rafmacalaba/datause-encoder-data huggingface dataset:
the household survey, it appears that the household survey under-represents the top earning households, particularly the top earning senior executive households.

Similarly, in Vietnam the top salaries recorded in their household survey are less than half of average executive salaries obtained from corporate salary surveys (World Bank, 2014). In the case of Argentina, Alvaredo (2010) finds that while the tax data have almost 700 observations with incomes exceeding 1 million USD, there are none in the Argentine household survey. In a comparison of 16 Latin American household surveys, the ten richest households have incomes similar to a managerial wage, which is arguably substantially smaller than the incomes of top capital owners (Sz ́ekely and Hilgert, 1999).

# **4 Empirical application**

This section presents our empirical application to Egypt. As outlined in the methodology section we combine data on household expenditures with data on house prices. The household expenditures are obtained from the 2009/10 Egypt Household Income, Expenditure and Consumption Survey (HIECS), which is also used for Egypt’s official estimates of poverty and inequality. The house prices represent listing prices for houses that have been put up for sale via two large real estate firms operating in Egypt. We use the real estate database to estimate the top end, defined as the top 5 percent, of the income distribution. The “bottom” 95 percent of the income distribution is estimated using the HIECS.

The following practical decisions and assumptions are made: (a) we restrict the analysis to urban Egypt only (this can be extended to apply to all of Egypt under the assumption that rural households do not rank in the top of the income distribution in Egypt), (b) it is assumed that house price quotes are proportional to (imputed) rental values (as the household expenditure survey contains data on rents only, and we rely on the survey to identify the relationship between house value and household income),<sup>17</sup> (c) it is assumed that the Pareto tail index of the income distribution has been stable between 2009/10 (the time of the survey) and 2013/14 (the time of the house price database), (d) it is assumed that one house constitutes one household (the fact that top income households could be associated with multiple houses may lead us to under-estimate inequality) and that all houses are domestically owned, and (e) we will only be using house price data for Cairo and Alexandria to estimate the top tails of their respective income distributions. For the rest of urban Egypt the entire income distribution will be estimated using the HIECS. The latter decision is motivated by the fact that: (i)

> 17Under a non-arbitrage condition, house prices and rental values are expected to move in parallel, see e.g. Himmelberg et al. (2005).

16
systemically important, both domestically and globally to help limit the economic damage posed by large financial institutions and to strengthen market discipline.

As regulations tend to be pro-cyclical (Dagher, 2018) and as crises often lead to changes in the political landscape, there is always a concern that regulatory changes implemented immediately after a crisis may not be motivated purely by economics. Political considerations and public sentiment may play an important role. As there has been a significant increase in negative public sentiment towards the global financial system after the GFC, there have been fears that governments may respond to the financial crisis by ineffective regulations driven by politics rather than addressing core issues related to capital. In particular, the GFC highlighted the crucial importance of defining bank capital narrowly to improve bank stability at times of crisis (Demirguc-Kunt et al., 2013). As we have discussed earlier, while regulatory capital requirements have increased, there were also changes in definitions as to what constitutes high quality capital.

In this section, we first examine whether countries affected by the financial crisis implemented different capital reforms compared to countries that were not affected. Using data from the BRSS survey, we analyze changes in regulatory capital ratios, simple leverage ratios as well as changes in definitions of the capital instruments that constitute Tier 1 capital. We then investigate the impact of these adoptions on bank risk. Finally, we examine the determinants of the adoption of the latest Basel III capital regulations. Our analyses contribute to recent papers that have examined the impact of the crisis on financial regulation.<sup>18</sup> We use the following regression models to examine changes in capital ratios and capital definitions:

> 18 Kara (2016) uses the years since the last systemic banking crisis to explain changes in the stringency of capital regulation. Masciandaro and Romelli (2018) find that recent episodes of systemic banking crisis significantly increase the likelihood that a country reforms its supervisory structure.

24
Policy Research Working Paper 5207

# **Abstract**

Sub-national estimates of HIV prevalence can inform the design of policy responses to the HIV epidemic. Such responses also benefit from a better understanding of the correlates of HIV status, including the association between HIV and geographical characteristics of localities. In recent years, several countries in Africa have implemented household surveys (such as Demographic and Health Surveys) that include HIV testing of the adult population, providing estimates of HIV prevalence rates at the sub-national level. These surveys are known to suffer from non-response bias, but are nonetheless thought to represent a marked improvement over alternatives such as sentinel surveys. At present, however, most countries are not in a position to regularly field such household surveys. This paper proposes a new approach to the estimation of HIV prevalence for relatively small geographic areas in settings where national population-based surveys of prevalence are not available. The proposed approach aims to overcome some of the difficulties with prevailing methods of deriving HIV prevalence estimates (at both national and subnational levels) directly from sentinel surveys. The paper also outlines some of the limitations of the proposed approach.

This paper—a joint product of the Human Development Economics, Europe and Central Asia Region; and Poverty and Inequality Team, Development Research Group—is part of a larger effort to publish policy-relevant research. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at oivaschenko@ worldbank.org and planjouw@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
our earlier findings would reflect this selection process rather than the effectiveness of investment promotion activities. However, the results presented in Table 10 suggest that this was not the case in the developing country subsample. In none of the four specifications, does the dummy appear to be statistically significant. The coefficients on the targeting variable remain positive and significant. The F-test included in the two lower rows of the table suggests that there is a statistically significant difference between the dummy and the targeting variable. In contrast, in the full sample, the two effects are not different from each other and the targeting variable is not statistically significant.

In Table 11, we present the results from a probit regression modeling the determinants of sector targeting. The dependent variable is equal to one if country c begins targeting industry i at time t, and zero if the industry is not targeted at time t.<sup>26</sup> The purpose of the exercise is to find out whether past FDI inflows or FDI stocks in industry i in country c (lagged one, two or three periods) can predict future targeting of the industry. The model also includes controls for country characteristics as well as country and year fixed effects.<sup>27</sup> In only one of twelve specifications, we find a significant coefficient on the FDI variable. The coefficient bears a negative sign which suggests that, if anything, developing countries chose for targeting sectors with lower FDI flows.

As another robustness check, we remove from the sample observations for targeted sectors in countries where the investment promotion agencies reported in the Census that the choice of priority sectors was based on the earlier success in attracting inflows to those sectors or the lack thereof. As seen in Table 12, removing these countries leads to a stronger rather than weaker effect of the investment promotion efforts.

# **4.2 Sector-level analysis – additional robustness checks**

A potential concern is that our findings could be driven by simultaneous opening to FDI and targeting of services industries where entry of foreign investors was restricted in the past. To eliminate this possibility, we exclude from the sample services sectors and utilities and show that this change does not affect our findings (see Table 13).

> 26 Thus observations for targeted sectors in years other than the first year of targeting are not included in the sample.

> 27 See table notes for the list of control variables.

21
# **Productivity and Health: Alternative Productivity Estimates Using Physical Activity**

Oladele Akogun Modibbo Adama University of Technology Andrew Dillon Michigan State University Jed Friedman World Bank Ashesh Prasann World Bank Pieter Serneels University of East Anglia

**Keywords:** labor productivity, productivity measurement, malaria, field experiment **JEL codes:** I12, J22, J24, O12

**Acknowledgements** : We would like to thank Adedoyin Adesina, Bashir Aliyou, Sani Njobdi, and the Nigerian Ministry of Health, Malaria Control Program. The plantation staff and sugarcane cutters welcomed us and patiently assisted us throughout the study, without which this study would not have been possible. ESRC/DFID, Michigan State University, USDA National Institute of Food and Agriculture, and University of East Anglia are gratefully acknowledged for providing resources and support for the study. We appreciate the efforts of the supervisors, enumerators, and health workers who implemented this study. This study was ethically reviewed by the National Health Research Ethics Committee, Nigeria, Adamawa State Health Research Ethics Committee, and Michigan State University’s Institutional Review Board. We thank the participants of seminars and presentations at CSAE’s 2017 annual conference at Oxford University, and SEEDEC 2017 at UEA.
_Zi_ = 0, otherwise Because the selection of MFIs for supervision is non-random, and because the error term of the model that summarizes this choice (namely, 2) could be correlated with the error term in the regression of interest (namely, 1), one must search for a set of valid instruments. These instruments should be highly correlated with the endogenous regressor (the supervisory dummy), but contemporaneously uncorrelated with the error term in (1) ( _i.e._ , truly exogenous).

We use three variables as instruments in the regressions that follow. The first is a dummy variable indicating whether large and medium-sized banks have annual (or more frequent) onsite supervision (“ _Big Bank Supervision”_ ). The variable is based on survey responses from bank supervisors in more than one hundred countries that were collected by Barth, Caprio, and Levine (2006).<sup>5</sup> We view this variable as a measure of a country’s general propensity to regulate the banking industry.<sup>6</sup> In countries with a high propensity to regulate banks, we expect that MFIs will also be more likely to face regulation and supervision. The variable is exogenous in that the propensity to regulate banks existed before the MFIs arrived. Even had MFIs and large banks arrived contemporaneously, it is highly unlikely that the regulation of MFIs would have influenced regulation of banks, owing to the small size of the MFIs.

As a robustness check on our main results, we replace the _Big Bank Supervision_ dummy variable with an index of the official powers of bank supervisors, also developed by Barth, Caprio, and Levine (2006). That index, which is described in detail in Appendix B, is based on sixteen questions about the powers granted to supervisors in monitoring and disciplining banks. Though the qualitative results of those models are similar to those that use _Big Bank Supervision_ as an instrument, we have 40% fewer observations for the _Official Supervisory Powers (“OS”)_ index, and thus significance

> 5 We use the 2003 version of that dataset because it corresponds most closely to the years covered in our data.

> 6 One might worry that regulation and supervision is more stringent in better developed financial systems, and thus our supervisory dummies reflect financial development more than a propensity to regulate. However, when we control for measures of financial development in our treatment effects regressions, qualitative results are almost identical to those presented below.

13
two randomly generated digits making possible a merge between the baseline survey with the micro data from ENLACE 9th grade.<sup>14</sup> For 75.5% of the students in our sample, we can recover their 9th scores ENLACE score, with 68% taking the exam in 2009, 1.1% in 2007 and 0.4% in 2006.<sup>15</sup> There are two potential explanations for the partial attrition of 9th grade scores: 1) the exam is voluntary and students enrolled in upper secondary might have not taken it; 2) matching related issues either because the information provided during the baseline was not sufficient to generate the quasi- _CURP_ , or for the presence of multiple individuals with the same identifier. Only for five individuals out of 4,145 we were not able to generate a quasi- _CURP_ .

Table 2 shows the baseline characteristics, for the full sample as well as separately for boys and girls, distinguishing between students in the treatment and the control groups. In the top panel we report the socioeconomic characteristics measured through the baseline survey, in the bottom panel the administrative information on 9th grade test scores. Overall, the characteristics of the treatment and control group seem well balanced in line with the randomized design of the evaluation. For boys we find that, out of 23 variables, only the probability that the father works is statistically higher in the treatment than in the control group (p-value=0.011), but the size of the difference is economically negligible. In order to account for these imbalances, our main specification controls for all the variables that display significant imbalances. On average, 93% of the fathers work, as opposed to 45% of the mothers; fathers tend to be more educated than mothers, since 31% of the formers have, at least, completed high school as opposed to 24% of the latter. About 40% of the students have access to an Internet connection at home.

Self-reported measures of effort do not display major differences between boys and girls, neither when we consider the number of hours spent doing homework (5.62 for boys and 5.11 for girls), nor when we look at the number of school days missed last month (2.8 for boys and girls). Girls report a much lower probability of having failed at least one subject in lower secondary school than boys (19% versus 30%).

Students in the treatment group are more likely to be matched with 9th grade test score results and the difference is marginally significant at 10%. In 9th grade girls perform better than boys in language, irrespective of whether we consider the test score or the probability of being classified as insufficient, and they do as well as boys in math (approximately 35% of them are classified as insufficient). Previous work using data from low- and middle-income 14Given that the _CURP_ and the information to form the quasi- _CURP_ is confidential, the merge between the baseline survey information and the micro information of ENLACE was done by staff at the SEP. 15Students who could be matched with 9th grade score are different from those who could not be matched along some dimensions (see Table AI) but most differences are economically small.

9
WPS3886

# **THE MICROECONOMICS OF CREATING PRODUCTIVE JOBS:**

# **A SYNTHESIS OF FIRM-LEVEL STUDIES IN TRANSITION ECONOMIES**

# **J. David Brown**<sup>*****</sup> **and John S. Earle**<sup>******</sup>

## World Bank Policy Research Working Paper 3886, April 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._

> * Heriot-Watt University.

** Upjohn Institute for Employment Research and Central European University. This paper was prepared as a background paper for the ECA regional study Enhancing Job Opportunities: Eastern Europe and the Former Soviet Union.
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Table 3: OLS estimates of the effect of IT use on firm-level productivity

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable log(sales/worker)|||||||
|Computers-per-worker|0.898***|0.845***|0.977***|0.911***|0.858***|0.843***|
||(0.228)|(0.256)|(0.222)|(0.215)|(0.212)|(0.218)|
|_R_<sup>2</sup>|0.848|0.849|0.852|0.856|0.858|0.858|
|Share of labor with Internet|0.786***|0.753***|0.814**|0.685*|0.650*|0.624*|
||(0.255)|(0.275)|(0.353)|(0.354)|(0.333)|(0.338)|
|_R_<sup>2</sup>|0.846|0.847|0.850|0.853|0.856|0.856|
|Share of labor with computer|0.857***|0.805***|0.883***|0.776***|0.721***|0.710***|
||(0.193)|(0.195)|(0.270)|(0.276)|(0.251)|(0.249)|
|_R_<sup>2</sup>|0.848|0.848|0.848|0.855|0.857|0.857|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses

* Significant at the 1% level, **Significant at the 5% level, *** Significant at the 1% level All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 17
**Reforming Land Valuation and Taxation in Ukraine: A Path towards greater Sustainability Fairness, and Transparency**

Klaus Deininger Daniel Ayalew Ali Eduard Bukin Andrii Martyn

**Keywords:** Mass land valuation, Ukraine, property taxation, geospatial modeling, land cadaster, economic reforms **JEL codes:** Q01, D23, H20, Q15, R14

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Funding support from the European Union (ENI/2017/387-093 and ENI/2020/418-654) is gratefully acknowledged. We thank Denis Bashlyk, Florian Blum, Ben Hell, Thea Hilhorst, Roman Hrab, Sergyi Kubakh, Vasyl Kvartiuk, Daria Manzhura, Sevara Melibaeva, Irina Schuman, Karlis Smits, and Taras Vysotskyi for helpful discussions and insightful comments and as well as Tania Khorzovskaya for outstanding coordination and administrative support.
an average annual rate of 1.9 percent per year among oil rich countries using the traditional decomposition, and this contribution is reduced to 1 percent per year when including natural capital as an additional input. In the case of countries abundant in minerals and metals, the contribution of TFP growth (1 percent per year) using the traditional method is wiped out when accounting for natural capital in the production function. In fact, its contribution becomes detrimental —as the TFP contracts at an annual rate of 0.3 percent. In sum, the contribution of physical and natural capital dominates the growth narrative of resource rich countries once we include natural capital in the production function.

# **3 Trade Integration in Sub-Saharan Africa**

This section examines the evolution of international trade integration in Sub-Saharan Africa by calculating the volume and the patterns of trade of Sub-Saharan Africa as well as the role of natural resources in their foreign trade baskets. Specifically, it examines three different dimensions of international trade integration: (a) the volume of trade (as percentage of GDP), (b) trade diversification (as proxied by indices of product and market concentration), and (c) the importance of natural resources in the export sector (where natural resource exports are normalized by either the country’s GDP or its total amount of exports).

**Table 1. Trade measures for Sub-Saharan Africa**

||Sub-Saharan<br>Africa|Reso|urce Rich|Non-Resource<br>Rich|
|---|---|---|---|---|
|||Oil|Metal||
|**Trade Openness**|||||
|Percent of GDP|||||
|1990 - 94|64|95|65|56|
|2010 - 14|77|94|93|70|
|**Herfindahl Index**|||||
|Product Concentration|||||
|1990 - 94|0.41|0.84|0.69|0.29|
|2010 - 14|0.30|0.67|0.46|0.21|
|Market Concentration|||||
|1990 - 94|0.20|0.30|0.08|0.19|
|2010 - 14|0.20|0.15|0.28|0.18|
|**Natural Resources**|||||
|Percent of GDP|||||
|1990 - 94|17.0|46.9|26.8|9.8|
|2010 - 14|13.2|43.0|16.3|8.9|
|Percent of total exports|||||
|1990 - 94|0.82|0.99|0.95|0.78|
|2010 - 14|0.73|0.89|0.71|0.72|

_Sources: WDI, and author's calculations using WITS, COMTRADE_ _page 11_