rafmacalaba/data-use-mentions-extended huggingface dataset:
summarizes the results; details can be found in the Appendix. In all cases, the main conclusions remain. Crucially, they imply that increasing the degree of segregation of children on economic grounds will harm worse off students test scores while increase the attainment of richer pupils. In other words, the gap in performance would deteriorate. As mentioned before, the effect on the average test score is unclear. For this, I now turn to simulations.

> 16The original variable is expressed in terms of levels attained. I impute years of education using data from the Permanent Household Survey (October 2000). For incomplete level categories, I impute using the median value of the category, by province and gender.

25
While the data may not be the most reliable, the Figures 14 (a) and (b) show that there has been some catch-up in total R&D spending and in the number of patent applications in the new BRIC growth poles. Particularly noteworthy is that China has more than doubled its total R&D spending as a share of GDP. While patent applications lag the high income countries, the ratio has fallen substantially from over 40 HIC patent applications for every BRIC resident application to only about 5 HIC applications for every BRIC application.

# **Figures 14(a) and (b):  R&D and patent applications**

<!-- Start of picture text --> (b) Patent Applications by Residents<br>(a) R&D Spending, % of GDP<br>3 900000 50<br>800000 45<br>2.5<br>40<br>700000<br>HICs<br>2 HICs 600000 (Number) 35<br>30<br>1.5 China 500000 BRICs<br>(Number) 25<br>Russia 400000 HIC to BRIC<br>1 20<br>Brazil 300000 Ratio (RHS)<br>15<br>0.5 India 200000<br>10<br>0 100000 5<br>0 0<br>1991 1993 1995 1997 1999 2001 2003 2005<br><!-- End of picture text --> Source: World Development Indicators The World Bank has developed a multidimensional measure of multi-polarity that starts with a country‘s contribution to economic growth and is then adjusted to take into consideration the country‘s spillover effects via trade, finance and technology linkages. The multidimensional polarity index places six developing countries among the top fifteen in the world over the period 2004-2008.<sup>17</sup> The index also documents the changing role of the old growth poles of Europe, the United States
estimation sample for intergenerational mobility (18-35 year old children in 2012). The average schooling is 10 years for sons and 9.36 for daughters. In the rural sample, the average education of fathers is much lower; 3.90 years (sons sub-sample) and 3.83 (daughters subsample). The gender gap in average schooling is also much more pronounced in the rural areas; 8.13 years (sons) vs. 6.24 years (daughters). The average educational expenditure in IHDS-2005 is higher for sons and it is true irrespective of geographic location and for both the estimation samples (6-22 year old) and (8-23 year old). The bias in educational expenditure against girls is also observed in the NSS-1995 data.

# **(5) Empirical Results**

The empirical estimates of the mobility and investment equations discussed below report robust standard errors. However, we also estimate the standard errors clustering at different levels: district and state levels for India, and county (only for investment) and province levels for China. We cannot cluster the standard errors at the county level for the mobility estimates for China, as the county location is not available for the non-resident members of a household. The estimated clustered standard errors are reported in the online appendix. All the main conclusions based on the robust standard errors, however, remain intact when we use clustered standard errors instead. If and when clustering makes a difference in inference, we will note that in the following discussion.

# **(5.I) Intergenerational Mobility in India: Evidence and Interpretations (5.I.1) Urban India**

The estimates of the effects of father’s schooling on children’s schooling in urban India are reported in the upper panel of Table T2.IU.<sup>20</sup> The first two columns contain the evidence from a linear CEF which provides estimates comparable to the existing literature where linearity is a maintained assumption. The IGRC estimate is
improve this situation. Statistics about statistics are vital if improvements to statistical systems are to be made. This concern applies across the range of administrative data sources. In the area of geospatial data, there is an opportunity for the Global Geospatial Information Management (GGIM) community to have a significant beneficial impact.

Third, data about statistical infrastructure is patchy. In particular it will be useful to collect further data on the financing of statistical systems. One of the Sustainable Development Goal indicators (17.18.3) relates to whether the national statistics plan is fully funded but there remains incomplete data on this. Financing is also a complex issue since the costs that are associated with producing high-quality and more frequent data vary across countries and are likely to reflect, at least to some extent, country statistical capacity. For instance, a study suggests that the average cost of implementing a recent household consumption survey (in 2014 or later) ranges from approximately US$800,000 to US$5 million, depending on the context and sample sizes (Kilic _et al._ , 2017). Countries with a better data infrastructure (and higher levels of data skills) may possibly offer lower costs regarding data collection.<sup>18</sup> Related to this, certain countries heavily rely on donor funds and technical assistance to implement data collection activities (e.g., household surveys). Everything else equal, these countries likely have lower statistical capacity than countries that implement surveys on their own. As such, it is useful to investigate further how each country produces its data and whether some aid-dependent countries, in a counterfactual scenario, can continue their statistical activities without interruptions in the absence of donors. Such scenarios are not far-fetched. Donors such as the Swedish Aid Agency (SIDA) or the United Kingdom Foreign, Commonwealth and

> 18 On a related note, further research into the different strengths of correlation between the five data pillar scores can be useful. For example, does a stronger correlation of
Adjusting the distribution of regional output wedges offers a way to evaluate the hypothetical effects of changes in the distribution of regional intersectoral productivity gaps on national labor productivity. Note however that our model independently maximizes output in each region. Thus, the model does not itself provide a unique mechanism to adjust the distribution of regional output wedges. The same level of disparity in regional intersectoral productivity gaps and the consequent level of national labor productivity can arise from different distributions of regional output wedge sizes. Moreover, equalizing regional output wedges at different wedge sizes can produce different levels of national labor productivity.

B. _Effect of the disparity in regional output wedges on national labor productivity_ We calculate regional output wedges using Equation 18. Actual regional output wedges are calibrated based on sectoral labor productivities and labor allocation in a region, and the base model parameter values.

Using the calculated values of regional output wedges, the ratio of the productivity level in the informal sector relative to that in the formal sector, averaged across the regions, is 0.19.<sup>17</sup> This figure is smaller than the corresponding average of 0.26 estimated solely using the IBES. The correlation between the actual ratio and that obtained from applying the calculated values of regional output wedges is .92. Additionally, the correlation between regional output wedges and regional intersectoral productivity gaps is .81, and the correlation between regional output wedges and regional productivity losses due to the intersectoral misallocation of labor is .93. All the correlations are statistically significant at the 1 percent level. These patterns provide support for the use of regional output wedges as a proxy for regional intersectoral productivity gaps to assess the effect of hypothetical adjustments in the disparity of regional intersectoral productivity gaps on national productivity.

To assess the effect of the distribution of regional output wedges on national labor productivity, we perform a series of simulations. Table 1 presents the results regarding the effects on national nonfarm labor productivity from three types of simulations: uniform percentage changes in actual regional output wedges in all
sirable. This is because anticipation of high taxes induces agents to exert low effort; as the result of this, the bulk of income heterogeneity in the distribution is due to luck, which in turn makes it optimal ex post to redistribute.

The 2006 World Development Report on _Equity and Development_ provides a rich account of how political, social, and economic inequalities combine to stifle social mobility and create inequality traps (World Bank, 2005). Furthermore, empirical evidence from around the world suggests that an important part of overall inequalities in outcomes, commonly measures in the space of incomes or household expenditures, is, in fact, due to inequality of opportunity, or inequality due to differences in circumstances for which an individual would not normally be held responsible, and thus inequality that is objectionable on normative grounds (Bourguignon et al., 2003; Roemer et al., 2003; Ooghe et al., 2007; Checchi and Peragine, 2010; Cogneau and , Mesple-Somps; Ferreira and Gignoux, 2008; Ferreira et al., 2008, 2010; Lefranc et al., 2008, Lefranc et al., 2009).

This paper aims to contribute to this literature, but its focus is somewhat different – it examines the link between _perceived_ inequality of opportunity and _perceived_ future mobility. I am not aware of other studies that examine directly the links between subjective perceptions of inequality and subjective expectations of mobility. Clearly, actual and perceived inequalities are correlated, although not perfectly. Figure 1 plots perceived inequality of opportunity against available estimates of relative inequality of opportunity from the Equalchances.org database. Likewise, actual and perceived mobility are also correlated, even if imperfectly, as theoretical models have demonstrated. This is also consistent with empirical evidence. Earlier studies have found actual occupational mobility to be an important predictor of perceived occupational mobility (Kelley and Kelley, 2009), and parental social class to be correlated with occupational aspirations of adolescents and with parental aspirations for their children (Schoon and Parsons, 2002; Guyon an Huillery
Cost recovery|% total cost|84.4|**100.0**|100.0|
|Total hidden costs|% of revenue|68.8|**595.3**|442.5|
|U.S. cents|**DRC**|Predomi<br>hydrogen|nantly<br>eration<br>Other develop|ing regions|
|Power tariff (residential at 75 kWh)|**4.0**|10.27|5.0–10.0||
|Power tariff (commercial at 900 kWh)|**11.0**|11.73|||
|Power tariff (industrial at 50,000 kWh)|**14.6**|11.39|||

_Source:_ Eberhard and others 2009, derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.** — = data not available.

9
# **TABLE 1: COMPARISON OF ESTIMATES FOR QUARTERLY ENEU DATA**

# **PANEL A: LOG SPECIFICATION**

Dependent Variable: Log Real Household Income

||(1)<br>OLS|(2)<br>OLS|(3)<br>IV|(4)<br>A-IV|(5)<br>Pseudo<br>Panel|(6)<br>Pseudo<br>Panel|
|---|---|---|---|---|---|---|
|Quarterly Lag of Household Income|0.668|0.598|0.990|-0.062|0.988|0.832|
|T-statistic|955.85|791.23|213.59|-2.36|159.14|45.25|
|[95% confidence interval]|[.666,.669]|[.597,.600]|[.981,.999]|[-.114,-.011]|[.976,1.000]|[.796,.868]|
|P-value|0.00|0.00|0.00|0.02|0.00|0.00|
|Cohort Effects|No|Yes|No|---|No|Yes|
|Household-quarter observations:|1113172|1112464|165275|757561|---|---|
|Cohort-quarter observations:|---|---|---|---|842|842|
|Adjusted R squared:|0.4508|0.4731|||0.9679|0.9703|

# **PANEL B: LEVELS SPECIFICATION**

Dependent Variable: Real Household Income

||(1)<br>OLS|(2)<br
- Kaboski, J. P. & Townsend, R. M. (2012). The impact of credit on village economies. _American Economic Journal: Applied Economics_ , 4(2), 98–133.

- Karlan, D., Osei, R., Osei-Akoto, I., & Udry, C. (2014). Agricultural decisions after relaxing credit and risk constraints. _The Quarterly Journal of Economics_ , 129(2), 597–652.

- Karlan, D. & Zinman, J. (2009). Observing unobservables: Identifying information asymmetries with a consumer credit field experiment. _Econometrica_ , 77(6), 1993–2008.

- Karlan, D. & Zinman, J. (2011). Microcredit in theory and practice: Using randomized credit scoring for impact evaluation. _Science_ , 332(6035), 1278–1284.

- Karlan, D. & Zinman, J. (2018). Price and control elasticities of demand for savings. _Journal of Development Economics_ , 130, 145–159.

- Karlan, D. & Zinman, J. (2019). Long-run price elasticities of demand for credit: evidence from a countrywide field experiment in mexico. _The Review of Economic Studies_ , 86(4), 1704–1746.

- Karlan, D. S. & Zinman, J. (2008). Credit elasticities in less-developed economies: Implications for microfinance. _American Economic Review_ , 98(3), 1040–68.

- Keen, M. (1990). Welfare analysis and intertemporal substitution. _Journal of Public Economics_ , 42(1), 47–66.

- Khandker, S. R. (2005). Microfinance and poverty: Evidence using panel data from bangladesh. _The world bank economic review_ , 19(2), 263–286.

- Lagakos, D., Mobarak, A. M., & Waugh, M. E. (2018). The welfare effects of encouraging rural-urban migration
considered in a wider range of data collection. It also hints that there are some glitches when translating the experience of research guidelines in clinical environments into field work undertaken by economic and other social sciences.<sup>1</sup> We group these issues into three broad categories, cautioning the reader that more often than not, we pose questions but have relatively little to say definitive about potential solutions.

Our first category considers the objectives of balancing transparency and confidentiality. Researchers familiar with the act of conducting a survey in a remote village are aware that there is seldom a single “respondent”. Even in the confines of his or her home, the survey respondent typically faces the entire family, with neighbors “dropping in” and a curious set of faces looking in from the window. How to maintain confidentiality in these settings, and what confidentiality _means_ in a context where certain information is public—and therefore “OK” to share—but other information is not (and the researchers seldom knows which is which) is an ethically complicated question. The ethical ambiguity is heightened in cases where such confidential information implicates certain members of the family in behaviors that, at least in the eyes of the surveyor, fall outside the moral norms of society. We present a specific case study—surveys on domestic abuse— that takes on this issue.

The second category relates to trust between the surveyor and the surveyed and again goes to the issue of informed consent, and the sharing of information from the survey with the surveyed population. There are three fundamental issues that we address in this context. First, surveys are never isolated from the social history of the surveyed population. For instance, _any_ survey on vaccinations among certain populations in India will have to tackle the surveyed populations’ perceptions, and in particular, whether they believe that the survey will lead to

> 1 Stark (2012), however, reports that the question of obtaining signatures of informed consent – not of consent _per se_ – was also controversial
6 Figure 2: Macroeconomic channels of transmission from global food prices

# (A) Share of agriculture in economy

<!-- Start of picture text --> Percent of total<br>70 Value added<br>Employment<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EMDEs LICs<br><!-- End of picture text -->

# (B) Net food imports and exports

<!-- Start of picture text --> Percent Share of countries Percent<br>Share in consumption (RHS)<br>80 8<br>60 6<br>40 4<br>20 2<br>0 0<br>EMDEs LICs EMDEs LICs<br><!-- End of picture text -->

- (C) Inflation in LICs

<!-- Start of picture text --> Percent Food Energy Other<br>16<br>12<br>8<br>4<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

- (E) Terms of trade in LICs

<!-- Start of picture text --> Index, 100=2000 Index, 100=2010<br>130 Terms of trade 150<br>Food prices (RHS)<br>120 120<br>110 90<br>100 60<br>90 30<br>80 0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# (D) Contribution of food prices to inflation

<!-- Start of picture text --> Percent 2007-08 2010-11<br>70<br>60<br>50<br>40<br>30<br>
**Table 5: Share of population with data on the dimensions of vulnerability in 2019 (%)**

||Income|Education|Social<br>protection|Financial<br>inclusion|Water|Electricity|
|---|---|---|---|---|---|---|
|_Region_|||||||
|East Asia and the Pacific|98|97|97|97|30|30|
|Europe and Central Asia|100|89|81|89|88|87|
|Latin America and the Caribbean|97|90|92|90|90|87|
|Middle East and North Africa|97|58|58|81|58|58|
|Other High-Income economies|93|30|0|30|30|30|
|South Asia|97|96|96|97|22|96|
|Sub-Saharan Africa|97|82|84|85|82|82|
|_Income group_|||||||
|High|93|36|5|36|36|34|
|Upper-middle|100|95|97|98|39|39|
|Lower-middle|98|93|92|94|51|93|
|Low|87|62|60|71|62|62|
|_FCV_|92|63|70|75|63|63|
|_World_|97|46|60|84|46|45|
|_World (incl. data older than 5 years and_<br>_adding in datafrom WDI)_|97|82|78|84|66|83|

Source: Authors’ compilation from the GMD, ASPIRE and Findex databases.

Notes: Income refers to having less than 1.5*$2.15 per household member, education refers to no adult having primary schooling, social protection
The rest of the paper is organized in the following sections: Section 2 describes the data, Section 3 reports on the international comparison of key financial ratios, Section 4 provides the analysis of financial ratios by year, Section 5 presents industry composition and trend in sales growth by industry, Section 6 presents analysis of financial ratios by firm size, Section 7 presents analysis of key ratios by leverage and size, Section 8 reports the results of regression analysis of the leverage ratios, Section 9 discusses capital market liquidity and concentration and Section 10 presents summary of results.

# **2. Data**

The data were obtained from the Capital Market Authority (CMA) and include market price and shares traded for about 1100 firms and financial information on approximately 900 firms. The data on market prices and volumes are available for 1997– 2001 and the data on financial statements cover 1995–2001. Because year 2001 is very underrepresented and biased toward the large firms, it is excluded from most of the analysis.<sup>3</sup> An important adjustment has been made to reported accounts in order to carry out the analysis. This is required because most firms continue to report fixed assets (PPE—property plant and equipment) at their gross values without deduction of any allowance for depreciation. (This is the case even though Egyptian accounting standards have been changed to comply with IAS, which requires reporting fixed assets at their net value, i.e., subtracting accumulated depreciation from gross assets). This creates a distorted picture of the true value of the assets: as fixed assets age their replacement value declines and therefore the “economic” value of the total assets should decline. By recording the PPE at gross value, the book value of the total assets might be constant (or even increasing) even if the true (economic) value were declining. To adjust for this the team estimated the net value of fixed assets by subtracting accumulated depreciation, which will also allow easy comparison with similar ratios
public utility networks. In many developing countries, the problem of deficient electricity supply is further exacerbated by financing constraints that prevent smoothing these fixed costs (Steinbuks, 2012). Electrification can lead to higher or lower operating costs. On the one hand, a lower need for supporting equipment like self-generating electricity machines will contribute to reduce them (Foster and Steinbuks, 2009, Steinbuks and Foster, 2010). On the other, improvements in electricity access are conducive to the technological progress that generates increasing returns, such as e.g., adoption of electric irrigation pumps in India (Rud, 2012), which contributes to raise those costs.

The quantitative analysis in our study is based on Brazilian data for the period 1970-2007, although the model simulations cover a slightly shorter time interval, 1970-2006, because we lose one observation when forming predictions. These data indicate that our analytical framework is well suited to examining the influence of electrification on structural transformation in Brazil. Unlike many developing countries, Brazil offers comparable data across sectors on gross value added, employment levels, and size and number of firms. The advances in electrification and the interconnection process in Brazil started as early as the 1960s, when high voltage transmission lines connected the Southeast states of São Paulo, Rio de Janeiro and Minas Gerais. The process accompanied the rapid pace of industrialization observed during the 1960s and 1970s, which required significant infrastructure investments. During 1970-2006, the value added shares of agriculture and manufacturing declined and that of services increased. As expected, these patterns are replicated by the sectoral employment shares. As regards firm size, the manufacturing sector has, on average, the largest firms, followed by the services sector, and the agriculture sector that has the smallest firms. The average establishment size remains relatively stable in the agriculture and services sectors and exhibits cyclical behavior in the manufacturing sector.<sup>4</sup>

> 4The evidence on the relationship between a country’s income per capita level and average firm size is not conclusive.