rafmacalaba/data-use-mentions-extended huggingface dataset:
# **provides further confirmation that poverty in India is lower in 2017 than in**

**2011** . Consumption trends in past rounds of the IHDS and NSS surveys have tracked each other closely -- both surveys were conducted in 2004 and 2011 and predicted comparable drops in extreme poverty over this period. A limitation of IHDS-3 is that it is limited to the states of Bihar, Rajasthan and Uttarakhand. For this validation exercise therefore, we restrict the CPHS sample to these three states.

The IHDS captures consumption using the mixed recall period whereas the CPHS consumption used in our analysis corresponds more closely to the uniform recall period. Furthermore, IHDS-3 consumption values reported in Desai (2020) are in constant 2017 values and deflated using the _monthly_ CPI-AL and CPI-IW series. The consumption values in our analysis are in constant 2011 terms deflated using _yearly_ CPI-AL and CPI-IW series. For these reasons, we will be comparing changes in real consumption across the two sources (rather than comparing levels).

Real consumption grew at an annualized rate of 2.7 percentage points between the IHDS 2011-12 and 2017. The average annualized consumption growth over the same period in our analysis (approach 2) is 1.5 percent.<sup>28</sup> Real consumption growth in the IHDS-3’s rural and urban samples are 3.8 and -0.7 percent per year. By comparison, consumption growth in rural and urban in our analysis is 1.7 percent and 0.6 percent, respectively. Both surveys therefore point to faster growth in rural areas than urban areas. The differences in consumption recall and deflators used in the two surveys could account for the difference in magnitudes of the observed growth rates.

Correlates of consumption, such as durable asset ownership, are similar across the two surveys. Thirty-two percent of households in the IHDS-3 states own motorcycles and cars and 21 percent possess air coolers and air conditioners. In the reweighted CPHS, ownership shares of these two assets are 34 and 22 percent
CFUWBES2|_d Survey-Measured (_<br>(Cumulative)|_including jobs lost_<br>|
|---|---|---|---|---|
||Modeled|Model-predicted|Survey-measured|CMMHH|
|By gender|||||
|Female|13.1|19.0|21.3|17.3|
|Male|16.2|26.0|26.1|24.8|
|By age group†|||||
|below 25|15.5/15.3|25.6/26.9|24.6/24.9|23.1|
|25-45|16.5/16.3|25.2/24.9|26.3/25.6|20.6|
|above 45|11.1/14.3|21.3/24.3|23.9/25.9|38.3|
|By nationality|||||
|Jordanian|14.7|23.8|24.2|21.2|
|Syrian|14.1|21.4|25.1|6.8*|
|Palestinian|13.1|28.2|20.7|40.5|
|Other|22.6|32.2|33.5|-|
|Total<br>Notes: The cumulative jobs lo<br>the modeled incremental jobs<br>*May be unreliable due to sm<br>†Age group results are shown<br>between 2016 and 2020.|15.8<br>st by the time of CFUWB<br>lost from R1 to R2.<br>all sample size.<br>assuming the same wor|25.2<br>ES2 is predicted by aggreg<br>kers aged /
children and increased economic stratification (Hsieh and Urquiola 2006). This, in turn, may reduce pressure on public schools to supply quality education (Fiske and Ladd 2003).

While the SDI data cannot be used to assess the validity of these various arguments, we can compare the performance of public and private schools in the sample. In general, indicators in private schools tend to be better: Private school teachers tend to put in more effort, show more knowledge, and exhibit better teaching practices than their public counterparts (Table 8). At the same time, it is important to note that private schools are not able to overcome many of the poor service delivery issues faced by public schools—the issues appear to be systemic. Indeed, even in the private sector, one-third of teachers are absent from the classroom. While the taught school day is four hours long on average, i.e., more than one hour longer than in the average public school, it is still well short of scheduled time. In addition, while teachers in private schools have significantly higher test scores, their pedagogical knowledge is similar to their public school counterparts.

The better performance of private school teachers is reflected in their students’ learning. The student score in mathematics and language is one-third and two-thirds higher in private schools. A student in private school is 50 percent more likely to be able to read a word, and his or her reading comprehension score is three times as high as those of public school students. However, there is also some evidence that at least some of this superior performance is due to sorting of students: the non-verbal reasoning score in private schools is 13 percent higher than in public schools.

# **10. Discussion and Conclusion**

In this paper, we report on what primary school teachers in Africa know and do, using representative data from an ongoing survey program: The Service Delivery Indicators data. The findings provide a concerning picture of teacher effort, knowledge, and skill,
- D. _Sensitivity test: Contribution of structural transformation to the change in regional intersectoral productivity gaps when the pace of structural transformation differs across regions_

In deriving our results, we have assumed a constant pace of structural transformation across regions. If structural transformation reduces the disparity in regional intersectoral productivity gaps, it weakens our argument that the disparity in these gaps undermines national productivity. It becomes important then to examine how the disparity in regional intersectoral productivity gaps relates to the differing pace of structural transformation across regions.

We cannot directly observe the long-term pattern or pace of structural transformation at the region level using the IBES since it is a single cross-section. However, the IBES collected retrospective employment data going back a year. Specifically, the IBES gathered information on total persons engaged in an enterprise for four different dates: November 30, 2013; February 28, 2014; May 31, 2014; and August 31, 2014. We use these data to estimate short-term net nonfarm employment creation between November 2013 and August 2014, adopting the calculation method used by GSS (2016b).<sup>19</sup> Across regions in general, net employment creation has been dominated by services, and within services, by the informal sector (Figure 11a). At the national level, 70 percent of net nonfarm employment creation between 2013 and 2014 was in informal services. We view these patterns as being part of the long-term process of structural transformation occurring in Ghana, although we recognize that these patterns may be due to unrelated short-term labor market adjustments. The patterns of nonfarm employment creation, combined with the patterns in sectoral employment trends presented in Section II, indicate that the largest flow of workers has been from agriculture to informal services.

At the national level, net employment creation (as a percentage of the employment level in 2013) in formal and informal enterprises in industry were 3.3 and 7.1 percent, respectively (Figure 11b). Net employment creation in formal and informal enterprises in services was slightly higher, at
# Data and descriptive statistics

## Data

This paper uses data primarily from Better Work Vietnam’s Compliance Synthesis Report. The compliance data are collected during unannounced visits where monitoring teams of usually two people carry out an assessment of working conditions in participating factories. In Vietnam, the monitoring instrument contains several hundred questions designed to evaluate working conditions relative to national law and international standards. For each question, Better Work Vietnam then decides whether the factory is or is not compliant. Like other studies, this paper relies on Better Work Vietnam’s assessment of compliance and does not re‐evaluate their compliance decision (see, for example, Robertson 2017).

An initial compliance assessment is undertaken when a firm first joins Better Work Vietnam. As part of membership in the program, Better Work Vietnam supports the factory through advisory services during the cycle and monitors firm compliance in the cycle. Factory visits and assessments are subsequently undertaken once a year for every year (or cycle) the firm is in the Better Work Vietnam program. The data set thus contains compliance data of a firm over time. The number of assessments per firm varies, depending on when the firm jointed (or exited) the Better Work Vietnam’s program. Table 1 shows the factory counts by the maximum number of compliance assessments. A total of 1,508 factory assessments are available for 461 firms.

The data are available starting in 2010 through 2018. The number of factories for which compliance data is available for each year is given in Table 2 (including both new and revisited factories). Data for 2018 run only through June, which explains why the number of factories is smaller in 2018 than other years. Table 3 presents the factory count by evaluation cycle and shows that there are both firms entering and exiting the sample each year.

**Table 1: Number of factory assessments per factory**

|**Number of factory assessments**|**Number of firms**|
|---|--
trained on historical data may be vulnerable. Yet, we do not find it to be the case that using GDP growth to project forward is problematic for some growth rates, suggesting, possibly, that even during recessions such as the one caused due to the pandemic, predicting changes from the last survey might be preferable to predicting poverty rates directly. In sum, we do not find overwhelming evidence that predicting poverty rates directly could be more attractive when compared to simply shifting the mean with adjusted GDP growth rates.

# **4.3 Predictors of poverty**

Though the findings so far suggest that growth in real GDP per capita is the overof we can look at this more ana- whelming predictor poverty, systematically by lyzing the features that are important for the predictions. For the lasso, this can be analyzed by looking at the order in which variables enter. For forests and gradient boosting, it can be analyzed through feature importance measures. Feature importance measures indicate the importance of a particular feature for the accuracy of the predictions.

In Figure 7, we use the feature importance measures from the conditional inference random forests, as this method generally performed best across the machine learning algorithms (judged by its average rank in Figure 4). For a particular feature, the feature importance is calculated by permuting the feature in the dataset and calculating how much the prediction error increases. The importance measure is standardized such that the variable with the highest value gets a value of 1. The equivalent plots for the other methods look similar.

The only six features that are substantively predictive of growth in the mean are all national accounts variables. They are all identical to, or highly correlated with, growth in GDP. The most predictive variable is growth in final consumption expenditure, which is the sum of two components of GDP, government expenditure and HFCE. The second most predictive variables is growth in HFCE, followed by growth in GDP, GNI, and gross domestic income (GDP measured from the income side). The
enough information to create a targeting tool based on expenditures of households, the World Bank and UNCHR created a targeting algorithm that mapped directly to all individuals in the ProGres data (Verme et al. 2015). This algorithm revealed that UNHCR’s targeting efficiency could be improved markedly. As a result, this targeting model has been embedded into the UNHCR operational portal and used to rank refugee households for targeting cash assistance. For humanitarian organizations, this has been a paradigm shift that revolutionized targeted programs in North Africa and the Middle East and beyond, and the tool has been applied to displaced populations in Egypt, Lebanon by the UNCHR and the WFP.

Interoperability can also induce cost savings for the private sector, particularly data containing key national identifiers of companies, individuals, geographical units, and other entities, which allow for easy linking with the company’s own data. National ID systems, for example, can increase private sector efficiency through cost savings such as removing the need for companies to create their own ID systems, and increased revenues by expanding the potential customer base (World Bank 2020a and World Bank 2018c). In India, Aadhar, the unique 12-digit identity number that can be obtained by residents and citizens of India, has reduced the need for identity verification for firms. As a result, it is estimated that a firm’s typical onboarding costs for new employees could decrease from 1,500 rupees to 10 rupees – a reduction of 99.3% (World Bank 2020a and World Bank 2018c).

# **Impartiality**

When data are not impartial, their value falls apart due to potential misuse. Lack of data impartiality can seriously affect countries’ credibility and creditworthiness. For example, in 2007 the Argentine government began interfering with the consumer price index and other official statistics. This led to inaccurate and non-credible reported inflation figures (Todesca 2017). Similarly, Greece’s debt statistics appear to have deliberately misrepresented the country’s financial situation in the lead-up to the 2009-euro crisis
WPS3811

# **Nonfarm Activity and Rural Income Inequality: A Case Study of Two Provinces in China**

Nong Zhu (INRS-UCS, University of Québec)<sup>°</sup> Xubei Luo (DECVP, World Bank)<sup>°°</sup>

**_Abstract:_** Non-farm activity plays an increasingly important role in rural household income. Based on data from the Living Standards Measurement Study in the provinces of Hebei and Liaoning, we study the distribution of non-farm income in rural China. First, we assume non-farm income as an exogenous transfer to total income to decompose the Gini index; second, we assume non-farm income as a potential substitute for farm income to take household choices into account and simulate household income. The results show that non-farm activity reduces rural income inequality by raising the income of poor households to a larger extent than that of rich households. Improving rural infrastructure and implementing universal basic education are critical to build up the capacity of households (in particular poor households) to participate in non-farm activity. Strengthening the linkages between farm activity and non-farm activity is essential to optimize the contribution of non-farm activity to pro-poor rural economic development.

## **JEL Classification:** D63, O15, Q12

**Key words:** Non-farm income, Inequality, China

## World Bank Policy Research Working Paper 3811, January 2006

_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 of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

>
conservative measure. If interest coverage falls below 0.75, it is highly likely that the firm will experience financial distress. The threshold of 0.75 is used in Jones and Karasulu (2006) who argue that it provides a better mapping to the NPL ratios by banks. Among the 18 firms available in the Worldscope no firm has interest coverage below 0.75, while 2 firms have relatively low interest coverage between 1 and 1.5. Based on the interest coverage ratios firms in Jordan appear healthier or comparable to other countries.

Finally, Altman’s Z-score is an aggregate measure of vulnerability, which takes into account several key ratios used to predict corporate default in the US and other countries.<sup>3</sup> In Jordan the average Altman Z-score ratio for the 18 non-financial companies in Worldscope is the highest in the region, which means these are on average the healthiest firms relative to comparable countries.

Based on the Wordscope data the benchmarking exercise shows that firms in Jordan show relatively low vulnerability. The one point of concern and further investigation are high proportions of short-term debt.

However, this analysis is limited to 18 Jordanian firms available in Worldscope. The results are thus subject to small sample concerns. These 18 firms are the largest of the listed nonfinancial firms – they comprise about 60% of total assets of all non-financial firms listed on ASE. Therefore they are not very representative of the total population of firms listed on ASE (although from the systemic perspective these firms are the most important). Below we perform more in-depth analysis for all listed firms on ASE.

# **3. In-depth Analysis of Corporate Vulnerability in Jordan**

Data on public firms available from ASE (Amman Stock Exchange) allow for more in-depth analysis of corporate vulnerability across different sectors, firm size and over time.

> 3 Altman’s Z-score is given by the formula : Z-score=0.717*WC_TA + 0.847*RE_TA+ 3.107*ROA+ 0.
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. Results for wood processing and wood products (ISIC 2000), manufacturing of plastic products (ISIC 2520), structural metal products (ISIC 2811) and furniture making (ISIC 3610) are reported in Tables 9 and 11.

The first column of Table 8, underscores that unweighted estimates of the revenue productivity equation of section 3.3 on data on firms pooled across the sector would conceal effects that would be observed in relatively homogenous industries. Thus, per this column, productivity would respond to changes in local market size as the theory would predict but not to shifts in transport costs or to entry license fees. This contrasts sharply with what we see in Tables 3 to 7 21
12 urban areas. These are nonetheless still relatively small. For context, Malawi’s urbanization status is well below the Sub-Saharan African (SSA) overall level. In 2020, less than 6% of the population lived in cities with more than one million people, compared to 15% in SSA overall, and only 17% of Malawians lived in urban areas at all, compared to 41% in all of SSA (World Bank 2021). The consumer food price index computed with these price data is considered representative of rural Malawi. Food items selected for price monitoring were revised at the end of 2012 based on nationally representative household survey data collected in 2010 to include any item accounting for more than 0.02% of total household expenditure (Kaiyatsa, Schneider and Masters 2021). The list of food items whose prices are monitored includes foods from all food groups.

We match households to the market corresponding to their district of residence (or in a few cases of multiple markets per district, their sub-district market) (National Statistical Office (NSO) [Malawi] 2011; National Statistical Office (NSO) [Malawi] 2012; National Statistical Office (NSO) [Malawi] 2018). Our emphasis is on the market and its ability to provide access to nutritious diets, as a metric of food system performance. The prices observed in the market are for standardized items of a particular quality, so they are not directly comparable to unit costs reported by households. Unit costs reflect both quality and price, and therefore differences are not necessarily attributable to different price environments (Gibson 2016; Gibson and Kim 2018; Gibson 2013). The food price environment faced by households and their preferences are reflected in the numerator of the affordability analysis, namely the food and total expenditure calculated using reported unit costs and standard methodology to value own produced goods.<sup>2</sup>

> 2 For the purpose of context, Supplementary Tables G-1 (by size of difference) and G-2 (by food
|-1.564|-1.178|-0.860|
|Temperature|-1.072|-0.504|-2.423|-1.228|-1.583|0.819|
|Constant|66.63|72.85|232.1|171.2|142.5|57.96|
|Out-of-Sample _R_<sup>2</sup>|0.615|0.799|0.365|0.515|0.449|0.565|

**Note** : Lasso linear model with 5-fold cross-validation. Mass PM2.5 ( _μg/m_<sup>3</sup> ) refers to the mass concentration of fine particulates with a diameter of fewer than 2.5 microns. Count (particles/100ml) refers to the count of all particles greater than or equal to 2.5 micrometers in diameter. Data is aggregated hourly before calibration.

To calibrate the low-cost monitor data to the local regulatory-grade monitors, we use the model that combines the data from the two locations and use the PA PM2.5 measure as the main predictor. Table 1 displays the coefficients and out of sample R<sup>2</sup> for each calibration conducted at the hourly level.<sup>14</sup> We apply the following calibration equation corresponding to the fifth column of Table 1 to generate a local calibration: Local Calibration PM2 _._ 5 = (Unadjusted PM2 _._ 5 _×_ 1 _._ 59) _−_ (Humidity _×_ 1 _._ 18) _−_ (Temperature _×_ 1 _._ 58)+142 _._ 2 (3) In the absence of data from regulatory-grade monitors, satellite data may provide an alternative for calibrating the data from low-cost monitors. Therefore, we also calibrate the Purple Air data with the satellite data using a similar
,208|<br>37,208|<br>37,208|<br>37,208|
|R-squared|0.361|0.361|0.361|0.361|0.361|0.362|

_Note_ **_:_** Results are from 2019-2022 panel regressions where the unit of observation is the village council and the dependent variable is the NDVI for fields planted with winter wheat in each of the relevant years. Crimea and areas that have been occupied since 2014 are excluded. GDD is cumulative growing degree days above 0°C between October 1and May 31 approximated hourly by sinusoidal distribution of minimum and maximum temperature (d’Agostino and Schlenker, 2016); and fall, winter and spring months are Oct-Nov, Dec-Feb, and Mar-May, respectively. ‘Any’ (Col. 5) is the maximum over the 8 two-week periods considered whereas ‘conflict intensity’ (col 6) accounts for the temporal dimension by taking a value of 1 for any of the 8 two-week periods when any conflict activity was observed. See appendix table 1 for full specification including weather variables. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

31