rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Poor exposed (millions, $1.90) #>0.15m¢>0.5m ¢>1.5m<br>180<br>°<br>160<br>140<br>120<br>100<br>°<br>80<br>60<br>40 °<br>20<br>00<br>02020 40 60(years) 80 100<br>Return40 period60(years) (years)<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed (millions, $2.15) >30% land affected<br>250 @>50% land affected<br>200<br>a<br>150 of<br>100 ;<br><‘<br>50<br>o<br>0<br>0 10 Return2020 period;; (years)3030 40 50<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed (millions, $1.90) #>0.15m¢>0.5m ¢>1.5m Poor exposed (millions, $2.15) >30% land affected<br>180 250 @>50% land affected<br>°<br>160<br>140 200<br>a<br>120<br>100 150 of<br>°<br>80 100 ;<br>60 <‘<br>40 ° 50<br>20 o<br>00<br>02020 Return40 period60(years) 80 100 0 10 Return2020 period;; (years)3030 40 50<br>Poor exposed (millions,$2.15) Poor exposed (millions, $2.15)<br>350 70 @Cat-1 ¢Cat-2 ¢Cat-3<br>a<br>300 >32C 60 e<br>250 #>33C 50<br>200 a >34C 40<br>150 sO sO >35C 30 + oa<br>100 @>36C 20 °<br>50 oa<br>0 OO OO 100 ¢ ‘° ¢_$ ra<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Return period (years) Return period (years)<br>Poor exposed exposed (millions, $5.50) #>0.15m ¢>0.5m ¢>1.5m Poor exposed (millions, $6.85) ¢>30% land affected<br>900 900 @>50% land affected<br>800 . 800<br>700 700 Md<br>600 600 ¢<br>500 500<br>400 ° 400 ry<br>300 300<br>200 ° 200<br>100 100 o<br>0) 0<br>0 20 Return4040 period60(years)60(years)(years) 80 100 0 10 Return20 period; (years)30 40 50<br>Poor exposed exposed (millions, $6.85) Poor exposed exposed (millions, $6.85) $6.85) @Cat-1 ¢Cat-2 ¢Cat-3<br>2500 600<br>#>32C bd<br>2000 500<br>© 733 733 400<br>1500 _—_ *>34C 300 :<br>1000 >35C °<br> a 200 ; .<br>500 ne ne @>36C too], ° a<br>9 0) 0 20 40 60 80 100<br>0 20 40 60 80 100 Return period (years)<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed (millions,$2.15)<br>350<br>300 >32C<br>250 #>33C<br>200 a >34C<br>150 sO sO >35C<br>100 @>36C<br>50<br>0 OO OO<br>0 20 40 60 80 100<br>Return period (years)<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed exposed (millions, $5.50) #>0.15m ¢>0.5m ¢>1.5m<br>900<br>800 .<br>700<br>600<br>500<br>400 °<br>300<br>200 °<br>100<br>0)<br>0 20 Return4040 period60(years)60(years)(years) 80 100<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed exposed (millions, $6.85)<br>2500<br>#>32C<br>2000<br>© 733 733<br>1500 _—_ *>34C<br>1000 >35C<br> a<br>500 ne ne @>36C<br>9<br>0 20 40 60 80 100<br>Return period (years)<br><!-- End of picture text --> <!-- Start of picture text --> Poor exposed exposed (millions, $6.85) $6.85) @Cat-1 ¢Cat-2 ¢Cat-3<br>600<br>bd<br>500<br>400<br>300 :<br>°<br>200 ; .<br>too], ° a<br>0) 0 20 40 60 80 100<br>Return period (years)<br><!-- End of picture text -->
7 instead of continuing to attempt phone contact at this time, we expanded plans for supplemental endline tracking. .

At endline, we again attempted to contact all participants to invite them for endline surveys and HIV/STI testing. Research staff first called the current primary phone number of each participant. Three attempts at different times of day were made for each participant. If all attempts on the primary phone were unsuccessful, all alternate phone numbers were attempted. Pamphlets were also distributed at common work sites such as brothels and bars, and in-person tracing was conducted by RDS seeds and study staff at 25 FSW business centers and sex work locations (bars, brothels, guest houses) to attempt to locate participants unreachable by phone. An endline lottery was introduced in September 2021 to incentivize participation, as a large proportion of participants had not completed surveys or testing: One of every five endline participants each week was selected to win 50,000 TZS for participating. Those who participated were also asked to locate any peers or colleagues enrolled in the study and bring them to PASADA to complete their survey and testing; one of every ten participants who recruited others in this way was selected to win 50,000 TZS. After September 2021, the endline lottery was replaced by a uniform incentive amount of 10,000 TZS to all participants who completed endline surveys.

# _Outcomes_

The primary outcome was combined incidence of HIV and/or HSV2, two incurable STIs, at 36 months (pre-specified for 24 months, but extended due to the COVID-19 disruption). Prespecified secondary outcomes included the prevalence of syphilis and trichomonas vaginalis, two curable sexually transmitted infections, self-reported sexual behavior, and incident HIV and HSV2 infection (separately). Participants were coded as having incident HSV2 only if they tested negative for HSV2 at baseline and positive at endline.

# _Sample size_

The RESPECT II trial was powered based on the primary outcome of combined incidence of HSV2 and/or HIV. Assuming 15% combined incidence in the control group and a target minimum detectable effect of 4.5 percentage points, we determined that an endline sample of 1,724 participants would be needed to achieve 80% power (using an alpha of 0.05). We increased this sample size by 20% to account for attrition at endline, for a minimum of 2,156 participants overall (at least 1,078 per study arm).

_Statistical analysis_
# Privatization and Labor Force Restructuring

# **around** the World

## Alberto Chong

Inter-American Development Bank and Georgetown University Public Policy Institute Albertoch(Iadb.Org Florencio Lopez -de-Silanes Yale University School of Management and National Bureau of Economic Research florencio.lopezdesilanes@yale.edu We are grateful to Alexander Dyck, Martin Rama, Jaime<sup>Saavedra, and seminar</sup> participants at a conference at Fondazione Eni Enrico Mattei,<sup>and theLatin Americanand</sup> Caribbean Economic Association Meeting for comments<sup>and suggestions.VirgilioGaldo</sup> and Alice Hong provided research assistance and Madison<sup>Boeker providedediting.The</sup> Research Committee of the World Bank supported this project<sup>throughgrant RPO683-69.</sup> The Yale School of Management provided support<sup>throughitsInternationalInstitute of</sup> Corporate Governance.
||||||||Root-m|ean-s|quare|error||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||n=|400|n=|1,000|n=|3,000|n= 1|0,000|n= 3|0,000|
|pT|pY|∆ATE|∆LAT E<br>∆AT E|BP|IV|BP|IV|BP|IV|BP|IV|BP|IV|
|0.1|0.1|0.07|<br>2.75|0.25|5.99|0.15|8.13|0.08|0.29|0.04|0.18|0.03|0.15|
|0.1|0.3|0.14|1.12|0.31|17.42|0.22|18.43|0.13|0.44|0.07|0.23|0.04|0.12|
|0.1|0.5|0.15|0.42|0.35|12.13|0.29|15.32|0.18|0.53|0.10|0.27|0.06|0.17|
|0.1|0.7|0.12|0.15|0.22|20.78|0.21|9.53|0.21|0.50|0.18|0.25|0.11|0.17|
|0.1|0.9|0.05|0.13|0.06|7.74|0.05|13.87|0.05|0.32|0.06|0.17|0.08|0.10|
|0.3|0.1|0.06|1.34|0.24|2.31|0.16|0.86|0.07|0.14|0.03|0.07|0.02|0.05|
|0.3|0.3|0.13|1.61|0.27|6.40|0.19|0.63|0.11|0.20|0.06|0.13|0.03|0.10|
|0.3|0.5|0.15|1.13|0.24|15.60|0.16|0.54|0.10|0.23|0.05|0.12|0.03|0.07|
|0.3|0.7|0.12|0.56|0.23|38.01|0.16|0.64|0.09|0.23|0.05|0.12|0.03|0.09|
|0.3|0.9|0.05|0.16|0.11|5.50|0.09|0.39|0.08|0.15|0.07|0.09|0.04|0.06|
|0.5|0.1|0.05|0.48|0.16|17.87|0.11|0.63|0.06|0.12|0.03|0.07|0.02|0.05|
|0.5|0.3|0.12|1.17|0.23|5.10|0.16|1.32|0.09|0.17|0.05|0.09|0.03|0.06|
|0.5|0.5|0.15|1.44|0.28|14.33|0.20|1.18|0.12|0.19|0.07|0.12|0.04|0.09|
|0.5|0.7|0.12|1.18|0.24|11.22|0.16|0.99|0.09|0.18|0.05|0.09|0.03|0.06|
|0.5|0.9|0.05|0.48|0.16|5.67|0.10|0.46|0.06|0.13|0.03|0.07|0.02|0.05|
|0.7|0.1|0.05|0.16|0.10|4.21|0.09|0.46|0.09|0.15|0.07|0.09|0.05|0.06|
|0.7|0.3|0.12|0.56|0.22|7.76|0.16|0.86|0.09|0.22|0.05|0.12|0.03|0.08|
|0.7|0.5|0.15|1.12|0.24|4.12|0.17|0.88|0.10|0.22|0.06|0.12|0.03|0.07|
|0.7|0.7|0.13|1.59|0.27|9.49|0.19|0.78|0.11|0.21|0.06|0.13|0.04|0.10|
|0.7|0.9|0.06|1.35|0.24|3.27|0.15|0.64|0.07|0.14|0.03|0.08|0.02|0.05|
|0.9|0.1|0.05|0.14|0.06|15.44|0.06|12.17|0.06|0.29|0.07|0.17|0.07|0.10|
|0.9|0.3|0.11|0.15|0.21|15.21|0.21|12.75|0.21|0.52|0.17|0.25|0.10|0.16|
|0.9|0.5|0.14|0.41|0.32|18.70|0.28|8.28|0.19|0.56|0.11|0.27|0.06|0.17|
|0.9|0.7|0.14|1.09|0.31|26.71|0.21|14.96|0.13|0.49|0.07|0.22|0.04|0.13|
|0.9|0.9|0.07|2.78|0.25|11.85|0.17|5.20|0.09|0.33|0.05|0.19|0.03|0.15|

Table 9: Root-mean-square error of ∆<sup>ˆBP</sup> ATE<sup>andˆ∆IVfortrueATEasafunctionofpTand</sup> pY , in bivariate probit model simulations with covariate X and ρ = 0.7.

32
# **Appendix 1 –** Variable Definitions

|**Variable**|**Description**|**Source**|
|---|---|---|
|Adult population|Adult population in 2009. When the 2009 data are not available,<br>we use the most recent|World Development Indicators|
|Bank cost/income|Bank Cost‐Income Ratio, 5‐year average (2002‐2007)|Financial Structure Database|
|Banking concentration|Share of deposits in the five largest banks|Barth, Caprio, and Levine<br>(2004)|
|Branches per adult|Number of branches per 100,000 adults|Financial Access Database<br>(2010)|
|Closing business ‐ days|The average time to close a business, 2007|World Bank Doing Business<br>Indicators (2008)|
|Contract enforcement ‐<br>procedures|The average number of procedures to enforce a contract, 2007|World Bank Doing Business<br>Indicators (2008)|
|Contract enforcement ‐ time|The time to resolve a dispute, counted from the moment the plaintiff<br>files the lawsuit in court until payment, 2007.|World Bank Doing Business<br>Indicators (2008)|
||Getting credit, Credit Information Index, 2007. This index measures||
|Credit information index|rules and practices affecting the coverage, scope and accessibility of<br>credit information available through either a public credit registry or<br>a private credit bureau.|World Bank Doing Business<br>Indicators (2008)|
|Domestic credit/GDP|Domestic credit to GDP, 2007|World Development Indicators|
|Expected real growth|Expected real growth rate of GDP per capita, 2009|World Development Indicators|
|Foreign owned banks, share|Percentage of banking system assets in banks 50percent + owned by foreign|Barth, Caprio, and Levine|
|of assets|entities|(2004)|
|Fuels and mining/GDP|Exports of fuels and mining products (current USD) as percent of GDP, 2007|WTO|
|GDP growth|Gross Domestic Product per capita in current dollars of 2007|World Development Indicators|
|GDP growth avg|GDP growth 5 year average 2002‐2007|World Development Indicators|
|GNI per capita|Gross National Income per capita in current dollars of 2007|World Development Indicators|
|Inflation|Inflation, consumer prices (annual percentage) 2007|IFS|
|Interest spread|Interest rate spread (lending rate minus deposit rate, percent) 2007|IFS|
||Getting credit, Legal Rights Index, 2007. This index measures the degree to|World Bank Doing Business|
|Legal rights index|which collateral and bankruptcy laws protect the rights of borrowers and<br>lenders and thus facilitate lending.|Indicators (2008)|

29
27 THE INTENSIVE MARGIN IN TRADE <u>1</u> _N_ (2003) show that Θ<sup> ̄</sup> _≡ N_ � _n_ =1<sup>_θ_</sup> _i_<sup>(</sup><sup>_n_)</sup> is a consistent estimator of Θ _i_ , while the covariance matrix of Θ<sup> ̄</sup> _i_ is given by the variance of Θ<sup>(</sup> _i_<sup>_n_)</sup> , so we use this to construct confidence intervals for _θ_<sup> ̄</sup> _i_ . For each origin, we run 5 different chains that start at a different random starting value _θi_<sup>(</sup><sup>_i_). We then</sup> explore whether the different parameters in _θi_ converged to the same values across different chains and discuss the convergence of the chains in the Online Appendix F.

Loosely speaking, identification works as follows. First, data on export flows and the number of exporters across country pairs helps in identifying _dij_ and _μ_ ̄ _f,ij_ . Second, the variance of firm sales within each _ij_ pair helps in identifying the sum of the dispersion parameters for productivity and demand shocks, ̄ _σφ,i_ + _σα,i_ . Third, the extent of correlation of firm sales from a particular origin across different destinations helps in identifying _σφ,i_ separately from _σα,i_ : the more correlated firm sales are across destinations, the larger is _σφ,i_ relative to _σα,i_ . Fourth, the correlation between fixed costs and demand shocks can be inferred from the distribution of sales of small firms. Intuitively, if correlation is negative, then a firm with a bad demand shock would also likely draw a high fixed cost shock and thus will not export, hence, we would not see a lot of small firms in the data. Finally, to understand how _σf,i_ is identified, imagine for simplicity that there is only one destination. We then have where _Ci ≡_ Pr _{_ ln _σ_ +ln _fi_ 1 _≤ Zi_ 1 _}_ and _gZi_ 1() is the probability density function of the latent sales<sup>_<u>fi</u>_</sup><sup><u>1</u></sup><sup>_<u>≤xi</u>_</sup><sup><u>1</u></sup><sup>_<u>|Zi</u>_</sup><sup><u>1=</u></sup><sup>_xi_</sup><sup><u>1</u></sup><sup>_<u>}</u>_</sup> _Zi_ 1. This implies that we can get the density of _Xi_ 1 by applying weights<sup>Pr</sup><sup>_<u>{</u>_ln</sup><sup>_σ_</sup><sup><u>+ln</u></sup> _Ci_ to the density of _Zi_ 1. The parameter _σf,i_ regulates how these weights behave with _xi_ 1. In the extreme case in which _σf,i_ = 0 then the weights are 0 for _xi_ 1 _≤ μfi_ 1 and 1 _/C_ for _xi_ 1 _> μfi_ 1 _,_ while in the other extreme with _σf,i_ = _∞_ the weights are all equal to 1. For intermediate cases the density of _Xi_ 1 will be somewhere in the middle, with the left tail becoming fatter and the right tail becoming thinner as _σf,i_ increases. This suggests that we can identify _σf,i_ from the shape of the density of sales.

We will use the results of the estimation to conduct exercises similar to those in the previous sections. First, we will compute the IME for all firms and for each percentile using the estimated model. Second, after removing origin and destination fixed effects, we will compute the correlation across the estimated values of _dij_ and _μ_ ̄ _f,ij_ , and between them and distance.

**4.2. Data**
be sanctioned for production—presumably resulting in the government having to seek out a costlier alternative.

# _Derailment of public finances_

The third area is the derailment of public finances. This area has already received considerable attention by scholars who have analyzed the impacts caused by giant oil and gas discoveries on various economic indicators. Arezki et al. (2017) find a decline in savings, and Cust & Mihalyi (2017) identify a growth disappointment, in the five years after discovery.

We do not aim to provide rigorous statistical evidence in this section, but would draw attention to only one indicator: debt sustainability. This is the result of the IMF/World Bank joint debt sustainability analysis across the 12 countries discussed in our study.

_Table 4. Change in debt sustainability risk, according to IMF and World Bank_<sup>_25_</sup>

|**Country**|**2011 Debt**<br>**Sustainability Analysis**|**2020 Debt**<br>**Sustainability Analysis**|
|---|---|---|
|Ghana|Moderate|High|
|Guinea-Bissau|High|Moderate|
|Kenya|Low|Moderate|
|Liberia|Low|Moderate|
|Mauritania|Moderate|High|
|Mozambique|Low|In debt distress|
|Niger|Low|Moderate|
|São Tomé and Príncipe|High|In debt distress|
|Senegal|Low|Moderate|
|Sierra Leone|Moderate|High|
|Tanzania|Low|Low|
|Uganda|Low|Low|

_Source: Authors’ collection from IMF and World Bank DSA reports_ As Table 4 shows, the IMF and World Bank’s assessment of debt sustainability worsened in 9 of the 12 countries, while it improved in only one case. Of course, there are regional and cyclical factors at play too, but various studies point towards resource sector disappointment being a major factor behind debt problems in Ghana (Bawumia and Halland, 2017), Mozambique (Hubert, 2019) and São Tomé (Frynas, Wood and Hinks, 2017). For example, both Ghana and São Tomé had taken on loans that were to be repaid in-kind using oil sector proceeds (Mihalyi, Adam and Hwang, 2020). With the

> 25 These represent the latest assessment as of April 2020. All were conducted before the 2020 global pandemic and economic crisis.

21
This research presents a new measure of corruption, the Public Administration Corruption Index (PACI), formulated using data on cross-border corruption cases. The proposal is motivated by the need to find viable alternatives to currently available cross-national measures of corruption, which, despite their shortcomings, have been and are being extensively used in academia,<sup>1</sup> within policy circles, and in public debates at large.

There are two types of cross-national measures of corruption currently available. The most widely used type is perception-based, such as the Transparency International Corruption Perceptions Index (TI-CPI; Saisana and Saltelli 2012; Transparency International 2012) and the World Bank Control of Corruption Indicator (WB-CCI; Kaufmann, Kraay, and Mastruzzi 2009). The shortcomings of the cited type, however, are well known. For one, perceptions may have weak correlations with actual experiences of corruption (Seligson 2006; Olken 2009; Razafindrakoto and Roubaud 2010). Moreover, often the precise definition of corruption being assessed is not precisely defined. The second type of cross-national measure, meanwhile, is based on surveys assessing first-hand experiences of corruption.<sup>2</sup> Surveys, however, are often costly endeavours, and their results are affected by respondents’ reticence in answering questions related to their participation in corrupt activities.<sup>3</sup> Proposing to use judicial statistics to develop a cross-national measure of corruption may appear ungainly. Even setting aside differences in legal definitions across jurisdictions, the fraction of observed corrupt transactions relative to their actual number is unknown and varies widely across countries. These differences could be so important that they could even imply a _negative_

> 1 On the use of perception-based measures to inquire into the nature, causes and consequences of corruption, see Lambsdorff (2006 and 2007), Treisman (2007), and Rose-Ackerman (1999).

> 2 A well-known example is Transparency International’s “Global Corruption Barometer” (TI-GCB). See http://www.transparency.org/research/gcb/overview.

> 3 See for instance Treisman (2007), Clausen et al. (2011), and Kraay and Murell (2013). Other types of measures of corruption have been proposed, but so far not at the cross-national level. See for example Golden and Picci (2005).
# TABLE 2

# YEARS OF SCHOOLING COMPLETED AND EXPECTED YEARS OF ENROLLMENT RATIOS BY SEX. FOR REGIONS FROM 1960 AND 1980: SELECTED AVAILABLE COUNTRIES

|Region<br>(no. of countries<br>observed)|Years of School<br> <br>Completed<br>A<br>|ing<br>1960-1980<br> <br>ae 20-24<br> <br>|Exoected<br>Y<br>|<br>ears of Enro<br>|llment<br>|
|---|---|---|---|---|---|
|1. High income industrially<br>advanced<br>coutries<br>(including<br>Japan, Israel,<br>and South Africa)|Female to Male<br>Ratio<br>(1)<br> <br>1<br>(24)|Male Level<br>Female to M<br>inYears<br>1960<br>(2)<br>(3)<br>8.9<br>0.94<br>(24)|ale Ratio<br> <br>1980<br>(4)<br>1|Male Level<br>1960<br>(5)<br>10.8|in Years<br> <br>1980<br> <br>(6)<br> <br>12.6|
|2. Latin America|0.94<br>(22)|5.5<br>0.92<br>(22)|0.97|5.6|9.8|
|3. East Asia<br>(excluding<br>Japan)|0.83<br>(8)|6.4<br>(8) Asia<br>||||
|||exclun||||
|4. South Asia|0.45<br>(5)|3.8 Arab)<br>0.65<br>(5)|0.75|7.5|9.4|
|5. West Asia<br>(excluding<br>Israel)|0.34<br>(2)|4.7<br>(2)||||
|6. North Africa|0.39<br>(2)|3.5 Arab<br>0.49<br>(2) States|0.73|5|9|
|7. Sub Saharan<br>Africa<br>(excluding<br>S. Africa)|0.44<br>(7)|3.3 Africa<br>0.56<br>(7) (excluding<br>Arab)|0.73|3.5|7|
|World Total|n.a|n.a<br>0.75|0.82|7.7|9.8|

Source: Columns 1 and 2 derived by averaging of the available sample of country estimates reported in Table _A-1_ by the author. Column UNESCO estimates of enrollment rates by level, with different regional groupings. 1987 Statistical Yearbook, Table 2.10. See footnote expected years of enrollment. Source: Schultz, 1988, TableWomDev 10/4/88. Note: (From next page)
the composition of investment via firms’ technology choice is statistically and economically significant: a one percentage point (pp) increase in inflation reduces the probability of innovation at the establishment level by 4.3%. Consistent with the prediction that firms economize on the use of liquid assets in periods of increased inflation, we find that inflation reduces the share of working capital financed by internal funds or retained earnings, but not the share financed by formal bank loans. Finally, the adverse impact of inflation on innovation increases with establishments’ reliance on internal funds or retained earnings to finance their working capital. The Compustat data, which allow for a tighter measurement of corporate liquidity holdings, further reveal that U.S. firms’ R&D expenditure is increasing with their liquidity holdings: a 1% increase in corporate liquidity holdings is on average associated with a 6% increase in firms’ R&D intensity. Corporate liquidity holdings, in turn, are negatively affected by inflation: a one pp increase in inflation reduces firms’ liquidity holdings by 5.5%. Hence, the adverse effect of inflation on the composition of aggregate investment and its transmission via reduced liquidity holdings is corroborated also in the context of an industrialized economy with developed markets.

Finally, the sector-specific predictions of our theoretical model enable us to apply a generalized difference-in-difference specification as in Rajan and Zingales (1998), which also helps to address any remaining endogeneity concerns. We find that the negative impact of inflation on establishments’ innovation or R&D activity is larger in (i) more volatile sectors, (ii) sectors with higher value added growth, (iii) more R&D intensive sectors, and (iv) sectors with higher asset tangibility. There is thus broad support for the specific mechanism laid out in our theoretical model.

The rest of this paper is organized as follows. Section 2 embeds our paper into the related literature. Section 3 presents the theoretical model. Section 4 discusses the data, the estimation strategy and our empirical findings. The final section concludes.

# **2 Related literature**

There is a vast literature studying the effects of inflation on growth. Theoretical models used to examine the effects of inflation on growth typically combine a variant of an endogenous 4