rafmacalaba/data-use-mentions-extended huggingface dataset:
Notes: Stars indicates levels of significance of t-test on equality of means between the two years: * p<0.1, ** p<0.05, *** p<0.01 Source: LFS 2019-20, 2020-21 In order to better gauge the differential labor market impact of the COVID-19 crisis between comparable men and women, we estimate the following linear OLS model on the pooled sample of individuals in the working age population observed in the 2019-20 and 2020-21 LFS survey waves: i 0 F F C F F ∗C C + F + F where (1) YY is the outcome of interest (labor force participation, employment and unemployment ii = αα + ββ F ii + γγ C CCii + δδ (F ii CCii) + ηη XXii ii ii + εεii<sup>9</sup> ) for individual _i_ in province _p_ and season _s._ The model further includes a dummy variable to identify the gender ii of the respondent ( YY _Female_ ), a dummy identifying the pandemic (corresponding to Persian year 2020-21 from March 2020 to February 2021) and an interaction between the two, which identify the differential impact of the pandemic on female and male individuals. The vector includes individual controls, such as age, age squared, education, marital status as well as a dummy indicating urban residence. Additional ii XX controls include season and province dummies. Moreover, in order to capture how the presence of children might have differentially impacted men and women after controlling for individual characteristics, the sample was partitioned to focus on the following groups: women and men with any child aged 0-5; women and men with any school-aged child (age 6-14) and women and men without any child aged 0-14. Results are presented in Table 6.

_Table 6: Gendered impact of the pandemic - regression results_

||**All**|**Any 0-5 child**|**Any 6-14 child**|**No child 0-14**|
|-
Building on the burgeoning literature that uses machine learning to study economic issues, we show that the outcome of the prediction is not significantly affected by the prediction approach usedstatistical or algorithmic. It is not substantially affected by the number of indicators considered either: as long as traditional and modern data are combined, predictions are very similar.

In passing, our analyses shed light to the ongoing debate on India’s urbanization rate. In recent years, studies using different population thresholds, or combining traditional indicators into more complex indices, have claimed that the actual urbanization rate is higher than official figures suggest. Analyses based on global urban layer products seemingly confirm this conclusion. However, we find relatively minor discrepancies with official figures, and identify the institutional and statistical mechanisms that could underlie the observed gaps. Importantly, we show that the discrepancy with estimates based on global urban layer products is mainly due to the population data used, not to the resulting land classification.

The methodology we propose could also serve as the basis for further research. In this paper we apply it to the classification of places at one point in time, but it would be interesting to explore how to use it to assess changes in urbanization. Our results also suggest that the discrepancies in the assessment of the urban areas vary across states. A relevant question is whether accuracy would increase if prediction approaches were fine-tuned for places operating under different institutional settings or facing diverse geographical conditions.

Finally, our methodology uses administrative boundaries as the unit of analysis, whereas global products  are built on grid cells of standard size. Digitized administrative boundaries the polygons delimiting the  jurisdiction of cities, towns, and villages are an important anchor for data integration. They can be used as the basis for combining traditional and modern data, and therefore to extend prediction beyond the places virtually visited by assessors. A more systematic evaluation of the tradeoffs faced when relying on administrative boundaries, instead of grid cells, is therefore warranted.

32
In terms of catches, boat owners reported in a survey conducted in 2007 that the main factors are the season, weather conditions, and the lunar calendar. Consistently, a voluminous literature in natural science and land economics (for example Smith, 2002; Smith and Wilen, 2005, and Watson and Pauly, 2001) supports the hypothesis that weather conditions, – – expertise the knowledge of when and where to catch a particular type of fish and luck are the main factors affecting the volume of catches.

Boat owners obtain daily weather forecasts from the radio and TV and are therefore aware of weather conditions when deciding whether and where to go fishing.

In addition, there is some evidence from the biology literature that lunar phases affect fish abundance. Some authors suggest that the correlation is due to night brightness. For example, Luecke and Wurtsbaugh (1993) state that when there is a full moon in Utah, lake fishes stay in deeper areas to hide from detection of potential predators. However, the most supported hypothesis is that lunar phases affect fish behavior via migratory patterns and reproduction cycles.<sup>14</sup> Biologists argue that the reason for such patterns is the existence of positive externalities in breeding and egg protection when fish synchronize their reproduction cycles, which happen to follow lunar periods.

# **2.2 Data and Descriptive Statistics**

We use several data sources in the analysis. First, from the middlemen and societies we use hand-written records of all sales and loan transactions. At the end of each year, they provide a copy of individual sales records to each one of their boat owners. Each boat owner thus has a precise record of his catches. These are the records we use in our analysis from January 2001 to July 2007, yielding a total of about 260,000 observations.

Unfortunately, neither the middlemen, societies nor boat owners keep track of the prices paid by the exporting and processing companies. Since frozen fish is sold internationally, however, the exchange rate and
share of electricity expenditures in household total expenditures (excluding expenditures on health, durables and rents) in Turkey was still the eighth lowest among the European and Central Asian countries after price hikes in 2008.

# _Model validation – out-of-sample test_

As discussed earlier, the empirical model does not capture the impact of regional variation in weather conditions on electricity demand. To examine the model’s validity, I predict household demand in 2009 using Equation (1) estimated with 2008 HBS data. Figure 6 and 7 compare the out-of-sample predictions to actual consumption levels in 2009. In Figure 6, the vertical-axis is the sum of the log of the monthly consumption; while Figure 7 plots the sum of monthly electricity consumption. In general, the actual and predicted series are close. Although, as more clearly shown in Figure 7, the model under-predicts overall consumption but it picks up the trend 14
fertilizer applied. Sheahan and Barrett (2017) in their review of LSMS-ISA countries also document that the average application of mineral fertilizer per unit of cultivated area across the six countries is 57 kilograms per hectare (26 kilograms per hectare in nutrients) but emphasize considerable variation across countries; per hectare applications are highest in Malawi, Ethiopia, and Nigeria. Average application rates conditional on use range from 1.2 kg per hectare to 146 kg per hectare in Malawi, across all households irrespective of fertilizer application. Among households using mineral fertilizers in the LSMS-ISA countries, the average application rate was 123 kg/ha (58 kg/ha of nutrients). Average application rates among households using mineral fertilizers varies substantially by country, with 81 kg/ha on average in Ethiopia, 189 kg/ha in Malawi, 26 kg/ha in Niger, 310 kg/ha

> 5 Findings are for the primary agricultural season though there are typically two agricultural seasons per year, with the secondary season generally in the drier stretch of months. For more information on the LSMS-ISA, visit: <u>https://www.worldbank.org/lsms</u>

> 6 For more information on RuLIS, visit: https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en

> 7 RuLIS defines small-scale food producers in accordance with the definition put forth by FAO for the monitoring of SDG 2.3, which is summarized as “producers who: [i] operate an amount of land falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of land size at national level (measured in hectares); and [ii] operate a number of livestock falling in the first two quintiles (the bottom 40 percent) of the cumulative distribution of the number of livestock per production unit at national level (measured in Tropical Livestock Units – TLUs); and [iii] obtain an annual economic revenue from agricultural activities falling in the first two quintiles (the bottom
indirectly estimate the village population density using the HIES survey weights, under the assumption that the survey weights reflect the inverse probability of housing unit being selected for the sample. The steps used to estimate village level population density from the HIES are described in Appendix A.

# **2.2 Publicly available areal estimates of population density**

We validate several publicly available gridded population data sets against each other and with the census, and compare them with our survey-based population density predictions. We examine the following five sources: (1) Landscan for the year 2010; (2) WorldPop for 2010 and 2015; (3) Facebook HRSL for 2015; (4) Gridded Population of the World v3 (year 2010) and v4 (year 2015), and (5) GHSL for 2015. All sources employ a top-down approach using a combination of areal interpolation techniques, including basic dasymetric approaches in conjunction with ancillary data, and statistical modeling methods to distribute census population to smaller grids. While GPW redistributes data based on an equal weighting technique, other sources use measures such as built-up area, road count and density, elevation, slope, and light intensity (“covariates”) to proportionally redistribute population. Facebook, WorldPop 2015, GPW 2015, and GHSL estimates are based on the 2012 Sri Lankan census; WorldPop 2010 and GPW 2010 are based on the 2001 census; and Landscan uses mid-year population of the country of the past year calculated by the Geographic Studies Branch, US Bureau of Census (Oakridge National Laboratory, 2018).<sup>11</sup>

# **2.3 Satellite data**

We use various sources of geo-spatial data in our models. The low-resolution public indicators are from three sources: (1) Night time lights from the Visible Infrared Imaging Radiometer Suite (VIIRS) at a resolution of 750 m per pixel. We use the maximum and mean intensity of two months, namely March and September, 2014; (2) Global Forest Change data based on
the treatment of housing expenses. The overall conclusion of these studies is that CPI inflation is likely to overstate COL inflation (Jacobs, <u>et al. 2014).</u> Formally, the CPI inflation is calculated based on the Lowe index<sup>2</sup> :

> 2 The Lowe index is a fixed-basket type index in which the quantity vector (or expenditure shares) corresponds to a reference period that may be different from the price periods compared. Although most countries claim that they use a Laspeyres (or “Laspeyres-type”) index for their national CPI (UN 2009), the claim is not strictly accurate. In the

6
# Appendix

Table A.1: Emissions and cost-effectiveness accounting for emissions from electricity generation

|**Variable**|**No**<br>**Policy**<br>**(level)**|**Current**<br>**Policy**<br>**(diff)**|**Sales tax**<br>**reduction**<br>**only (diff)**|**Tariff**<br>**reduction**<br>**only (diff)**|**Carbon**<br>**tax**<br>**(diff)**|
|---|---|---|---|---|---|
|Lifetime CO2emissions<br>(thousand tons)|**Panel A:**<br>3416.8|**Year 2019** <br>-11.4|<br>-2.1|-6.1|-52.6|
|Fiscal cost (USD) per ton of CO2|0.0|562.7|188.1|357.9|-1107.8|
|Welfare cost (USD) per ton of<br>CO2|0.0|61.5|-47.0|113.9|-564.3|
||**Panel B:**|**Year 2020**||||
|Lifetime CO2emissions<br>(thousand tons)|2126.0|-13.9|-2.7|-7.1|-36.1|
|Fiscal cost (USD) per ton of CO2|0.0|671.3|374.4|580.6|-833.6|
|
Policy Research Working Paper 5759

# **Abstract**

Stakeholder dialogue, as an alternative institutional strategy for environmentally and socially sustainable development, has received little attention from researchers and practitioners in developing countries such as China, even though the dialogue strategy can potentially lead public governance to a more efficient level. This paper first discusses the potential of stakeholder dialogue as an institutional tool for promoting sustainable development in China, and then presents a pilot program of stakeholder dialogue recently developed in China—the community environmental roundtables. Community leaders organize roundtable dialogues where representatives from government agencies, companies and the local residents exchange their views toward certain environmental issues they are facing and discuss possible ways to resolve the issues. Informal agreements are reached during the dialogues and implemented after them. This community roundtable dialogue strategy has been piloted in dozens of Chinese municipalities, addressing various environmental issues. A survey of dialogue participants shows that significant impacts have been generated on environmental protection, community management, as well as social and institutional development at the community level. Mutual understanding and trust among the government, companies, and local citizens are enhanced, environmental and social conflicts are reduced, and the public performance of various parties has been improved. This approach is expected to help solve other conflicts and public governance issues in China as well. The potential challenges of institutionalizing such a program in China are also discussed in the paper.

This paper is a product of the Environment and Energy 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 hwang1@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
capital requirements. Baumol et al. (1981) defined entry costs as _“anything that requires an expenditure by a new entrant into an industry, but that imposes no equivalent cost upon an incumbent_ ”, stressing the importance of sunk costs. The presence of sunk costs was later on shown by Baldwin (1989), Baldwin et al. (1989) and Dixit (1989) to lead to hysteresis in market structure and trade flows,<sup>1</sup> something that is close to our earlier observation that entry barriers into commercial farming lead to hysteresis in poverty.

If the conceptual issues are clear, the search for empirical evidence on the presence of entry costs is still largely limited to high-tech sectors in industrial countries, where, according to Geroski (1995) “ _econometric estimates of the height of entry barriers suggest that they are high_ ”. In the trade and development literature, the focus has been on the existence of barriers to export. Using Colombian manufacturing census data, Roberts et al. (1997) and Sanghamitra et al. (2001) found that sunk costs related to lack of exporting experience affected the aggregate export response, leading to hysteresis in trade flows. Sivadasan (2003) tested how reducing entry barriers through measures like trade and FDI liberalization would affect manufacturing productivitiy in India. Porto (2005) attacked the problem from a slightly different angle, looking at how removing government-related barriers to export (transport costs, custom practices, bureaucracy, regulation and corruption) would affect poverty in developing countries. He found those barriers to be a large 24.5% of the value of goods shipped, their removal having a non-negligible effect on poverty.

The type of informal barriers considered by Porto is likely to be relevant for entry into agricultural markets. Remoteness, whether resulting from geography or infrastructure decay, is likely to be a substantial barrier to entry into commercial farming, as is the lack of credit or intermediaries (sometimes from ethnic groups that were marginalized by nationalistic policies).
Policy Research Working Paper 8007

# **Abstract**

The emergence of global value chains has opened up new ways to achieve development and industrialization. However, new evidence shows that not all countries have gained from participating in global value chains, and that country-specific characteristics matter for economic upgrading in global value chains. This paper uses two panel data sets of developing and industrialized countries at the sectoral level to relate global value chain participation as a buyer and seller to domestic value added. These are combined with a wide range of policy measures at the country level that can play a role in economic upgrading through global value chains, by targeting global value chain integration or the quality and conditions of input and output factors.

First, the study finds that global value chain integration increases domestic value added, especially on the selling side, which holds across all income levels. Second, the results highlight the importance of policy for economic upgrading through global value chain integration. Although the study cannot claim causal evidence, all the assessed policy areas are consistently shown to mediate the effects of global value chains and magnify the gains for domestic value added. Third, a detailed analysis shows that several policy areas mediate the gains from global value chains more through integration as a seller. Finally, the study observes that many of the results are driven by high- and upper-middle-income countries.

This paper is a product of the Trade and Competitiveness Global Practice 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 authors may be contacted at dtaglioni@worldbank.org and dwinkler2@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
media improves dictators’ government quality by providing incentives to bureaucrats to gather and transmit information from across different sectors to the higher government levels. However, the extent of media freedom that a dictator chooses varies with resource endowments of a country. The findings show that media is less free in resource-rich economies, with stronger effect in nondemocratic regimes. Further, Garcia-Sanchez et al. (2016) use the World Bank Governance Indicators data for 2002–2008 to analyze the relationship between free media and government effectiveness. The authors demonstrate that government effectiveness level can be determined by media, political characteristics and organizational environment, considering the level of economic development of an economy.

Arguably, one of the key roles of media in promoting democratic principles and good governance lies in encouraging public activism and enabling better informed political and economic debates in a society. A sizable body of research examines the effects of media in the context of political discourse and expression of public opinion. McCombs and Shaw (1972) argue about the importance of media in setting the political agenda and shaping political reality by not only informing the audience about certain issues, but also determining the level of their importance. Likewise, Stromberg (2001) in his analysis of the impact of news media on regulatory policy shows that media, due to its special role in transmitting information to mass audiences, increases political influence of dispersed large groups at the expense of small interest or minority groups, thus strongly influencing policy setting. Furthermore, one of the 4
variables are included together with income, income losses its significance because the institutional quality variables can already capture the impact of income. The age dependency ratio has the correct sign, but its estimated coefficients are not significant for the sub period of 1994-2001.

When we compare these new results with the ones reported in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it can be seen that the number of observations increased from 884 (covering the period of 1994-2003) to 1322 (covering the period of 1994-2009); corresponding to an almost 50 percent increase. The R-squared are almost 0.15-0.20 points higher in the new results, indicating a better fit of empirical specifications. One interesting difference between these two sets of results is that the significance and the magnitude of the income variable (GDP per capita) drop in the new set of results. It is true especially in the specifications where institutional variables are included as determinants of taxes. While the estimated coefficient of income was around 2.2 in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it is only 1.11 in the traditional tax specification; and it gets even lower and insignificant (0.503 and 0.002 with bureaucracy index and corruption index, respectively) when the institutional variables are included in the specifications. It means that with the recent improvements in taxation in developing countries and on-going effort by highincome countries to rationalize their tax systems toward greater efficiency and lower tax burden, particularly in direct income taxes, the income level becomes less important now in determining their tax revenues. On the other hand, the institutional quality of countries is more important (even more than income levels) in determining their tax revenues. The higher significance and magnitude of the estimated coefficients of the institutional variables in the new results support this argument. For example, while the estimated coefficient of the corruption index was only - 0.560 in Table 1 of Le, Moreno-Dodson, and Rojchaichaninthorn (2008), it is now