rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> richest<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>poorest<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>IME Textile Transport Tourism Construction Commerce Agriculture Other<br><!-- End of picture text --> <!-- Start of picture text --> b. By gender<br>Female<br>Male<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>IME Textile Transport Tourism Construction Commerce Agriculture Other<br>c. By region<br>Sud Ouest<br>Sud Est<br>Centre Ouest<br>Centre Est<br>Nord Ouest<br>Nord Est<br>Grand Tunis<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>IME Textile Transport Tourism Construction Commerce Agriculture Other<br><!-- End of picture text --> Source: Author’s calculations based on EBCNV 2015 Note: IME refers to Mechanical and Electric Industry 7
hypothesis testing (Costinot and Rodríguez-Clare 2014). Unfortunately, widely used estimators generate quite different results, making it particularly important to recognize which are suffering from bias due to econometric challenges in estimating the gravity model.

This paper considers three major econometric challenges for estimation of the gravity model—limited-dependent variable bias; a combination of heteroscedasticity and nonlinearity; and missing data mapped to zeros. Many attempts have been made to assess, using Monte Carlo simulations, which estimators are unbiased. Unfortunately, many of these have ignored key features of the underlying data generating processes—whether by omitting all zeros from the analysis; choosing arbitrary distributions for key variables; or examining only arbitrary patterns of heteroscedasticity.

This paper follows Leung and Yu (1996) in developing Monte Carlo simulations that reflect the key parameters of the system. The analysis begins by identifying and removing nearly 2100 observations from the widely used SST (2006) dataset that are identifiable as missing values mapped to zero. Simple quantile plots supported use of log-linear rather than linear specifications for the Monte Carlo distributions of predicted trade flows and model residuals, so this element of past practice was continued. The estimated degree of heteroscedasticity was quite similar across three approaches to estimation—OLS, PPML, and Eaton-Kortum corrected for heteroscedasticity (EK-H)—with larger proportional deviations from small trade flows than from large. There were large differences in the distribution of predicted trade from the PPML model, which predicts censored trade, and the EK-H model, which uses an unbounded latent variable. To ensure robustness, Monte Carlo simulations were performed with both sets of parameters.

The Monte Carlo results point to very large downward bias in coefficients when the traditional estimator (OLS in logs) is used. The EK-H estimator accounting for heteroscedasticity 26
Policy Research Working Paper 6302

# **Abstract**

This note examines in detail Brazil’s export performance over the past 15 years, focusing not only on growth and composition, but also on different performance dimensions, including diversification, sophistication, and firm dynamics. The analysis uses international comparisons to better situate the Brazilian performance, and explores different databases, including firm-level data recently published by the World Bank. The note uses a recent diagnostic toolkit developed by the World Bank in order to suggest some hypotheses about the factors that have been inhibiting exports and industrial production expansion. Among the latter, it is noted how service sectors, as the largest beneficiaries from favorable terms of trade, accommodated larger wage increases and “exported” cost pressures to other sectors of the economy. Furthermore, although a stronger currency can be appointed as one of the elements behind the lower competitiveness in Brazilian exports, sluggish productivity performance and a real wage uptrend explain a significant part of the overall loss of competitiveness. This diagnostic reinforces the importance of resuming the agenda of microeconomic reforms, increasing the investment-to-gross domestic product ratio, and advancing toward better-skilled human capital.

This paper is a product of the Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ocanuto@worldbank.org, mcavallari@worldbank.org or jreis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
***<br>(0.008)|-0.043***<br>(0.008)|
|Public = Private (p-value)|||0.000|0.000|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses work participation on the density of public and private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3- 6 in each district, a dummy for having a preschool-aged eligible child, and the respective double and triple interactions. Comparison group includes all mothers of children of the wrong ages and nonmothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. The last row tests the equality of ‘Public * Eligible’ and ‘Private * Eligible’ coefficients and reports the associated p-value. There are 226,400 female-year observations.

56
growth. The latter may occur when the marginal productivity of additional workers in expanding sectors is below that of existing activities in those sectors.<sup>8</sup> China’s development has been similar to that of other Asian countries—benefiting both from rapid within-sector productivity growth and significant structural change (Timmer et al., 2015). Over 1979-2018, productivity growth within sectors contributed 5.7 percentage points to aggregate labor productivity growth of 7.3 percent per year. The movement of workers between sectors generated the rest (Figure 2). The cross-sector term is positive, suggesting that labor generally moved to sectors with positive productivity growth, but its effect is very small.

**Figure 2 Within-sector productivity growth versus movements of labor**

(annual average contributions to growth in labor productivity, percentage points)

# **Figure 3 Labor moving out of agriculture**

(log ratio of sectoral to aggregate labor productivity in 1978, y-axis; change in employment share 1978– 2010, x-axis) <!-- Start of picture text --> Within-sector Between-sector Cross-sector 2.5<br>10 Utilities<br>2.0<br>8 Fin., real estate, business services<br>1.5<br>Distribution Trade, rest., hotels<br>6 1.0<br>Construction<br>Mining<br>0.5 Manufacturing<br>4<br>Government services<br>0.0<br>2 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2<br>Agriculture -0.5 Other non-market<br>services<br>0<br>1979-2018 1979-1988 1989-1998 1999-2008 2009-2018 -1.0<br><!-- End of picture text --> Source: NBS; authors’ calculations.

Source: GGDC 10-Sector Database, Timmer et al. (2015); authors
<!-- Start of picture text --> % of Baseline % of Baseline<br>1 14<br>° 1.2<br>8<br>7 l<br>68<br>5<br>4 6<br>34<br>22<br>wl<br>0 0<br>1990 1995 2000 2005 2010 2015 2020 1990 1995 2000 = 2005 2010 = 2015 2020<br>—— Average Emissions ———— Phaseout Schedule —— Average Emissions——— Phaseout Schedule<br>% ofBaseline<br>% of.. Baseline..<br>1<br>4.5<br>8 4<br>3.5<br>6 3<br>2.5<br>4<br>5<br>1.5<br>2<br>1<br>0 5<br>1990 1995 2000 2005 2010 2015 2020 9<br>1990 1995 2000 2005 2010 2015 2020<br>—— Average Emissions Phaseout Schedul -<br>—— Average Emissions——— Emissions—————— Phaseout Schedule<br><!-- End of picture text --> <!-- Start of picture text --> % of.. Baseline..<br>4.5<br>4<br>3.5<br>3<br>2.5<br>1.5<br>1<br>5<br>9<br>1990 1995 2000 2005 2010 2015 2020<br>-<br>—— Average Emissions——— Emissions—————— Phaseout Schedule<br><!-- End of picture text -->
**Figure 3.2: Costs of Identification (expressed as % of income per capita)**

<!-- Start of picture text --> Costs of identification<br>0.99<br>0.86<br>Fees charged for passport 1.32<br>0.44<br>0.04<br>0<br>Fees charged for NID 0<br>0<br>0 0.2 0.4 0.6 0.8 1 1.2 1.4<br>Percentage of income per capita<br>Chile Ghana India Vietnam<br><!-- End of picture text --> _Source: Living Life data. Notes: Cost expressed as percentage of GNI per capita using the Atlas Method in current (2016) U.S. dollars._ The efficiency of ID services can also be assessed by the time taken to obtain a NID and passport from the day of request until the day of delivery of the document. Legally prescribed timeframes vary significantly among the four economies from 1 day to obtain an ID in Ghana to 60-90 days in India. In India, obtaining a passport should take 3 days, and in Ghana the process lasts 15 days according to the law. However, time required in practice may deviate from the time prescribed by law.

Contributors reported much longer times required in practice for obtaining passports. For example, it takes Ghanaian citizens twice as long to get a passport at 30 days in practice compared to the 15 legally prescribed. Similarly, Indian citizens seem to experience significant delays while obtaining their passports at 30 days, as opposed to the 3 days prescribed legally. In contrast, the provision of national ID cards in India is more streamlined in practice at 30 days within the legal timeframe of 75 days. However, due to the limited number of respondents, the data on days required in practice should be interpreted with caution.

Figures 3.3.
In terms of missing or incomplete data, the Korean dataset is missing several cases for which hard-copy documents were not available, although antidumping investigations had been notified to the WTO, and this problem is especially severe between 2000 and 2004. The data is also missing the details on the government publications related to antidumping investigations at the initiation, preliminary, and final stages. Finally, the database lacks some information on preliminary outcomes in certain cases and it does not contain information on when antidumping measures were revoked.

# **3.9 Mexico (MEX)**

In Mexico, determinations of dumping and injury are made by one body, the Unidad de Prácticas Comerciales Internacionales (UPCI, International Trade Practices Unit), which is part of the Secretaría de Economía. Unlike many other countries, the Mexican authority is quite likely to continue an investigation through the preliminary phase without placing duties. Mexico was one of the earlier Latin American countries to introduce a dumping investigation system, in place since 1987. The current law which governs this process can be found at the OAS SICE website, <u>http://www.sice.oas.org/antidumping/legislation/mexico/LCEXT.asp. The data for this set was found in</u> electronic copies of the Mexican case files. For a discussion of Mexico’s antidumping process see Leycegui and de la Torre (2005), as well as Niels and Francois (forthcoming).

In addition to the standard data described in section 2, the Mexican file contains additional data described in the table below.

**Table 3.9.1 : Additional Mexican Data Included in AD-MEX-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-M**|**EX-Master :**||
|**U.**|**RELATED_CVD*
Columns 1-3 report estimates of the impacts on highway provision when the presidential party status is interacted with a dummy equal to one if the PAN is in power, and zero otherwise (i.e. the PRI is in power). The coefficient estimates on the interaction term are statistically insignificant at standard confidence levels, regardless of the order of the spline approximation. In other words, by focusing only on closely contested elections, there is no statistical evidence of political bias in highway construction when we contrast a PAN presidency with a PRI presidency. This is not surprising given that both parties expressed similar taste for public spending on infrastructure as evidenced by their large infrastructure investment programs. What our results show is that they both resort with the same intensity to pork-barrel politics.

Table 6: Potential Motives: Divided vs.

||LLR|with bandw|idth <sup>ˆ</sup>_h_|Poly|nomial of or|der_p_|
|---|---|---|---|---|---|---|
||ˆ_h_1= .3|ˆ_h_2= .2|ˆ_h_3= .1|<br>_p_= 2|<br>_p_= 3|<br>_p_= 4|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. Divided|||||||
|President|1.364*|1.972**|3.979***|1.593*|2.779***|3.107***|
||(0.729)|(0.879)|(1.342)|(0.822)|(1.023)|(1.131)|
|Observations|2,751|1,930|1,037|4,496|4,496
<sup>15</sup> Out of 3,139 bank-year observations from around 240 banks between 1995 and 2017, 420 observations represent privatization events, of which 71 events are IPOs and 47 events are foreign issuances. On average 13.8 percent of shares were sold in these events.<sup>16</sup> We use a 30 percent threshold to represent large privatizations and these sales constitutes 23.8 percent of all the events (60 events in developing countries and 40 in high-income countries). We also try to identify whether bank privatizations involved large increases in equity (i.e. more than a 33 percent growth in equity last year) and/or presented high NPL ratios (specifically, higher than 8.7 percent, the 75<sup>th</sup> percentile of large privatization observations).

# **3.1. Bi-variate analysis of banks’ pre- and post-privatization performance**

We start the empirical analysis by calculating and comparing bank outcome variables before and after a large privatization event, where the sold amount was at least 30 percent of the book equity (similar to Otchere, 2005). The pre-privatization mean ratios are calculated over the year t-3 to year t-1 period and the post-privatization mean ratios are calculated over the year t+1 to year t+3 period relative to year t=0 (the year of issue).<sup>17</sup> Thus, this analysis provides insights into shortterm differences in banking outcomes around the privatization events. The t-stat test results regarding the difference post- and pre-privatization are reported in Table 2. Comparing the means

> 14 The summary statistics are based on the multivariate regression including the bank and country level control variables (regression 3 in Table 3).

> 15 This is slightly smaller than the 270 observations used in the previous section due to missing bank-level data. 16 The median sale in these events was 22.6 percent.

> 17 These mean ratios are calculated for banks
MT|Guatemala, Mauritius, South Africa|
|0.2-0.45 mln MT|Colombia, Dominican Republic, Philippines, Swaziland,<br>Guyana, Fiji|

Source: See Data S6 for details Data S6 lists the major exporting countries and their exports for the six years, 1965, 1975, 1985, and 1 990-93. The countries in the table account for about 91 % of world exports. It is interesting to note that over 80% of raw sugar exports come from developing countries.

8
comparable across the two POF surveys, a general tendency in the direction of higher saving across all age-groups will also increase the average saving rate.

If saving rates increase the most for older age-groups, the weighted average of the saving rate is likely to increase substantially since the elderly will constitute a larger share of the population and, thus, be assigned a larger weight in the average saving rate. If the saving rate mainly increases for younger age-groups, there may not be a marked difference in the average saving rate. In conclusion, more research is needed in this area in order to determine the likely implications for saving of changes in age-structures and poverty. However, the main finding from the partial equilibrium analysis is that the saving rate is likely to increase in the future — especially if poverty falls. This, the topic of the next section, depends crucially on whether there will be endogenous changes in agespecific saving rates.

# **4. Aging, Wealth, and Demographic Dividends**

This section provides a general equilibrium (general equilibrium) analysis of the policy challenges and tradeoffs the Brazilian government faces in the context of aging. This analysis complements the partial equilibrium analysis described in the previous section. The partial equilibrium model does not allow households to change their saving behavior as a response to neither aging nor the economic implications aging may have. Instead, households are assumed to display the same economic behavior in the future as they did when the POF household surveys were conducted in 2002 an 2008, respectively.

However, aging is likely to have major general equilibrium implications for household behavior in terms of, for example, consumption, saving, human capital investments, and labor supply. Moreover, macroeconomic responses to aging that endogenously affects growth — such as reductions in the availability of labor in production — were also not taken into account by the partial equilibrium model.

The general equilibrium model captures these behavioral and macroeconomic responses to aging. A general equilibrium model has its own advantages and disadvantages compared to the partial equilibrium model since
Herfindahl index (H) of exports for each country using the HS 4-digit, HS 6-digit, and SITC 3- digit export data, and estimate the following equation<sup>6</sup> : We use GDP per capita rather than the log of GDP per capita to remain consistent with the approach of Imbs and Wacziarg (2003). The results, summarized in Table 3, indicate that, similar to total production, a country’s export basket becomes more diversified as income rises until a relatively high level, at which point the process reverses itself and specialization occurs. This result is also found when employing a fixed-effects OLS regression on the SITC data from 1972-2002.

**<u>Table 3: Stages of Export Diversification</u>**

|**Variable**|**HS 4-Digit**<br>**1995**|**HS 6-Digit**<br>**1995**|**SITC 3-Digit**<br>**1972-2002 (FE)**|
|---|---|---|---|
|Coefficient on GDP per capita|-.0000333<br>(-4.55)|-.0000183<br>(-3.07)|-.0000173<br>(-7.01)|
|Coefficient on GDP per capita squared|9.53e-10<br>(3.30)|5.25e-10<br>(2.44)|4.98e-10<br>(6.71)|
|Minimum Point(highest levelofdiversification)|$17,471|$17,429|$17,369|
|AdjustedR-Squared (OLS) /F-Statistic (FE)|.2683|.2047|60.79|
|NumberofObservations / Groups|100|53|146|

Note: parentheses indicate t-statistics. Source: Author’s calculations All three data types yield a level
# **Estimating Poverty Using Cell Phone Data: Evidence from Guatemala**

By MARCO HERNANDEZ, LINGZI HONG, VANESSA FRIAS-MARTINEZ, ANDREW WHITBY, and ENRIQUE FRIAS-MARTINEZ<sup>1</sup> _JEL Classification: O12, I32, B41, C80. Keywords: Big data, cell phone call detail records, poverty measurement, Guatemala._ 1 M. Hernandez: The World Bank, Washington, D.C. (email: marcohernandez@worldbank.org); L. Hong: University of Maryland, College Park, MD. (email: lzhong@umd.edu); V. Frias-Martinez: University of Maryland, College Park, MD. (email: vfrias@umd.edu); A. Whitby: The World Bank, Washington, D.C. (email: awhitby@worldbank.org); E. Frias-Martinez: Telefonica Research, Madrid, Spain (email: enrique.friasmartinez@telefonica.com). We are grateful to the Guatemalan authorities for their support to this project, in particular the Ministry of Public Finance and the National Statistics Institute. We thank Oscar Calvo-Gonzalez, Manuela Francisco, Humberto Lopez, Pablo Saavedra, Kinnon Scott, and participants of the World Bank Group’s Innovation Challenge for helpful comments. Maryam AliLothrop and Sean Lothrop provided excellent research and editorial assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.
**Figure 1.7. Medium-Term and Long-Term Correlates of Mobility, Ordered Logit Model with Random Effects, Marginal Effects, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: 1994-2004 Panel B: 2004-2015 Panel C: 1994-2015<br>15<br>10<br>5<br>0<br>-5<br>To full-time employmentNo transitionUpward skills mobilityNo transition To formal sectorNo transitionTo full-time employmentNo transitionUpward skills mobilityNo transition To public sectorNo transitionTo formal sectorNo transitionTo full-time employmentNo transitionUpward skills mobilityNo transition<br>Mobility(%)<br><!-- End of picture text -->

**Note:** Orange/green lines are related to 95% confidence intervals. The dependent variable is individual labor earning mobility between year _t-1_ and year _t_ . The terciles are defined using the cross-sectional sample for each year. Data on formal sector are available since 1998 and data on public sector are available since 2004.

66
Table 2 presents balance on baseline child-level characteristics for the full sample. About 49% of the children in our sample are female, 94% are living with their biological mothers and 86% are the children of the heads of households. On average, children are 3 years old, with children in the control group being slightly older than children in the treatment group (2.97 years old vs 3.02, _p<_ 0.05). The average child development score in our sample is 85 points out of a total of 152 points, with an average gross motor score of 36 points (ranging from a minimum of 0 to a maximum of 52), an average fine motor score of 20 points (ranging from a minimum of 0 to a maximum of 54), and an average language score of 29 points (ranging from a minimum of 0 to a maximum of 46).

Although our tracked sample, used in the analysis, mainly appears to be balanced on baseline characteristics, we find statistically significant differences at the 5% level in 4 of the 22 baseline household characteristics and 5 of the 18 baseline child characteristics we test, including some key employment and time use variables.<sup>12</sup> To create a scale-free measure of overlap in the distribution of covariates in the two treatment groups, we estimate normalized differences following Imbens and Rubin (2015). The only variable with a normalized difference above their suggested cutoff of 0.25 standard deviations is total hours worked per week, with a normalized difference of 0.27. Given that the public work sites were randomly assigned to receive a childcare center, we view these differences as resulting from chance. We address this imbalance by using an ANCOVA model as our preferred specification in our regression analysis.

# **4.2 Use of Childcare Centers**

At baseline, only a very small proportion of households (2%) reported having any child in the household attend a childcare center in the last