rafmacalaba/data-use-mentions-extended huggingface dataset:
transition probabilities for Egypt, also find that most workers who enter the public sector come from unemployment. For workers with a tertiary education, a significant proportion of public sector workers also come from formal self‐employment, most likely as professionals. For more educated workers public sector employment is very stable— over 98% of those with a tertiary education in the public sector were in the public sector 3 years before. On the other hand, there is much less stability in the public sector for less educated workers.

In summary, the transition data from developing countries suggest that since less‐skilled public sector employees who leave the public sector tend to go to the informal sector, their appropriate comparison includes informal sector employment. On the other hand, since workers with a tertiary education do not tend to move into the informal sector when they lose their jobs, but often move back and forth between the private formal sector and the public sector, the appropriate comparison for them is the private formal sector. In the discussion below we focus on developing countries and present evidence for both comparators (all private employees and only private formal employees).

These patterns of public sector employment transitions also suggest that the estimates of public sector earnings differentials should differentiate between less‐skilled and more‐skilled workers. When we estimate earnings differentials by education level and compare to private formal employees, we find that the relationship between education level and the public sector earnings premium is not monotonic. The average premium across countries is 7.8 percent for employees with a primary education, 11.6 percent for employees with a secondary education, while employees with a post‐ secondary education pay an average penalty of 3.4 percent. Figure 9 shows that for most countries, public earnings premiums are higher for employees with secondary education than for workers with post‐secondary education. Compared to private formal sector workers, secondary school graduates working in the public sector earn statistically significant premiums in 54 percent of countries (and penalties in only 10 percent),
, when many structural reforms were implemented. We estimate that TFP grew by 3.5 percent a year in the first decade (1979-1988) (Figure 1). Annual TFP growth averaged 3.1 percent in 1979–2008, generating 40 percent of growth in output per worker.<sup>7</sup> Bosworth and

> 6 We use China’s official national accounts. PWT 9.1 adjusts China’s GDP growth down by an average of 2.1 percentage points in 1953–2017 (with a standard deviation of 4.4) compared with NBS data, arguing that “official statistics systematically overstate growth” (Feenstra et al. 2013 and 2015). In the PWT, average TFP growth in 1979-2008 was 1.6 percent and TFP contributed about 30 percent to growth in output per worker. Although the PWT output and TFP growth rates for China are on average lower than ours, the two sets of variables are highly correlated (correlation of 0.78 for output growth and 0.70 for TFP growth) and tell a similar story over time.

The quality of China’s official statistics has received a lot of attention in the literature (see, for example, Adams and Chen, 1996; Holz, 2014; Maddison and Wu, 2008; Perkins and Rawski, 2008; Rawski, 2001). Four recent papers explore this issue after 2007. Clark et al. (2018) and Hu and Yao (2019) use satellite–recorded nighttime lights to estimate GDP. The former conclude that growth in recent years has been underreported in official statistics, while the latter find that official GDP has been systematically overestimated. Re-estimating GDP using value added tax revenues, Chen et al. (2019) find that official statistics overstated growth by 1.7 percentage points in 2008-2016. In contrast, linking trading-partner data on exports to China and China’s GDP, Fernald et al. (2019) conclude that official statistics have become generally more reliable over
that responding is positively correlated with incomes in the US CPS and that correcting for this raises the mean income in the top percentile by around 40%. The authors note that only regional level response rates are required to implement the method.

The Korinek et al. (2006) method has subsequently been used in several papers to correct for selective compliance on income. One of these is Hlasny (2020), who used 66 Luxembourg Income Study surveys for 38 upper- and middle-income countries and collected regional unit non-response rates to implement the Korinek et al. (2006) corrections. He finds extremely large effects across the countries for some statistics- the mean top 1% share of income is 6.5% across the 66 surveys but the Korinek et al. (2006) non-response adjustment increases this to 17% (Hlasny (2020), appendix A6). There are also several extreme or even implausible cases- the Italian top 1% share rises from 6% to 44% in the 2008 survey after the unit non-response corrections but only from 5% to 16% in the 2010 survey.

# Imputation for item non-response

Like in the case for unit non-response, it is possible to ignore item non-response by using only the data from responders. To the extent that item non-response is ignorable this is reasonable. It would still mean the sample size is reduced, however, which matters for the variance of any estimates and can worsen sparsity problems.

But the missing data is very unlikely to be ignorable and so other solutions are required. The most common solution is imputation. This involves using responses to other questions in the survey and/or external data to predict earnings for those who did not respond to the question on income. A common method for incomes is hotdeck imputation, in which the income of a person or household with the same set of specified characteristics is “donated” to the individual with missing income (Lohr, 2009).

The problem with single imputation methods, including hotdeck imputation, is that
the magnitude of the gender gap in Saudi Arabia is among the largest in the world, significant differences in achievement between boys and girls are also observed in many other countries (Mullis et al. 2017; 2020). A large body of research has explored the factors contributing to the gender gap in student performance internationally (Autor et al. 2016; 2019; Bertrand and Pan 2013; Buchmann and DiPrete 2006; DiPrete and Buchmann 2013; DiPrete and Jennings 2012; Fortin, Oreopoulos, and Phipps 2015; Jha and Pouezevara 2016; Legewie and DiPrete 2012; OECD 2021). Evidence from this research suggests that social norms, school characteristics, students’ social and behavioral skills, and family background are the main factors associated with the achievement gap between boys and girls.

Prior research indicates that, compared to boys, girls tend to have better noncognitive skills, such as self-regulation and persistence, and spend more time doing assignments and homework (Buchmann and DiPrete 2006; Cornwell, Mustard, and Van Parys 2013; DiPrete and Jennings 2012; Downey and Vogt Yuan 2005; OECD 2021). Other studies found that school characteristics such as school quality, disciplinary practices, and school climate (i.e., the institutional norms, practices, structures, values, and relationships underpinning a student’s experience of school) can affect boys and girls differently. An important study combined birth records with school administrative data from the US state of Florida to identify the effects of school quality (with school-level gains in mathematics and reading scores used to indicate quality) on the gender achievement gap between opposite-gender siblings who attend the same sets of schools (Autor et al. 2016). This study found that boys benefitted more than girls from studying in higher quality schools. Similarly, another recent study (OECD 2021) based on data from two large-scale international assessments showed that school discipline problems affect boys more negatively than girls. Combining data from the Teaching and Learning International Survey (TALIS) 2018 and PISA 2018, the study found
towards larger companies may be problematic if one were studying the effect of the Asian financial crises on the corporate sector. It does not pose a problem here, since we focus on the years preceding the crisis, when (as critics argue) large companies were at the root of the corporate and financial sector difficulties.

Whenever possible, we have compared the main variables of interest with those reported in other studies, in particular Demirguc-Kunt and Maksimovic (1995), Glen et al. (1998), and Goldman Sachs (1998).2 We also cross-checked the data for Japan with the _Comparative Economic and Financial Statistic for Japan and other Major Countries,_ published by the Bank of Japan and the OECD Financial Statistics Part 3, _Financial Statements of Non-Financial Enterprises._ The similarity in calculations-large companies are also used there-provides some comfort in the robustness of our results.

> **2** Pomerleano (1998) also analyzes East Asian corporations. He uses alternative measures of performance and leverage that are not easily comparable with the statistics in this study.

3
after Peru had received a low-interest rate IMF-loan (Hudson, 1993: 174), while Robert Mugabe’s behavior first deteriorated during the 1980s - a decade in which his government was well-funded thanks to large aid inflows.<sup>29</sup> These aid inflows were meant to reward Mugabe for his benevolent behavior. Paradoxically, our model however implies that through this reward-system, Mugabe’s promising start may actually have contributed to his subsequent derailment (also see Meredith (2009: 81) who recounts how Mugabe abused aid donations for purposes of repression).

At the same time, Daniel arap Moi’s rule of Kenya became less repressive after the US reduced their aid flows to Moi’s regime once it had become of less strategic interest following the collapse of the USSR.<sup>30</sup> This suggests that policies aimed at limiting the amount of fungible resources available to a non-accountable ruler can be useful. The reason is

> 29From 1979 to 1984, foreign aid to Zimbabwe increased by a factor 32 (measured in 2011 dollars, it went from 26 to 849 million dollars according to World Bank data). Over the same five year period, aid to neighboring and similarly-populated Angola only doubled (it went from 120 to 285 million dollars).

> 30According to World Bank data, aid to Kenya fell from 2 billion dollars in 1989 to 442 million in 1999 (all measured in 2011 dollars) - a reduction of more than 75 percent.

26
- 11 -

# II. Privatization in LDCs

It is in the past six years that privatization has been widely debated and acted upon in the developing world. This is not to deny that in the 1970s, a few countries (Chile, Bangladesh and Pakistan, for example) had resorted to privatLzation or reprivatization because they were in effect returning nationalized enterprises back to the private sector. Their privatization programs have continued into the eighties too. The focus of this section, however, is on the post 1980 period when a much larger number of LDCs, facing severe economic crises, designed policies and programs for privatization. A recent survey of such programs in 28 LDCs shows that of their 3,975 public enterprises, 35 were liquidated, 102 were closed, 85 were sold (fully or partially) and 45 were leased or their management contracted out.<sup>19</sup> The value of assets of these enterprises is not known. It is therefore difficult to pass any judgment on the significance of the divestiture that has occurred. But as a percentage of the total number of public enterprises in these countries, the number of public enterprises sold is only two percent, the number leased or contracted out is about one percent and the number closed or liquidated is 3.4 percent. There is some evidence that the divested enterprises in many countries vere relatively small enterprises. In Brazil, for example, of the 17 public enterprises sold, only one had employment exceeding 1,000 workers. On the other hand, even a small country such as Jamaica had sold a much larger number of public enterprises (33 jute mills and 26 cotton 19/ Elliot Berg and Mary Shirley, Divestiture in DeveloDing Countries, World Bank Discussion Paper No. 11, 1987, pp. 25-30.
all national statistical offices have already implemented the new definition of employment and continue to classify ‘persons exclusively engaged in own‐use production’ as employed. One reason is that distinguishing agricultural households that _mainly produce for own final use_ from agricultural household that _mainly produce for the market_ is difficult (Global Strategy, 2018). This requires at least one additional question in household surveys about the intended destination of the produced goods (Benes & Walsh, 2018d).

The resolutions of the 19<sup>th</sup> ICLS do not affect the definition of unemployment. Person are unemployed if they i) are not currently employed; ii) are seeking employment (desire to work for pay or profit) or to start a business; and iii) are available to start a new job in a specified time period (19<sup>th</sup> ICLS, §47). This requires at least two additional questions: one evaluating whether the person was actively seeking a job (during the last four weeks), often operationalized by asking about job search strategies, and a second question evaluating whether the person is available to work (in the next two weeks). These questions should also be asked to subsistence farmers since they are not considered employed but may be looking for employment. While keeping the definition of unemployment unaltered, the 19<sup>th</sup> ICLS complemented the unemployment rate<sup>8</sup> , the traditional indicator of labor slack, with the time‐ related underemployment indicators that measures the desire and availability to work additional hours (19<sup>th</sup> ICLS, §40). These new indicators address the growing demand for

> regard to own‐use producers. 40% of the national statistical offices include at least some own‐use producers in the definition of employment. This approach is particularly prevalent for persons producing agricultural products for own use, whereas only a few statistical offices include persons constructing their own dwelling
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data where i 1 i S denote the detection probabilities for sales, raw materials, and labor costs respectively and is the subjective expectation of tax office efficiency. For given tax office efficiency ( ), the detection probabilities increase with the relative gap between the true values and the reported values to tax office. We further assume that once a firm is chosen for tax auditing, any tax evasion will be detected and the true sales/raw materials/labor costs of the firm will be fully revealed.<sup>18</sup> Therefore, a firm maximizes the expected self-retained profit over the choices of reported sales, raw materials and labor costs, taking true sales/raw materials/labor costs as given. A firm’s expected retained profit can be written as: t r t t t t t maxS<sup>ESv</sup> v S 1 S v v P SS<sup>t</sup> S tS t v v `(8)` where denotes firm’s retained profit, and the corporate profit tax rate and payroll tax rate respectively<sup>19</sup> ; and P is the penalty multiplier. Plugging in the three audit probabilities into equation (8), we obtain three first-order conditions with respect to S<sup>ttt</sup> : 18 We can also assume that only part of the underreporting will be detected but this does not change the main implications of the model.

19 In 2003, Mongolia there were two profit tax rate levels for registered firms: 15% if the taxable income is below 100 million MNT and 40% above. We calculated the gross taxable incomes using tax office data and no firms have taxable incomes above the threshold of 100 million MNT. The calculated taxable incomes could be even lower if there are other tax deductible costs not included in the calculation. Hence assuming a single profit tax rate is reasonable. Also the payroll
for constructing consumption aggregates for welfare analysis._ 2000, World Bank: Washington DC.

23. Hentschel, J. and P. Lanjouw, _Constructing an indicator of consumption for the analysis of poverty. Principles and illustrations with reference to Ecuador._ 1996, World Bank: Washington DC.

24. Buhmann, B., L. Rainwater, G. Schmaus, and T. Smeeding, Equivalence scales, wellbeing, inequality and poverty. _Review of Income and Wealth,_ 1988. **34:** p. 115-142.

25. de Onis, M., C. Monteiro, J. Akre, and G. Clugston, The worldwide magnitude of proteinenergy malnutrition: An overview from the WHO global database on child growth _Bulletin of the World Health Organization,_ 1993. 71(6): p. 703-712.

26. World_Health_Organization, _WHO Global Database on Child Growth and Malnutrition._ 1997, World Health Organization: Geneva.

27. Contoyannis, P. and P. Forster, The distribution of health: a theoretical framework. _Hournal of Health Economics,_ 1999. **18:** p. 605-622.

28. Wagstaff, A., _If the health of the poor matters more: Child survival inequalities in nine developing countries._ 2000.
Congo, Ethiopia, Kenya, Mali, Mozambique, and Nigeria). The Democratic Republic of Congo is an extreme case, as it uses an 11-block IBT. Interestingly enough, despite the large number of blocks, the price difference across blocks is very small, suggesting that the regime is complicated without really discriminating very much between large and small consumers.

<!-- Start of picture text --> Figure 2. Characterization of residential block pricing<br>a. Number of pricing blocks b. Tariff price differential between first and highest<br>block (%)<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>one (linear two three four or more 0% < 100% 100-200 200% ><br>rate)<br>number of blocks<br>number of countries number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database The conventional wisdom is that IBTs are designed so that the first and smallest “lifeline” block covering subsistence consumption is subsidized to promote equity, while the subsequent blocks are priced at a higher level that will ultimately enable cost recovery. This of course assumes that poorer customers will have lower consumption. This assumption is more reasonable in the case of power – where usage is driven by ownership of appliances more prevalent among wealthier households and businesses – than it is for example in the water sector – where usage is more correlated with the size of the household.

The first question is whether African countries tend to define the consumption level of the first block at a level low enough to be consistent with subsistence consumption and hence with the “lifeline” principle underlying this tariff design (table 2). The response to this question is quite positive. Two-thirds of the sample countries define the first block at 50 kilowatt-hours (kWh)
# **IV. Final remarks**

This paper assesses the predictive capacity of specific microsimulation models using a minimum set of perfect macroeconomic input data and under different assumptions on how family income is estimated. Using actual input data allows for isolating the analysis from any possible bias from macroeconomic inputs, so the predictive capacity of the model variations relies only on the changes in the assumptions made in the proposed microsimulation methodology. The study makes two contributions: first, it extends previous microsimulation methodologies by accounting for labor income movements within each economic sector given by informality, and second, it tests three versions of the model (i.e., the old model, the new model with rescaling, and the new model without rescaling) to identify the one that fits better the actual income distribution. The _old model_ corresponds to the first option in which labor income varies only at the macroeconomic sectoral and total output growth rates; the _new model with rescaling_ is the second, which incorporates within-sector variation in labor earnings using the formal (informal) changes in average labor income in the main activity, provided by the labor income-pseudo-labor productivity elasticities for each employment sector, and rescales for sectoral and total output growth. The last option is the _new model without rescaling_ , which only considers changes in average income earnings given by changes in average labor earnings by employment sector. The proposed alternatives are tested using the SEDLAC harmonized household data for 2016 or the closest year to predict poverty, inequality, and the GICs in the short (2017 – 2018) and medium run (2019 – 2020) for 15 LAC countries.

Overall, results suggest that incorporating more flexibility in the labor income estimation by adding labor market attributes such as informality produces a smaller bias across time and a better estimation of the changes along the income distribution. For poverty, results indicate that the three model variations generally have outstanding performances in the short run, irrespective of the country's poverty level. Hence, in the short run, these alternative models do not differ
however, it was learned that there was in fact considerable variation in the program’s impact. The average of this variation may have been close to zero, but for certain groups the program had worked quite well, for others it had had no impact, while for still others it had been detrimental. Who were these different groups, and what was it about them that led to such variable outcomes? A companion qualitative process evaluation<sup>17</sup> was able to discern that the key difference was the quality of implementation received by different groups, the level of support provided to them by managers and political leaders, and variations in the nature and extent of local-level inequalities (which in turn shaped which groups were able to participate, and on what terms). The administrative rules and implementation guidelines provided to all groups were identical, but in this case a qualitative process evaluation was able to document the ways and places in which variable fidelity to them yielded widely different outcomes (albeit with no net impact). Moreover, the qualitive data was able to discern subtle positive effects from the program that reliance on the quantitative survey instrument alone would have missed.

# 2. Measurement becomes conflated with management

<mark>An extension of the above point is that undue emphasis on quantitative data can lead to measurement becoming a</mark> _<mark>substitute for</mark>_ <mark>rather than a</mark> _<mark>complement to</mark>_ <mark>management. This is evident when only that which is quantifiable receives any significant form of managerial attention, an outcome made possible when the easily quantifiable becomes the measure of success, in turn becoming the object of management’s focus, typically to the exclusion of all else. As Wilson (1989: 161) famously intoned in this classic study of bureaucratic life, “[w]ork that produces measurable outcomes tends to drive out work that produces immeasurable outcomes” (Wilson 1989
**ample**|||
|Children|5.84|5.00||9056|5.56<br>5.00||3873|
|Father|3.38|2.00|0.85|7126|3.53<br>2.00|1.01|3713|
|Mother|1.45|0.00|0.51|7261|1.64<br>0.00|0.62|3648|
|Average of Parents|2.34|1.00|0.65|9010|2.59<br>1.50|0.75|3844|
|||||**Daughter**|**s Sample**|||
|Children|4.14|4.00||9531|5.44<br>5.00||1979|
|Father|3.41|2.00|1.05|6891|4.16<br>3.00|1.27|1886|
|Mother|1.47|0.00|0.63|7266|2.14<br>0.00|0.83|1875|
|Average of Parents|2.33|0.50|0.80|9495|3.14<br>2.50|0.96|1962|
|||||**IND**|**IA**|||
|Years of Education of|||**Both S**|**ons and Da**|**ughters Sample**|||
|Children|6.23|7.00||14877|6.97<br>8.00||9132|
|Father|4.37|2.50|0.91|14877|4.74<br