rafmacalaba/data-use-mentions-extended huggingface dataset:
economic standing. This analysis uses the results from Filmer and Pritchett (1998) which argued that an index of housing characteristics and assets owned by the household members, which are collected in the DHS, is a good rneasure of a household's long run wealth in predicting educational outcomes.

The particular goal here is to investigate the association between educational disparities and gender, household wealth, adult education, and "access" to schools. The analysis leads to four main findings. First, the extent of the female disadvantage in education varies enorrnously across countries. At one extreme there are some countries, primarily located in Western and Central Africa, North Africa, and South Asia where the gaps are large in all the measures used. For example, in India there is a 16.6 percentage point difference between the school enrollment of girls and boys aged 6 to 14. In Benin, the enrollment rate of boys aged 6 to 14 is 63 percent higher than the enrollment rate of girls. At the other extreme there are countries, mostly in Latin America, where there is no female disadvantage in and in fact a small female _advantage_ in some of the measures used. In Colombia, the enrollment r ate of boys is 98 percent that of girls.

Second, while gender gaps are large in a subset of countries, wealth gaps are large in almost all the countries studied. For example, in Senegal the enrollment of 6 to 14 year olds from the poorest households is 52 percentage points lower than for those from the richest households. In Zambia, there is a 36 percentage poilnt difference in the enrollment rate of children from the richest and poorest households. Disturbingly, in some countries where there is a high degree of female disadvantage in enrollment, wealth interacts with gender to exacerbate gaps in educational enrollment among the poor (Niger, Egypt, Morocco, India, and Pakistan). The magnitude of this difference can be quite large. For example, in India there is a 2.5 percentage point difference in
-- End of picture text --> Note: The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. 95% Confidence Interval. Math performance on a scale from 0 to 350 (SAEB scale). reading performance on a scale from 0 to 325 (SAEB scale). Figures (a) and (c) show the estimates for the TD. Figures (b) and (d) show the estimates for DiD. All regressions are weighted by fifth-grade enrollment at the school level. Standard errors clustered at the municipality level. The controls are municipal fixed effects, students’, teachers’, schools’, and principals’ characteristics, the percentage of teachers that work in a state and a locally-managed school at the same time, and the percentage of teachers that work in a state-managed school that implemented the managerial practices intervention. The triple difference specifications also include schools’ fixed effects. All triple DiD estimates exclude state-managed schools in which the state government implemented managerial practices intervention. We perform the estimates using the _cic_ (changes in changes) Stata command.

The more detrimental effects in the state-managed network might indicate that locally-managed

> 19The share of students with insufficient performance in 2007 considers all state and locally managed schools affected by the shutdowns.

> 20We also assess the effects of the school shutdowns on grade promotion, retention, and dropout. DiD estimates indicate a small increase in promotion rates and a decrease in school retention and dropout rates in locallymanaged schools. Triple difference estimates point to nil effects on those outcomes in state-managed schools (Table A.14).

> 21We use the command _cic_ in Stata to perform the estimation. This command allows the estimation of the confidence intervals with bootstrapped standard errors. However, it does not perform the _Wild Bootstrap_ to adjust the standard errors when the number of clusters is relatively small.

18
<sup>7</sup> of Colombia’s. Other countries at similar level of GDP

> 5 Lanau, S, Rodríguez-Delgado, D., and Toscani, F. 2018. ‘Colombia Selected Issues’. IMF. p 17. Gurría, Á. 2019. Presentation of the 2019 Economic Survey of Mexico. OECD.

> 6 Source: ILO and Encuesta Nacional de Ocupación y Empleo. Data on the year of 2020. Retrieved Jan 2022.

> 7 Source: ILO and Gran Encuesta Integrada de Hogares. Data on the year of 2020. Retrieved Jan 2022.

6
2 KENYA AND CHINA’S TRADE RELATIONSHIP

|Poisson Q|uasi Maximum Likelihood|
|---|---|
|Importer GDP per capita|0.00(0.00)<sup>_∗∗∗_</sup>|
|Population Kenya|_−_0.01(0.01)|
|Population Importer|0.00(0.00)<sup>_∗∗∗_</sup><br>|
|Distance|_−_0.00(0.00)<sup>_∗∗∗_</sup>|
|Regional Trade Agreement|1.16(0.10)<sup>_∗∗∗_</sup>|
|Contiguous|2.30(0.09)<sup>_∗∗∗_</sup>|
|Common Language|0.19(0.06)<sup>_∗∗_</sup>|
|GATT Kenya|0.78(0.23)<sup>_∗∗∗_</sup>|
|GATT importer|1.50(0.09)<sup>_∗∗∗_</sup>|
|Colonial History|2.53(0.08)<sup>_∗∗∗_</sup>|
|China dummy|_−_1.74(0.42)<sup>_∗∗∗_</sup>|
|AIC||
|BIC||
|Log Likelihood||
|Deviance|98819.06|
|Num. obs.|6118|

> _∗∗∗ p <_ 0.001, _∗∗ p <_ 0.01, _∗ p <_ 0.05. Standard errors in parentheses.

**Kenya’s exports falls with distance. China is far** Figure C36 presents the relationship between distance and
about some management practices. Both the human capital of the manager and the management practices potentially determine the capacity of the business to adjust to the COVID-19 shock, and we therefore include these variables in our specifications. The measures of the human capital of the manager are the years of experience in the sector; whether he or she has a post-graduate degree; whether he or she studied abroad; and whether he or she has worked in a multinational or a large business before. The indicators on management practices in the FAT are whether the business offers incentives to improve performance; the number of key performance indicators (KPI) monitored; the frequency of measurement of KPI; and the time horizon of production targets.<sup>3</sup> We follow ( _18_ ) and combine these four measures into a z-score, which we then include in our linear regressions.<sup>4</sup> Our specifications control for other pre-pandemic characteristics also available in the FAT: the age, size, and sector of the business; whether the business is an exporter; and whether the business is foreign owned.

# **2.2 The COV-BPS data**

The COV-BPS is an initiative of the World Bank Group to track the impact of the COVID-19 pandemic on the private sector across the world ( _1_ ). In Cear ́a-Brazil, Senegal, and Vietnam, the COV-BPS was applied on a sub-sample from the FAT. Combined, the FAT and COV-BPS data offer a representative sample of businesses in each country right before and soon after the COVID-19 shock.<sup>5</sup> Our measure of business performance from the COV-BPS is the reported percentage change in sales in the 30 days prior to the interview relative to the same period of 2019. The COV-BPS

> 3See Appendix A for more details on these questions.

> 4The answers to each question in the management module take values in ascending order starting with 1 for no, none, or never (see Appendix A). We compute the
higher by at least 31 % in Cˆote d’Ivoire, 67% in Ethiopia, 76% in Ghana, and 162% in Kenya.

Even though the method utilized to measure misallocation is fairly straightforward to apply, we highlight the biases that can be incurred in the interpretation of the results from limitations in the underlying datasets. To emphasize this point, we compare our results, obtained from Census-based datasets, with those obtained from an alternative and readily available source, the World Bank’s Enterprise Surveys (ES). We start assessing the accuracy of the ES in terms of capturing the features of the size distribution of firms relative to the Censuses. We show that except in Kenya, where the sample in the ES is taken straight from the Census, the size distribution in Cˆote d’Ivoire’s, Ghana’s and Ethiopia’s ES diverges from their census-based counterparts. In particular, the pattern is that the ES overestimates the size of the highest percentiles in the firm size distribution. We then evaluate the implication of this bias for the resulting measures of misallocation and the counterfactual gains in productivity from its reversal. When weighting sectors according to sectoral value added shares in the Census, we find that the degree of productivity losses implied by misallocation in the ES are significantly smaller. We see this finding as raising a warning to the precipitate application of the methodology. Ensuring adequate size and sectoral representation in the data stands as an important ingredient for the robustness of the results.

As a first step in an attempt to connect the observed misallocation to concrete policies and distortions, we explore two additional dimensions of the distribution of distortions: its decomposition into capital/labor ratio wedges and revenue wedges, and its evolution over the firm’s life cycle.<sup>2</sup> The first dimension is informative to identify whether it is the policies affecting the functioning of financial and labor markets that are a more binding constraint for the economy, or if it is the case that policies affecting capital
Figure B19: Decomposition of Theil indexes, sorted by GDP per capita <!-- Start of picture text --> (A) DOU (B) DB<br>1 1<br>.8 .8<br>.6<br>.6<br>.4<br>.4<br>.2<br>.2<br>0<br>0<br>WDI three letter country codes<br>Between Within National<br>Source: International Urban Poverty Database.<br>Note: Countries are sorted in ascending order of log of GDP per capita, measured in PPP (constant 2017<br>international $). GHSPOP 1km is used for the DOU and DB methods. See Figure B20 for the same chart with<br>countries reordered by the highest to the lowest Theil indexes.<br>Figure B20: Decomposition of Theil coefficients and GDP, sorted by Theil index<br>(A) DOU (B) DB<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>National Between Within<br>NERMWITCDETHGNBBFAUGA GINTZALSOSENGHA CIVMRTAGOGAB<br>NERMWITCDETHGNBBFAUGA GINTZALSOSENGHA CIVMRTAGOGAB<br>MWIAGO LSOUGA BFAGHA TZAGABNER ETHSEN CIVTCDGNBMRT GIN MWIAGO LSOUGA BFAGHA TZAGABNER ETHSEN CIVTCDGNBMRT GIN<br>Theil index<br>Theil index<br>Theil index Theil index<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, GHSPOP 1km is used.

58
The rest of the paper is organized as follows. Section 2 presents the Global Findex data and some descriptive statistics on the differential use of financial services by gender across income groups and regions across the globe. Section 3 describes the additional data used in the regression analysis, including our data on legal discrimination against women and gender norms. Section 4 discusses the regression methodology used in the paper and section 5 presents the results. Section 6 concludes.

# **2. Measuring Financial Inclusion**

# **_2.1 Global Findex Data_**

Our data on the use of financial services come from the 2011 Global Findex database.<sup>5</sup> The Global Findex data was collected in conjunction with the annual Gallup World Poll Survey. The 2011 Gallup World Poll surveyed at least 1,000 individuals in 148 economies, using randomly selected, nationally representative samples.<sup>6</sup> The target population is the entire civilian, non-institutionalized population, age 15 and older. In our descriptive analyses, we focus on 140 countries<sup>7</sup> (see Appendix A for the list of countries in the sample).

The Global Findex dataset includes 41 indicators on the use of financial services around the world. In this paper, we focus on three main dimensions of financial inclusion: (1) ownership – individual or joint – of an account at a formal financial institution; (2) savings in the past 12 months; and (3) credit in the past 12 months. For savings and credit, we distinguish between cases when individuals save at or borrow from a formal financial institution (such as a bank) or if they use only informal means.

> 5 See Demirguc-Kunt and Klapper (2012) for a more description of the database.

> 6 The complete individual-level database, as well as detailed country-level information about the data collection dates, sample sizes, excluded populations and margins of error can be found at: www.worldbank.org
**Introduction** 1.

<mark>Following the 2011 Arab Spring uprisings, several countries in the Middle East and North Africa (MENA), including the Arab Republic of Egypt, underwent economic transitions, with the objective of boosting growth, providing employment opportunities for the youth, stimulating investment, and fostering economic development.</mark> <mark>Youth unemployment is one of the main economic challenges facing the MENA region. Indeed, poor labor market prospects for educated youth were one of the main drivers of the 2011 uprising (Campante and Chor, 2012). This paper focuses on Egypt, the most populous country in the MENA region, with a population approaching 100 million inhabitants. Egypt’s large population is also very young—individuals aged 24 or younger are set to comprise 51% of the population in 2020 (UN World Development Prospects, 2019). As in much of the developing world, Internet use in Egypt has increased exponentially in recent years. According to the</mark> ITU World Telecommunication/ICT Indicators database (2019), the percentage of Internet users in Egypt increased from less than 1 percent in 2000 to reach approximately 47 percent in 2018 (Figure 1).

The growth and diffusion of the Internet constitutes a major shock to information flows, as it facilitates the transfer of vast amounts of information at low-cost. By reducing the cost of information flows, Internet adoption therefore affects local labor markets. For instance, the Internet made it possible to access a large amount of information on available jobs, vacancies, as well as company and career information, all at a low cost. These changes likely altered job search behavior by leading to a shift in job search methods, away from traditional methods towards increased use of Internet related search methods. These changes affect job search intensity, as well as the probability of success of the job-hunting process. Thanks to the Internet, job searchers can have access to a larger pool of available jobs and can therefore apply for more
development that has been made possible by still high oil revenues.

> 4 In the context of the ongoing conflict, reserves of foreign exchange declined drastically during the first half of 2011, according to IMF’s Arab News on 3 August 2011, downloaded from: < _http://arabnews.com/economy/article482640.ece_ >.

> 5 In addition to accounting for over one–third of agricultural production, as said, the production of qat requires a quarter of total water resource use. Furthermore, according to the Yemen Poverty Assessment (Government of Yemen and others, 2007: p. 43), ―qat impacts the economy in a negative manner through the opportunity cost of lost savings as well as lost work-hours. Although the authorities have developed public awareness campaigns, citing the drawbacks of qat consumption, discouraging qat consumption is a complex and difficult task; it is far too well integrated into the Yemeni economy and social structure to be eliminated on a short-term basis without adverse effects‖.

9
, 1994), Jamaica (Glewwe et al, 1995), India (Kingdon, 1996) and the Philippines (Tan et al. 1997) confirm that the quest for a causal link between teacher characteristics and school resources on the one hand, and educational attainment on the other, is still far from complete. Indeed, in the studies cited most variables proved statistically insignificant, and while each investigation found some teacher and school characteristics to be statistically significant, the specific resources tested or found significant varied greatly across the different studies.

45. Krueger (1999) highlights that “much of the uncertainty in the literature derives from the fact that the appropriate specification of the _education production function_ is uncertain”. Along the same line, Glewwe and Kremer (2005) stress that omitted variables at either the student, class, school, or local level may bias the estimates based on retrospective data either upward or downward. For example, unobservables such as children‟s innate ability and parents‟ motivation may be positively correlated with school overall performance, if parents enroll high-ability children in better performing schools. Similarly, teachers‟ unobservable characteristics, such as motivation, are likely to bias the estimates upward, if they are correlated with the observable characteristics included in the empirical specification.<sup>8</sup> 46. Researchers have tried to overcome these limitations by employing data generated by “natural experiments” or by conducting randomized control trials (RCTs). The resulting evidence on the impact of school resources on education outputs is still mixed. While studies exploiting natural experiments suggest, for instance, that reducing the pupil-teacher ratio increases test scores (Krueger, 1999; Angrist and Levy, 1999, among the others), RCTs conducted by Duflo et al. (2009)

> 8 The differences in the results obtained by different studies may therefore be the result of the different specifications used and, consequently, different omitted variables. Possible multicollinearity between the many school and teacher variables used with the attempt to reduce omitted variable
Figure 4.2, weather conditions vary considerably across regions and over time.

Similarly, lagged output prices may affect input use as they are usually a good predictor of current prices, as evidenced by the fact that commodity prices (particularly maize and banana) are often characterized by autocorrelation models of order one (Deaton and Laroque 2003, Cafiero at al. 2005). Thus, if prices were high in the previous cropping season, the farmer may expect similar prices for the current season and decide in favor of adopting inputs. For the analysis, we use the same set of prices used to estimate the financial returns in Section 3: wholesale prices collected in eight different markets by UBOS for the construction of the CPI.

The availability of information, technical advice as well as proper functioning credit markets have been also identified as key factors for input use. Thus, we analyze whether adoption rates are higher among 18 In a focus group with three groups of farmers in the Northern region (Gulu), farmers reported that poor weather conditions and low market price for their crops are two major sources of uncertainty and vulnerability for their livelihood. Uncertain outcomes and returns likely constrain the decision to adopt improved inputs, as the expected value is too ambiguous.

> 19 The WRSI is calculated for each pixel using a maize crop model calibrated to the growing seasons across Uganda. Specifically, the geoWRSI v 3.0 was used with the global PET and RFE v2 (2001‐2017) time series.

24
Combining the effect of the Family 500+ and the increase in the change in the tax-free allowance leads to a further reduction in poverty and inequality. Most of the change results from the introduction of the Family 500+ program, as shown in Table 7, with inequality declining by an additional 0.2 to 0.5 Gini points and extreme poverty by 0.3 to 0.6 percentage points, depending on the size of the allowance. The Figure 10. Progressivity and impact of direct taxes and <u>contributions under alternative reforms</u>

|0000<br>0.010<br>0.020<br>0.030<br>0.040<br>0.050<br>0.060<br>0.070<br>0.080<br>Kakwani Index<br>gressive <----------------><br>Progressive|||0000<br>0.005<br>0.010<br>0.015<br>0.020<br>0.025<br>0.030<br>0.035<br>Marginal contribution|
|---|---|---|---|
|.<br>2014<br>Baseline<br>Re|Includes<br>Family<br>Family<br>500+ and|Family<br>500+ and|.|
||500+<br>Tax-free|Tax-free||
||amount|amount||
||PLN 5000|PLN 8000||
|Kakwani|Redistributive effect (G|ini points)||
|Source: WorldBankestimate|s based on HBS2014.|||

cost of these programs ranges from an additional PLN 7.5 (0.4 percent of GDP) to PLN 19 million (1.1 percent of GDP
<br>0.183|0.155|0.231|0.185|0.155|0.232|

_Source_ : Authors based on HIES 2014 and 2016. Note: Standard errors in parentheses clustered at the district level. *** p<0.01, ** p<0.05, * p<0.1. All models control for Enumeration Area fixed effects and observations are weighted to be representative of the population.

22
–139.

- Huettner, F. and M. Sunder (2012, January). Axiomatic arguments for decomposing goodness of fit according to Shapley and Owen values. _Electronic Journal of Statistics 6_ (none), 1239–1250. Publisher: Institute of Mathematical Statistics and Bernoulli Society.

- Ivancic, L., W. Erwin Diewert, and K. J. Fox (2011). Scanner data, time aggregation and the construction of price indexes. _Journal of Econometrics 161_ (1), 24–35.

- Jaravel, X. (2018). What Is the Impact of Food Stamps on Prices and Products Variety? The Importance of the Supply Response. _American Economic Association Papers and Proceedings 108_ , 557–61.

- Johnson, J. P. (2017, March). Unplanned Purchases and Retail Competition. _American Economic Review 107_ (3), 931–965.

- Karadi, P. and A. Reiff (2019, July). Menu Costs, Aggregate Fluctuations, and Large Shocks. _American Economic Journal: Macroeconomics 11_ (3), 111–146.

- Lee, D. S., J. McCrary, M. J. Moreira, and J. R. Porter (2021, August). Valid t-ratio Inference for IV. Working Paper 29124, National Bureau of Economic Research. Series: Working Paper Series.

- Leung, J. H. (2021, October). Minimum Wage and Real Wage Inequality: Evidence from Pass-Through to Retail Prices. _The Review of Economics and Statistics 103_ (4), 754–769.

- Meckel, K. (2020, June). Is the Cure Worse Than the Disease? Unintended Effects of Payment Reform in a Quantity-Based Transfer Program. _American Economic Review 110_ (6), 1821–1865.

- Nakamura, E. and J. Steinsson (2014, March). Fiscal Stimulus in a Monetary Union: Evidence from US Regions. _American Economic Review 104_ (3), 753–
and data on total credit to domestic enterprises and financial institutions, we can generate a rough estimate of the government's total implicit liabilities stemming from guarantees to the financial sector.<sup>23</sup> Table 6: Estimated Total Non-Performing Bank Loans

||||No|n-Performin<br>(estimat|g Credit<br>ed)|
|---|---|---|---|---|---|
||Non-<br>Perform.<br>Loans<br>(% of<br>all loans)|Credit to<br>Domestic<br>Enterprises<br>and<br>Financial<br>Institutions*<br>(6/97)|% f<br>MB|% of<br>1997<br>GDP**|% of<br>1997<br>Central<br>Gov't.<br>Revenue"|
|Indonesia|17|343|129.6|9.4|63.0|
|Korea|16|589|454.1|22.1|103.7|
|Malaysia|16|403|88.8|23.3|98.3|
|Philippines|14|1365|84.5|7.8|41.1|
|Thailand|19|7912|289.6|30.2|176.7|
|Hong Kong|4|2223|91.6|6.6|33.6|
|Singapore|4|160|35.0|4.4|11.8|
|Taiwan|4|12157|26.8|6.0|50.0|

*For Indonesia and Korea, expressed in trillions of local currency units. For all others, in billions of local currency units "**Estimates.

Columns 2, 3 and
similar shares of their total expenditures to telecom services.

**Table 3. Percentage of the population reporting expenditures on telecom services**

|**Telecom Service**|**% Consumed**|
|---|---|
|Fixed,mobile or cellular telephone,fax or fax machine|1.0%|
|Repair of telephonyand fax equipment|0.2%|
|Purchase of card for landline / mobile / transfer credit|15.5%|
|Telephone communication in aphonebooth(cabine)|8.6%|
|Subscription fee landline / rural telephone|1.9%|
|Other telephone and telegraph services|0.0%|
|Internet subscription fees|2.2%|
|Internet connection fee(includingcybercafe use)|0.5%|
|Telecom bundle(anytelecom service)|21.8%|

Source: own calculations using EDAM2017.

Notes: Consumption is based on reporting expenditures in the category during the last reference month.

> 15 This will clearly underestimate current users as many households may have phones but not purchased these items in the reference month when the survey was conducted. As noted above, about 75% of the population has a mobile phone, but the percent of users with recurrent payments is much lower.

11