rafmacalaba/data-use-mentions-extended huggingface dataset:
(Dervisevic et al. 2022). The breadth of sources from administrative data to surveys make this category of outcomes the most disparate.

Height outcomes include height for age z-scores (HAZ) and a binary variable for stunting, defined as low height-for-age, which is a proxy for chronic or recurrent malnutrition. Height is usually a direct observation, measured by a trained interviewer. Height measures cumulative nutrition and is particularly valuable prior to age 5, when growth distributions in healthy populations have been found to be similar worldwide (WHO, 2006). Three studies, all of them from non-Western lower middle-income countries use height outcomes (Rashad and Sharif, 2019; Shajan and Sumalatha, 2020; Dervisevic et al., 2022).

Weight outcomes are based on weight for age z-scores (WAZ) or body mass index (BMI), and – depending on the context – capture deviation from normal towards malnutrition or obesity.

Seven studies (Von Hinke Kessler Scholder 2008; Hubbard 2009; Anderson et al 2003; Bishop 2011; Pekkurnaz 2014; Greve 2011; Agiro and Huang 2020) considered binary indicators for obesity and overweight, which in most cases where constructed based on information on BMI. Two studies (Haaland, Rege, Votruba 201 and Ziol-Guest, Dunifon & Kalil 2013) use BMI. Three studies included other weight measures such as WAZ (Shajan and Sumalatha 2020) or an indicator of “underweight” (Dervisevic et al., 2022; Rashad and Sharif, 2019). Unlike height, weight fluctuates over time and therefore reflects current and acute as well as chronic malnutrition. The studies had a trained professional take this data.

15
Figure 4. Share of Sub-sectors in Services, %.

<!-- Start of picture text --> 50 45.4<br>36.6<br>40<br>30<br>20.6 19.3<br>20<br>9.6 8.7 11.1 12.5 8.7 8.8<br>10 2.8 3.4<br>0<br>Trade Communication Financial services Insurance Business services Recreation and<br>nec nec other services<br>Non-High Income Countries High Income Countries<br><!-- End of picture text --> Source: Authors’ computations using Version 9 of the Global Trade Analysis Project (GTAP) data in 2011.

# **5. Conclusion**

This paper estimates the impact of electricity consumption on the value added in three sectors: agriculture, manufacturing, and services. It uses panel data with annual observations for 126 countries for the period of 1996-2014 that is compiled using data from the International Energy Agency and the World Development Indicators database. The selection of these sectors and the timeframe for the analysis is driven by data availability for both the value added and electricity consumption, as well as for such control variables as capital and labor. Estimating the electricity-value added relationship on the sectoral level helps in the consideration of sector-specific patterns, as these sectors vary considerably in terms of their energy intensity and production technologies. For example, manufacturing substantially relies on powerintensive technologies and uses some level of automation, even in developing countries. By contrast, the electricity consumption in agriculture is small compared to other sectors across all countries, despite the use of electricity-intensive technologies in advanced economies. Also, the sectoral dimension can be useful for analyzing the growth implications of power in different countries, as the composition of their GDPs varies by sector. Finally, this paper estimates regressions for two samples of countries (all countries and non-high-income countries) and finds that the impact
# **_Domain_**

Globalization in the majority of papers is defined as increased openness in trade policy. Only two papers (Milanovic, 2005a, and Behrmann, Birdsall and Szekely, 2003) look at capital account liberalization. None define globalization in terms of increased labor mobility. And with respect to inequality, both global inequality (inequality between all citizens in the world) and within-country inequality receive attention in the context of multi-country studies. Interestingly, there is also a significant group of studies that focus on wage-inequality, typically in the context of single-country studies exclusively in Latin America.

Clark, Dollar and Kraay (2001) is a good example of a paper dealing with global inequality. They find that inequality between the population-weighted mean incomes of the globalizing countries has decreased substantially between 1975 and 1995. This reflects increases in average incomes in key developing countries (especially China) relative to the OECD average, so that population-weighted between-country inequality has fallen. At the same time, they point to rising within-country inequality for this group of countries but not enough to offset the (population-weighted) between-country decline. This appears to be a fairly firm result but does not of course incorporate the countries that have not globalized. Here, Clark, Dollar and Kraay point to decline and divergence. Since these are some of the poorest countries in the world, this is clearly a matter of concern. Nevertheless, they show that inequality worldwide has declined between the second half of the seventies and the second half of the nineties despite these disparate experiences.

If, however, one looks at the world as a whole, the conclusions regarding global inequality (inequality between all individuals) are not so firm. Milanovic (2005) finds a sharp increase between 1988 and 1993, and then a moderate decline during the next five-year period. Sala-i-Martin (2002) and Bhalla (2002) argue that global inequality has gone down although their approach which combines household survey and national accounts (GDP data) is questionable, and the results may be driven by a
# **Conclusion**

This paper is the result of a collaboration between the World Bank and UNICEF to estimate the global and regional magnitude of child poverty between 2013 and 2019 and specifically includes nowcasts of extreme child poverty for 2020 – 2022, to help monitor progress towards SDG 1.1.1.

The trend analysis shows that in the absence of COVID-19, we would have expected a reduction of 79.7 million children in extreme poverty between 2013 and 2022; but our estimates suggest a reduction of only 49.2 million children, due to pandemic disruptions. The extreme child poverty nowcasted estimate in 2022 (15.8 percent) is on par with the child poverty rate in 2019, indicating approximately three years of lost progress.

With a staggering 333.5 million children living in extreme poverty in 2022, addressing the structural root causes that affect children living in extreme poverty is imperative. A key step towards that is to monitor and understand which children and their families are being left behind, what are their characteristics and where do they live, and why existing policy interventions are not effective in curbing child poverty for countries to develop the appropriate policy portfolios to address and end extreme child poverty for good.

The analysis shows that these policy portfolios need to consider both the common and distinct vulnerabilities and deprivations of children living in fragile and conflict prone settings, children in large households, younger children, children in rural settings, children in households where the head has no or little education - as these are more likely than other children to be living in extreme poor households. In addition, the focus needs to be on the large concentration of children living in extreme poverty in Sub-Saharan Africa, where a staggering 40 percent of children live in extreme poverty. And while about 45 percent of extremely poor children live in low-income countries; half of all extremely poor children live in lower middle-Income countries. As a result, the design of effective policy portfolios needs to take into consideration too that context matters. For example,
it is representative of the consumption sector of any economy. However, since it includes both traded and nontraded goods, it is not the ideal index for measuring competitiveness of traded goods. In Western and Central Africa, due to a weak statistical base, it is frequently the only inflation indicator available for economic analysis and hence, was widely used in the exercise. However, for trade with Western Europe and Northern America, while the CPI’s were used, a set of simulations was conducted using the WPI (wholesale price index), which is more heavily weighted towards tradeables.

# **4. CHOICE OF TRADE WEIGHTS**

Country weightings were chosen based on shares of total trade for both regional imports and regional exports averaged over two years (2000 and 2001). In the CEMAC and UEMOA zones, many of the trading partners are similar for exports and imports so it was not necessary to disaggregate the REER’s. The sum of the weights was adjusted to equal 1. Furthermore, since the analysis was for only five years, there was no need to provide annual updates of the trade weights. However, a set of simulations were run using trade shares including unrecorded trade obtained from survey data, especially from Nigeria and Ghana, and the weighting schemes were adjusted accordingly. Fortunately, for the sake of the analysis, much of this unrecorded trade did not occur in parallel markets, since both Nigeria and Gabon have reformed their exchange rate regimes, and differentials between parallel and market rates in these countries is minimal. Thus, for the simulations using informal trade, one can safely use measures of official exchange rates. Finally, third-country competition used in several weighting schemes was not used because of data limitations.<sup>2</sup>

# **5. CHOICE OF CURRENCIES**

Currencies were chosen from countries which had a share of no less than 0.5% of total UEMOA exports and imports. The currency of the country or area which had the share of no
. Inequality (Dissimilarity Index) in Health and Survival by Year**

<!-- Start of picture text --> 25<br>20<br>20<br>15<br>14<br>15<br>12 2003/4<br>2011<br>10 8<br>7 2012<br>4<br>5 3<br>1 1<br>0<br>Prenatal Delivery Fully Infant mortality<br>Immunized<br>Dissimilarity index<br><!-- End of picture text --> Source: Authors’ calculations based on DHS 2003/04, ENPSF 2011, and ONDH panel 2012.

These inequalities translate into very different chances for the most and least advantaged children in society (Figure 11). The most advantaged child is almost certain to have good early 16
children in each region separately.

There are 1,995 rural children less than 36 months in the ENN dataset. Our sample consists of only 1,540 children.<sup>33</sup> We only include those children whose mother has not moved in the past year to ensure that the weather shocks match what the child experienced. Of the excluded children, there are 138 children with missing height information and 91 children with improbable z-scores,<sup>34</sup> suggesting data entry errors. The other excluded children have incomplete information on the covariates. The children measured (and with probable z-scores) are statistically significantly older than those who are not measured. This poses a problem

> 29 We use, WHO Anthro for personal computers, version 3, 2009: Software for assessing growth and development of the world's children. Geneva: WHO, 2009 (http://www.who.int/childgrowth/software/en/ ) for calculating the standardized height-for-age scores.

> 30 The measure does not capture any differences in mortality from unusual weather.

> 31 The asset index is based on the principal factor analysis of  the household‘s ownership of a radio, a television, a VCR, a telephone, a computer, a refrigerator, a washing machine, a stove, a heater, and motor vehicle.

> 32 Only when analyzing the effects by participation in a nutritional program, we also include nutritional program participation as a regressor.

> 33 This is the pre- _canícula_ sample without nutritional supplement program variables and access to health care included as explanatory variables.

> 34 That is, their height-for-age z-scores are less than -6 or more than 6.

23
|
|Hourly Work Earnings|5.565|7.312|0.300|61.317|119,770|
|Age|34.924|11.936|16|65|119,770|
|Male|0.631||||119,770|
|High School|0.131||||119,770|
|College Incomplete|0.046||||119,770|
|College|0.064||||119,770|
|PNAD 2007||||||
|Hourly Work Earnings|5.659|6.978|0.312|62.500|155,979|
|Age|36.367|12.025|16|65|155,979|
|Male|0.589||||155,979|
|High School|0.259||||155,979|
|College Incomplete|0.085||||155,979|
|College|0.094||||155,979|

A comparison of Tables 1 and 2 reveals considerable differences between the two labor 10In 1995, PNAD did not survey households in the rural areas of Acre, Amap ́a, Amazonas, Par ́a, Rondˆonia and Roraima — six states in the Amazon region.

25
Policy Research Working Paper 10762

# **Abstract**

This study documents the impacts of climate change on firm-level productivity by matching a globally comparable and standardized survey of nonagricultural firms covering 154 countries with climate data. The findings show that the overall effects of rising temperatures on productivity are negative but nonlinear and uneven across climate zones. Firms in hotter zones experience steeper losses with increases in temperature. A 1 degree Celsius increase from the typical wet-bulb temperature levels in the hottest climate zone (25.7 degrees Celsius and above) results in a productivity decline of about 20.8 percent compared to firms in the coldest climate zone. The effects vary not only based on the temperature zones within which firms are located, but also on other factors such as firm size, industry classification, income group, and region. Large firms, firms in manufacturing, and those in low-income countries and hotter climate zones tend to experience the biggest productivity losses. The uneven impacts, with firms in already hotter regions and low-income countries experiencing steeper losses in productivity, suggest that climate change is reinforcing global income inequality. If the trends in global warming are not reversed over the coming decades, there is a heightened risk of widening inequality across countries. The implications are especially dire for the poorest countries in the hottest regions.

This paper is a product of the World Bank Office of the Chief Economist, Africa Region and the International Monetary Fund. 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 wkassa1@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
In the housing sector, the capital-output ratio was below that in the United States until 2010 (Figure 15, Panel B). Since then the ratio for the housing sector in China has moved above that in the United States and this development will continue unless there is an abrupt fall in housing investment in China.

# **Total factor productivity growth by sector**

The incremental capital-output ratio is one measure of the efficiency with which capital is used but it is only a partial measure of the productivity of the economy. The overall efficiency of the economy is better measured by the productivity of both labor and capital – total factor productivity. Most growth accounting studies use the total amount of employment as the labor input and add a separate variable that measures the qualifications of the overall labor force. But the overall labor force is far from being homogenous. The growth accounting analysis assumes competitive markets, so employee compensation is assumed to reflect marginal productivity.

There is a long history of not using just the numbers employed as a measure of labor input. Kendrik (1956) argued for weighting physical labor inputs by a baes-period average compensation which, as Denison (1961) pointed out, enables the contribution of structural change in the distribution of labor in the economy to productivity growth be, at least partially, eliminated. Jorgenson (1991) argued for a much fuller decomposition of labor input covering education, age and gender with each input weighted by compensation.

# **_Labor quality indicators_**

A full decomposition of labor inputs by sector would be difficult to achieve in China due to lack of underlying data and restricted publication of data from labor market surveys. However, Wu _et al_ . (2015) have published estimates of average employee compensation by industry, which can be taken as a proxy of the quality of labor input by sector. Such data permit a breakdown of employee compensation by the sectors used in the analysis of capital formation (business and government/infrastructure). However, the business sector itself is far from
Delmotte _et al._ (eds.) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33−144.

Kalkuhl, M., & Wenz, L. (2020). The impact of climate conditions on economic production. Evidence from a global panel of regions. _Journal of Environmental Economics and Management_ , _103_ , 102360.

- <mark>Karim, A., & Noy, I. (2016). Poverty and natural disasters: a regression meta-analysis.</mark> _<mark>Review of Economics and Institutions</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 26.</mark>

- Kolstad, C. D., & Moore, F. C. (2020). Estimating the economic impacts of climate change using weather observations. _Review of Environmental Economics and Policy, 14_ (1), 1–24.

- Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M., & Levermann, A. (2021). Day-to-day temperature variability reduces economic growth. _Nature Climate Change_ , _11_ (4), 319– 325.

- Kotz, M., Levermann, A., & Wenz, L. (2022). The effect of rainfall changes on economic production. _Nature_ , _601_ (7892), 223–227.

- Kummu, M., Taka, M., & Guillaume, J. H. (2018). Gridded global datasets for gross domestic product and Human Development Index over 1990–2015. _Scientific Data_ , _5_ (1), 1–15.

- Lakner, C., Mahler, D. G., Negre, M., & Prydz, E. B. (2022). How much does reducing inequality matter for global poverty? _Journal of Economic Inequality_ , _20_ (3),
8958***|1.9215***|3.0930***|
||(0.0492)|(0.2853)|(0.2004)|(0.1945)|(0.2174)|(0.2131)|(0.2141)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|

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.

86
1−β1<sup>′</sup> <u>i2</u> 2−β2<sup>′</sup> <u>i2</u> 1 2) = Φ2 , − (C4) ~~�~~ zz xx zz xx where Φ 2(. ) stands for the bivariate normal cumulative distribution function (cdf), and PP(yyii1 < zz aai yyii2 > zz σσεε1 σσεε2 , −ρρ� 2(. ) stands for the bivariate normal probability density function (pdf). Note that in Equation (1), the estimated parameters obtained from data in both survey rounds are applied to data from the second φφ survey round (x2) (or the base year) for prediction, but we can use data from the first survey round as the base year as well. It is then straightforward to estimate quantity (C3) by dividing quantity 1−β1<sup>′</sup> <u>i2</u> (C2) by Φ , where Φ (. ) stands for the univariate normal cumulative distribution � ~~�~~ zz xx function (cdf). σσεε1 In Equation (4), the parameters and are estimated from Equation (C1), and ρ can be estimated using an approximation of the correlation of the cohort-aggregated household ii εεii ββ σσ consumption between the two surveys ( ). In particular, given an approximation of , where c indexes the cohorts constructed from the household survey data, the partial correlation yycc1yycc2 yycc1yycc2 coefficient ρ can be estimated by ρρ ρρ <u>i1 i2</u> ) 1<sup>′</sup> 2 (C5) ρρyy yy �vvaavv(yyii1)vvaavv(yyii2 −ββ vvaavv(xxii)ββ An alternative way to estimate ρρ= is to further assume that there is a cohort fixed effect in the σσεε1σσεε2 error terms and aggregate all the time-invariant variables to the cohort level and use the following equation ρρ
22 investing across borders: do fdi-specific regulations matter?

of contract enforcement rights, due process, and access to justice). It gives parties autonomy to create systems tailored to their disputes. According to World Bank Group (2010), countries that score well on these indicators have a strong arbitration legal framework, receive support from local courts for arbitration proceedings and efficient enforcement, adhere to international conventions, and provide autonomy to parties seeking to resolve their commercial disputes.

# 1.4.3 _Starting a Foreign Business_

Starting a Foreign Business indicators record the time, procedures, and regulations involved in establishing a local subsidiary of a foreign limited liability company. Here, I use the number of procedures required to establish a foreign business. This resembles a cost that affects the decision about whether and how much a firm invests in a foreign country. The steps include both pre- and post-incorporation procedures with which foreign businesses are officially required to comply. According to World Bank Group (2010), countries that score well on the Starting a Foreign Business indicators have simple and transparent establishment processes that abolish unnecessary steps (which create opportunities for rent-seeking). High scorers also treat foreign and domestic investors equally, and differences in treatment vary only by company size, legal form or commercial activity, not the nationality of shareholders. Tables 19 to 21 describe how indicators of FDI-specific regulations are constructed in the IAB data set. I separately construct composite country scores for the quality of selected FDI regulations in Table 22.<sup>22</sup>

> 22 The Investing Across Sectors score (1) averages the maximum percentage of equity permitted for foreign ownership in the 11 sectors mentioned in Table 19. The Ease of Process Index (2) and the Ease of Judicial Assistance Index (3) are obtained by normalizing the country score for those indices using the min-max rule: the score for a country is subtracted from the best-performing country, divided by the difference in scores between the best and worst-performing countries. The number is then multiplied
8 _Table 2: Percent Change in Health Indicators by Provinces, 1983-93 (Provinces ranked by per capita income)_

||||_P_|_ercent Chan_|_ge_|||
|---|---|---|---|---|---|---|---|
|_Quintile_<br>_of_||_Public_<br>_Health_<br>_Spending_|_Infant_<br>_Mortality_<br>_Rate_|_Maternal_<br>_Mortality_<br>_Rate_|__Covera_<br>_DPT3_|_ge_<br>_of_<br>_Sanitation_|_Safewater_|
|_Provinces_||||||||
|Poorest|I|93|-4.1|-4.3|68.8|58.6|89.1|
||II|77|-21.8|-4.4|58.9|85.4|90.2|
||III|69|-20.3|-10.7|50.2|77.4|59.9|
||IV|65|-30.1|-17.3|67.1|77.3|73.1|
|Richest|V|58|-32.5|-18.3|57.3|38.2|35.4|

**_Source:_** Staff Estimates from MOH data.

# **Costs and Quality:**

The average private out-of-pocket costs per visit to public health services vary widely across quintiles. Private expenditures on health care are shown in Table 3, ranked according to quintiles, from poor to rich in urban areas. Although the wealthiest households pay larger absolute amounts on medical items, as a proportion to non-food household spending, the poorest households spend about 1.5 times more on medical care than do richer households. Of total non-food expenditure, drugs and medicine