2 percentage points but reduced it by 4.8 percentage points for men. The negative effect on men’s labor force participation could be related to the effect of higher income on leisure.

There is also evidence that electrification increases women’s decision‐making power. In households gaining access to electricity, the probability of women making their own decisions rose by 16.3 percentage points for decisions on purchasing food items, 9.5 percentage points for decisions on purchases of clothing and shoes, 2.6 percentage points for decisions on education, and 1.7 percentage point for decisions on employment. The explanation may lie in women’s increased participation in the labor force (and thus greater economic empowerment) and their increased access to electronic media, which broadened their horizons on opportunities for social and market participation and raised their awareness of gender equality.

Another important dimension of electrification is the reliability of supply. A basic regression analysis using household‐level data from the IFC survey shows that each additional hour of daily outages is associated with an income loss of roughly 1.6 percent. The regression model is where _Dit_ denotes average daily duration of power outages, and all other variables are as defined in equation (1).

# **VI. Conclusions**

11
The OECD’s FDI Regulatory Restrictiveness Index reflects these developments: the Communications sub-index fell significantly from 0.595 to 0.120 between 1997 and 2003 as a result of the 1999 reforms, but increased again to 0.410 between 2006 and 2010 as a result of the increasing restrictiveness described above.

# **4. Data & Methodology**

What are the productivity losses that manufacturers face due to policy restrictiveness toward FDI in the service sector? Are some manufacturers more affected than others? If so, what determines these effects? Are there any service sectors for which policy restrictiveness towards FDI has particularly large effects on manufacturers, and are there specific restrictions that matter more than others?

In what follows we first describe the data used to answer these questions, as well as the empirical strategy.

# **4.1 Data**

There are four main sources of data used in this paper: (1) Manufacturing census data at the firm-level for about 20,000 firms (the census collects information on those firms that are registered and employ at least 20 workers), (2) OECD FDI regulatory restrictiveness index both for services and for manufacturing, (3) input-output tables constructed by the Indonesian statistical office (BPS), and (4) the World Bank Enterprise Surveys for Indonesia (2003 and 2009)..

From the manufacturing census, TFP was calculated using a multilateral index following Aw, Chen and Roberts (2001). The index is an extension of that derived by Caves et al. (1982), and allows for consistent comparison of TFP in plant-level data with a panel structure.<sup>10</sup> The index expresses each individual plant‘s output and inputs (capital, labor, materials and energy) as deviations from a single reference point. As the reference point, the index uses a hypothetical plant operating in the base time period (the first year of the data being 1983) and having average input cost shares, average logarithm of inputs
.

- Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., ... & Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publ. Off. Eur. Union.
forecasts based on yearly data. The largest part of these studies employs univariate time series methods with exogenous regressors. Few other studies use multivariate time series methods or state space econometric models. With the exception of Baltagi et al. (2002), none of these studies attempt to compare the forecast accuracy of different forecasting models.<sup>3</sup> Given significant variation in country coverage, time frame, forecast horizons, and econometric methods, the results of these studies are difficult, if not impossible, to reconcile.

The purpose of this study is to assess the accuracy of different econometric methods in forecasting electricity demand in developing countries. Based on the time series econometrics literature we first develop an econometric framework for forecasting electricity demand. We then obtain a number of electricity demand forecasts based on historical time series of 106 developing countries over the period 1960-2012. Finally, we evaluate the accuracy of the electricity demand forecasts resulting from different econometric methods and model specifications.

Our results demonstrate that time-series econometric forecasts yield highly accurate predictions for the evolution of electricity demand in the majority of developing countries. The forecasts based on the best performing method do significantly improve over the predictions of two heuristical models, commonly used by development practitioners, which assume that electricity demand grows at an exogenous rate or is proportional to real GDP growth. The quality of the forecasts, however, diminishes for the countries and regions, where rapid economic and structural transformation or exposure to conflicts and environmental disasters makes it to establish stable historical demand trends.

# **2 Forecasting Methods and Accuracy Tests**

This section briefly documents the econometric framework for forecasting electricity demand and evaluating its forecast accuracy. It first discusses implications of the stationarity property on forecastability of electricity demand time series. It then summarizes econometric methods employed for forecasting electricity demand. Finally, it describes measures of forecast errors for assessing forecast accuracy and comparing the quality of different forecasting methods.

> 3Baltagi et al. (2002) only focus on a small set of estimators within Autoregressive distributed
# Figure 4. Supply chain bottlenecks and trade integration

A. Supply chain pressures B. Impact of supply bottlenecks on goods trade and industrial production <!-- Start of picture text --> Standard deviations from average value<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> <!-- Start of picture text --> Index, 100 = December 2019<br>Trade: Counterfactual<br>120 Trade<br>Industrial production: Counterfactual<br>110 Industrial production<br>100<br>90<br>80<br>Dec-19 Feb-20 Apr-20 Jun-20 Aug-20 Oct-20 Dec-20 Feb-21 Apr-21 Jun-21 Aug-21 Oct-21<br><!-- End of picture text --> C. Global trade, tariffs, and Russia’s share in global oil production D. Foreign value added content of gross exports <!-- Start of picture text --> Share of GDP Percent Percent 1995 2008 2018<br>80 Trade 30 60<br>Tariff rate (RHS) 50<br>60 Russian Fed. share of oil production (RHS)<br>20 40<br>40 30<br>10 20<br>20<br>10<br>0 0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 Advanced economies EMDEs<br>Sources: Benigno et al. (2022); BP Statistical Review; Organisation for Economic Co-operation and<br>Development; Federal Reserve Bank of New York; Penn World Tables; World Bank.<br>Note: EMDEs = emerging market and developing economies.<br><!-- End of picture text --> A. Chart shows the New York Federal Reserve’
and the TIN of the importing firm as identifiers to construct an equivalent of firmproduct.

**Firm registration data** We construct firm characteristics using formal registrations of firms. Each firm is identified by a unique TIN, and the registrations contain the firm’s ISIC sector classification (which we use to identify textile manufacturers) and sub-District.

**Census** We construct data on socioeconomic characteristics of sub-Districts using the 2012 Population and Housing Census.

# **2.2 Context**

Our analysis of the impacts of mask manufacturing covers the early stages of the COVID-19 pandemic in Rwanda. The Ministry of Health (MINISANTE) announced the first case of COVID-19 in Rwanda on March 14, 2020. On March 22, the Prime Minister announced a lockdown including school closures, the suspension of international travel, a work-from-home mandate, and the prohibition of non-essential movement, all with meaningful impacts on economic activity (Byrne et al., 2021). These restrictions were partially lifted on May 4, accompanied by the introduction of a curfew which then remained in place, in some form, over the remainder of our study period. Other restrictions that were lifted (and sometimes reinstated) after May 4 included the resumption of domestic travel on June 2, and the reopening of Kigali International Airport on August 1.

During the early months of the pandemic, Rwanda rapidly implemented recommendations and national mandates for the use of masks in public. We present the timeline of key events and policies that relate to the mask mandate during the early 7
# 2. Data

We use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by the University of Delaware (Willmott and Matsuura 2001) that has widely been used in the economics literature (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015, among others). This data set provides monthly total precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each month between 1901 and 2014. Annual precipitation at the cell level between 1990 and 2014 ranged between 3mL in Sudan to 10,187mL in India.

To define when a grid cell experienced a shock, the z-score of rainfall is calculated in each grid cell, and in rainit−raini each time period as per zit = , where zit is the z-score of rainfall in grid cell _i_ at time _t_ , rainit is σi rainfall in grid cell _i_ at time _t_ , raini is mean rainfall in grid cell _i_ over the full time period of the climate dataset, and σi is the standard deviation of rainfall in grid cell _i_ over the same time period. The z-score for rainfall is interpreted as the number of SDs away from the long-run mean in time _t_ . Once z-scores are calculated, binary variables are generated to indicate whether the z-score passes certain thresholds. A grid cell is considered to have a _dry shock_ if rainfall in a given year is lower than the long-run annual mean for the grid cell by at least X SD ( z≤X), where X can be any range of integer values. Similarly, a grid cell is considered to have a _wet shock_ if rainfall in a given year is higher than the long-run annual mean for the grid cell by at least X SD ( z≥X). Doing this allows for a semiparametric test of the impacts of deviations from the long-run mean. It also allows for impacts to be both nonlinear and nonsymmetric around zero. These properties are important for distinguishing the possible heterogeneous impacts of deluges versus droughts.

Annual grid-level GDP data between 1990 and
Policy Research Working Paper 5603

# **Abstract**

Trade policy reforms in recent decades have sharply reduced the distortions that were harming agriculture in developing countries, yet global trade in farm products continues to be far more distorted than trade in nonfarm goods. Those distortions reduce some forms of poverty and inequality but worsen others, so the net effects are unclear without empirical modeling. This paper summarizes a series of new economy-wide global and national empirical studies that focus on the net effects of the remaining distortions to world merchandise trade on poverty and inequality globally and in various developing countries. The global LINKAGE model results suggest that removing those remaining distortions would reduce international inequality, largely by boosting net farm incomes and raising real wages for unskilled workers in developing countries, and would reduce the number of poor people worldwide by 3 percent. The analysis based on the Global Trade Analysis Project model for a sample of 15 countries, and nine stand-alone national case studies, all point to larger reductions in poverty, especially if only the non-poor are subjected to increased income taxation to compensate for the loss of trade tax revenue.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmartin1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank
6 than in production, this leads to the underestimation of early-career wages, implying an overestimate of the returns to experience due to learning-by-doing only, particularly in developed economies where more time is spent in training. Given that we do not have information on how much time each worker in our data spends working or training, we cannot directly assess the contribution of each mechanism. However, indirect robustness checks based on cross-country data on the number of hours spent in formal or informal training suggest that our results are more consistent with learning-by-doing than the learning-versus-doing model of Ben-Porath (1967).

# **2. Data Used to Estimate the Returns**

**Sources.** The data source for the analysis is the _International Income Distribution Database_ (I2D2) of the World Bank. The database consists of a large number of individual-level surveys and census samples. The surveys include household surveys and labor force surveys. The data was initially compiled by the World Bank’s _World Development Report_ unit between 2005 and 2011. The original version was used by Montenegro and Hirn (2009) to study the labor market characteristics of developing countries for the 2009 _World Development Report_ of the World Bank. The database has since been expanded and used by some of the members of the original _World Development Report_ unit.<sup>7</sup> We use the expanded December 2017 vintage of the I2D2 database. Only select members of the research team or individuals in charge of harmonizing the data can access the database. However, a significant number of the surveys can be accessed online or after entering an agreement with the countries’ respective statistical office. Finally, the surveys are nationally representative and large enough (i.e., typically have more than 10,000 observations) for our purpose.

**Sample Size.** The version of the I2D2 database that we use includes about 1,500 survey/census samples. However, wages are only reported for about two thirds of them. In addition, we restrict our
is more costly. These consist of irregular border crossing or migrant workers entering legally with a permit and then overstaying their visas. These two effects move the demand for work permits in opposite directions with an ambiguous net impact. In addition, the informal labor market could alter the incentives of native workers who might sell their right to work but still earn wages on the informal market, resulting in an increased supply.

The magnitude of these effects, and their impacts on the size of the market and the prevailing price of work permits, depends on the extent to which informal and formal labor markets are integrated. Law enforcement, which could be partly financed by the tax on work permits, would then determine the _tax_ associated with informal work as well as the _cost_ of taking an irregular migration pathway. Admittedly, the ability of governments to regulate the informal labor market depends on the initial size of that market. This would ultimately determine whether such markets are viable and be an effective instrument to provide social protection to native workers.

Another implementation issue that the model does not consider is the existence of a sizable share of the population that is unemployed or inactive. Data from the US Bureau of Labor Statistics suggest that only 62 percent of the working-age population is actually working, implying that 95 million US citizens would add to the supply of work permits (Bureau of Labor Statistics, 2018). For our suggested scheme to be viable, therefore, eligibility provisions would need to be implemented. For instance, mirroring unemployment insurance eligibility requirements, minimum prior employment duration could be a prerequisite for a worker before she can sell her work permit.

# **7 Conclusion**

The paper has assessed the scope for financing a basic income guarantee for working-age citizens by creating and regulating a market for temporary work permits in the labor markets of destination countries.

17
(Carter et al., 2007; Little et al., 2006). Although this approach facilitates causal identification, the choice of an extraordinary phenomenon undermines the external validity of the findings. Such studies are informative only of the impact of the dramatic weather event and not of recurring droughts or floods. Our analysis focuses on droughts and excess rainfall during the main growing season in Malawi (November to April) and evaluates the implications of weather shocks on households’ vulnerability to poverty. This vulnerability analysis first identifies the impact of extreme weather events during the growing season on the consumption per capita in the months following the harvest time. The study uses the spatial and temporal variation in droughts and excess rainfall experienced by the 35,627 households surveyed in the three rounds of the Integrated Households Survey (IHS) (2004– 2016).<sup>1</sup> The Self-calibrating Palmer Drought Severity Index (SC-PDSI) is used as a measure of drought at the second lowest administrative division in Malawi (traditional authority [TA]). SCPDSI is calculated from temperature, precipitation, and locally available soil water content (Barichivich, Osborn, and Jones, 2018; van der Schrier et al., 2013). To measure floods, we use raw precipitation data to calculate the number of days with rainfall above the 98th percentile in each TA (NASA GES DISC, 2019). This is a rough approximation of flooding. In reality, flooding is a function of topography, rainfall amount and intensity, soil moisture, and vegetation. The use of SC-PDSI and extremely high rainfall enables us to address the endogeneity in the measurement of weather shocks. Self-reported shocks, quick postdisaster assessment of damages, and the Normalized Difference Vegetation Index (NDVI) are all potentially endogenous. For instance, the amount of losses could be higher in relatively wealthier areas that have more physical assets that could be damaged, but the households may as well be more resilient. Farmers may also delay planting due to a late onset of rainy