rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 1: Employment Growth by Size and Age <!-- Start of picture text --> Size < 10 Size 10−19<br>−.2 0 .2<br>Three−year Change in Employees Labor Growth, Three year annualized<br>Age < 5 Age 5−9 Age < 5 Age 5−9<br>Age 10−14 Age 15−19 Age 10−14 Age 15−19<br>Age 20+ Age 20+<br>Size 20−99 Size 100+<br>−.2 0 .2 −.2 0 .2<br>Labor Growth, Three year annualized Labor Growth, Three year annualized<br>Age < 5 Age 5−9 Age < 5 Age 5−9<br>Age 10−14 Age 15−19 Age 10−14 Age 15−19<br>Age 20+ Age 20+<br>−6 −5 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 1010−20 20+<br>.8 15<br>.6<br>10<br>.4<br>Density<br>Density<br>.2 5<br>0<br>0<br>15 15<br>10 10<br>Density Density<br>5 5<br>0 0<br><!-- End of picture text --> Note: 10 employees, change in employees displayed where increase in 8 employees corresponds roughly to 20% three year annualized growth rate for firms with 10 employees in base year. Source: Enterprise Survey 2013-2014 wave.
as Misali Island, west of Pemba, and Mnemba and Chumbe islands, off Zanzibar. There are fringing and patch reefs along much of the mainland coast and these islands (Leon and others, 2004). Tanzania’s continental shelf covers an estimated 17,900 km<sup>2</sup> , with waters up to 200 meters in depth. It is generally narrow—typically extending about 4 km offshore—except at this survey with demographic data and detailed fishing gear usage data provides a unique opportunity for a detailed analysis.

5
the past two decades by international institutions and NGOs, studies that focus on the carbon benefits as well as the distributional impact of subsidies based on large scale household surveys are extremely lacking.

This study aims to fill this gap using the first available national household survey that collects data on SHS installation in rural Bangladesh. Bangladesh is one of a few countries that have made significant progress in providing electricity access to rural population through SHS. The SHS programs that were financed from Global Environment Facility (GEF) and International Development Assistance (IDA) had installed over 300,000 SHS (accounting for about 1.6 % of non-electrified rural households) in rural Bangladesh by 2009. This study focuses on (1) the quantification of the carbon benefits, particularly on kerosene displacement; (2) SHS affordability; and (3) the distributional consequences of SHS subsidies.

This paper is structured as follows: section 2 reviews the development of SHS dissemination in LDCs. Section 3 summaries the progress of rural electrification in Bangladesh. In section 4, we present the statistical summary of the 2005 national household survey. Section 5 presents methodology and results in the above three areas. Section 6 concludes.

# 2. **SHS dissemination in less developed countries**

Despite technology maturity and the constant decline in SHS prices, the current level of SHS dissemination among rural populations is low. According to the Global Status Report 2009, out of the 400 million households who lacked access to grid electricity in 2007, only about 2.5 million received electricity from SHS.<sup>4</sup> A recently published IFC report, entitled “Selling Solar “, summarizes the lessons from more than a decade of experience in SHS dissemination in developing countries and concludes that many IFC programs financed through GEF have not been able to create sustainable SHS business in rural areas.

Several factors underlie the slow progress in SHS dissemination for rural electrification. These include lack of information about SHS and grid-extension plans, lack of financial resources for SHS businesses and
<!-- Start of picture text --> Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>CHN 20012011 CHN 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 10 20 30 40 50 0 20 40 60<br>Total LVAX share (%) Total LVAX share (%)<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **Figure 21: Skilled and unskilled labor content of exports in GVC-intensive sectors 2001 vs. 2011**

<!-- Start of picture text --> Wearing Apparel Motor Vehicles<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 4,000 5,000 0 2,000 4,000 6,000 8,000<br>US$ mil. US$ mil.<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br>Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0
number of people living in each of those populated centers. This allows us to exploit the information content in the dispersion of population within a given municipality.

The human resources of each municipality are available through the annual National Registry of Municipalities ( _Registro Nacional de Municipalidades, RENAMU_ ), a census of municipalities run by the National Institute of Statistics. This registry allows us to know not only the total number of staff working in a given municipality but also the type of work they perform, whether it is managerial, administrative and technical, or manual and support work.

The living standards of the population in a given municipality are available thanks to the work produced by World Bank staff for operation purposes using the Poverty Maps 9
||
|Share_Infrastructure2011_|0.670***|-0.668***|-0.069||
||[0.124]|[0.222]|[0.098]||
|Homeland*Share_Infrastructure2011_|-0.722***|0.751***|0.046||
||[0.132]|[0.228]|[0.102]||
|Observations|9,562|9,562|9,562|9,562|
|R-squared|0.641|0.637|0.636|0.642|
|1996 Controls|Yes|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). Column 4 includes all infrastructure variables (coefficients for these not presented). The dependent variable is the difference in log per capita income for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
to the emerging literature. First, using a globally comparable and standardized survey of firms rather than an individual country presents opportunities for better understanding the potentially heterogeneous and uneven impacts of temperature changes on firm outcomes. Most earlier studies that examined climate change impacts using micro-data relied on individual country observations due to limitations of data comparability across countries and regions especially at the micro level. The survey data also allows for estimation of impacts across regions, climate zones, industries, and incomes. This structure enables estimation of the impact at the global level. Comparison of these impacts is essential given the global nature of climate change and the potentially distinct types and magnitudes of the impacts. This may be the only study that estimates the impact of temperature changes on productivity at the widest geographical coverage using a representative sample of firms in more than 150 countries, providing the most comprehensive study in the literature. In addition, despite the relatively rich literature on the impacts of climate change on agriculture, studies on the non-agriculture sector are relatively scarce. This study contributes to understanding the impact on the nonagriculture sector, using firm-level data.
Policy Research Working Paper 8995

# **Abstract**

This paper documents an inverse U-shape in the evolution of wage inequality in Latin America since 1995, with a sharp reduction starting in 2002. The Gini coefficient of wages increased from 42 to 44 between 1995 and 2002 and declined to 39 by 2015. Between 2002 and 2015, the 90/10 log hourly earnings ratio decreased by 26 percent. The decline since 2002 was characterized by rising wages across the board, but especially among those at the bottom of the wage distribution in each country. Triggered by a rapid expansion of educational attainment, the wages of college and high school graduates fell relative to those with primary education. The premium for labor market experience also fell significantly. But the compression of wages was not entirely driven by changes in the wage structure across skill groups. Two-thirds of the decline in the variance of wages took place within skill groups. Changes in the sectoral, occupational, and formal-informal composition of jobs matter for the process of reduction in inequality, but do not fully account for the fall in within-skill variance. Evidence using longitudinal matched employer-employee administrative data suggests that an important driver was falling wage dispersion across firms.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 jsilva@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
In comparison to the non-camp settings in Jordan and Kurdistan, male refugee children in Lebanon have the highest school enrollment rate, with 63 percent of boys in school. Refugee children living inside camps primarily attend schools run by the UN, NGOs, or charitable groups, whereas refugee children living outside camps attend mostly public or private schools. Almost 90 percent of out-of-camp children in Jordan attend public schools. Likewise, as a result of the “second-shift” policy in Lebanon, which teaches Syrian children in an afternoon shift, about 60 percent of school-going children attend public schools.

Syrian refugees spend less on children’s education than host communities, but the difference is owing to the difference in the type of school and area of settlement. For instance, 43 percent of host community children in Lebanon attend private schools compared with less than 4 percent of Syrian refugee children. After accounting for the type of school and the district of settlement, there is no significant difference in education expenditure between the two groups.

The quantitative survey shows that many school-age children are currently out of school in the three host countries, and one of the most common reasons for refugee children to be out of school is economic hardship: they need to work to support the family, or school expenses are not affordable. This is also consistent with significantly higher school enrollment rates for school-age children before leaving Syria in all three regions. In KRI, roughly half of the Syrian refugee and IDP children who are not in school cited economic hardship. Lack of financial means prevents 40 percent of refugee children from attending school in Lebanon, and unaffordability is the most commonly stated reason why children are not in school in Jordan, as well. In Jordan, 50 percent of both Syrian and host community children who are out of school are not interested in going to school.

Even though rates of enrollment among refugee and IDP children are uniformly lower than enrollment rates in the host community (which are more than 85 percent in all localities), the influx likely has placed
_Figure 1: Comparison of the official equivalence scale with the square root equivalence scale and the per capita equivalence scale_ <!-- Start of picture text --> Comparison of three equivalence scales<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00<br>Household size<br>Official ES SQRT ES Per-capita ES<br>Equivalence scale<br><!-- End of picture text -->

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : The calorie requirement considered are 3,400 calories for a male, 2,750 for a female, and 2,000 for a child. The imputed share of food is computed from the coefficients regressed in 2015 in Upper Rural Egypt.

# **4.2 Comparison with alternative poverty lines**

In the final subsection, we contrast the official poverty lines with two benchmark alternative poverty lines. The objective is to further investigate the joint implications of the two non-standard aspects of the official poverty lines, namely their hybrid definition and their endogenous implicit equivalence scale. We assess: (a) how different the poverty picture would be if these aspects were taken out; and (b) whether the official poverty lines perform well in terms of identifying disadvantaged households. We also explore whether the official poverty lines behave more similarly to purely absolute or purely relative poverty lines.

# **Definition of the purely absolute and purely relative lines**

As shown in the previous subsection, the most natural alternative to the current endogenous equivalence scales would be to use the per capita equivalence scale. That is, the poverty line of any household is obtained by
1962)).

**Heterogeneity by religion.** We examine heterogeneity by religion across the two largest religious groups in India, Hindus and Muslims, because they are known to differ in attitudes to abortion. Previous work shows that Muslims, who are more averse to abortion due to religious reasons, are more likely to attain their desired number of sons through son-biased stopping rules, whereas Hindus are more likely to practice sex-selective abortions to achieve the same end (Almond and Edlund (2008), Bhalotra and Cochrane (2010), Bhalotra et al. (2018b)). In line with this, Tables A.17, A.18, and A.19 show that our results on fertility, postnatal health investments, and mortality are driven by Hindus rather than Muslims. In the pre-ultrasound period, Muslim women with a firstborn girl had significantly more children and had significant under-5 EFM relative to those with a firstborn boy as displayed by the coefficients of _Firstgirl * Female_ in Table A.17 and in Panel B of Table A.19. In the post-ultrasound era, when some Hindus switched from fertility continuation or postnatal neglect to sex-selective abortion, Muslims continued, as in the pre-ultrasound 50The one case where group differences in EFM decline are significantly different is in rural relative to urban households. Table 9 shows that absolute declines are significantly larger in rural families but, in fact, the proportional declines are smaller. This is in line with greater prenatal sex-selection in urban areas offsetting the role of higher baseline mortality in rural areas.

> 51We include households of all religions and use the self-reported caste of the household for our analysis while using religion as a control variable. These results are available in Table A.16.

> 52According to the 2005 India Human Development Survey, only 4.4 percent of women were married to a spouse from a different caste.

23
give the population totals in terms of individuals, the average household size is calculated from the dataset for each category in the disaggregated data and the totals calculated in terms of households. Within each category the weighted total number of observations is calculated and the census projection is divided by this number for the corresponding category. This correction is then applied to all the observations in the category. For example, if the weighted total of households in the capital city is calculated from the survey to be 157,304, but the census projections give a total of 193,484, the poststratification adjustment factor would be 1.23. The weight of each household in the capital city would then be multiplied by 1.23.

The final panel weight would then be:

# **Weight Calculations for Rounds Subsequent to Round 2**

The weights for subsequent rounds of panel data follow the same steps as round 2 with one important difference. In round 2, each household member was classifiable into one of four categories: a reinterviewed household member (observed in round 1), a new household member (new in round 2), a round 1 member who has died, or an attriter (observed in round 1 but not re-interviewed in round 2). In round 3, there would be an additional category of returning household member, which would apply to those members that were present in round 1, not found and thus classified as attriters in round 2, and then present in round 3. In the course of the calculation of round 2 weights, an attrition adjustment was applied to compensate for the departure of these members. As these members have now returned, simply using the weights from the previous round as the base weights for the panel calculations would overestimate the 9
studies focused on the relevance of the opportunity cost for money particularly in the short run in addition to the redeployment of classical modeling technologies in the short and long runs using partial adjustment models and auto-regressive distributed lag models.

In his paper, Ball (2012) challenged the conventional wisdom about the instability of the short-run money demand by examining quarterly data for the US from 1959 through 1993. Following the work of Hoffman and Rasche (1991) and Stock and Watson (1993), Ball interpreted long-run money demand as a co-integrating relationship among real narrow money balances M1 interest rates and output. To explain short-run deviations from the long-run relationship, Ball used Goldfeld’s partial-adjustment model. The key innovation of his paper is the choice of the interest rate in the money demand function. Instead of using a short-term market rate, such as the treasury bill rate or the commercial paper rate, Ball used the average return on ‘‘near-monies.’’<sup>9</sup> His findings showed that the long-run money demand is stable regardless of whether the interest rate is measured by the return on near monies or a money-market rate (the T-bill rate). However, the deviations of money holdings from their long-run levels are smaller with the return on near monies. In addition, Goldfeld’s partial-adjustment model yielded reasonable parameter estimates using the return on near monies, and it is not rejected in favor of a less structured error-correction model. Most importantly, he found that the deviations of money holdings from the predictions of the model are small. Thus, there is little evidence of shifts in the money demand function, even in the short run.

Hossain (2012) also challenged the conventional wisdom of the instability of money demand function in developed countries since the mid-1980s. He investigated the presence of an economically meaningful, stable narrow money demand relationship using annual data for Australia for the period 1970–2009. He employed an Auto-regressive Distributed-lag (ARDL) cointegration approach. The results suggested the presence of a long-run equilibrium relationship
|0.445|0.328|0.470|0.489|0.500|
|HH belonged to non-poor group (T60)|HH wealth index generated by Principal<br>Component Analysis. 1 if HH wealth index<br>belonged to upper three quintiles|0.729|0.445|0.672|0.470|0.511|0.500|
|**_Districts:_**||||||||
|South|1 if HH was located at Southern Kerala state|0.328|0.470|0.293|0.456|0.272|0.445|
|Central|1 if HH was located at Central Kerala state|0.181|0.385|0.133|0.340|0.302|0.459|
|North|1 if HH was located at North Kerala state|0.491|0.501|0.574|0.495|0.426|0.495|

Source: Author’s calculations based on KMS 2013.

18
(2022)). Specifically, firms responded by accelerating the use of existing digital technologies and investing in new digital solutions. This expansion in digitalization allowed firms to better reach consumers (or reach new ones) and organize production more flexibly and efficiently.

Figure 2 shows that nearly 45% of businesses had increased the use of digital platforms in the latest available data, compared to 32% in the first wave. Furthermore, 28% of firms had invested in new digital solutions, a 8 percentage points increase from wave 1. Although the intensity of these investments is not measured in these survey data, the number of firms taking actions to accelerate digitization has been unprecedented.

<!-- Start of picture text --> 60<br>45<br>44<br>40<br>28<br>23<br>20<br>0<br>Increase in digitalization<br>Increased use of digital platforms Have online sales<br>Invested in digital solutions Have remote workers<br>Share of firms<br><!-- End of picture text --> Note: Results from the latest round of available data.

Figure 2: Fraction of businesses by responses to the shock.

In addition, the increase in digitalization has been steady. For countries with all three rounds of survey data, the percentage of firms increasing the use of digital technologies has gone up from 28 percent to 41 percent, and 47 percent in the latest round (Figure 3). Moreover, the increase in digitalization has been widespread across the world. Even after controlling for the magnitude of restrictions on mobility, there was little difference in the uptake of digital technologies between countries at different levels of income. In fact, the advent of digitalization in response to the pandemic was lower in many high-income countries across Europe, compared with low- and middle-income countries across Asia and Africa. For example, the likelihood of firms starting or increasing their use of digital technologies in Hungary,
Table <u>6. Placebo effects of COVID-19 restrictions on educational outcomes (</u> _<u>continued</u>_ <u>)</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_C. Switched to public school_|||||
|Warning index x 2019|-0.001|-0.000|-0.000|-0.000|
||(0.000)***<br>[0.004]|(0.000)***<br>[0.012]|(0.000)***<br>[0.004]|(0.000)***<br>[0.004]|
|Warning index x 2020|0.002|0.002|0.002|0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2019=2020)|0.000|0.000|0.000|0.000|
|Mean outcome|0.010|0.010|0.010|0.010|
|Adjusted R<sup>2</sup>|0.006|0.014|0.107|0.105|
|Observations|12,342,065|12,294,413|12,341,959|12,294,343|
|_D. Switched to private school_|||||
|Warning index