rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 1. Emerging Economy Imports Affected by TTBs through 2011 (cont.)**

<!-- Start of picture text --> a. By policy<br><!-- End of picture text -->

- **b. By category of affected trading partner**

<!-- Start of picture text --> percent Indonesia<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Mexico<br>2<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1991 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent South Africa<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Turkey<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> <!-- Start of picture text --> Stock: imports subject to any imposed TTB in effect<br>Stock: imports subject to AD only in effect<br>Flow: imports subject to any newly initiated TTB investigation<br>Flow: imports subject to newly initiated AD investigation only<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia<br>percent<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Mexico<br>percent<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1991 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>South Africa<br>percent<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Turkey<br>25<br>20<br>15<br>10<br>5<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>All trading partners' exports under any TTB in effect<br>China's exports under any TTB in effect<br>Other emerging economies' (non-China) exports under any TTB in effect<br>High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

20
After 2007, however, employment declined to 324,871 in 2008. Employment levels in Sri Lanka’s apparel sector increased from 102,000 in 1990 to 340,367 in 2004, accounting for more than a third of manufacturing employment in 2004 (Staritz and Frederick 2011b). As the industry started consolidating<sup>8</sup> firms after the end of the MFA, employment declined by approximately 20 percent to 270,000 in 2008 (see figure 2).

**Figure 2 Total Employment in the Apparel Sector in Cambodia and Sri Lanka**

<!-- Start of picture text --> 400,000<br>350,000<br>300,000<br>250,000<br>200,000 Sri Lanka<br>Cambodia<br>150,000<br>100,000<br>50,000<br>0<br>1995 2001 2002 2004 2005 2007 2008<br>number employed<br><!-- End of picture text --> _Source:_ Kelegama 2005a, 2005b, 2006, 2009; Tait 2008; Barrie 2009; Saheed 2010; Garment Manufacturers’ Association in Cambodia (GMAC).

_Note:_ Operating employment numbers are presented for Cambodia..

# **3. Description of the Data and Methodology**

# _3.1. Methodology_

The paperfollows the theoretical model from Robertson and Trigueros-Argüello(2011), which uses a simplified version of the model used in Do, et al. (2011).The model decomposes the different wage effects of trade liberalization to provide the theoretical ground for the MFA phase-out implications for women in apparel manufacturing.

Assumethat there are two factors, males ( _m_ ) and females ( _f_ ), and two industries, apparel manufacturing ( _a_ ) and another ( _b_ ). The output of the two goods( _ya, yb_ ) can be summarized with linear homogeneous, differentiable, and positive and declining marginal productproduction functions:

> 8Consolidating the apparel industry in Sri Lanka means concentrating apparel production in a smaller number of plants, mostly the larger enterprises. This phenomenon was accompanied by a decrease in the number of small firms and overall employment.

5
- **11** .

Figure 5. Decomposition<sup>of changes in</sup> RPCs for African export<sup>crops</sup> <!-- Start of picture text --> average annuol chonge averoge RPC<br>0.2 /1.28<br>-0.2 -0.8<br>-0.4 -0.6<br>-0.6- 0.4<br>1971 1973 1975 1977 1979 1981 1983 1985 1987<br>_ prod'er price effect exchange rate effect<br>FII inter'l price effect - RPC (right scale)<br><!-- End of picture text -->
**Table 6** : Comparison of Returns to Schooling using Mincerian and Wald Estimators

||**Pooled sample**|**Males**|**Females**|**Urban**|**Rural**|
|---|---|---|---|---|---|
|**_Panel a. Birth cohort data_**<br>Mincerian earnings function|9.40%***|10.60%***|7.30%***|9.50%***|8.80%***|
|[std. error]|[0.013]|[0.020]|[0.008]|[0.010]|[0.024]|
|Wald estimate [std. error]|7.90%***<br>[0.018]|9.90%***<br>[0.024]|4.30%***<br>[0.011]|10.21%***<br>[0.017]|2.90%<br>[0.026]|
|**_Panel b. Individual-level data_**<br>Mincerian earnings function|9.7%***|8.9%***|10.7%***|8.6%***|10.0%***|
|[std. error]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|Wald estimate<br>[std. error]|11.2%***<br>[0.004]|15.0%***<br>[0.003|7.8%***<br>[0.003]|10.6%***<br>[0.004]|10.1%***<br>[0.004]|
|Wald estimate using IV<br>estimator<br>[std. error]|12.02.%***<br>[0.003]|17.2%***<br>[0.004]|7.4%***<br>[0.004]|13.8%***<br>[0.004]|9.4%***<br>[0.004]|

Note: The Mincerian return to schooling estimates are taken from specification II for each group in Table 5 while the Wald estimates for pooled sample is derived by dividing the coefficients of life expectancy at birth from specification IV in Table 3 by the coefficient of life expectancy at birth in specification IV in Table 1. Similarly, for each gender and Rural-Urban group, the Wald estimates are obtained by dividing the life expectancy coefficients from specification II in Table 4 by life expectancy coefficients from specification IV in Table 2. Standard errors for Wald estimates are obtained by 500 bootstrap replications.

44
||[0.143]|[0.108]|[0.156]|[0.212]|[0.190]|
|---|---|---|---|---|---|
|Registration Fees|261.58|543.478|319.667|-58.086|223.812|
|(CFA Francs)|[79.570]|[154.603]|[85.403]|[116.727]|[176.623]|
|Education Expenses|2351.689|3012.625|2556.167|-204.477|456.459|
|(CFA Francs)|[334.717]|[476.950]|[291.944]|[444.147]|[559.207]|
|PTA Fees|718.937|699.666|801.5|-82.563|-101.834|
|(CFA Francs)|[74.925]|[92.651]|[75.513]|[106.377]|[119.526]|
|Child is boy|0.495|0.52|0.504|-0.010|0.016|
||[0.008]|[0.017]|[0.012]|[0.014]|[0.021]|
|Age|9.783|9.793|9.837|-0.054|-0.044|
||[0.069]|[0.081]|[0.076]|[0.103]|[0.111]|
|Father has some formal ed.|0.014|0.024|0.026|-0.012|-0.002|
||[0.005]|[0.012]|[0.008]|[0.009]|[0.014]|
|Father has some Koran ed.|0.169|0.164|0.202|-0.034|-0.038|
||[0.041]|[0.040]|[0.077]|[0.087]|[0.086]|
|Mother has some formal ed.|0.004|0.011|0.011|-0.007|0.000|
||[0.003]|[0.007]|[0.004]|[0.005]|[0.008]|
|Mother has some Koran ed.|0.029|0.06|0.107|-0.078|-0.048|
||[0.016]|[0.020]|[0.068]|[0.070]|[0.071]|
|Maternal orphan|0.032|0.029|0.024|0.008|0.005|
||[0.009]|[0.010]|[0.006]|[0.011]|[0.012]|
|Paternal orphan|0.04|0.07|0.055|-0.015|0.015|
||[0.009]|[0.010]|[0.016]|[0.018]|[0.019]|
||Household l|evel variables||||
|Head age|42.881|45.669|45.629|-2.748*|0.040|
||[1.052]|[1.190]|[1.223]|[1.613]|[1.707]|
|Head is male|0.976|0.978|0.978|-0.002|0.000|
||[0.005]|[0.007]|[0.006]|[0.008]|[0.010]|
|Head is Mossi|0.129|0.094|0.101|0.028|-0.007|
||[0.064]|[0.045]|[0.048]|[0.079]|[0.066]|
|Head is Fulani|0.389|0.46|0.411|-0.023|0.049|
||[0.090]|[0.101]|[0.102]|[0.136]|[0.144]|
|Head is of Blacksmith descent|0.041|0.027|0.02|0.021|0.007|
||[0.021]|[0.010]|[0.006]|[0.021]|[0.011]|
|Head is of Noble descent|0.377|0.37|0.557|-0.180|-0.187*|
||[0.084]|[0.071]|[0.075]|[0.113]|[0.103]|
|Head is of Captive descent|0.35|0.391|0.193|0.157|0.198*|
||[0.088]|[0.085]|[0.058]|[0.105]|[0.103]|
|Head is Muslim|0.967|0.978|0.987|-0.020|-0.009|
||[0.018]|[0.012]|[0.007]|[0.019]|[0.014]|
|Household asset value (1000 CFA)|66.522|92.109|78.966|-12.443|13.143|
||[12.659]|[19.772]|[6.027]|[14.020]|[20.670]|

Robust standard errors in brackets.

* Significant at 10%; ** significant at 5%, *** significant at 1%

- 1 USD = +/- 500 CFA Francs.

Mossi and Fulani are two ethnic groups from the region. Blacksmith, Noble or Captive descent are castes used to categorize households within these ethnic groups.

25
# **3. Research Design**

# 3.1. Data

The key variables for our analysis include indicators of structural change, productivity convergence, and imported capital and intermediates. The first two variables are the outcome variables—which we employ as key components of economic transformation. Indicators of imported capital and intermediates, on the other hand, are the main explanatory variables. To minimize potential omitted variable bias, it is important that we control for other variables in the empirical model specification. Guided by a closely related literature on the drivers of structural change and productivity efficiency (see Danquah & Ouattara, 2015; Owusu, 2024; Ndubuisi _et al.,_ 2022; Ndubuisi & Owusu, 2023), our specified empirical model will control for human capital, institutional quality, inflation rate, natural resource, and infrastructure (mobile broadband and transport). We also control for initial industrial structure since this has strong implication on structural change and productivity growth. To operationalize this variable, we use each country’s initial agricultural value-added share. Table A1 in the appendix describes and lists the data sources of these variables.

Structural change involves a compositional shift in the sectoral structure of an economy. From a production standpoint, structural change can be assessed by examining the evolution of sectoral value-added shares or employment shares (Herrendorf _et al.,_ 2014). Due to limited manufacturing employment data for Africa, our empirical analysis relies on value added data. Both the employment and value-added data, whenever available are used interchangeably to measure structural change. Following Kaba et al. (2022), we measure structural change as the ratio of manufacturing value-added to agriculture value added. Higher values of the ratio indicate a compositional shift of an economy’s sectoral structure from agriculture to manufacturing. We adopt a similar measure in our study. The sectoral value-added data used to compute the structural change index is taken from the UNCTAD Statistical Database.

<mark>For the productivity convergence</mark> , we use a measure of labor productivity efficiency computed as a country’s labor productivity relative to the global labor productivity frontier. To compute the global labor productivity frontier, we consider all the countries in the world as contained in the Penn World Table 10.1. The global labor productivity frontier in a year is then defined as those 9
Policy Research Working Paper 8428

# **Abstract**

Business and politicians’ interaction is pervasive but has mostly been analyzed with a binary approach, i.e. either a firm is connected to a politician or not. Yet the network dimensions of such connections are ubiquitous. This paper uses use a unique data set for seven economies that documents politically exposed persons and their links to companies, political parties, and other individuals. The data set is used to identify networks of connections, including their scale and composition. The analysis finds that all country networks are integrated having a Big Island. They also tend to be marked by small-world properties of high clustering and short path length. Matching the data to firm-level information, the paper examines the association between being connected and firm-level attributes. The originality of the analysis is to identify how location in a network, including the extent of ties and centrality, is correlated with firm scale and performance. In a binary approach, such network characteristics are omitted and the scale and economic impact of politically connected business may be significantly mis/ under-estimated. By comparing the results of the binary approach with the network approach, the paper also assesses the biases that result from ignoring network attributes.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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://www.worldbank.org/research. The authors may be contacted at mbussolo@worldbank.org, scommander@alturapartners.org, s.poupakis@ucl.ac.uk.

_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 not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
£

# Tble **of** Contents

Paae

|1.|<br>INTRODUCTION<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>1|
|---|---|
|2.|A REVIEW OF THE LITERATURE ON INTERNATIONAL WORKERS' REMITTANCES<br>.<br>.<br>3|
||2.1.<br>The Endogenous Migration Approach<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>3|
||2.2.<br>The Portfolio Approach<br>.<br>. . . . .<br>.<br>. . . . . . . . . ..<br>. .<br>6|
||2.3.<br>A Synthesis of the two Approaches<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>10|
|3.|EXTERNAL MIGRATIONAND REMITTANCES<br>IN NORTH AFRICA ANDEUROPE.<br>.<br>.<br>.<br>12|
|4.|ECONOMETRIC<br>ESTIMATION<br>OF THE REMITTANCE<br>FUNCTION<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>17|
||4.1.Model Specification<br>. . . . . . .<br>. .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>17|
||4.2.<br>overview of the Results.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>21|
||4.3.<br>Some Diagnostic<br>Tests and Additional Estimates<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>22|
||4.4.<br>Interpretation of theResults<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>25|
|5.|CONCLUSIONS<br>. .<br>. . .<br>. . .<br>.<br>.<br>.<br>.<br>.<br>.<br>. .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>28|
|APPEN|DIX-DataSourcesandConstruction<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>44|
|APPEN<br>RFRNC|DIX TABIE8<br>. .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>48<br>ES<br>.<br>.<br>.<br>.<br>.<br>. ...........<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>54|
**<u>Table 14.  Robustness to Dropping Poorly Fitting Placebos</u>**

|||<br> <br>|<br>|Ho<br>|dgesLehmann<br>|
|---|---|---|---|---|---|
|RMSPE<br>Ratio|Number of placebo<br>firms used|Fraction<br>of<br>all<br>placebofirms used|Mean percentile<br>rankstatistic (�)|Estimate|95% confidence<br>interval|
|||**(log) Median Cl**<br>|**earance Time**<br>|||
|.|525|1.00|0.434|-0.065|(-0.216, 0.067)|
|20|486|0.93|0.427|-0.068|(-0.216, 0.054)|
|10|449|0.86|0.428|-0.064|(-0.215, 0.056)|
|5|410|0.78|0.422|-0.064|(-0.215, 0.052)|
|2|318|0.61|0.409|-0.081|(-0.243, 0.033)|
|||**(log) Clearance tim**<br>|**e- 75**<sup>**th**</sup>**percentile**<br>|||
|.|525|1.00|0.309***|-0.404|(-0.618,-0.167)|
|20|479|0.91|0.310***|-0.404|(-0.611,-0.157)|
|10|443|0.84|0.310***|-0.382|(-0.597,-0.180)|
|5|406|0.77|0.310***|-0.381|(-0.605,-0.198)|
|2|329|063|0334***|-0404|(-0677-0105)|
|||.<br>**(log) Clearance tim**<br>|. <br>**e- 90**<sup>**th**</sup>**percentile**<br>|. <br>|., .<br>|
|.|525|1.00|0.377*|-0.261|(-0.626, 0.004)|
|20|483|0.92|0.372**|-0.259|(-0.613,-0.011)|
|10|451|0.86|0.374**|-0.234|(-0.583,-0.004)|
|5|409|0.78|0.369**|-0.238|(-0.580,-0.021)|
|2|325|0.62|0.383*|-0.194|(-0.4690.008)|
|||<br>**Inspectio**<br>|<br>**n rates**<br>|<br>|, <br>|
|.|525|1.00|0.484|-0.002|(-0.021, 0.015)|
|20|474|0.90|0.463|-0.005|(-0.023, 0.012)|
|10|428|0.82|0.464|-0.004|(-0.022, 0.012)|
|5|361|0.69|0.463|-0.004|(-0.022, 0.012)|
|2|236|0.45|0.496|-0.001|(-0.018, 0.014)|
|||**(log) Impo**|**rt value**|||
|.|525|1.00|0.588|0.110|(-0.049, 0.253)|
|20|494|0.94|0.589|0.106|(-0.049, 0.246)|
|10|466|0.89|0.590|0.102|(-0.048, 0.240)|
|5|424|0.81|0.585|0.089|(-0.048, 0.214)|
|2|338|0.64|0.620*|0.127|(-0.005, 0.245)|

Notes: *, **, and *** indicate significance at the 10%, 5%, and 1% confidence levels, respectively. Placebos are assessed by the match quality of the synthetic control firm constructed by the benchmark SCM model. The RMSPE ratio is the ratio of pre-treatment root mean square prediction error for the placebo relative to that of the treated firm. Rows show results for sub-samples where placebos are limited to those whose RMSPE ratios are less than X, where X = . indicates the full sample.

# **7.  Testing for Heterogeneity**

The tests presented so far evaluate the null hypothesis of no impact of the IHC program on the outcome variables of interest for all treated firms. Formally, the null hypothesis could be rejected if there is a common impact on all firms, or if there is large enough impact on a subset of firms (even just one) without any impact on the remaining firms. To better inform these alternatives, we apply tests for heterogeneous impacts of the IHC program across treated firms.

41
of some of the more recent theoretical breakthroughs. These authors link worker-level panel data with ...rm-level and industry-level data to obtain a rich dataset that allows them to test many implications of the most advanced trade models (e.g. heterogeneous-...rm models that incorporate heterogeneous labor) for Brazil. In so doing, the authors are able to assess the impact on jobs of Brazil’s trade liberalization during the 1990s while controlling for a number of worker-speci...c, ...rm-speci...c, industry-speci...c, and economy-wide structural reforms. Menezes-Filho and Muendler’s dataset allows them to follow workers throughout the liberalization period and observe the path of their employment histories in greater detail than previous studies. They are particularly interested in the e¤ects of trade liberalization on employment status, type of employment (formal or informal), and job reallocations.

Menezes-Filho and Muendler’s results show that exporting ...rms and ...rms in industries with a “comparative advantage" shed workers more frequently. Moreover, these same ...rms also hire workers less frequently than the average ...rm. Thus, on net, trade liberalization leads to net employment losses in these ...rms. This is surprising given the standard predictions of international trade models that would indicate that these sectors and ...rms would potentially hire more workers when liberalization occurs. Menezes-Filho and Muendler also show that tari¤ reductions and increased import penetration are associated with an increase in the likelihood of a worker transitioning into informality and unemployment, as well as with a lower probability of a transition from informality back to formal employment. Furthermore, they ...nd that trade liberalization in Brazil has been associated with longer reallocation times for workers moving from one formal-sector job to another formal-sector job. Their results are robust to di¤erent levels of exposure to trade, ...rm-level productivity, worker heterogeneity, as well as other general trends that occurred in the country during the period studied–such as skill-biased technological change and labor market reforms.

Kaplan and Verhoogen (2009) use matched employer-employee data from Mexico to examine the wage premia paid by exporters in the Mexican manufacturing sector. Wage premia are 31
behavior to avoid embarrassment, or if they believe the enumerator has a role in influencing their selection into the program and they fear that telling the truth will harm their chances of being selected.<sup>1</sup>

# **2. Data**

# **2.1 Girls’ Sexual and Romantic Activity**

This study is embedded within a baseline survey for an impact evaluation of the Sisters of Success (SOS) Program, in which mentors and girls’ groups delivered life skills, including sex education, to adolescent girls aged 12‐15. The baseline survey took place from October 2013 through January 2014, with girls who registered for the SOS program. This paper uses data from the 1,264 girls aged 14‐15 during baseline, as incidence of reported sexual activity among the younger girls is too low for statistical inference. We further drop 46 observations for whom some data used in this analysis are missing, for a final working sample of 1,210 girls. Among these, 17% reported having ever had sex, 10% having had sex in the past thirty days, 20% having ever had a boyfriend, and 15% currently having a boyfriend.<sup>2</sup>

# **2.2 Interviewers’ Characteristics**

We examine how girls’ self‐reports vary with the characteristics of the 40 enumerators who interviewed them. We use data from a survey self‐administered by the interviewers in May 2014 to construct measures of attitudes towards gender, attitudes towards the SOS program, and ability using selected questions that predict girls’ responses. We also rated interviewers’ physical attractiveness.

# **2.2.1 Physical Attractiveness**

To measure physical attractiveness, we follow a similar procedure to that used in the economics literature on beauty [e.g. Hammermesh and Biddle 1994]. Each interviewer was rated independently on a scale from 1 (“Very unattractive”) to 10 (“Very attractive”) by a male‐female pair of young Liberian adults, hired as research assistants for this study. The raters have similar standards: the mean enumerator physical attractiveness is 5.6 (s.d.=1.6) for the male rater, 5.2 (s.d.=1.5) for the female rater, and the Cronbach’s

> 1 One cannot also rule out the possibility that girls leave wiggle room in their verbal responses, and the enumerators with different attitudes code it differently.

> 2 In this paper we focus on these extensive margins of girls’ sexual and romantic behaviors. We find little variation on the corresponding intensive margins (e.g. number of sexual partners the girl ever had).

3