rafmacalaba/data-use-mentions-extended huggingface dataset:
The NSS is a weighted survey but the weights do not affect the estimated downward trends in poverty rates in Table 1. This is reassuring because the spatial panel estimators that we use in the econometric analyses below do not handle variable sampling weights. We therefore rely on the unweighted data; based on Table 1 this should provide similar patterns to the weighted data. The other pattern in Table 1 is the regional divergence in poverty rates; the coefficient of variation of rural poverty rates based on the NSS regions rises monotonically over time, for both poverty measures and whether using sampling weights or not. In other words, even though India has been escaping from mass absolute poverty it is doing so at an uneven rate over space.

The spatial pattern of rural poverty at baseline is shown in Figure 1, for the headcount index (left panel) and poverty gap index (right panel). The spatial autocorrelation in the data is apparent, with regions having high poverty likely to be neighbors with other high poverty regions and _vice versa_ for regions with low poverty rates. Roughly speaking, if one drew a straight line from Nashik in Maharashtra (which is about 100 miles northwest of Mumbai) that went at a 50 degree angle to pass west of Kanpur in Uttar Pradesh (UP), all but three regions to the southeast of that line would be in the highest three poverty classes, which had headcount poverty rates of 42 percent or more in 1993/94.<sup>9</sup> Conversely, almost all regions along the Arabian Sea coast, and in northwest India were in the lowest classes for poverty. A common test for spatial autocorrelation is Moran’s _I_ test, which has some parallels with the Durbin-Watson statistic.<sup>10</sup> This test reveals significant positive spatial autocorrelation with _I_ =0.30 for the headcount index in 1993/94 and _I_ =0.26 for the poverty gap index.

The spatial pattern of rural poverty at the endpoint of our panel,
is ranked higher for R&D expenditures (51) and quality of research institutions (51), but worse for international co‐inventions (90) and patent applications (97). Indeed, while the overall number of patent filings has increased significantly in recent years, the proportion of patents filed by Moroccan residents has actually declined, from about 32 percent in 2014 to less than 9 percent in 2017 (Figure 2.17).<sup>1</sup> The fact that non‐residents file the vast majority of patents in Morocco raises some fundamental questions about the dynamism of R&D activities by nationals. Similarly, according to the global innovation index of INSEAD‐WIPO (2018), Morocco is ranked 76 out of 126 countries. In terms of the innovation input sub‐index (institutions, human capital and research, and infrastructure), the ranking is 84, and in terms of the innovation output sub‐index (knowledge creation, impact, and diffusion), the ranking is 69. Among lower middle‐income countries, it is ranked No. 10 out of 30 countries, behind Vietnam, India, Armenia, and the Philippines.

21
**Table 6. Interacting** **_Doing Business_ indicators with the Contract Enforcement indicator**

||Lgrow<br>(1)|ln(LP)<br>(2)|
|---|---|---|
|DB Get Electricity|-0.156|0.104**|
||(0.405)|(0.044)|
|DB Get Electricity * Contract Enforcement|0.990**|0.012|
||(0.387)|(0.052)|
|DB Get Credit|0.097|-0.031|
||(0.327)|(0.055)|
|DB Get Credit * Contract Enforcement|-0.486|-0.073|
||(0.373)|(0.070)|
|DB Construction Permit|-0.172|-0.057|
||(0.419)|(0.078)|
|DB Construction Permit * Contract Enforcement|-0.025|0.205*|
||(0.508)|(0.117)|
|Contract Enforcement|0.773|-0.137|
||(0.499)|(0.119)|
|Year, Industry FE|Yes|Yes|
|Firm, Country Ctrl|Yes|Yes|
|Observations|57124|51710|
|Adjusted_R_<sup>2</sup>|0.054|0.339|

SEs are clustered at country level. All BE indicators are standardized with their sample mean and standard deviation. Firm level control variables include biggest owner share, small/mediuam/ large scale firm, age 6-10, age 11+, exporter, foreign firm, foreign share, city population category dummies, and number of competitor dummies. Country level variables include ln(GDP pc), ln
for both cargo and<br>passenger transport companies.<br>**Consequences of subsidies**: Some 3,500 retail outlets closed<br>between 2005 and Mar 2010 because of poor profitability.<br>**Use of export tax to lower domestic prices:**Until Jan 2013, the<br>export tax on crude oil was 100% above US$45/barrel (bbl), making<br>the export price US$42/bbl. Government relaxed the export tax in<br>Jan 2013, raising the oil export price from US$42 to US$70/bbl.<br>**Information**: There is comprehensive coverage on the Web of<br>current and historical gasoline, kerosene, and diesel prices by<br>location and company. Government uses price surveys to boost<br>transparency and indirectly press companies to reduce prices.<br>Monthly price surveys are carried out at filling stations and the<br>National Statistics and Census Institute (Instituto Nacional de<br>Estadistica y Censos, or INDEC) posts price and volume<br>information for each filling station every month on its Web site,<br>going back to Dec 2004.|<br> <br> <br> <br> <br>year), on the condition that the participating<br>companies sell diesel at government-<br>controlled prices.<br>**Upstream sector**: The exceptionally low<br>export price of oil until Jan 2013 has led to<br>oil production falling from 0.72 million bpd<br>in 2006 to 0.61 million bpd in 2011, while<br>consumption grew from 0.47 million to<br>0.61 million bpd. Government increased the<br>export tax on natural gas from 45% to<br>100% in 2008. Natural gas production
, contracts and human resources, occupational health and safety, and working time. There are 39 compliance points across the eight clusters. Table A.1 lists the different compliance points under each of the eight clusters.

Moreover, not all questions in the Compliance Synthesis Report are compliance questions. For example, the Compliance Synthesis Report collections information on the number of staff employed by the factory. The data set on firm compliance was restricted to just questions that directly imply compliance. These non‐compliance questions were either saved as part of the firm characteristics when appropriate, or dropped. Table A.2 lists the variable names and their definitions available on the Compliance Synthesis Report.

The eight compliance clusters have not changed over time, however the compliance questions asked in the Compliance Synthesis Report have changed. Though changes to the monitoring instrument are needed to accommodate learning and shifting emphasis of working conditions, this creates difficulties in tracking compliance to individual questions over time. Moreover, question codes were also changed, and more frequently than the individual questions (that is, the same questions would be coded differently over time). To maximize comparability of compliance over time, careful attention was given to identifying and using only compliance questions that were available over the entire sample period. The individual compliance questions from each Compliance Synthesis Report were matched manually. The resulting data set contains 161 individual compliance questions that appear consistently throughout the sample period.<sup>6</sup> Better Work Vietnam identified 26 “critical issues” for which public disclosure would be assessed, listed in Table A.3. The analysis of whether transparency through public disclosure accelerates adoption of compliance requirements focuses on these critical issues. However, not all 26 critical issues have

> 6 Dropping questions that enter or leave the sample introduces potential bias, depending on why questions were added or removed. In some cases, multiple questions were replaced with a single question and vice‐versa. As an extension, the average across component questions could be used to measure compliance,
regions is exploited in later sections to estimate household-level demand. We refer the reader to Appendix 2 for additional validations conducted on the temperature and rainfall data.

# **SECTION IV: Modeling Demand for Energy Services**

The model makes use of the data from the household survey of electricity-consuming appliance ownership and use in order to estimate a model of the demand for energy services. It captures the wellknown fact that electricity is a derived demand. Specifically, a household’s overall demand for electricity is derived from the demand for an energy service provided by an electricity-consuming capital good such as a light bulb, fan, air conditioner, etc. Moreover, the amount of electricity consumed to provide a fixed quantity of electricity services, say an hour computer use, is uncertain because of back- ground factors such as the background temperature and intensity of use of the appliance. Let equal the households demand for energy service in hours of use per day within the billing cycle, and ii uu 1 2 )<sup>′</sup> equal the vector of energy services demand for the N services that the household consumes. Let equal the household’s electricity consumption in kilowatt-hours (kWh) per day during uu ss= NN (uu , uu , ... , uu the billing cycle. EE A household’s demand for a vector of energy services is related to its use of electricity during the bill cycle through the electricity production function, . This function characterizes the technological relationship between the vector of energy services a household consumes and the electricity use they require. This function also depends on EE = oo (ss, AA, εε), a vector of weather and household characteristics that impacts how the vector of energy services demand translates into electricity AA consumption, and , a random variable that is unobserved by the household and the researcher that captures the uncertain amount of electricity, , used by a household that consumes the vector of energy services, . εε EE Let ss equal the potentially nonlinear price schedule
|
||tons||tons||tons|
|India|19.4|Brazil|9.3|Russia|5.2|
|EU|18.6|EU|4.2|Indonesia|1.7|
|Brazil|18.5|Australia|3.8|Japan|1.6|
|U.S.|7.9|Thailand|3.6|U.S.|1.4|
|China|7.8|Cuba|3.2|Korea, Rep.|1.2|
|Thailand|5.4|R. S. Africa|1.3|Canada|1.2|
|Mexico|5.1|Guatemala|1.1|Iran|1.0|
|Australia|4.9|Colombia|1.0|Malaysia|1.0|
|Cuba|3.8|Turkey|0.6|Algeria|0.9|
|Pakistan|3.0|Mauritius|0.5|Nigeria|0.7|
|All Other|38.9|All other|10.3|All other|20.7|
|World|133.3|World|38.9|World|36.6|

<mark>Source: USDA PS&D Database 2002. Note: Data is in raw sugar equivalents.</mark> Among the important changes that have occurred in the world sugar market in the past 20 years has been the shift of production and exports to low-cost producers, the shift of consumption and imports increasingly to developing countries, and the declining share of preference markets in total imports. Beginning in the 1980s, sugar production by the low-cost producers began to increase rapidly, with Australia, Brazil, Colombia, Guatemala, South Africa, and Thailand increasing their combined share of world production from 19 to 29 percent and their share of world exports from 24 to 52 percent
supplement this database with data on government spending that we collect from the central government and different provincial government websites. As a result, we can zoom in on provinces that are good (or bad) performers. Furthermore, this rich panel database also allows us to employ rigorous econometric modeling techniques to investigate the channels affecting the province-level income distributions.

Our estimation results suggest that within-province inequality has steadily increased over time. Within-province inequality is also much larger than between-province inequality, with the former type of inequality being almost three times the latter type of inequality in 2020. Average incomes and expenditures appear to converge across provinces, while poverty significantly declines during this period. The remaining poor seem to be regionally segregated among provinces with a greater share of ethnic minority population. We find beneficial impact of economic growth on poverty reduction. While the relationship of inequality with headcount poverty appears weak and not statistically significant, inequality is positively and strongly statistically correlated with poverty gap and poverty severity and might impede economic growth. Provinces with greater population density and a larger share of urban population have faster growth, whereas the opposite result holds for those with a greater share of ethnic minority groups. The transitions from an agriculture-based economy toward wage-based and services-based economy could be beneficial for the country’s economic growth and poverty reduction.

This paper consists of four sections. In the next section, we describe the data (Section 2.1), discuss the overall trends in economic growth, inequality, and poverty at the country level (Section 2.2) and at the regional level as well as analyze poverty segregation across provinces (Section 2.3). We subsequently investigate in Section 3 the relationship between economic growth, inequality, and poverty, including convergence in inequality. We finally conclude in Section 4.

4
BMGF) that supports governments in eight African countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, Uganda, and Burkina Faso. The goal is to generate several rounds of nationally representative panel surveys with a multi-topic approach, designed to improve the understanding of the links between agriculture, socioeconomic status, and non-farm income activities. This paper uses data from the latest rounds of LSMS-ISA surveys from four countries: Ethiopia (September/December 2018 and June/August 2019), Malawi (April 2019 to March 2020), Nigeria (July/August 2018 and January/February2019), and Uganda (March 2019 to February 2020).

In particular, we use respondents’ industry of work from the LSMS-ISA data to complement the HFPS data for our analysis of the labor market impacts of COVID-19. Here, we also summarize the LSMS-ISA data on employment status disaggregated by gender to describe the pre-COVID-19 labor market in each of the four countries.

The pre-COVID-19 LSMS-ISA surveys show that labor force participation was between 58% and 70% before the onset of the pandemic (Figure 3); participation being highest in Uganda and lowest in Ethiopia. In all four countries, the labor force participation of women was below that of men and the gender difference was largest in Ethiopia, with 20% of men and 40% of women economically inactive.

In all four countries, a large share of the working population is employed in the agriculture sector. Before the pandemic, Nigeria had the most diverse workforce, with four out of ten workers in the agriculture sector, and two out of ten in commerce (Figure 4). In Ethiopia, almost eight out of ten workers were in agriculture.

5
44|-6***|50|49|-1||
|_Autonomy over major household purchases_|39|41|2**|44|50|6***||
|_Autonomy over visits to friends and family_|52|49|-3*|62|58|-4***||
|N|1127|1443||9596|11711||23877|
|**Women’s earnings decision-making**|89|92|3***|84|89|5***||
|**autonomy**||||||||
|N|1039|1383||7449|10823||20694|

Source: Authors own calculation using data from the 2008 and 2013 Nigerian Demographic and Health Survey Note: *** Difference in means between before BH and during BH is significant at the 1% level. ** Difference in means between before BH and during BH is significant at the 5% level. * Difference in means between before BH and during BH is significant at the 10% level.

The proportions of women with decision-making autonomy changes during the BH insurgency in both the BH affected areas and the non-BH areas. However, there is not a systematic pattern of decreased decision-making autonomy in the BH affected areas compared to the non BH areas. In both areas, autonomy over health care decisions and autonomy over decisions to visit friends and family decreases and autonomy over major household decisions and disposition of earnings increases. Reductions in autonomy over health care decisions are greater in the BH area than the non BH area (6 percentage points vs 1 percentage point) but there is little difference between the BH and non BH areas in the reductions in autonomy over visits to friends and family. The increase in the proportion of women who have autonomy over major household purchases is larger in the non BH areas than the BH areas (6 percentage points vs 2 percentage points). Finally
003|
||||||[0.018]|[0.034]|[0.089]|[0.143]|
|Other capital*PreCrisis|||||-0.097***|-0.079|-0.053|-0.214*|
||||||[0.034]|[0.059]|[0.083]|[0.112]|
|Common equity*Crisis|||||0.114**|0.165**|0.283**|0.617**|
||||||[0.044]|[0.067]|[0.126]|[0.278]|
||||||(0.047)|(0.014)|(0.012)|(0.035)|
|Other capital*Crisis|||||-0.015|0.002|0.324**|0.561*|
||||||[0.076]|[0.102]|[0.144]|[0.293]|
||||||(0.407)|(0.251)|(0.008)|(0.015)|
|Controls*Crisis|x|x|x|x|x|x|x|x|
|Controls*PreCrisis|x|x|x|x|x|x|x|x|
|Country*Year FE||||||x||x|
|Nber. Obser.|4049|3726|827|736|3654|3700|745|748|
|R squared|0.23|0.23|0.32|0.33|0.23|
sheet vulnerabilities and the attendant contingent fiscal liabilities into their policy formulation.

The subsequent resilience emerging markets displayed during the global crisis of 2008-09 is an eye-opener, not just for developing countries but OECD countries as well. A key lesson is

> 28 They looked at the impact of the March 2008 1.5% tax on fixed income investment, and the October 2009 2 % tax on all foreign portfolio investments).  Using fund level capita flows data (fixed income), they found that the regulations induced capital outflows from Brazil.

29 From July 25 2011 onward, financial institutions operating in Korea are no longer allowed to buy FXdenominated bonds issued onshore by Korean companies (―Kimchi bonds‖) who swap proceeds into local currency. A Bank of Korea official explained the policy intent saying that ―local firms should raise funds in won when they use the money here.‖ Kimchi bond issuance had risen considerably in the months before the policy change, because all-in borrowing costs are lower than for comparable won-denominated debt. [ _Financial times_ , ―Seoul takes kimchi bonds off the menu,‖ July 19, 2011].

24
2 to the operations of firms. Therefore, it could be that work experience contributes significantly to human capital in a way that deserves more attention.

Few studies have consistently estimated returns to experience and explored how they vary across a sample of developing and developed countries (Lagakos et al., 2018b,a). Even less is known about the patterns of returns to experience for a global sample, and how they are affected by policy. Human capital accumulation at work could have important implications for long-run growth and the divergence in income between rich and poor countries. Since output per worker is the main contributor of output per capita, if workers see their earnings increase due to higher returns to experience in their economy, then tracking the evolution of these returns over time could provide valuable information regarding the ability of developing economies to eventually catch up to the income level of developed economies. In this paper, we provide additional evidence that work experience is a key factor of economic development.

First, we study wage-experience profiles, and obtain returns using data from 1,084 household surveys and census samples across 145 countries (1990-2016). We find average population-weighted returns to experience of 2% for the world as a whole. Returns to education are 4 times higher, at 8%. However, returns to education do not vary much across income levels, whereas returns to experience are strongly correlated with economic development. We find returns of 1.7% for developing economies and 3.2% for developed economies, which suggests that workers in developed economies accumulate almost twice as much human capital at work than workers in developing economies. In our preferred specifications, returns are then higher in middleincome countries than in low-income countries, but only significantly so for a few specifications. Significantly higher returns to experience in all specifications are only observed when an economy achieves high-income status. These patterns could support our conjecture that the evolution of a country’s returns to experience could proxy for this country’s ability to “catch up” with
household surveys should perhaps be provided as a robustness check for estimates on the GDP growth rate using the national account (Indicator 8.1.1).

# _Missing Data_

We have discussed in the previous section the important role of time trends with interpreting poverty reduction results, which similarly holds for other development outcomes. Building on this result, we show in Figure 5 the different patterns of GDP per capita growth over a five-year period during 2011-15 for all countries in the WDI database. We divide growth patterns during this 5- year interval into two groups: decreasing (Panel A) or increasing (Panel B). For most readers, the notion of increasing GDP growth may be continuous growth over all five years (Figure 5, Panel B, group 1); in fact, the real growth patterns are much more complicated. There may, for instance, a decrease in GDP per capita in the first year, and a continuous increase for the remaining years (Panel B, group 2). Or growth may be continuous for the first two or three years, then slip back in the remaining years (Panel B, group 4 and group 5). Or growth may be continuous for the first three years, decrease in the fourth year, and then increase again in the fifth year (Panel B, group 3).

These results help highlight the impacts that missing data can have on interpreting the SDG progress. More precisely speaking, missing data can result in an incorrect interpretation of the actual trends. For example, if the data for country groups 4 and 5 are available not for the whole five years but only for the last two, the growth pattern for these two years would be decreasing despite the general increase over the 5-year period. The pattern can be even more complex if not all countries have data in the same years, and some groups have data only sporadically—which is 19
<mark>freely available, World Bank approved microdata cataloging tool available at</mark> <u><mark>http://nada.ihsn.org/</mark></u> <mark>that can serve as a guideline for NSOs.</mark> Suggested practice 3: Provide links to other websites with country’s data Earlier we reported that not all microdata sets are hosted on the NSOs websites and about half of the publicly available microdata sets are accessible through international repositories. Where this is the case, providing a link to the websites with the relevant country data is best practice. Microdata available in other Microdata Library of the WB, IHSN, Eurostat, MICS and IPUMS data can be easily linked on the NSO’s website whether the NSO owns all the data available on these websites. Djibouti sets a good example for providing external links to its country’s data. At the time of the study, on the landing page of the website of the National Institute of Statistics of Djibouti, there is a tab named “database” with 3 dropdown tabs as follows (1) survey data. (2) Open data (3) key indicators. The survey data tab links to the World Bank's microdata library.

Suggested practice 4: Provide clarity for requesting restricted data In the classification of microdata accessibility in section 3.2, we differentiate between two classes of microdata accessibility - “satisfactory openness” and “excellent openness” where the former involves a situation where authorization and/or registration is required before a data user can access available data and the later a situation where microdata is available for immediate download on the website. As discussed earlier, “excellent openness” is ideal from the perspective of a data user, however requiring registration, authorization, and clearance before data is released by data guardians is acceptable. When microdata has “satisfactory openness”, it is important that NSO’s provide clarity regarding the steps that need to be