rafmacalaba/data-use-mentions-extended huggingface dataset:
size-city cell. For instance, in Nepal’s data there are 18 cells, indicating 18 different groups of firms of same size in the same industry and city. In our baseline model, there are 2,617 cells covering 39,732 firms in 111 countries. Each cell includes at least 5 firms and the mean and median numbers of cells per country are 25 and 18 respectively.

The mean values of productivity, corruption and regulation are 9.79, 0.12, and 0.09, respectively. Standard deviation of productivity is among the highest, 1.66; while the standard deviation of corruption and regulation are among the lowest, 0.15 and 0.08, respectively, compared to other indicators of the model (Table A.1 and Figure 1). Our model does not have multicollinearity as the highest correlation coefficient between the explanatory variables is 0.35 (excluding the correlations between regulation and corruption and their interactive terms and squares).

An overarching finding of the simple statistical analyses of the main variables of interest is that association of productivity with corruption and the interactive term between corruption and regulation is consistently negative across countries, regions and different income groups (Figures 2 to 4). That is, countries, regions and income groups with lower corruption and the interactive term between corruption and regulation also tend to have higher productivity. Furthermore, productivity increases with the income levels of countries, while corruption and the interaction term between corruption and regulation decreases. There is no such clear pattern for regulation.

# **5. Regression analysis of corruption, regulation and productivity**

Our estimation is based on the following standard linear Ordinary Least Square (OLS) model that incorporates country, industry, sector and region fixed effects and time trend: 18
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tries. Even if Chinese workers, building on the learning by doing of the workforce over the last 20 years, are able to move into complementary higher paying jobs, where will automation leave the follower countries with very unskilled labor that in the past might have expected to inherit the routine jobs that China is now shedding, but which are now in fact, disappearing altogether? Vietnam, which has benefited from redirection of off-shoring as China’s wages have risen, also has seen substantial automation of, for instance, the textile industry over the last decade.<sup>7</sup> The worry, then, is that there remains a large global population with relatively weak cognitive and non-cognitive skills who will not be writing code and programming robot routines anytime soon, but who will not inherit unskilled jobs either.

# **6 Conclusion**

This paper has used census data to explore to what degree findings of polarization in the advanced world can be found in the developing world. We first offer several reasons why we might not expect to see the same tendencies found in the advanced world, or at least not yet. It is also possible that between being destinations for off-shoring, and the fact that new technologies may enable LDC citizens to overcome myriad structural and political economy obstacles that form barriers to creating better jobs, the net impact might be expected to be positive in the medium term. We then confirm previous findings of polarization graphically for the US and France. However, we do not find strong evidence for polarization in LDCs. The key category- plant and machine operators and assemblers, does not show absolute or relative decrease in most developing countries across the last decades. Regression analysis supports the graphical findings that on average, we do not find evidence of polarization in LDCs.

This does not imply much about the future. We find that Indonesia, Brazil and Mexico do show relative decline in the operators category which could suggest potential polarizing

> 7John Luke Gallup _End of Development? The Peril of Automation for
# _5.3.Did the Encouragement Work?_

We estimate the following linear regression equation to test whether the randomized encouragement succeeded in increasing readership of the Development Impact blog among the treated group: where the αs are controls for randomization strata (Bruhn and McKenzie, 2009) and the coefficient of interest is β.

The first column of Table 4 shows that the encouragement succeeded in increasing the proportion of respondents who read Development Impact by 9.9 percentage points, a more than 50 percent increase relative to the 18 percent of the control group who had read development impact in the last month. Columns 2 and 3 then re-estimate equation (1) by gender, and by whether or not the individual at baseline said they wish to become a researcher in an academic institution. We see that the encouragement worked for males, but not females, and for individuals who wish to become academic researchers. It seems reasonable that encouragement to read a research-oriented blog is likely to work better for individuals who are more interested in pursuing a research career. Some of the gender difference is due to females being less likely to say they want an academic research career, but the encouragement treatment also has no effect on females who want research careers. The lack of effect for females could potentially also be related to some of the reasons hypothesized for why few female economists blog (Kahn, 2011).

# _5.4. Impacts of the Development Impact blog_

We are then interested in using our follow-up survey data to estimate the impact of reading development impact on various outcomes. We can do this by 21
# **3. Data**

_Data collection and survey rounds_ The pre-assistance baseline survey (PAB) was conducted with a stratified random sample of 8,690 applicant households, after the decision on their eligibility was made, but before any assistance was provided. The sample was drawn from over 268,000 assessed eligible and ineligible applications, representing 1.6 million individuals, nearly 50% of the refugee population during the first half of 2017 (Cuevas et al. 2019). The sample was stratified by region, which included Istanbul, Aegean, Anatolia/Thrace, Mediterranean, and South-East (Appendix Figure 1). Within each stratum, the households to be interviewed were drawn as a simple random sample from the list of all applications. The data collection for the PAB took place between February and May 2017 (Appendix Figure 2).

Three follow-up panel surveys, called post-distribution monitoring (PDM) surveys, were collected to reinterview all PAB households at 6-month intervals (Appendix Figure 2). The schedule of the first three followup surveys used in this paper (PDM2, 4, and 6) and the numbers of beneficiary and non-beneficiary households interviewed successfully are presented in Table 1 below.<sup>17</sup> The surveys were designed by the WFP with inputs from the World Bank and the data were collected remotely by the TRC monitoring and evaluation operators based in Gaziantep. This phone-based data collection modality allowed for a large sample size, while remaining cost efficient. However, the surveys had to remain shorter than face-to-face household surveys due to the higher likelihood of survey fatigue. <mark>Informed consent was obtained from each household in the study sample, according to guidance from the Turkish authorities.</mark>

**Table 1: Data collection rounds and sample sizes**

|**Date of data collection**|**Feb-May 2017**|**Nov 2017-Jan 2018**|**April-Jul 2018**|**Nov-Dec 2018**|
|
Policy Research Working Paper 10868

# **Abstract**

Are changes in the labor market in response to changes in exports contained specifically within exporting industries, or do they disperse throughout the economy through supply chain linkages? This paper studies the case of Viet Nam, an example of a successful export-led growth economy, to examine this question. Combining UN COMTRADE data, input-output tables from the Global Trade Analysis Project, and 2010 to 2019 annual labor force survey data for Viet Nam, the study constructed a measure of each worker’s total exposure to export shocks. The measure accounts for changes due to both direct export exposure (increase in exports in the worker’s own industry) and indirect exposure (from increased exports in other industries that use inputs from the worker’s industry). Estimates of the repercussions from increasing exports on labor market outcomes show that both direct and indirect exposure significantly increase workers’ wages and employment, while reducing inactivity and inequality. Wage premiums for attending college decrease, and the gender wage gap narrows. Wages increase more for the lowest-income workers and employment gains accrue more to unskilled workers, while employment decreases for more skilled workers.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gacevedo@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
in mathematics, reading, science, history, social science, and so on. Some students with disabilities take the California Alternative Performance Assessment (CAPA) and their test scores are excluded from our sample. However, students in grade 8 and beyond tend to take different CST depending on their course selection in school. We chose to focus on math and reading achievement in grade 5 and grade 7, so we have a manageable number of test outcomes that correspond to elementary school level and secondary school level.

# **International education systems—PISA**

Much has been written about the international variation in knowledge and skills emerging from exercises such as the OECD‟s Program for International Student Assessment (PISA) that

> 5 The website is <u>http://www.cde.ca.gov. The current expenditure data are adjusted to June 2000 dollar values based on the</u> Consumer Price Index (CPI) available at the Bureau of Labor Statistics website ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.txt. 6 14 of these school districts have some test score or enrollment data missing in some years because of their relatively small size.
For each country in the dataset, the out of sample forecasts are chosen based on the fore-

> 6For a more detailed description of the Diebold and Mariano (1995) test please refer to appendix section A.3.

> 7Bhattacharyya and Timilsina (2010) point out that the reliance on consumption data for the demand forecasting implies that only the satisfied demand is captured the suppressed demand is not taken into consideration. This problem can be potentially important in the presence of electricity market distortions and, correspondingly, unrealized demand (e.g., load shedding). As estimating unrealized demand typically requires high-quality micro-level panel data of enterprises and households, which are typically not available, addressing this problem is beyond the scope of this paper.

> 8These difficulties include the inaccurate recording of electricity consumption due to the poor technical capacity of electric utilities (Jamasb 2006), the absence of reliable electricity meters (Victor and Heller 2007), and large unaccounted losses from electricity theft (Smith 2004, Joseph 2010).

9
44.34|14.00|47.95|12.52|43.85|
|Labor income|405.96|4347.76|330.66|1465.98|415.75|4591.45|
||||Panel B:|Child outcom|es||
|Number of children|3994||3512||482||
|Weight-for-age z-score|-1.08|1.30|-0.93|1.37|-1.10|1.29|
|Weight-for-height z-score|-0.39|1.48|-0.30|1.47|-0.40|1.48|
|Height-for-age z-score|-1.43|1.89|-1.24|1.93|-1.46|1.88|

Notes: This statistics is calculated from ESS data by restricting to rural samples only. For child outcomes, biologically implausible height and weight records that lead to implausible values of any of WAZ, WHZ or HAZ (i.e above 5 or below -5) are dropped in the calculation of this statistics and in all the regressions below. Consumption spending are in Adult Equivalent (AEq) terms.

25
models. Finally, we perform the Diebold and Mariano (1995) test to assess whether differences between competing forecasts are statistically significant or simply due to sampling variability.<sup>6</sup>

# **3 Electricity Demand Measurement, Data and the Forecast Horizon**

The ultimate goal of this study is to forecast electricity demand, i.e., the total final consumption.<sup>7</sup> However, in many developing countries, particularly in South Asia and Sub-saharan Africa regions, these data are either not available or available for a relatively short time frame due to difficulties with an accurate accounting of electricity at the end use level.<sup>8</sup> In light of these limitations, we have to rely on the more accurate electricity production (output) data for forecasting purposes. As electricity is a nonstorable and poorly tradable commodity, the output is a reasonable proxy for the total final consumption. However, we have to acknowledge that using electricity output data may lead to biased forecasts in a handful of developing countries with high exposure to electricity trade.

As regards data sources, the electricity generation (output) data come from the OECD/IEA Extended World Energy Balances database (IEA, 2016). The data on population and real GDP come from Penn World Tables, version 8 (Feenstra et al., 2013). The resulting dataset covers 106 developing countries over the period between 1960 and 2012.

Finally, we have to specify the within sample forecast horizons for assessing the accuracy of the forecasting methods. These are set to five and ten years, conditional on at least ten observations in the forecast validation sample. Additionally, we report out of sample forecasts over the period 2013-2022. For each country in the dataset, the out of sample forecasts are chosen based on the fore-

> 6For a more detailed description of the Diebold and Mariano (1995) test please refer to appendix section A.3.

> 7Bhattacharyya and Timilsina (2010)
the measure (0.056). Since the import data is converted to logs – the size of the effect is not particularly easy to interpret. Nevertheless, the impact of an increase from the mean value of the explanatory variable of 16.857 (the log of roughly $21 million in targeted imports) to 17.857 (the log of roughly $57 million in targeted imports, less than a one standard deviation increase) increased the likelihood of dispute initiation by 5.6 percentage points. Keeping in mind that the model’s predicted probability at the means of the data is roughly a 10% likelihood of WTO dispute initiation, this is a sizable effect.

> 27 A potential contributing explanation for the result that the “injury” determinants are not particularly successful in predicting antidumping decision-making is the _sample_ of U.S. trade remedy investigations used in the estimation here – i.e., we only examine investigations against GATT/WTO members. For example, this implies omitting antidumping and countervailing duty investigations against China occurring before its WTO accession in 2001.

17
# Abbreviations and Acronyms

|BAT|Build and Transfer|
|---|---|
|BENECO|Benguet Electric Cooperative|
|BLT|Build lease and transfer|
|BOO|Build own and operate|
|BOT|Build Operate and Transfer|
|CAIDI|Customer Average Interruption Duration Index|
|DoE|Department of Energy|
|EC|Electric co-operative|
|EPIRA|Electric Power IndustryReform Act|
|EPM|Empresas Públicas de Medellín|
|GENCOS|Generation Companies|
|GWh|Giga Watt hours|
|IPP|Independentpowerproducer|
|IRR|ImplementingRules and Regulations|
|JCPC|Joint Congressional Power Commission|
|MERALCO|Manila Electric Company|
|NEA|National Electrification Administration|
|NGCP|National Grid Corporation of the Philippines|
|NPC|National Power Company|
|O&M|Operation and management|
|PDP|Power Development Program|
|PSALM|Power Sector Assets and Liabilities Management Corporation|
|QfD|Quasi-fiscal deficit|
|RCOA|Retail Competition and Open Access|
|RES|Retail electricitysuppliers|
|ROO|Rehabilitate own and operate|
|ROT|Rehabilitate operate and transfer|
|SAIDI|System Average Interruption Duration Index|
|SAIFI|System Average Interruption FrequencyIndex|
|TSC|Transition SupplyContract|
|WESM|Wholesale ElectricitySpot Market|

4
**Table 3.1. Real macro indicators: level in 2012 (constant US$ million) and base growth by period (Percent per year)**

||**2012**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|
|**Absorption**|3,403.1|0.4|4.7|3.5|
|**Consumption - household**|1,550.8|5.6|5.4|5.5|
|**Consumption - UNMIL**|469.9|-84.5|-1.0|-40.8|
|**Consumption - government**|391.1|12.0|4.0|6.2|
|**Fixed investment - private**|717.0|-14.1|8.1|1.4|
|**Fixed investment - government**|274.4|7.8|-7.5|-3.5|
|**Exports**|697.9|21.6|5.2|9.5|
|**Imports**|2,253.3|2.7|4.6|4.1|
|**GDP at marketprices**|1,847.7|8.3|5.1|6.0|
|**GDP at factor cost**|1,638.7|8.8|5.2|6.2|
|**Total factor employment (index)**|100.0|5.9|3.2|3.9|
|**Total factorproductivity (index)**|100.0
evaluations dataset with additional project characteristics from the World Bank’s Projects and Operations portal data. The resulting dataset includes 4,671 projects covering 146 countries over 2000-2021.

Previous work that used World Bank project data to examine the determinants of project outcomes, includes Ashton et al. (2021), Bulman et al. (2017), Chauvet et al. (2010), Denizer et al. (2013), and Dreher et al. (2013). We build on their work to quantify cross-country heterogeneity in project outcomes while controlling for project-level characteristics. Our aim in this exercise is not to estimate the drivers of project outcomes. Instead, we control for project-specific, sector-specific, and World Bank intervention-induced factors to obtain a residual term which captures countrylevel factors that are determinants of project outcomes. Formally, we start by estimating the following model: where PSi,s,c,t is the numeric project outcome score (ranging from 1 to 6) for project i in sector s, country c, and year t; Xi,c,s,t is a matrix of project-specific characteristics; yc,t is real per capita GDP, gc,t is real GDP growth in country c, year t (we include these variables to control for the — level of economic development and the business cycle effects the results are robust to estimating the model without yc,t and gc,t), μs and τt are sector and year fixed effects, and εi,s,c,t is the error term.

The matrix of project-specific characteristics includes _Project length, Project volume_ , _Agreement type_ , _Quality at entry_ , and _Quality of supervision._<sup>_11_</sup> _Project length_ measures the actual duration of the project and ranges between 1 and 20 years, with an average value of 7.6 years. _Project volume_ is a categorical variable aimed at measuring the total cost of each project. It includes five categories ranging from less than 10 million USD to over 100
1246***|
||(0.0838)|(0.3548)|(0.2404)|(0.3276)|(0.3100)|(0.3053)|(0.2559)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.367|0.167|0.109|0.149|0.229|0.152|0.093|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

85
less than three years, three to five years and more than five years (i.e. 1,500 observations in each category). Because the program targets the poorest women, non-members tended, on average, to have slightly higher incomes than SHG members.

16 The consent protocol and written summary of instructions to the surveyors are available from the authors on request. PSM analysis uses dichotomized indicators.

> 17 The difference might not be entirely surprising, as the program seeks to target for participation those who are worst off. Income data are often missing, not surprisingly, so the household type is perhaps a better indicator of household asset values. The propensity score

15
liabilities increase. The assets increase because the central government acquires a portfolio of government guaranteed bonds. The liabilities increase due to larger issuance of government securities. This increases transparency (the debt is now included in the sovereign balance sheet) and allows the related risks to be better included in the consolidated risk management of the central government; ‐Risk indicators from SOEs (transactions, liabilities, duration of assets and liabilities before and after swaps, etc.) are being collected quarterly and used as an input to assess the risks of the sovereign balance sheet.

# **Box 5.**

**Three examples of transactions designed to address risk exposure across the sovereign balance sheet in Uruguay**

Amante _et al._ (2018, forthcoming) describe and analyze different strategies implemented by Uruguayan authorities to reduce financial balance sheet vulnerabilities in the consolidated public sector, based on the identification of exposures under a sovereign ALM balance framework. In the case of Uruguay, the public‐sector balance sheet comprises the central government, the central bank, the four major non‐financial SOEs, and the state‐owned insurance bank.

‐ (i) <u>Liability management operations in managing FX risks during periods of high capital inflows:</u> From late 2010, Uruguay experienced strong capital inflows, and by 2015 the central bank had accumulated increasing levels of foreign‐currency reserves, sterilized by short‐term CB securities. Reserves were well above prudential metrics, and the CB was facing balance sheet pressures from the cost‐of‐carry of reserves. In turn, the government was keen to pursue its debt de‐dollarization strategy.

In July 2015, the central government and the central bank launched a joint operation of issuance and exchange of public securities which resulted in improving the ALM of the individual balance sheets of both the central government and central bank. In the case of the latter, a reduction in USD assets and local‐currency liabilities reduced the balance sheet mismatches and cost of carry. The