rafmacalaba/datause-encoder-data huggingface dataset:
7

# TABLE I

AwnV(ioas on the Evolution of Key Macroeconomic Indicators and Housing Fund Bond Rates

|Years:<br>CP|(a)|195<br>15.7|1959<br>17|1990<br> <br>19.5|_1e-_<br>15|I9<br>12|1993<br>10|1994<br>|1995<br>|1996<br>|1997<br>|199<br>|I99|2000|2001|2002|_203_|2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|CostFunds|(b)|14|21.5|17|<br>'4|<br>12|8<br>t0|7<br>9|6<br>|5<br>|5<br>|5|5|5|5|_|5|
|BondRate|(c)|18|25.5|21|is|<br>16|<br>14|<br>13|S<br>|7<br>|7<br>|7|7|7|7|7|7|
|GDP (d)|1418|1705|2020|2317|<br>2614|<br>2917|<br>3203|<br>3487|12<br>|I<br>|I I<br>|I I<br>|11|I|II|if|II|
|GDP nonmnal|<br>(e)|20.2|1.5|14.7|12.8|<br>116|<br>98|<br>|3762<br> <br>|4051<br>|4370<br>|4720|5104|5520|5970|6456|695|
|GDP real|(1)|2.77|-0.86|-0.26|<br>073|.<br>145|.<br>167|3.9<br>|<br>7.9<br>|7.7<br>|7.9<br>|3.0|3.3|8.2|8.2|8.2|<br>3.2|
||||||.|.|.|1.74|1.78|2.55|2.74|2.37|299|3.00|<br>3.00|<br>3.00|3.00|
|Years:|20052|006<br>|2007<br>2|003<br>2|009|2010<br>|2011<br>|2012|2013<br>|2014|2015<br>|036||<br>||||
|CPI(a)|5|5|5|5|5|5|5||<br> <br>|<br>|<br> <br>||2017|2013<br>|219|20.19|2021|
|CostFunds|7|7|7|7|<br>7|<br>7|<br>7|5<br>|5<br>|5<br>|5|5|5|5|5|_5_|5<br>1|
|BondRate|11|it|I L|I I|<br>I I|<br>11|<br>I|7<br>I I|7<br>|7|7<br>|7|7|7|7|7||
|GDP (d)|7552<br>|8167<br>|8533<br>|95'3<br>1|<br>M31|<br>11173|<br>12034|<br>13069|I I|II<br>14134||I I<br>|I<br>|II I|11|3 1|II|11|
|GDP nominal|3.2|3.2|3.2|3.2|3.2|<br>82|<br>32|<br>32|<br>|15236<br>|16531<br>|17t79|9336|20Q12|22646|24459|26453|
|GDP rea|3.00|3.00|3.00|3.00|<br>3.00|.<br>3.00|.<br>300|.<br>300|3.2<br>300|8.2<br>|8.2<br>|3.2<br>|5 .2<br>|3.2|3.2|8.2|3.2|
||||||||.|.|.|3.00|3.00|3.00 ||3.00|3-00|3.00|3.00|3.00|
|Yeatr<br>CMI(a)|2022<br>5|2023<br>5|2024<br>_5_|2025<br>5||||||||||||||
|CostFund|7|7|7|7||||||||||||||
|BondRate|II|II||11 II||||||||||||||
|GDP (d)|23603<br>|30940 1|33462|36139||||||||||||||
|GDP n<br>a|3.2|3.2|3.**2**|.2||||||||||||||
|GDP**1ea**|3.00|3.00|3.01|3.00||||||||||||||
|tes:<br>(a)|Consme|Pnce Ind|ex in S|||||||||||||||

Notes: (b) **Aubor'** estimates, **amed** to be equal in **chbnge** in CPI plus 2% (except _ror_ 1959) (c) Audwr. estinte, Billion Fodata (d) In Audwr. estinte, Billion Fodata aumed to be equl to cos of finds plus 4% (3O% ral yield plus 1 of management fee) (e) GDP erowh (in _)_ in nomnnol terms (M * P gowth (in %)in e tenms
totally bear this assumption out. There is no steady trend in economic outcomes as the length of time that a household has electricity increases (Table 11). However, those households with grid electricity for more than 10 years are distinctly better off than the rest, and this is probably due to a combination of being better off at the outset and then making income gains over the years. The findings for education are quite different with both school enrollment and years of completed schooling consistently improving with increasing duration of having electricity in the home. For instance, for those households with electricity 10 or more years boys have a 100 percent enrollment rate, while girls’ enrollment rate is 93 percent.

Having observed a trend in electricity consumption and welfare indicators as the exposure to electricity increases, it is now worth exploring the benefits of electrification by such exposure. For example, the first round data of 2002 in our sample already contains some electricity users; therefore, we can make a distinction between those households that got electricity in or before 2002 and those received electricity after 2002, and investigate the difference in benefits between them. A comparative assessment of benefits of these two types of beneficiaries can be done using the triple difference (DDD) estimation technique, which is basically the difference between doubledifferenced benefits for early and recent connectors. It is formally given by, where, _YTE1_ = 2002 outcome of treated group who got electricity in or before 2002

- _YTE2_ = 2005 outcome of treated group who got electricity in or before 2002

- _YTL1_ = 2002 outcome of treated group who got electricity after 2002

28
Policy Research Working Paper 8418

# **Abstract**

This paper analyzes the use of capital flow measures in emerging markets. Drawing on a specially compiled new database of capital flow measures, it establishes that policy makers in emerging market economies do not use capital flow measures as an active tool at business cycle frequency. While there is a general trend toward the liberalization of capital accounts, the use of capital flow measures as a countercyclical policy tool is rather sporadic. Instead, countries show a distinct preference for using monetary policy, exchange rate adjustments, macro prudential measures, and adjustments in external reserves to modulate the impacts of domestic business cycles, international liquidity cycles, and shocks to capital flows. Regulation of different kinds of capital flows—resident and nonresident flows; inflows and outflows; and foreign direct investment, portfolio, and banking sector flows—is changed infrequently and is acyclical to domestic business and external liquidity cycles.

This paper is a product of the Strategy and Operations Team, Development Economics Vice Presidency. 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 pgupta5@worldbank.org and omasetti@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 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
percent level. Hence technology may also reduce the distortions caused by historical legacies. We find no significant difference in the effect of free-trade agreements, borders, or languages.

Columns (3) and (7) of Table 1 add shipping costs to the set of explaining variables. Since these are not available for offline data, they are not usually included in gravity equations. But since our eBay data include shipping costs, we include this bilateral ad-valorem average as a control both online and offline where it may also be a valid proxy for shipping costs. Surprisingly, we find no significant effect for shipping costs,<sup>13</sup> and our results are unaffected by this inclusion, which can be explained by the fact that shipping costs are not necessarily correlated with distance.<sup>14</sup> Columns (4) and (8) provide the results using the Poisson pseudo-maximum likelihood estimator which was suggested for gravity models by Santos Silva and Tenreyro (2006) to control for heteroscedasticity. Again we find that distance matters more offline. The estimated distance elasticity is around 55 percent smaller online.

To check that our result is not driven by a composition effect within the online and offline bundles, we estimate gravity equations for each eBay category using the specification of column (2) of Table 1. The estimated coefficients, using both LSDV and Poisson pseudo maximum likelihood estimators, are summarized in Figure 5 which shows that distance always has a bigger effect offline. It is on average 2.5 times bigger. Pooling the product regressions together and estimating an average effect using importer-category and exporter-category fixed effects yields distance coefficients of -0.287 online and -1.167 offline (columns 1-2 of Table 5).

In Table 3 we include the results of various robustness checks. As an important part of eBay trade is in used goods (25 percent) or occurs through auctions (65 percent) we replicate Table 1 disaggregating imports into used vs. new goods (this is done on a 2008 cross section because it is the only year for which we have the used versus new good information) and auctions vs. direct sales. We also report results when looking at all trade flows reported

> 13This could be explained by endogeneity problems, as larger trade flows create incentives for investment in transport infrastructure along those routes, or measurement error problems.

> 14This result also suggests that the death of distance online is not due to a reduction in shipping costs. Adding other controls such as bilateral average tariffs or trade-restrictiveness indices does not affect the results (not shown).

11
<u>Table B.6: Fiscal (pro)cyclicality of corporate tax rates, whole sample (2000-2016): Impacts of rigidity, fiscal space and business cycle</u>

|Dependent variable: Individual income tax|(1)|(2)|(3)|
|---|---|---|---|
|Cyclicality of real GDP|0.560**|-9.308|-2.741|
||(0.227)|(12.916)|(5.671)|
|**Business cycle**||||
|Crgdp*good times|-1.230e+08***|-3.576e+09|2.221e+08|
||(26210474.704)|(3.773e+09)|(5.662e+08)|
|Crgdp*good times*rigidity||0.535<br>(0.652)||
|Crgdp*rigidity||2,743.200||
|||(2,791.339)||
|Rigidity of public spending||0.034||
|||(0.046)||
|Crgdp*good times*fiscal space|||-0.017|
||||(0.028)|
|Fiscal space|||162.312|
||||(279.729)|
|Crgdp*fiscal space|||0.003**|
||||(0.001)|
|good times|0.453|-0.164|0.932|
||(1.037)|(0.775)|(1.115)|
|Constant|59.088***|38.562***|58.122***|
||(1.508)|(2.595)|(1.662)|
|Adjusted R-squared|0.733|0.818|0.744|
|Rmse|4.359|3.029|4.275|
|Joint significance (p-value)|0.000|0.168|0.009|
|CD test (p-value)|0.000|0.000|0.000|
|Order of integration|I(0)|I(0)|I(0)|
|Cluster|country|country|country|
|Covariates|Yes|Yes|Yes|
|Observations|831|407|828|
|# countries|50|26|50|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

35
2013).<sup>6</sup> _Capital regulation_ captures the amount of capital banks must hold and the stringency of regulations on the nature and source of capital. It is an index ranging in value from 0 to 10, with higher values indicating greater stringency. _Activity restrictions_ is an index that measures the degree to which the national regulatory authorities restrict banks from engaging in securities, insurance, and real estate activities. Securities activities refers to underwriting, brokering, dealing and all aspects of the mutual fund industry. Insurance activities include underwriting and selling, and real estate activities refer to investment, development, and management. The activities restrictions index takes values from 3 (where each of the three activities is permitted) to 12 (where each activity is prohibited). _Disclosure requirements_ is an index that captures the type of information banks must disclose about their financial condition. It indicates whether the income statement includes accrued or unpaid interest or principal on nonperforming loans, whether banks are required to produce consolidated financial statements, and whether bank directors are legally liable if information disclosed is erroneous or misleading. The variable ranges from 0 to 3, with higher values indicating greater bank disclosure. _Diversification requirements_ is an index which measures whether regulations support geographical asset diversification. It is based on two variables: whether there are explicit, verifiable, and quantifiable guidelines for asset diversification and whether banks are prohibited from making loans abroad. The index takes values from 0 to 2 with higher values indicating more diversification. _Loan Classification Stringency_ measures the actual minimum number of days beyond which a loan in arrears must be classified as sub-standard, doubtful, or loss. _Provisioning Stringency_ measures the minimum provisions (as a percentage of loans) required as a loan is successively classified as sub-standard, doubtful, and lastly as loss. _Supervisory powers_ is an index measuring supervisory authorities’ power and authority to take specific preventive

> 6 The 2007 survey covers the 2005-2006  period.

7
_-55 -_

# ANwx 3.1 BAsic EARNiNGS FuCTIONS

# Colombia 1989

|||Ge|nder|Se|ctor|
|---|---|---|---|---|---|
|Variable|Entire<br>Sample|Males|Females|Private|Public|
|Years of Schooling (S)|0.140|0.145|0.129|0.137|0.119|
|Experience (EX)|0.047|0.059|0.035|0.046|0.035|
|Experience-squared (EX<sup>2</sup>)|-0.0005|-0.0006|-0.0004|_-. OOOS_|-0.0003|
|Log Hours (H) Worked|0.645|0.428|0.719|0.664|0.326|
|Constant Term|6.190|6.897|6.108|6.140|7.980|
|R<sup>2</sup>|0.33|0.32|0.36|0.30|0.38|
|N|27,021|16,272|10,749|23,974|3,047|
|MM||||||
|Earnings (Y)|53,643|60,592|43,124|50,279|80,116|
|S|8.2|8.1|8.3|7.8|11.2|
|EX|20|21|18|19|20|
|H|48|50|46|48|_45_|
|Var (Ln Y)|0.947|1.010|0.815|0.956|0.458|

Source: _Encuesta Nacional de Hogares, CEPAL._ Notes: The earnings variable (Y) is in pesos per month. H is the weekly hours worked.

All coefficients are statistically significant at the 5% level or better.
Table 5: Labor-Market Responses: Fishery Workload and Having Secondary Jobs

||Weekly workload<br>(1)|Having extra Jobs<br>(2)|
|---|---|---|
|**[Panel A] Geographic control **|**group**||
|(HaTinh & QuangBinh) _×_ post|1.227<br>(1.917)|-0.122<br>(0.093)|
|(QuangTri & Hue) _×_ post|-28.825***<br>(1.085)|0.138***<br>(0.020)|
|Observations|872|872|
|R-squared|0.240|0.066|
|**[Panel B] Industry control gr**|**oup**||
|(HaTinh & QuangBinh) _×_ post|1.720*|-0.114|
||(1.015)|(0.091)|
|(QuangTri & Hue) _×_ post|-29.562***<br>(0.674)|0.141***<br>(0.017)|
|Observations|2,090|2,090|
|R-squared|0.119|0.015|
|Month FE|Yes|Yes|
|Individual FE|Yes|Yes|
|Standard errors in parentheses,|*** _p <_0_._01, ** _p_|_<_0_._05, * _p <_0_._1|

Note: the dependent variables are (1) fishery work hours per week (column 1), and (2) the probability that the affected fisher worked extra jobs (column 2). The heterogeneous labor-market responses are shown for the upstream (Ha Tinh & Quang Binh) and downstream (Quang Tri & Thua Thien-Hue) Formosa-affected regions. “Post” indicates May-2016 or after. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. All regressions include month and individual fixed effects. Standard errors are clustered at the district level.

35
bustness check (see Section 4), we repeat these entry regressions using a version of the GDP-by-field and relative employment variables that is exclusively based on “movers,” namely students who work in a department but obtained their degree in a different one. Table A.4 shows these results, which are practically identical to the ones from panels A and B in Table 3.

We would expect changes in labor demand not only to motivate the opening of new SCPs but also the enrollment of new, additional students—either in existing or new programs. We examine aggregate enrollment effects (Table A.5) and find that, indeed, enrollment in a field and department rises in response to a local demand increase for graduates in the field, as in Grosz (2022). Consistent with entry responses, the enrollment elasticity with respect to labor demand changes is higher for private than public HEIs (1.2 v. 0.9, respectively). Enrollment elasticities are larger than entry elasticities because they account for additional students not only at new but also at existing programs. Nevertheless, the main message from these results is that not only do HEIs respond to economic activity changes; students respond as well.

We further explore heterogeneous responsiveness to local labor market demand by looking at the three HEI types that provide SCPs (Table A.6.) Both the estimates using GDP-by-field and field relative employment show that technological and technical institutes (which specialize in SCPs) as well as technological schools (offering SCPs as well as bachelor’s programs) are more responsive than universities (which teach the full spectrum of degrees, ranging from SCPs to PhDs). Through greater specialization in SCPs, non-university HEIs have an institutional setup readily geared towards SCPs, which makes them nimble and fast. Anecdotal evidence indicates that these institutions are in closer contact with the private sector than universities and operate more flexibly. Because of their larger variety of products, universities lack this flexibility. Based on conversations with university leaders, universities that offer both SCPs and bachelor’s programs struggle to manage the two very different sets of students and faculty. In addition, high fixed costs limit their ability to change their offerings.<sup>15</sup> Overall, the main takeaway from this analysis is that SCP entry is indeed responsive to local labor market demand, particularly on the part of private and non-university HEIs.

> 15We observe similar patterns when looking separately at public and private institutions for each of the three HEI types. Results are available upon request.

22
to be matched with census data. This allows us to create a year-ISIC panel with information about exports and imports on 86 industry sectors in Peru.

# **4.2 Outcome Variables**

In the first part of our analysis, we examine the impact of local labor market conditions on self-reported information on experiencing discrimination as reported by Venezuelans surveyed in ENPOVE. Overall, 36.4% of Venezuelans report having experienced discrimination, with this being slightly more common among women (38.1%) than men (35.0%). Figure 2 shows the distribution of reported discrimination in different municipalities in Peru. There is clearly variation both across and within regions. Report discrimination is least common in Tumbes (23.4%), which is the typical entry point to Peru for Venezuelans and currently hosts 5% of ENPOVE sample, while Cusco and Lima, where 7% and 48% of Venezuelans are located, show the highest (47.8%) and median levels (37.1%) of reported discrimination, respectively. Individuals who experienced discrimination are then asked in which locations did the episode took place. We examine reports for the three most common locations, at work (20.0%), on the streets/in public places (25.0%), and on public transit (9.8%).

In the second part of our analysis, we examine the impact of Venezuelans on a wide variety of outcomes for Peruvians. First, we examine impact on labor market outcomes, specifically employment, formal employment, log wages if employed, log household income and log household expenditure. Second, we examine the impact on crime and opinions about personal safety. Specifically, we look at the reported (log) number of crime in each district from administrative data split into non-violent and violent crimes (data starting in 2011, means 3.54 for log violent crime and 3.31 for log non-violent crime), from ENAHO whether crime is a major national problem (12.7%), from LAPOP whether they have been a crime victim in the last two months (32.0%) and standardized variables from LAPOP on opinions about neighborhood safety and from Gallup on personal security. Lastly, we examine the impact on community outcomes. Specifically, we look at standardized indexes measuring quality of local services and trust in neighbors from LAPOP and indexes from Gallup on community attachment, the quality of the local community and whether the community likes diversity.

# **4.3 Control Variables**

Table 1 show the descriptive statistics for the control variables used in each analysis. The information we have available from each dataset varies, but we can always control for age, gender, education, marital status, whether employed and household size. ENPOVE collects additional relevant data about Venezuelans including how long they have been in Peru, 10
|Table G5|. Rule of Law|||
|---|---|---|---|
|Code|Table|Row|Concepts measured|
||(Appendix 1)|||
|Represe|ntative sources|||
|DRI|A 1.3|18|Losses and Costs of Crime|
|||26|Kidnapping of Foreigners|
|||9|Enforceability<br>of private contracts|
|||8|Enforceability<br>of govemment contracts|
|EIU|A 1.5|10|Corruption in banking|
|||11|Crime|
|HFWSJ|A 1.9|10|Black Market|
|||8|Property Rights|
|PRS|A 1.10|9|Law and order tradition|
|WDR|A 1.13|6|Theft and crime (now)|
|||7|Confidence in authority to secure property (now)|
|||8|Unpredictability<br>of the judiciary (now)|
|||14|Crime and theft as obstacles to business|
|Non-repr<br>|esentative sourc<br>|es||
|BERI|A 1.1|12|Enforceability of contracts|
|CEER|A 1.2|6|Rule of Law|
|FHNT|A 1.6|7|Ruleoflaw|
|GCS98|A 1.8|||
|GCS97||8|Extent of tax evasion|
|||31|Costs of organized crime for business|
|||30|Police effectiveness in safeguarding personal security|
|||14|Intellectual property protection|
|||23|Compliance with court rulings and/orarbitration awards|
|||20|Legal system effectiveness at enforcing commercial|
|||19|Independence<br>of the judiciary from interference by the<br>govemment and/or parties to the dispute|
|||26|Private business has recourse to independent and impartial<br>courts for challenging the legality of govemment actions|
|||27|Likelihood of winning a court case filed against the govemment|
|||20|<br>Legal system effectiveness at enforcing commercial contracts|
|||22|Private business capacity to file lawsuits at independent<br>and impartial courts against govemment|
|||25|Citizens' willingness to accept legal means to adjudicate|
||||disputes rather than depending on physical force<br>or illegal means|

58
**<u>Figure 1:</u>** EOI requests sent and received by African countries since 2014

<!-- Start of picture text --> 700<br>628<br>600 568 569<br>505<br>482 460 592<br>500<br>421<br>379<br>400 439<br>279<br>300<br>215<br>194<br>200<br>109<br>67<br>100 38<br>0<br>2014 2015 2016 2017 2018 2019 2020 2021<br>EOI requests sent EOI requests received<br>Number of requests<br><!-- End of picture text --> Note: The graph reflects the situation for the 34 African countries which have provided data. The 2020 data includes feedback from five new respondents who did not participate in last year’s survey (Congo, Sierra Leone, Gambia, Zambia and Zimbabwe) but excludes feedback from three countries (Guinea-Bissau, Niger and Malawi) who participated in last year’s survey but did not provide feedback for this year’s survey.

Source: Tax Transparency in Africa 2022: Africa Initiative Progress Report, OECD (2021).

Figure 2 displays the average tax revenue as share of GDP in both groups of countries (GFM on the one hand and non-GFM members on the other). The graph shows that over the entire period 1990-2020, GFM membership countries performed better in tax revenue mobilization than the non-membership countries.

**Figure 2:** Tax revenue in percentage of GDP, membership vs non-membership of GFM

6