rafmacalaba/data-use-mentions-extended huggingface dataset:
order to compare the global GDP factor with other measures of global economic activity, Figure 3 (bottom) plots the global GDP factor alongside an index of world industrial production and the Kilian’s index (see Kilian, 2009; Hamilton, 2019). The global GDP factor and the world industrial production are highly correlated, with correlation 0.85, whereas the correlation between the global GDP factor and the Kilian index is 0.14.<sup>20</sup> The histogram in Figure 13, Appendix VII.1, provides a closer look at the importance of the global GDP factor in accounting for fluctuations in economic activity across countries. The figure shows the distribution of the amount of variance in country GDP growth rates accounted for by the global GDP factor. The results indicate that in the majority of countries, the global GDP factor explains a significant fraction of GDP growth volatility; it accounts for 21.6 percent of output variability across countries on average.<sup>21</sup> The contribution of the global GDP factor to country GDP growth rates is highest for oil importing advanced economies (32.9 percent) followed by oil exporters (21.8 percent), and its contribution to the fluctuations in output growth rates of oil importing emerging markets and developing countries is 12.4 percent.

# **IV.3.2 Global Economic Activity and U.S. Energy Market Shocks**

This section provides the results from the FAVAR Model 5 described in Section IV.2.2. I first discuss the impulse responses of the endogenous variables to U.S. energy efficiency and U.S. oil supply shocks. These results compare the average effects of the two shocks over the period 1980– 2019. I then present the historical decompositions to assess the impact of U.S. energy efficiency and oil supply shocks in the evolution of global economic activity during 2010–2019, a period that overlaps with the shale revolution.

**_Impulse Responses
<!-- Start of picture text --> Figure 2a: Growth-incidence Curve - Urban (2002-2010) Figure 2b: Growth-incidence Curve - Rural (2002-2010)<br>Urban Rural<br>6 Growth-incidence 95% confidence bounds 6 Growth-incidence 95% confidence bounds<br>Growth at median Growth in mean Growth at median Growth in mean<br>Mean growth rate Mean growth rate<br>5 5<br>4 4<br>3 3<br>2 2<br>1 1<br>1 10 20 30 40 50 60 70 80 90 100 1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles Expenditure percentiles<br>Figure 3a: Growth-incidence Curve - Urban (2008-2010) Figure 3b: Growth-incidence Curve - Rural (2008-2010)<br>Urban Rural<br>Growth-incidence 95% confidence bounds Growth-incidence 95% confidence bounds<br>16 Growth at median Growth in mean 16 Growth at median Growth in mean<br>Mean growth rate Mean growth rate<br>13 13<br>10 10<br>7 7<br>4 4<br>1 1<br>1 10 20 30 40 50 60 70 80 90 100 1 10 20 30 40 50 60 70 80 90 100<br>Expenditure percentiles Expenditure percentiles<br>Annual growth rate % Annual growth rate %<br>Annual growth rate % Annual growth rate %<br><!-- End of picture text --> _III.5. However All Trends Reversed Starting in 2008_ Despite an overall improvement in welfare over the past decade, the increase in inequality observed since 2008 has contributed significantly to the population’s overall level of discontent and the general feeling of worsening welfare conditions. The issue of public perception highlighted earlier can also be linked to the economic performance observed in the second half of the decade, especially since 2008. Looking at the data from the 2008 household income and expenditure survey, the
Policy Research Working Paper 8639

# **Abstract**

This paper uses Mongolia’s Household Socio Economic Survey for 2016 to estimate the distributive impact of taxes and transfers. The findings show that the system is progressive and contributes to reductions in poverty and inequality. The Gini coefficient of the pre-tax-and-transfer income is 0.4183 and decreases to 0.3507 after-tax-andtransfer. This is a reduction of 6.76 Gini points (around 16 percent). Something similar happens with the poverty rate, which decreases from 47.31 to 31.96 percent. Despite the progressiveness of the whole system, there are some caveats and policy warnings. First, pensions are the most redistributive instrument in the system, but their actuarial and fiscal sustainability is weak. Second, two programs (the child money program and the mortgage subsidy) do little redistribution—the latter is actually regressive—but represent a large share of the budget (around 2.5 percent of gross domestic product). These two factors, and the fact that up to a 35 percent of total expenditures in monetary and in-kind transfers is funded by corporate taxes and royalties—which are highly dependent on volatile commodity prices—make the redistributive impact of the tax-andtransfer system susceptible to fiscal unsustainability.

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/research. The authors may be contacted at sfreijerodriguez@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
Can Medium-Resolution Satellite Imagery Measure Economic Activity at Small Geographies? Evidence from Landsat in Vietnam Ran Goldblatt, Kilian Heilmann, Yonatan Vaizman<sup>_†_</sup>

# **1 Introduction**

The analysis of satellite imagery is now a key methodology in economics and other applied scientific research. Coming straight from impartial satellites, remotely sensed data has the advantage of not being filtered through national data agencies that are potentially inefficient or biased. As Donaldson and Storeygard (2016) lay out its main benefits, remote sensing allows researchers to access information that would otherwise be difficult to obtain due to low state capacity, provides high spatial resolution, and a wide (if not global) geographic coverage. Since the marginal cost of collecting more data is low, repeated consistent samples are often available to researchers to learn about the world. Lastly, satellite imagery ignores administrative boundaries and can therefore be flexibly combined with other data at any

> _†_ Ran Goldblatt is Chief Scientist at New Light Technologies, Washington, DC; his email address is ran.goldblatt@nltgis.com. Kilian Heilmann (corresponding author) is a Postdoctoral Researcher at the Dornsife Institute for New Economic Thinking (INET) at the University of Southern California, Los Angeles, CA; his email address is heilmann@usc.edu. Yonatan Vaizman is a Senior Researcher at Comcast Applied AI Research, Washington, DC; his email address is yonatan ~~v~~ aizman@comcast.com. The authors are grateful to Gordon Hanson, Amit Khandelwal, Klaus Deininger, Fang Xia, and Anh Pham for invaluable advice. We also thank participants at the USC Dornsife INET workshops, the Annual Meeting of the Urban Economics Association in Vancouver, and the Pacific Conference for Development Economics for helpful comments.
The remainder of the paper is as follows. We first revisit existing technology identification methodologies, drawing attention to newly identified shortcomings of the Max-Share approach before outlining several proposed improvements. We then show the performance of the existing and new identifications in Monte Carlo simulations on DSGE-generated data and data generated by a simple 2-variable model. Finally, we take each specification to data for the United States and several major emerging and developing economies (EMDEs), finding that there are several sources of contamination in traditional VAR approaches that can be addressed with our new methodologies.

# **2 Empirical Approaches**

In this section, we briefly revisit the standard long-run and Max-Share identification of a technology shock. The potential for the Max-Share approach to be contaminated by other non-technology shocks is then explained, before we detail three approaches that can reduce contamination, depending on its source.

5
Figure A.8. Treatment Variables correlation with Conflict Indicators <!-- Start of picture text --> 1 1<br>.8 .8<br>.6 .6<br>Taliban Exposure U.S. Allies Exposure<br>Fitted Values Fitted Values<br>.4 .4<br>.2 .2<br>0 0<br>0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 0 500 1000 1500 2000 2500 3000 3500<br>Number of Affected and Killed People in Afghanistan Number of Affected and Killed People in Afghanistan<br>Note: Correlation=0.28*** Note: Correlation=-0.33***<br>1 1<br>.8 .8<br>.6 .6<br>Taliban Exposure U.S. Allies Exposure<br>Fitted Values Fitted Values<br>.4 .4<br>.2 .2<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 110 120 130 0 10 20 30 40 50 60 70 80 90 100 110 120 130<br>Number of Individuals Displaced (Thousands) Number of Individuals Displaced (Thousands)<br>Note: Correlation=0.23*** Note: Correlation=-0.26***<br>Taliban Exposure by Province<br>U.S. Allies Exposure by Province<br>55<br>Taliban Exposure<br>U.S. Allies Exposure<br>IDPs Province of Origin IDPs Province of Origin<br><!-- End of picture text --> _Source:_ United Nations Office for the Coordination of Humanitarian Affairs and United Nations Assistance Mission conflict data
, with some level of social sanctions or rewards expected for

> 11 Nepal Demographic and Health Survey, 2016 and 2022.

> 12 Ministry of Labor, Employment, and Social Security, Nepal, 2020.

> 13 For a deeper discussion of social norms theory and concepts, refer to Bussolo et al. (Forthcoming)

> 14 **<u>Annex D</u>** includes details about the qualitative survey.

5
This period is one of substantial commodity price volatility. It included the plunge from mid-2014 to its trough in early 2016, the subsequent rebound, the collapse during the pandemic, in spring 2020, and again the subsequent rebound—for many commodities to historic highs. Since many of these swings were unusually strong by historical standards, model performance is likely to be generally poorer than in previous studies.<sup>10</sup>

# **3.1 Futures prices and consensus forecasts**

Consensus forecasts were drawn from Consensus Forecast Reports accessed through the IMFWorld Bank library for 2000-2022. Data is available monthly, with quarterly forecasts until the end of the subsequent year.

Generic futures prices were drawn from Bloomberg for all seven commodities for 2000-2022: Oil (tickers CL3-CL6-CL9-CL12); copper (tickers HG3-HG6-HG9-HG12); aluminum (tickers AA3AA6-AA9-AA12); lead (tickers LL3-LL6-LL9-LL12); nickel (tickers LN3-LN6-LN9-LN12); tin (tickers LT3-LT6-LT9-LT12); and zinc (tickers LX3-LX6-LX9-LX12). Monthly average data is used, with 3-, 6-, 9, and 12-month-ahead futures prices.

# **3.2 Bivariate correlations**

Alquist, Kilian, and Vigfusson (2013), in their review of oil price forecasting models, identify several variables that are strongly correlated with oil prices over the subsequent 6-12 months. These include the Commodities Research Bureau (CRB) Raw Industrial Commodity Index, U.S. M1 growth, futures prices, and consensus (or other professional) forecasts. These variables are augmented with indicators for China, which plays a large role in global metal markets, and purchasing managers indicators. These include China’s Manufacturing Purchasing Managers’ Index (PMI), the Global Manufacturing PMI, and the Global Composite PMI.

Based on Alquist, Kilian, and Vigfusson (2013), the forecast is obtained by regressing the future change of a commodity price against past independent variables: ∆ hh∆ tt tt tt,tt+hh YY = ββMM + εε where ∆∆ indicates the percent
|São Tomé and Príncipe|0.2|0.3|n.a.|n.a.|0.2|0.3|n.a.|n.a.|
|Senegal|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Sierra Leone<sup>c</sup>|0.6|1.2|0.6|1.2|0.6|1.4|0.6|1.4|
|South Africa|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|Swaziland|0.0|0.1|n.a.|n.a.|0.0|0.2|n.a.|n.a.|
|Tanzania|0.8|1.3|0.9|1.3|1.0|3.1|1.0|3.2|
|Togo|1.1|2.7|1.1|2.6|1.1|3.5|1.1|3.5|
|Uganda|0.3|0.8|0.3|0.8|0.3|1.5|0.4|1.5|
|Zambia|0.3|1.3|0.3|1.4|0.3|2.6|0.4|2.8|
|Median|0.3|0.8|0.4|0.9|0.3|1.4|0.4|1.5|

_Source_ : World Bank staff calculations based on household survey data. _Note_
* **_Mais Educação_**

_Mais Educação_ is a federal initiative to promote full-time schooling among Brazilian public schools. The program promotes the extension of the school day to at least seven hours a day.<sup>20</sup> The average daily duration of school day in Brazilian public schools has been 4.5 hours,<sup>21</sup> and so the extension of the school day promoted by _Mais Educação_ represents an increase of more than 50 percent in instructional time. The ultimate goal of the program is to improve schooling outcomes among public schools, reducing evasion and dropout rates, and improving learning.<sup>22</sup> _Mais Educação_ promotes the extension of the school day through financial support to schools, enabling them to implement extra activities during the extended time.

> 19 To our knowledge, other existing rigorous evaluations only look to state results of _Mais Educação._

> 20 Seven hours a day is the minimum period considered full-time education by the federal government.

> 21 We calculate average duration of school day using the 2007 School Census.

> 22 Additional goals of _Mais Educação_ are to provide educational services for students with special educational needs, prevent and combat violence against children and teenagers, promote cultural diversity in public schools, encourage human development, citizenship, and solidarity through sports and other leisure activities, and create a closer bond between families/communities and schools.

9
# **1. Introduction**

The impacts of climate change are expected to become more severe, especially on the agriculture sector. Changes in the weather pattern are more and more associated with extreme events that have short- and medium-term impacts (Day et al., 2019; Stevanović et al., 2016; Key and Sneeringer, 2014). However, the distribution of these impacts varies widely by region. Southeast Asia and Sub-Saharan Africa are more prone to floods, while the Middle East and North Africa (MENA) regions are experiencing recurrent droughts. The MENA region is already one of the driest areas on the planet and drought shocks are expected to become more frequent, longer-lasting, and more intense in the future (Fischer et al., 2021; Chiang et al., 2021, Pörtner et al., 2022). Recently, Alfani et al. (2022) have documented that severe drought shocks significantly and persistently reduced the agricultural employment in Morocco during the period 2000-2009, with a limited reemployment of displaced workers and residual unemployment. However, while the impact of climate change on the agriculture sector in Africa is well documented (Abidoye and Odusola, 2015; Asafu-Adjaye, 2014), we know much less about the effects of climate extremes in the MENA region.

In this paper we investigate the effects of drought shocks on the labor market in Tunisia using rich laborforce survey data aligned with granular weather information at the governorate level. Building on Alfani et al. (2022), we test whether agricultural workers - who are the most exposed to climate variability - faced a job displacement as a consequence of severe drought events that occurred in Tunisia from 2000 to 2019. We also look at the employment rate of other economic sectors and at the unemployment rate to investigate whether a drop in agricultural employment is offset by job reallocation or translates into higher unemployment. We frame the analysis in a difference-in-difference setting by considering multiple and non-absorbing shocks at different points in time to account for differential treatment dynamics (Chaisemartin and
3 %|3.7 %|35.0 %||

_Notes:_ This table shows point estimates for each of the elements of the indices. Panel A shows the elements of the beliefs index, Panel B the behaviors index, and finally Panel C shows both the policy preferences index and green voting. Outcomes are binary, so multiplying the point estimate by 100 yields the percentage point increase in the likelihood of having a pro-climate stance on the given outcome from an additional year of education. Standard errors clustered at the country _×_ law level in parentheses and p-values in brackets. Estimates include country fixed effects and country-specific linear time trends. “CC” means “climate change”.

11
contribution of manufacturing is barely visible for most years. It is important to understand, however, that the relevant growth contribution of services does not stem from productivity increases within the aggregate service sector. If anything, productivity in services was declining after 2005 (see Figure 2.D; the more detailed analysis in section 3 finds no productivity change). Services’ relevant growth contribution rather stems from the fact that the overall size of the sector was large (a level effect) and growing (an effect of structural change).

By and large, one could expect this to be a favorable pattern of structural change, as labor productivity in services is about double as high as in agriculture (Figure 2.D). This aggregate picture, however, masks the productivity differences that exist within the services (and also industry) sector and the detailed changes in employment taking place. This motivates our more detailed analysis of structural change in section 3.

# **3. A detailed analysis of structural change**

In this section, we have a more detailed look at patterns of structural change and recent literature of decomposing aggregate productivity changes into productivity changes that take place within

> 5 Note that a revision of the national account statistic was responsible for the considerable increase of the service sector in 2005/2006 as several communication and personal services, recreation services, and professional services were not included in GDP before the revision. Figure 2.B may thus quantitatively overstate the described trends which are nevertheless qualitatively present.

6
Research Working Paper_ , No. 4559. Washington, D.C.: World Bank.

- Le, T. M., Moreno-Dodson, B., and Bayraktar, N. (2012). Tax Capacity and Tax Effort: Extended Cross-Country Analysis from 1994 to 2009. _World Bank Policy Research Working Paper_ , No. 6252. Washington, D.C.: World Bank.

- Leuthold, J. H. (1991). Tax Shares in Developing Countries: A Panel Study. _Journal of Development Economics_ , 35(1), 173-185.

- McNabb, K. (2017). Toward Closer Cohesion of International Tax Statistics: The ICTD/UNU-WIDER GRD 2017. _WIDER Working Paper_ 2017/184. Helsinki: UNU-WIDER.

- Naeher, D. (2015). An Empirical Estimation of Asia's Untapped Regional Integration Potential Using Data Envelopment Analysis. _Asian Development Review_ , 32(2), 178-195.

- Naeher, D. and Narayanan, R. (2020). Untapped Regional Integration Potential: A Global Frontier Analysis. _Journal of International Trade & Economic Development_ , 29(6), 722-747.

- Naeher, D. and Narayanan, R. (2021). Attracting Private Capital for Development: Are Poorer Countries Less Efficient?. Unpublished working paper.

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11 Since the aspirations of the women workers is simply to get paid and be independent they can also become passive in facing classic problems of labor, such as low pay, bad working conditions and not receiving their rights such as maternity leave, menstrual leave and so on. These women typically do not make the effort to find out about their rights, let alone to fight for their rights. In research conducted in Central Java, Wolf (1982:6) finds that women workers are often unaware of labor legislation and their rights and are reluctant to create conflicts. The situation is made worse by a lower level of education and lack of adequate mechanisms to provide necessary information. The passive attitude of women workers continues to contribute to their general reluctance to form a union (United Nation 1995). According to Scott (1986) the observation that women are at the bottom of the job structure indicates a marginalization process. In a broad context, marginalization is defined as a shift in power away from a party until it loses access to land, water, investment, work, education etc. to another party which will have a monopoly of these resources. Women are more likely to experience marginalization even though it is not exclusively a female phenomenon (Grijn, 1993). For women workers marginalization ensures them less chance to work, and less than adequate job related services, including health benefits. According to Scott (1986), lack of ambition is only one of the contributing factors to marginalization, and the main factor is the whole process of supply side industrial capitalism.

# _1I.3 Migration and Job Mobility_

Migration is an important indicator of the availability of work in an area. In general, the steady move of people from the rural areas to the city is an indication of economic growth (Boserup 1970:174). A survey of migration in Indonesia has concluded that migration has been dominated by men and that women are either following their husband or are avoiding economic hardship in their home town (Khoo,
Accommodation and food services Agriculture, forestry, fishing, and hunting Retail trade Other services, except government Educational services Administrative and waste management services Arts, entertainment, and recreation Transportation and warehousing Health care and social assistance Construction Real estate and rental and leasing Manufacturing Wholesale trade Mining Professional, scientific, and technical services Utilities Finance and insurance Management of companies and enterprises Information 0 50,000 100000 150000 Average annual earnings 2022 (USD) <!-- Start of picture text --> Accommodation and food services<br>Agriculture, forestry, fishing, and hunting<br>Services to households<br>Administrative and waste management services<br>Construction<br>Real estate<br>Manufacturing<br>Leasing and business services<br>Transportation and warehousing<br>Wholesale and retail<br>Public administration<br>Education<br>Arts, entertainment, and recreation<br>Mining<br>Utilities<br>Health care and social assistance<br>Professional, scientific, and technical services<br>Finance and insurance<br>ICT services<br>0 10,000 20,000 30,000 40,000<br>Average annual earnings 2022 (USD)<br><!-- End of picture text -->
..|..|..|5|2|2.5|6|1|6.0|
|Former FSU|..|..|..|10|2|5.0|5|3|1.7|
|TOTAL: All countries|100|100||100|100||100|100||

Notes: .. = data not available.

**  China and Chinese Taipei became WTO members after 2001 accordingly.

Rad = the ratio of share in total antidumping investigations relative to the share in world exports.

Sources: WTO antidumping database and World Bank WDI database (trade data).

The Table also shows the very large incidence of anti-dumping actions taken against China, Chinese Taipei and non-WTO members, especially so called 'non-market economies' mostly countries other than the Baltics that emerged from the former Soviet Union, in particular Russia, Ukraine, Belarus and Kazakhstan which have seen their exports being targeted for anti-dumping measures at a far greater rate than their share in world trade. Indeed, controlling for the value of total exports over the last three decades anti-dumping measures were at least four times more likely to be directed against a product from a non-market economy which is not a WTO Member than a product from a developed market economy. These conclusions are entirely consistent with the Bown’s findings (Bown, 2013, p21) regarding the changing use of trade remedies against different countries over time. He shows a decreasing but still substantial use of trade remedies against countries like Ukraine, Russia and Kazakhstan between 2001 and 2011 and an increasing use against China and Korea.

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