rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Solutions in the literature for addressing MSB, NSB and interval data.

|Stage|Challenge|Solution in the literature|Main issues with the solution|
|---|---|---|---|
|Elicitation|MSB|Elicit a single switchpoint:<br>sMPL, iMPL|Error misattributed to WTP<br>variation, changed outcome<br>variance|
||MSB|Stop MPL questions after<br>frst switchpoint|Measurement bias; error<br>misattributed to WTP variation,<br>changed outcome variance|
|Analysis|Inconsistent<br>choices|Drop observations|Selection bias; loss of<br>information, reduced outcome<br>variance|
||NSB|Impose minimal/maximal<br>WTP|Measurement bias; potentially<br>changed outcome variance|
||Interval data,<br>NSB|Assign single point in the<br>interval, impose<br>min./max. WTP (NSB)|Measurement bias; reduced<br>outcome variance|
||Interval data,<br>MSB|Impose interval bounds<br>(MSB) and use interval<br>regression|Loss of information, potentially<br>changed outcome variance|

Notes: Overview of common approaches to addressing MPL data challenges arising from inconsistent choices (MSB and NSB with dominated choices) and interval identification (NSB and interval data). “Stage” refers to whether the solution is implemented during data collection (elicitation) or after (analysis). “Solution” and “Main issues” are discussed in more detail in the text.

interval identification when analyzing MPL data _ex post_ , at the analysis stage. However, we argue that these solutions do not full address the issues with both idiosyncratic and systematic choice error in MPL. They often misattribute the individual error to variation in WTP, fail to account for order biases, and potentially even introduce new sources of bias, for example through selection. Table 1 summarizes these issues. In this section, we discuss how these solutions can lead to biased WTP measurement or incorrect standard errors. In Section 3.6, we illustrate this using data from South Africa.

At the elicitation stage, MPL design approaches often focus on avoiding or suppressing MSB by enforcing a single switch. The problem is that these solutions may not change the incidence of error, only its visibility. As a result, errors are never observed; variation that may simply be due to error is instead attributed to heterogeneity in WTP. For example, a “switching MPL” or sMPL shows subjects the full MPL and ask them at which point they would switch from option A to option B (e.g., Tanaka et al. (2010), Andersen et al.

13
Bright Lights, Big Cities: Measuring National and Subnational Economic Growth in Africa from Outer Space, with an A lication to Ken a and Rwanda <u>pp y</u> By Tom Bundervoet; Laban Maiyo and Apurva Sanghi<sup>1</sup>

> 1 Bundervoet: Sr. Economist, World Bank in Kigali; Maiyo: GIS Specialist, World Bank in Nairobi; Sanghi: Lead Economist and Task Team Leader, World Bank in Nairobi. Please direct comments to asanghi@worldbank.org. We are grateful to Andrew Dabalen, Vernon Henderson and Somik Lall for useful peer review comments; participants in the World Bank seminar series; members of the Kenya National Bureau of Statistics for their inputs, and Thomas O’Brien and Carolyn Turk for insightful comments.
achievement of fourth graders and eighth graders in a large number of countries.<sup>7</sup> In addition to internationally comparable standardized test scores, TIMSS also collected student surveys, teacher surveys, and school surveys.

Because TIMSS asked principals whether they grouped students into classrooms on the basis of ability in mathematics and science, and sampled at least two classrooms from numerous schools in several countries, I am able to exploit classroom level variance of student age within each ability-mixing school through a school fixed effects estimator.<sup>8</sup> The focus on ability-mixing schools is important, as principals in these schools are less likely to selectively assign teachers according to the age distribution of students or the prior achievement of students across classrooms. Similarly, as age and achievement are positive correlated, the classroom age distributions of ability-mixing school are also less likely correlated with students’ prior achievement and other determinants of achievement. Since eighth graders tend to attend various mathematics and science classes with different levels of difficulties and with different set of peers, even within schools claiming not to group students based on ability, classroom age heterogeneity measured in the eighth grade is more likely confounded with unobserved factors and measurement error. Consequently, I focus primarily on fourth graders and only examine eighth graders to assess whether the effect persists into the eighth grade. I include student data

> 7 However, countries were not consistently covered across different waves of TIMSS or across grade levels within each wave of TIMSS. Furthermore, each student was only tested once and individual schools and students were not followed over time in TIMSS, limiting the use of various estimation techniques.

> 8 An ability-mixing school is one in which students of different ability levels are mixed in a classroom. I only include schools that do not ability group students in math and science classes, based on principals’ responses to the survey.

11
# **Measuring Ownership, Control, and Use of Assets**

Cheryl Doss<sup>†</sup> , Caitlin Kieran<sup>‡</sup> and Talip Kilic<sup>*1</sup> JEL Codes: C83, D31, J16.

Keywords: Gender, Asset Ownership, Wealth, Survey Methodology, Household Surveys.

1†Corresponding author: cheryl.doss@qeh.ox.ac.uk. Senior Departmental Lecturer in Development Economics, Department of International Development, Oxford University.<sup>‡</sup> Ph.D. Student, Agricultural and Resource Economics, University of California, Davis, <u>kieran@primal.ucdavis.edu.</u><sup>*</sup> Senior Economist and Survey Methods Team Leader, Living Standards Measurement Study (LSMS), Survey Unit, Development Data Group, The World Bank, tkilic@worldbank.org. This paper is part of a broader collaboration on methodological experimentation among several researchers from the World Bank Gender Innovation Lab, the World Bank LSMS, the CGIAR Program on Policies, Institutions, and Markets led by the International Food Policy Research Institute, the International Rescue Committee, and Oxford University to improve the measurement of time use, women’s agency, and ownership and control of assets: three key constructs in women’s empowerment, known both for their centrality in the current policy debate on gender equality and for the challenges posed by their measurement. The broader collaboration aims to achieve three goals: (1) shed light on the relative quality of the existing methods of measuring these constructs; (2) design and test new ideas to measure these constructs; and (3) generate evidence on which measurement method is most appropriate given the policy and research question at hand.
# **1. Introduction**

In the early months of 2020, the world experienced the largest school closure in history. Within a period of six weeks, from February 16 to April 1, 2020, 172 countries globally closed their schools due to the emerging COVID-19 pandemic. Sub-Saharan Africa was no exception to this trend. By April 1, 2020, 38 countries in the region had closed their schools due to COVID-19, while in an additional eight countries schools were closed due to a scheduled academic break. Only two African countries, Burundi and Seychelles, kept their schools fully or partially open, respectively.<sup>1</sup> Schools stayed largely closed in most African countries until mid-September 2020, after which re-openings accelerated (Figure 1). However, throughout 2021 and into early 2022, school closures continued to be used as a public health measure to promote physical distancing and curb the spread of the pandemic. In Uganda, for example, students were only allowed back into class in January 2022, making it one of the world’s longest pandemic school closures.

**Figure 1: School closures in Sub-Saharan Africa, Feb 16, 2020, to April 1, 2022**

_Notes:_ Based on UNESCO (2022).

From the outset of the pandemic, there were concerns that girls would be disproportionately affected by COVID-19 induced school closures. A survey of frontline organizations in Sub-Saharan Africa conducted in March 2020 shows that 71 percent of respondents believed that girls were more likely than boys to be negatively affected by closed schools (Akmal et al. 2020). Many international organizations also expressed concerns that, in the context of developing countries, adolescent girls’ school attendance and achievements would be more negatively affected than boys’ (e.g., Azevedo et al. 2020; de Paz et al. 2020; Giannini and Albrectsen 2020; Malala Fund 2020). As mentioned by de Paz et al (2020) as well as UNESCO (2021), girls were expected to be more strongly affected by the increased burden of unpaid work within households, including greater responsibilities in household chores and caring for younger children (now

> 1 Throughout this paper, Africa refers to Sub-Saharan Africa.

2
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TABLE **13:** PETROCEEHICALS - EFFECTIVE PROTECTION RATES

|Product|Year|**Nominal**Pro<br>Output|tection Rate<br>**Inputs**|Effective<br>Protection<br>Rate(Z)|
|---|---|---|---|---|
|IPCL: PS|1987|144|217|64|
|LDPE|1987|102|116|90|
|PBR|1987|66|135|17|
|LAB|1987|67|50|177|
|DMT|1987|227|113|428|
|Overall|1987|110|41|225|
|EPDM Rubber|1988|67|47|121|
|ABS|1989|77|73|86|
|Nitrile Rubber|1989|77|82|68|
|Alpha-Olefins|1989|77|111|25|
|MA|1989|61|61|61|
|PA|1989|100|59|101|
|PO/PG/Polyols|1989|83|n.a.|135|

Source: Pursel_ (1988), DFIs.

55. Existing firm level data has been reestimated to separate the effect of higher prices of inputs and capital, and the required level of nominal protection to compensate for these high prices is termed "compensatory protection." Table 14 compares the official tariffs with the realized nominal protection (ratio of domestic to international prices) and the level of nominal protection required to compensate the firms for the high costs of investment and inputs (compensatory protection) in India.
**Decoupling and recoupling**

answer is no –not surprisingly, since equity inflows tend to be closely correlated with foreign equity holdings (Levy Yeyati and Williams, 2011).

**Figure 13** presents a graphical illustration of these results by plotting country-by-country equity betas against US holdings, and showing how, unlike in the case of frontier markets, the expected positive link between betas and FG fails to materialize.

As noted, there are important difference between the average international investor and the professional asset manager, not least of them the fact that the latter are usually benchmarked, which limits their decisions to small deviations from the market weights, forcing them to purchase or liquidate in all markets proportionally in the event of massive allocations or withdrawals from the asset class. In other word, global flows may explain the growing correlation of EM assets with US markets better than the holdings of the average U.S. resident investor.

With that in mind, in **Table 10** we replicate the exercise in column 9 of **Table 9** using EPFR flow data. Now, we find a significant amplifying effect: larger fund flows (both in and out of individual countries) are associated with higher equity betas. Interestingly, the effect is asymmetric: as columns 2 and 3 show, the volume of fund flows plays a significant role only during equity sell-offs. A similar asymmetric pattern is found for credit spreads (relative to U.S. high yield corporate spreads) and currency returns (relative to the inverse of the Broad Dollar Index).<sup>19</sup> The directional causation of the previous findings is not trivial. They could reflect the behavior of benchmarked professional investors facing contributions and redemptions while keeping a balanced portfolio. However, they could alternatively reflect money-chasing returns (or rushing to the exit) whereby asset managers increase their positions in countries with excess returns and cut their exposure in falling markets. To control for this possibility, we re-estimate our models in **Table 11** using Arellano-Bond panel estimator with external and internal instruments. As external instruments, we compute, for each country, capital flows to the rest of EM, the assumption is that fund flows, which are highly correlated across EM, can only affect

> 19 A digression should be made about currency markets. Because there is no data on currency flows to make comparable estimations, we use data from equity funds as a proxy. The assumption is that many professional investors go into equity markets using unhedged positions, so that currency and equity cross-border flows are closely correlated.

<u>15</u>
- 15 -

# **3. The Corporate Sector**

**_Corporate Governance._**<sup>**_26_**</sup> At the end of the 1980s, both corporate governance and disclosure systems were weak, and capital markets played a limited role in the governance of firms and exhibiting at least three interrelated problems: (i) concentrated ownership; (ii) weak information standards; and (iii) poor protection of minority shareholders.

**_Concentrated Ownership._** One of the salient features of the corporate sector in Thailand is the dominance of family control over business operations. Thai firms were (are) generally closely held and managed by majority-often family-interests, while a relatively limited number of families controlled many of the corporations listed onL the stock exchange. The concentration of ownership can be largely attributed to the relative youth of Thailand's corporates as ownership concentration is common in emerging market economies. Nevertheless, while ownership concentration can have advantages,<sup>2 7</sup> empirical evidence suggest that concentrated ownership structures may impede the development of professional managers that are required as firms mature and become more complex, and may lead to increase risk taking by firms (in particular if ownership links between financial and nonfinancial firms exist) as other stakeholders (creditors and employees) share in the downside risk. Moreover, in order not to loose control, large shareholders have incentives to dilute market pressures for improved disclosure and protection for minority shareholders.

**_Weak Information Standards._** In Thailand in the late 1980s, the scope for market monitoring was limited as disclosure was weak and accounting standards and practices were not up to international practice, thus limiting investors' ability to monitor corporate performance.<sup>28</sup> Standards for financial statement disclosures, asset classification, marketable securities, loss recognition and debt restructuring needed improvement. As (large) firms-at the individual firm level as well as at the country level-had easy access to financing, firms and insiders had little to gain from improving disclosure and corporate governance.

**_Protecting Minority Shareholders._** An important factor influencing external financing patterns is the degree of protection from abuse by corporate insiders that is provided by legal and regulatory mechanisms to outside investors. There is growing international evidence that the quality and efficacy of these protection mechanisms influence whether and at what costs outside investors are willing to fund corporations. La Porta et al. (1997) suggest that poor protection mechanisms will limit the availability of

Thai banks' operating ratios were lower than those of other East Asian economies (Philippines 4.2 percent, Indonesia 2.9 percent, Korea 2.8 percent). World Bank (1999).

- 26 See for an extended discussion Alba, Claessens, Djankov (1998).

- 27 It solves the agency problem since large shareholders are able to more easily assert control over a firm and limit management inefficiency and abuse.

- 28 Alba, Claessens, Djankov (1998).
**<mark>Table 5. 2SLS regression of household-level outcomes on child care attendance</mark>**

|Explanatory variables|Log of<br>income per<br>capita|Household is<br>poor|Living with<br>grandparents|Women are<br>migrating|Household<br>size|
|---|---|---|---|---|---|
|Child care attendance|0.428*|-0.222*|0.009|0.029|0.047|
||(0.237)|(0.124)|(0.053)|(0.050)|(0.363)|
|Ethnic minority|-0.970***|0.547***|0.021***|-0.017***|0.527***|
||(0.030)|(0.018)|(0.008)|(0.005)|(0.058)|
|Dummy year 2010|_Reference_|||||
|Dummy year 2012|0.328***|-0.011|0.039***|-0.008|0.112**|
||(0.034)|(0.019)|(0.006)|(0.006)|(0.050)|
|Dummy year 2014|0.530***|-0.070***|0.034***|-0.007|0.094*|
||(0.039)|(0.021)|(0.007)|(0.007)|(0.057)|
|Dummy year 2016|0.678***|-0.106***|0.041***|0.005|0.127**|
||(0.041)|(0.021)|(0.009)|(0.009)|(0.061)|
|Constant|9.316***|0.323***|-0.008|0.014|4.193***|
||(0.101)|(0.053)|(0.022)|(0.021)|(0.153)|
|Observations|3,863|3,863|3,863|3,863|3,863|

Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1.

Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

37
# _Identification_

For some countries, the CET equation (5) might have identification problems as export supply could co-depend on global demand, expressed as an Armington import demand condition similar to equation 4: The demand for Et as a ratio to the global domestic good Dtw is a function of their relative price PPtdwte ~~;~~ β<sup>w</sup> is Previous literature often uses just the aggregation of industrial countries for Dtw and Ptdw with the bilateral trade flows as weights. However, the trade weights are shifting significantly over time, difficult to derive, or unavailable consistently for each country's entire 1970-2018 period. For this reason, we use the global aggregation of national accounts already available in the WDI database to derive Dtw and Ptdw, which are consistent with the specification of the 1-2-3 model. Since the global GDP and its components are also expressed in current and constant U.S. dollars, the global variables are consistent with the country variables.

However, for the 1-2-3 model, we are interested in the CET elasticity Ω from equation 5 and not the CES elasticity σ<sup>w</sup> linked to global demand in equation 8. Whenever the identification issue arises (usually if there is an incorrect sign for the CET coefficient), we consider including the variables of equation 8 as additional cointegrating or exogenous variables in the long-term cointegration equation or part of the error correction of the VEC.

Equations 5 and 8 could also be solved simultaneously, yielding equation 9 as another option for estimating Ω. We apply a time series technique like VEC to it.

# _Fluctuations and Breaks_

For many developing countries, the variables are often characterized by fluctuations rather than a single break, mainly due to policy reversals, crises, conflicts, or exogenous shocks from the weather (e.g., drought, hurricanes, etc.). Figure 1 shows the frequent fluctuations of relevant variables in Benin in contrast to the smoother movements in the United States. In these situations, we find that the growth rates of output, real exports, and real imports, as well as the appropriate share of trade in GDP, work well as extra exogenous variables in the cointegration equation or the error correction part. The growth rates are stationary, so they do not add to the number of cointegration equations for estimation if introduced as endogenous variables in rare instances. We adopt this approach for most countries.

10
- 10

-

receives close to one-third of its revenues from import tariffs and lowering of tariffs and QRs would effect external and internal balances requiring adjustments in public expenditures, other taxes and exchange rates. Ideally, the simulation of reduced protection should assume that all trade taxes are removed and compensating fiscal adjustments are made through lump-sum taxes. In this study, a more realistic and therefore theoretical **l** ess correcty adjustment is assumed. First, the protective tariffs are assumed to be lowered to a uniform 20% rather than to zero. This is because, for a considerable amount of time, the revenue from tariffs will be required in India. Second, the QR premium is assumed to be a uniform 25% and all QRs are assumed to be eliminated. Third, MODVAT is extended to allow those sectors registered under MODVAT to claim full credit for excise and CVD paid on capital goods against their output tax liability.

19. This simulation is made using a 72-subsector general equilibrium model developed by Mitra and Go (1991) using the database for 1987/88. The model has an integrated macro-fisc **a** l nd micro-sectoral perspective and attains micro and macro equilibrium simultaneously. It is capable of tracing the effects of policy changes on the major macroeconomic aggregates and deriving the impact of tax, tariff and expenditure changes on output, value added, prices and real rates of return on the disaggregated sectors of the economy. (See Mitra and Go, 1991 for details of the model.) 20. These adjustments are simulated to achieve balance of payments neutrality, i.e., maintaining the existing current account deficit. Foreign savings is therefore given and domestic savings must adjust to ensure equality between total savings and investment. Domestic savings may be raised either by cutting public expenditure or by increasing tax revenue with fixed public expenditure. Maintaining the current account deficit with tariff cuts requires a tightening in the management of domestic demand. While this can in practice be brought about through a range of instruments including monetary policy and administered prices, the focus here is on fiscal adjustment. The first alternative is to raise excise taxes-cum-CVD while keepin **g** overnment expenditure constant in real terms. In the second alternative, the government
# **Figure 1: The Relationships between Citizens, Service Providers and Government**

<!-- Start of picture text --> CITIZENS/USERS<br> Access OUTCOMES<br> Price<br> Quality<br> Equity<br>SERVICE PROVIDERS<br>STATE/GOVERNMENT<br> Overall resources  Governance<br> Overall governance  Basic infrastructure Service Quality<br> Access<br> Outputs<br><!-- End of picture text -->

# **2.2 DSI Categories and the Selection Criteria**

There are a host of data sets available in both education and health. To a large extent, these data sets measure inputs and outcome/outputs in the service delivery process, mostly from a household perspective. While providing a wealth of information, existing data sets cover only a sub-sample of countries and are, in many cases, outdated (for instance there are five standard or interim DHS survey completed in Africa since 2007). Therefore, to compile an index of service delivery for Africa it is necessary to (i) have comparable data for all countries to be included in the index; and (ii) collect these data at the same regular intervals.

For the aforementioned reasons, it is difficult to rely on existing data sources (like DHS/LSMS/WMS). We therefore propose that all the data required for the indicator be collected through one standard instrument administered in all countries.

Our starting point is that service delivery can be measured using micro data. Moreover, while a ranking of countries on service delivery is obviously one of the key objectives of the DSI, country governments, and citizens, may be just as interested (or more) in benchmarking their own service providers; i.e., to make comparisons within a country. From the micro level data observations one can then build up inter-country comparisons.

Given this micro focus, we have essentially two options for collecting the indicators. We could