rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Data**

In this paper, we focus on the effects of some major components of SP1 on household consumption, income and debt, because this was the first major targeted response to the crisis. Our main interest is in understanding how households responded to these anti-recessionary policies, in anticipation of, as well as after, the policies were introduced and implemented. These questions are particularly relevant in the current economic climate, where the trade-offs of a large-scale government stimulus in a sudden crisis (and hence rising government deficits) are still being debated. Examining these trends is difficult without intra-year data on fluctuations in household consumption and income, to match with the timing of fiscal and monetary policy interventions.

In the case of Thailand, however, relatively detailed nationally-representative data are available on a monthly basis through the Socioeconomic Survey (SES) conducted by the National Statistics Organization (NSO). This survey, which covers about 4,000 households each month, has extensive information on household consumption, sources of income and employment, household participation in government programs, household debt, as well as household perceptions of their economic status. In what follows we present a discussion of findings from the SES rounds before and after the financial crisis, month by month from January 2006-December 2010. The purpose here is to illustrate the potential impacts of the crisis and its aftermath in Thailand. While the rounds of the SES do not form a household panel, each round/month surveyed the same rural/urban areas within Changwats across the five major regions in the country. There were a total of 157 of these communities, 79 urban and 78 rural. Within each community, there were about 90 primary sampling units (PSUs: villages/neighborhoods), and 10-15 households were interviewed within each PSU. We use this synthetic panel of communities to examine the effects of SP1 after 2009; unfortunately we do not have panel data at the household level.

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determining the change in real per capita growth (α=-0.024). The forecasts obtained with the pure cross section model are close to those obtained with the dynamic panel model. On average the forecasts differ by 0.12 percentage points, and in only 4 cases they differ by more than 0.5 percentage points. A comparison of the confidence intervals suggests that the dynamic panel model estimated with the GMM estimator provides a better regression fit than the pure cross section model estimated with the OLS estimator.

# _4.1 Evaluation of the forecasting model_

Next we undertake a formal and systematic evaluation of the ex-post performance of forecasting model (5). For this purpose we conduct an out-of-sample and with-in sample forecasting exercises.

# _4.1.1 Out-of-sample forecasting_

The dynamic panel dataset used in the estimation of forecasting model (5) includes data up to 2000, and we use the model to obtain forecasts for 2004. We then compare these forecasts with actual data. We do so using two simple statistics that capture respectively the bias and mean squared error of the forecasts – the cumulative forecast error statistic (CFE) and the Theil-U statistic (TU). Since in this case there is only one period the CFE statistic is given by the following expression: where<sup>_y_</sup> ˆ _it_ + 5 is the forecast and<sup>_y_</sup> _it_ + 5 is the actual outcome. The statistic is scaled by the actual outcomes in order to make it comparable across countries. Similarly, the Theil-U statistic, which measures the variability or precision of the forecasts, is defined as follows:<sup>30</sup>

> 30 For comparison purposes we compute and report the TU statistics following Kraay and Monokroussos (1999).

22
between Teaching Practices and Thinking Skills that are Promoted in Students]. Enseñanza de las Ciencias. 36 (2): 81 - 103 Garet, M. S., A. J. Wayne, F. Stancavage, J. Taylor, M. Eaton, K. Walters, M. Song, S. Brown, S. Hurlburt, P. Zhu, S. Sepanik, and F. Doolittle. 2011. “Middle School Mathematics Professional Development Impact Study: Findings after the Second Year of Implementation.” NCEE 2011-4024, National Center for Education Evaluation and Regional Assistance. Washington, DC.

Glewwe, P., M. Kremer, and S. Moulin. 2009. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” American Economic Journal: Applied Economics 1 (1): 112–35.

Glewwe, P., M. Kremer, S. Moulin, and E. Zitzewitz. 2004. “Retrospective vs. Prospective Analyses of School Inputs: The Case of Flip Charts in Kenya.” Journal of Development Economics 74 (1): 251–68.

Glewwe, P., N. Ilias, and M. Kremer. 2010. “Teacher Incentives.” American Economic Journal: Applied Economics 2 (3): 205–27.

Gulamhussein, A. 2013. "Teaching the teachers: Effective professional development in an era of high stakes accountability". Alexandria, VA: Center for Public Education, National School Board Association 1: 1-47 Harris, C. J., W. R. Penuel, A. DeBarger, C. D’Angelo, and L. P. Gallagher. 2014. Curriculum Materials Make a Difference for Next Generation Science Learning: Results from Year 1 of a Randomized Controlled Trial. Menlo Park, CA: SRI International. https://www.sri.com/sites/default/files/publications/pbis-efficacystudy-y1-outcomes-report-2014_0.pdf.

He, F., L. Linden, and M. Margaret
on poverty trends in Colombia as a consequence of the pandemic.

3. _Crises:_ this module’s objective was to verify whether the COVID-19 module made the crisis more salient to the treatment group. For this purpose, we asked individuals two questions. The first was an open-ended question about the worst crisis faced by Colombia in the last 50 years. Using the answers to this question, we created an indicator variable equal to one for any answer that included the words “pandemic,” “COVID-19,” or “Coronavirus.” The second question asked individuals to rank the following three crises in Colombia from worst to least bad: illegal drug trafficking, internal armed conflict, and COVID-19. We then created a variable that gave a score of three to anyone who ranked COVID-19 as the worst crisis, two if COVID-19 was the second-worst crisis, and one if it was the third-worst crisis.

> 8We used a reduced scale since Facebook surveys must be short to increase response rates. Since the original index counts each question as one or zero depending on the answer, summing the number of questions still yields changes in the same direction. Dahr et al., 2022 also applied a reduced-form version of the scale.

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of the 2,208 entrepreneurs at 2 months (72%, 66% control, 75% treatment) and 1,613 at 6 months (73%, 70% control, 74% treatment). Appendix B shows that the sample answering the surveys remain balanced on baseline characteristics. We account for possible bias due to selective attrition in several ways. Our main specification (noted below) uses the postdouble-selection lasso of (PDS lasso) of Belloni et al. (2014). This selects covariates that either predict the outcome of interest (which can potentially improve power), or that predict treatment status (which could arise from unbalanced attrition). Appendix B shows our results remain robust to alternative approaches to accounting for attrition, such as probability re-weighting, or using bounding approaches.

We supplement these quantitative surveys with a qualitative survey of 20 treated firms, selected to comprise of a sample of 10 firms that had attended training and had improved their sales and business practices a lot by the 2-month survey, and 10 firms that had attended training but not shown improvement. We also use our own observations from observing training sessions to provide more qualitative information on content and process.

The main primary outcomes of interest are those that are the focus of the majority of the business training literature: whether training gets business owners to adopt new business practices, and whether it improves business performance in terms of profits and sales. Our AEA registry includes a short pre-analysis plan specifying these measures. We estimate the effect of being assigned to training using the following specification for outcome _Y_ for firm _i_ in randomization stratum _s_ : This regression includes the lagged outcome variable ( _Y0_ ) where available, dummies for the different randomization strata, and a set of control variables _X_ selected via PDS Lasso. Robust (Eicker-White) standard errors are used. The coefficient of interest _β_ corresponds to the intention-to-treat effect, which is the effect of being offered the live Zoom training, compared to just being offered the asynchronous version in the control group. We
Table 3 shows country-specific access to improved water and sanitation infrastructure following the classification in Table 2. On average, about 63% of the children in our sample do not have access to any form of sanitation (including improved and flush toilets) and about 37% lack access to safe drinking water; figures that are very much in line with estimates by the WHO/UNICEF<sup>11</sup> for Sub-Saharan Africa (68% without access to sanitation and 40% without access to safe drinking water).

# **3. Methodology**

As discussed in the introduction, the analytical work of this paper is divided into three parts. In the first part, we estimate the empirical relation between water and sanitation infrastructure and child mortality. In the second part, we use the model parameters empirically fitted in the first part to predict country-specific aggregate improvements in child mortality that could be achieved by investments in water and sanitation infrastructure. In the last part, we combine our mortality improvement estimates with cost estimates for the required infrastructure upgrades to compute country specific investment needs as well as the implied costs per lifeyear saved.

The estimation of the mortality effects of water and sanitation infrastructure is based on a logistic model. The dependent variable is the probability of a child under the age of 5 dying during the 12 months preceding the survey. The model we estimate is given by: where _pijk_ is a binary mortality indicator for child _i_ in household _j_ in the DHS survey _k_ . _W_ 1, _W_ 2 are the two indicators marking improved and privately piped water access of the household respectively, and _S_ 1, _S_ 2 are indicators representing improved latrines and flush toilets, respectively. _Cij_ and _H j_ are vectors of child and household characteristics, and _Surveyk_<sup>are survey fixed effects. Standard errors are adjusted for clustering at the primary</sup> sampling unit (cluster) level. The estimated coefficients of equation (1) are reported as odds

> 11 http://www.
rural areas, are more dynamic and tend to move out of the UNPS sample more frequently than poor individuals. In the 20013/2015 panels, figures stabilize. All statistics are consistent with the national-level estimates from the UNPS 2015 cross-sectional sample. The refresh of the sample in 2013/14 seems to have mitigated the attrition of non-poor individuals. Rural and urban areas of residence show the same trends. Comparing the percent relative variance among the three estimates of the poverty headcount ratio, two from the longitudinal estimates and one from the cross-sectional estimator, we note that for the rural sub-population, the UNPS estimators has 5.3% of percent relative variance whilethe calibrated GWSM estimator has 3.5%. The estimates for the urban sub-population show the UNPS estimators has 0.6% percent relative variance, the calibrated GWSM estimator has 3.0%, while the unweighted has 5.4%. So in this case we are not able to indicate which one is the more stable estimator.

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and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labor force includes the armed forces, the unemployed and first-time job-seekers, but excludes homemakers and other unpaid caregivers and workers in the informal sector. _TNTx_ 1 : Time Cyclical and Hicks neutral technological progress. _R_ & _DNTx_ 1 : Research Expenditures for research and development are current and capital expenditures (both public and private) on creative work undertaken systematically to increase knowledge, and development including knowledge of humanity, culture, and society, and the use of knowledge for expenditure<sup>4</sup> new applications. R&D covers basic research, applied research, and experimental development. New businesses registered are the number of new firms, defined as firms registered in _NBRNT_ : New the current year of reporting. Businesses Registered<sup>5</sup> _TNTx_ 1 : Year Time-varying inefficiency effect.

Notes: 1International Finance Corporation's micro, small, and medium-size enterprises database <u>(http://www.ifc.org/ifcext/sme.nsf/Content/Resources).</u> 2World Bank national accounts data, and OECD National Accounts data files. 3International Labour Organization, using World Bank population estimates. 4United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics. 5International Finance Corporation's micro, small, and medium-size enterprises database (http://www.ifc.org/ifcext/sme.nsf/Content/Resources). Source: World Bank’s World Development Indicators (2009), and authors’ calculations.

The data source used for this analysis is the World Bank’s World Development Indicators (WDI). This database provides more than 800 development indicators, with a time series for 209 countries and 18 country groups from 1960 to 2007. From the World Bank’s World Development Indicators (WDI), we have time series observations (T=
top tail of the distribution.

A lack of documentation on this and other edits and decisions taken when processing data from household surveys is likely to be true in other LMICs. Kerr and Wittenberg (2021) note that earnings are imputed in South Africa’s Quarterly Labour Force Surveys, that these imputations are of poor quality but that they are undertaken and how they are undertaken is not mentioned in any public documentation. The obvious recommendation is that survey organizations and statistical offices should release as much documentation and data as possible without compromising respondent anonymity.

# _Lack of income data in household surveys in some LMICs_

A key challenge in measuring the top tail of the income distribution in LMICs is that many household surveys do not collect data on incomes. This is mainly because reported incomes are seen as less 9
, and rice as “paddy”. For tea, we aggregate high, medium, and low elevations into a single sector.

# **E Inversion Details**

In Section VI.B we describe our general approach to backing out unobserved exogenous variables (productivities, preference shifters, fertilizer endowments, and number of estate workers) by combining observed variables with the equilibrium structure of the model. In this section, we explain the details of this “inversion” procedure.

## **E.A Inversion Procedure**

It is possible to rewrite the market clearing equations (22, 25, 26, 27) of the model without QRs to obtain the following system of equations:<sup>116</sup> 115This data is from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023. 116More specifically, to obtain the system of equations, substitute equation (20) and _p_<sup>_f_</sup> _i_<sup>=</sup><sup>_pf_</sup> _RoW_<sup>into fertilizer market</sup> clearing condition (22), substitute equations (7), (17), and (18) into crop market clearing condition (25), and substitute equations (16) and (19) into labor market clearing condition (26).
financing than the introduction of public credit registries. To identify the introduction of credit bureaus (CBs) across countries, we rely on the World Bank Doing Business database. Specifically, we identify the countries and the year in which they introduced credit bureaus, as well as the level of coverage in each year following introduction.

In the ES sample, 20 countries have a survey both before and after the introduction of a CB. An additional 30 countries that have at least two surveys, but no CBs, are part of the control group. Fifteen countries in the Orbis data sample introduced CBs during the sample period, while data from an additional 14 countries that do not have a CB serve as the control group.<sup>14</sup> Empirically, we capture **_CB reform_** with a dummy variable that takes the value 1 for countries that introduce CBs in the years after the reform. In the years prior to the introduction of CB for the reform countries, and for the control group of countries with no CB, the dummy variable is coded 0. Thus, the CB reform dummy variable can be thought of as the interaction of a dummy variable that identifies the treatment-control sample with a dummy variable that identifies the pre-post reform years.<sup>15</sup> For countries that introduce a CB, World Bank Doing Business database also tracks the number of firms and individuals covered by the CB as a proportion of the total population. We refer to this variable as **_CB coverage_** .

# **_C. External finance dependence_**

We also construct an industry-level measure of the dependence on external finance, **_EFD_**

**_Index_** following the methodology proposed by Rajan and Zingales (1998).  The index is based

on the assumption that the well-developed US financial markets have fewer market

> 14 Appendix tables A1 and A2 provide the full list of countries in the Orbis and ES datasets, as well as when they are used as treatment
.

# **2.3 Data sources**

To conduct this work, several data sets have been compiled and combined from various resources (table 1).

Table 1. Data used for Somalia Enumeration Areas.

|**Source**|**Data description**|
|---|---|
|Road Data (OSM)|Lines|
|Waterway (OSM)|Lines|
|River (OSM)|Lines|
|Residential area (OSM)|Part points and part polygons|
|Building (OSM)|polygons|
|‘places’, ‘hamlet’, and ‘villages’ (OSM)|Points|
|DLR Global Urban Footprint (GUF)|Binary raster|
|BMGF/DigitalGlobe (DG) population estimates|scatter points|
|World Bank/Flowminder/WorldPop building<br>counts from Google Satellite imagery|polygons|
|UNFPA/PESS urban Enumeration Areas|Polygons|
|UNFPA/PESS urban Enumeration Areas (EAs)|# households per EA.|

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# **Table V.2 Per Household Upgrading Costs**

|**Per Household Cost**|**2**|**011/12 Prices**|
|---|---|---|
|Management Structure||90|
|Project Preparation % of Civil Cost|1|470|
|Project management % of Civil Cost|2.5|1,175|
|Survey, Household Data||7,200|
|Community Consultation & Design|||
|Civil Works||47,000|
|TOTAL||55,935|
||Approx.|**56,000**|

Source: Authors’ calculations

# **Impact on the Budget of the AMC**

# **Table V. 3 AMC Budget 2008-09 through 2011-2012**

||||all figure|s in Rupees Thousands|
|---|---|---|---|---|
||**2008-09**|**2009-10**|**2010-11**|**2011-12**|
||FINAL|FINAL|REVISED|APPROVED BY AMC|
|**Total Capital Income**|**8,762,959**<br>|**10,365,515**|**17,762,152**|**28,607,000**|
|**Total Capital Expenditure**|**10,311,889**|**12,950,453**|**17,762,152**|**28,607,000**|
|**Total Revenue Income**|**15,541,668.00**|**16,169,373.00**|**22,353,514.00**|
# Côte d’Ivoire’s Infrastructure: A Continental Perspective

## **_Vivien Foster and Nataliya Pushak_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Daniel Sellen (sector leader), Issa Diaw (power), Ibou Diouf (transport), Lorenzo Bertolini (private sector development), Yao Badjo (water and sanitation), and Richard Doffonsou (macro).
arguing that firms were better able to adapt to the new regime in regions where regulations were more pro-industry. Harrison et al. (2011) find that market-share reallocations played an important role in aggregate productivity gains immediately following the start of India‟s trade reforms in 1991. However, aggregate productivity gains during the overall 20-year period from 1985 to 2004 were driven largely by improvements in average productivity, which can be attributed to India‟s trade liberalization and FDI reforms. Goldberg et al. (2011b) investigate the impact of liberalization on Indian firms‟ product choice and find little evidence of “creative destruction” in the 1990s, i.e. Indian firms infrequently discontinued product lines even during a period of trade and structural reform. They argue that remnants of industrial licensing and rigid labor market regulation in the Indian economy prevented firms from adjusting fully to reforms.

The emphasis on attributing changes in manufacturing performance to changes in trade, investment and labor market policies in goods characterizes much of the existing empirical work on liberalization in developing countries. For instance, Pavcnik (2002) uses plant level data from Chile to find that trade liberalization forces exit of the least productive firms while increasing productivity of the remaining firms in the import competing sectors. Amiti and Konings (2007) delve deeper into the channels through which liberalization affects productivity by separately identifying the impact on Indonesian manufacturing of input and output tariff reductions, and find that the positive effect from increased availability of inputs to production is twice as strong as the effect from import competition. Halpern et al. (2009) estimate a structural model of importers using product-level data for all Hungarian manufacturing firms and reach a similar conclusion.

Empirical research on liberalization of foreign direct investment has produced mixed results. Aitken and Harrison (1999) find what they term „the market stealing effect‟ of foreign direct investment which swamps the positive effect of technology transfer on firm productivity in Venezuela. Javorcik (2004) explicitly distinguishes between intra- and inter-industry effects of foreign direct investment using firm level data