rafmacalaba/datause-encoder-data huggingface dataset:
datasets of Bangladesh for the years 2007, 2011, and 2014. We compute the point estimates and standard errors incorporating the sampling weights as well as respecting the survey design.<sup>14</sup> Excluding the non-usual residents, we were able to consider the information on 50,215 individuals from 10,398 households in the 2007 survey, 79,483 individuals from 17,139 households in the 2011 survey, and 77,680 individuals from 17,299 households in the 2014 survey.

Table 2: The five ordered categories of access to sanitation facilities

|Category|Description|
|---|---|
|Open defecation|Human faeces disposed of in felds, forests, bushes, open bodies of water, beaches or <br>open spaces or disposed of with solid waste|
|Unimproved|Pit latrines without a slab or platform, hanging latrines and bucket latrines|
|Limited|Sanitation facilities of an otherwise acceptable type shared between two or more house|
|Basic unsafe|A basic sanitation facility which is not shared with other households, but excreta ar<br>disposed safely, such as fushed but not disposed to piped sewer system, septic tank <br>latrine|
|Improved|Sanitation facility which is not shared with other households and where excreta are <br>disposed in situ or treated of-site and includes fush/pour fush to piped sewer system,<br>tank or pit latrine, ventilated improved pit latrine, composting toilet or pit latrine w<br>slab|

One target of the United Nations’ sixth Sustainable Development Goal (whose aim is to ‘ensure availability and sustainable management of water and sanitation for all’) is ‘by 2030, [to] achieve access to adequate and equitable sanitation and hygiene for all and end open defecation.’ In order to hit the target, the Joint Monitoring Programme (JMP) of the World Health Organisation and the UNICEF proposes using ‘a _service ladder approach_ to benchmark and track progress across countries at different stages of development”, building

> 14See NIPORT et al. (2009, 2013, 2016) for details about the survey design.

27
# **Data Appendix**

**Table A.1: Wage Labor variable construction notes**

||ETHIOPIA|MALAWI|TANZANIA|UGANDA|
|---|---|---|---|---|
||2011-12|2010-11|2010-11|2010-11|
|_Job_<br>_tracking_|Up to two jobs, Productive<br>Safety Net Program (PSNP)<br>labor, and casual labor|Up to two jobs and casual<br>(_ganyu_) labor|Up to two jobs|Up to 4 activities including jobs,<br>own farm and NFE (2 most<br>important in past 7 days and up<br>to 2 more if they are more<br>important over last year)|
|_Time use_<br>_aggregate_|For jobs: months last year; typic<br>typical hours / week worked. Fo<br>in Ethiopia): days / last year|al weeks / month worked;<br>r casual labor (and PSNP<br>(hours per day assumed)|Months last year;<br>typical weeks /<br>month worked;<br>hours last week|Months / year; weeks / typical<br>month worked; hours last week|
|_Cleaning_<br>_(time)_|Assume unreported time obser|vation = 0. Truncate unreasona<br>week)|bly large values (e.g.,|_>_16 hours / day or _>_7 days /|
|_Sector in-_<br>_formation_|Observed for jobs. PSNP is<br>classifed as ag. Casual labor is<br>classifed as “unknonwn”|Observed for jobs. Casual<br>labor is classifed as<br>“unknonwn”|Observed for both<br>jobs|Observed for all activities (for<br>4th activity sector is observed<br>but not whether it is a job,<br>family farm, or NFE)|
|_Wage rate_|Earnings reported per pay<br>period (for jobs) and annually<br>(for casual and PSNP labor)|Earnings reported per pay<br>period (jobs) and per day<br>(casual labor)|Earnings reported<br>per pay period|Earnings reported per pay<br>period (lots of missing pay<br>period observations)|
|_Cleaning_<br>_(wages)_|Winsorize hourly|wage rate (p = 0.01) and recons|truct annual returns w|ith imputed wage|
|_Annual_<br>_returns_||For each job: cleaned wage r|ate * annual hours||

A-1
- Brown, Drusilla and Robert Stern (2001), ―Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,‖ _Review of International Economics_ , 9(2): 262-286.

- Coe, David T., and Elhanen Helpman (1995), "International R&D Spillovers,‖ _European Economic Review_ 39 (5): 859-887.

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- Cockburn, J., (2001). ―Trade Liberalisation and Poverty in Nepal: A Computable General Equilibrium Micro Simulation Analysis‖, Discussion paper 01-18, Centre de Recherche en Économie et Finance Appliquées, Universite Laval, Quebec, Canada, October.

- Copenhagen Economics, East Europe Institute of Munich and the Institute for Economic Research and Policy Consulting (2006), ―Analysis of Economic Impacts of Ukraine‘s WTO Accession to the WTO: Overall Impact Assessment.‖  Available from www.ier.kiev.ua.

- Cororaton, Caesar B. and John Cockburn (2007), "Trade reform and poverty—Lessons from the Philippines: A CGE micro-simulation analysis," _Journal of Policy Modeling_ , Vol. 29(1), 141-163.

Daniels, P.W. (1985), _Service Industries: A Geographical Appraisal_ , New York: Methuen & Co.

- Dantzig, George and Alan Manne (1974), "A Complementary Algorithm for an Optimal Capital Path with Invariant Proportions," _Journal of Economic Theory_ , 9, 312-323.

- Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), ―Measuring the Costs of Barriers to Trade in Services,‖ in Takatoshi Ito and Anne Krueger (eds.), _Trade in Services in the Asia-Pacific Region_ , Chicago: University of Chicago Press.

84
# **I. Introduction**

Analyzing dynamic aspects of poverty is important for the design of poverty reduction policies. Without specific insights into the dynamics of poverty, the same poverty rate of, say, 20 percent in two periods could well mask dynamic processes ranging from complete stagnation (i.e., zero mobility where all households see no change in their welfare) to extreme volatility (i.e., perfect mobility where all poor households in the first period escaped poverty and were all replaced by households that had previously been non-poor in the first period). Policies to deal with these situations are clearly very different. If poverty is mostly transitory, attention should perhaps be focused on designing safety net programs to prevent non-poor but vulnerable households from falling into poverty. On the other hand, if poverty is mostly chronic, attention could perhaps better be directed to structural and longer-term interventions such as investment in human capital and building infrastructure. Transitory and chronic poverty typically require different policy instruments and no single policy is likely to successfully address both.

Senegal’s annual GDP per capita growth rate averaged 1.6 percent and 0.8 percent between the periods 2001-2006 and 2007-2011, respectively, which translates into an overall annual growth rate of 1.2 percent for the 2000s—around half of the corresponding figure of 2.3 percent for Sub-Saharan African developing countries (World Bank, 2014a). This aggregate performance has been accompanied by poverty rates falling steadily from around 55 percent in 2001-02 to 48 percent in 2005-06 and 47 percent in 2010-11.<sup>1</sup> While its growth trajectory has hardly been dizzying, Senegal appears to be progressing ahead with poverty reduction objectives; consistent with the “slow-growth/high stability economy” characterization frequently used to describe the country (Azam and Dia, 2004). However, while much is known about poverty figures at single

> 1 Unless otherwise noted, all numbers shown are our estimates using the household survey data for Senegal or the World Development Indicators database provided by the World Bank.

2
0 1S (6) Equation (6) adds to Equation (5) a vector of control variables cc,cc cc,cc cc,cc cc cc cc,cc , which includes the added YY = γγ + γγ SS + γγ<sup>′</sup> XX + θθ + σσ + εε values in manufacturing, agriculture, forestry, and fishing, and trade values (all as shares of cc,cc XX country GDP), which are measures of the sectoral composition of the economy. All these three variables are measured as a percentage of the country GDP. also includes the gross primary school enrollment rate. further includes either an index of political institutions as measured by cc,cc XX WGI (when the outcome variable is log of GDP per capita) or country income levels as measured cc,cc XX by log of GDP per capita (when the outcome variable is WGI).

Since we do not have all the country-year observations for these control variables (except for the WGI), we impute for the missing observations with the nearest available values. The percentage of missing values ranges from 1 percent (agriculture, forestry, and fishing added values as a share of GDP) to more than 25 percent (gross primary school enrollment). We focus on the resulting balanced panel data for 159 countries with data for the SPI, WGI, and other control variables between 2016 and 2022. The main reason that most countries do not have an overall SPI score in 2016 is due to data unavailability from Open Data Watch’s Open Data Inventory (ODIN), which was used for the SPI measures of data openness and geospatial information. The other reasons are missing human capital index scores or trade data. As such, Equation (5) is our preferred model for analysis but Equation (6) can offer useful robustness checks.

It is important to emphasize that these econometric models are unlikely to allow us to identify the causal impacts of the SPI on GDP growth or governance (which is beyond the scope of analysis in this paper). Yet, these models can help shed exploratory, useful insights on the correlational relationship between a country’s statistical performance and its economic growth and governance.

22
type of respondent, there is almost no difference between the statistics generated by self and proxy (except for slightly fewer boys working in “other sectors”).<sup>12</sup> Together this suggests that the additional questions contained in the detailed version work as “screening questions,” filtering out at least part of the children that equate domestic duties with employment. It appears that individuals who would classify themselves as “working in domestic duties” if assigned the short questionnaire are “screened out” when using a detailed module and end up classified as “no work.” This most frequently happens for girls. At the same time, a non-negligible proportion of children that would classify themselves as mainly engaged in domestic duties in the short questionnaire are classified in agricultural activities in the detailed questionnaire.

# _Regression results: Survey assignment effects_

To obtain the marginal effect of each survey assignment, we estimate Equation 1 controlling for individual characteristics (age, gender, and education [highest grade attended]), household characteristics (household size, composition, asset holdings,<sup>13</sup> and land holdings), and district indicators. In each case, we include separate indicator variables for the short module and the proxy module. Including an additional indicator variable for the short proxy interaction yields very similar results (results not presented). The results for child LFP, obtained by using a probit model, are reported in the first columns of Table 5, and indicate that the short module yields 19 percentage points lower participation rates for boys and 16 percentage points lower for girls (note that this is after re-classifying all domestic duties into “no work,” following the ILO definition of employment). The use of proxy respondents also produces underestimation of child labor with respect to self-reporting, but the effects are much smaller (although, again, larger for girls) and not statistically significant for our sample size. These effects are large and their variation is consistent with the widespread differences in child labor statistics noted by Guarcello et al. (2009), who,

> 12 The non-agricultural sectors were: mining/quarrying/manufacturing/processing, gas/water/electricity, construction, transport, buying and selling, personal services, education/health, and public administration. Only 9 children did work in these sectors in the past 7 days.

> 13 The household asset index is constructed from a list of 14 durable assets, 7 livestock categories, and 7 housing characteristics. It has mean value 0.11 and standard deviation 0.9.

16
In addition, in line with the literature, we find evidence that some country characteristics such as the level of financial development can affect the impact of global factors. However, the results are not consistently statistically significant implying that the global cycle is mostly driven by push factors and does not structurally discriminate between EMDEs.

Taken together, our findings provide strong support for synchronized primary issuance flows across EMDEs driven mostly by global factors. As a result, both sovereigns and corporates in EMDEs have collectively been able to take advantage of ample international liquidity by lowering their borrowing costs and extending maturities which can improve risk profiles, although in the wake of the crisis, maturities in EMDEs remain below pre-crisis levels.

The massive and widespread external issuance in EMDEs raises important questions regarding the impact of procyclical investor behavior once the global cycle winds down, or if global shocks materialize, with potential systemic implications for EMDEs. Moreover, while issuance at lower cost and maturity extension can help lower individual borrower risk profiles, large foreign currency exposures raise risks, particularly for unhedged issuers. The recent trend of a rapidly strengthening U.S. Dollar against most EMDE currencies further heightens currency risks.

In this context, the inevitable exit from EMPs will tighten international funding conditions, which could prove disruptive for currencies, balance sheets, and funding capacity in EMDEs. Additionally, fragility in EMDEs can be further compounded by their shallow local financial markets and a lack of strong institutions, supervisory and surveillance capacity, and technical experience. As such, in terms of financial sector policies, there is a continued need for, inter alia: i) creating vibrant local currency (corporate) bond markets and an active, diverse domestic investor base; ii) building macroprudential tools and monitoring capacity to deal with synchronized foreign investor activity to prevent or manage a situation where certain flows create a variety of risks which jeopardize undoing financial and (socio-)economic progress made; and iii) strengthening data collection efforts, particularly regarding foreign currency exposures and hedges.

# **References**

Adrian, T., and H. S. Shin (2010). “Liquidity and leverage,” Journal of Financial Intermediation, 19(3), pp. 418-437.

Avdjiev, S., M. Chui, and H.S. Shin (2014). “Non-Financial Corporations from Emerging Market Economies and Capital Flows”, BIS Quarterly Review, December 2014.

Baker, S., N. Bloom, and S. Davis (2015). “Measuring Economic Policy Uncertainty”, Stanford University Working Paper.

Bekaert, G., M. Hoerova, and M. Lo Duca (2013). “Risk, uncertainty and monetary policy” Journal of Monetary Economics 60, pp. 771-788.

Borio, C. and H. Zhu (2012). “Capital regulation, risk-taking and monetary policy: a missing link in the transmission mechanism?” Journal of Financial Stability, 8(4), pp. 236-251.

Bruno, V. and H.S. Shin (2015a). “Cross-Border Banking and Global Liquidity”. Review of Economic Studies 82, pp. 535-564.

20
# PRE Working Paper Series

|||Autho<br>IQ||Contact<br>for1aper|
|---|---|---|---|---|
|WPS74|2 The Cost of the District Hospital:<br>A Case Study from Malawi|A. J. Mills|August 1991|0. Nadora<br>31091|
|WPS743|Antidumping Enforcement in the<br>European Community|Angelika Eymann<br>Ludger Schuknecht|August 1991|N. Artis<br>37947|
|WPS744|Stainless Steel in Sweden: Anti-<br>dumping Attacks Good Internationa<br>Citizenship|Gunnar Fors<br>l|August 1991|N. Artis<br>37947|
|WPS745|The Meaning of "Unfair in U.S.<br>Import Policy|J. Michael Finger|August 1991|N. Artis<br>37947|
|WPS746|The Impact of Regulation on<br>Financial Intermediation|Dimitri Vittas|August 1991|W. Pitayatonakarn<br>37666|
|WPS747|Credit Policies in Japan and Korea:<br>A Review of the Literature|Dimitri Vittas|August 1991|W. Pitayatonakarn<br>37666|
|WPS748|European Trade Patterns After the<br>Transition|Oleh Havrylyshyn<br>Lant Pritchett|August 1991|N. Castillo<br>37947|
|WPS749|Hedging Commodity Price Risks in<br>Papua New Guinea|Stijn Claessens<br>Jonathan Coleman|August 1991|S. Lipscomb<br>33718|
|WPS750|Reforming and Privatizing Poland's<br>Road Freight Industry|Esra Bennathan<br>Jeffrey Gutman<br>Louis Thompson|August 1991|B. Gregory<br>33744|
|WPS751|A Consumption-Based<br>Direct Tax for<br>Countries in Transition from Socialis|Charles E. McLure, Jr.<br>m|August 1991|CECSE<br>37188|
|WPS752|Inflation and Stabilization in<br>Yugoslavia|Roberto de Rezende<br>Rocha|August 1991|L. Ly<br>37352|
|WPS753|The CMEA System of Trade and<br>Payments: The Legac; and the<br>Aftermath of Its Termination|Martin Schrenk|August 1991|CECSE<br>37188|
|WPS754|Estimating Returns to Scale with<br>Large Imperfect Panels|James R. Tybout<br>M. Daniel Westbrook|August 1991|S. Fallon<br>37947|
|WPS755|Hedgins, Crude Oil Imports in<br>Developing Countries|Stijn Claessens<br>Panos Varangis|August 1991|D. Gustafson<br>33714|
|WPS756|Taxes Versus Quotas: The Case<br>of Cocoa Exports|Arvind Panagariya<br>Maurice Schiff|August 1991|S. Fallon<br>37947|
|WPS757|Managing the Transition: Enhan&cg<br>the Efficiency of Eastern European<br>Governments|Eric Rice|August 1991|P. Infante<br>37642|
# **Methodology for a World Bank Human Capital Index**

Aart Kraay<sup>1</sup>

**JEL Codes:** I1, I2, O1, O4

## **Keywords:** human capital, health, education, development accounting

> 1 World Bank Development Research Group, <u>akraay@worldbank.org.  This paper was prepared as a background</u> paper for the World Development Report 2019 and for the World Bank’s Human Capital Project.  It has benefited from extensive discussions with Roberta Gatti, Simeon Djankov and David Weil (Brown).  Particular thanks to Rachel Glennerster (DFID), Bill Maloney, Mamta Murthi and Martin Raiser for peer review comments; to Chika Hayashi (UNICEF) and Espen Prydz for guidance on stunting data; to Noam Angrist (Oxford), Harry Patrinos and Syedah Aroob Iqbal for the harmonized test score data; to Deon Filmer and Halsey Rogers for extensive discussions on converting test scores into learning‐adjusted school years; to Husein Abdul‐Hamid, Anuja Singh (UNESCO) and Said Ould Ahmedou Voffal (UNESCO) for help with enrollment data; to Patrick Eozenou and Adam Wagstaff for help with DHS data; and to Krycia Cowling (IHME), Nicola Dehnen and Ritika D’Souza for tireless research assistance.  Valuable comments were provided by Sudhir Anand (Oxford), George Alleyne (PAHO), Ciro Avitabile, Francesco Caselli (LSE), Matthew Collins, Shanta Devarajan, Patrick Eozenou, Tim Evans, Jed Friedman, Emanuela Galasso, Michael Kremer (Harvard), Lant Pritchett (Harvard), Federico Rossi (Johns Hopkins), Michal Rutkowski, Jaime Saavedra, Adam Wagstaff, and Pablo Zoido‐Lobatón (IDB).  This paper has also benefitted from the discussion at two workshops on measuring the contribution of health to human capital held at the World Bank on March 1, 2018 and May 14, 2018, and a Bank‐wide review meeting held June 11, 2018.  The data used in this paper have benefitted from an extensive consultation process organized by the office of the World Bank Chief Economist for Human Development, which resulted in many expansions and refinements to the school enrollment and stunting data used in the HCI.  The HCI will be published in the 2019 World Development Report and accompanying special report on human capital. _The views expressed here are the author’s, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent._
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additional units. In the model, this cost is constant and equal to one. Naturally, we could extend the model to allow for congestion effects on the cost of capital as in the popular Q-theory of investment. But it seems unlikely that such an extension would be able to explain much of the rise in the value of the stock market.<sup>10</sup> Neither can this rise and fall be explained by an increase in the stock of capital. In the model world savings grow at a constant rate γ, and so a large increase in the US stock of capital would have to be associated with a decline in ROW’s stock of capital. However, the increase in stock market capitalization took place all around the industrial world. Naturally, one could extend the model to allow for increases in savings and therefore the capital stock. But this would not get us very far quantitatively. Since the US capital stock is about twice US GNP, a three-fold increase in the stock of capital during the second half of the 1990s would have required astronomical investment rates! The question remains: how did the value of the stock market grow so much in the second half of the 1990s and then drop in the first half of the 2000s?<sup>11</sup> Of course, there have also been many voices arguing that the US stock market during this period was fueled by a bubble rather than by an increase in US productivity relative to the rest of the world. According to this alternative view, foreign investors were not buying US firms in the IT sector because of their high productivity, but instead because they were expecting to resell them later at a higher price. The appearance of a bubble brings huge capital gains to those that are able to “create” it, and this could explain the massive increases in equity prices during the second half of the 1990s. But to realize these capital gains one must first find buyers for the bubble, and this is only possible if the bubble

> 10 Hall [2001] estimates the price of installed capital in the US since 1946, and finds that this price increased by only about 25 percent during the second half of the 1990s. See also Hall [2004] for an attempt to measure the cost of capital.

> 11 Hall [2001] argues that the increase in the total value of the stock market reflects massive accumulation of intangible capital. This increase is mostly a windfall, since it did not require significant investment expenditures. As Ventura [2001] points out, it is hard to see why this intangible capital suddenly disappeared in 2000.

13