rafmacalaba/datause-encoder-data huggingface dataset:
As a consequence of their increased visibility and use by policymakers and scholars worldwide, the WGI have also attracted considerably scrutiny by others. This natural and healthy process of scholarly debate over data and methodology has resulted in several written critiques of the WGI, to which we have provided detailed responses and rebuttals. Interested readers can refer to Kaufmann, Kraay and Mastruzzi (2007a) for a detailed response to several critiques of the WGI that have been raised by other authors, as well as the exchange over the WGI in the April 2007 issue of the Journal of Politics.

Finally, we reiterate, as we have with each previous round of the WGI, that the composite indicators we construct are useful as a first tool for broad cross-country comparisons and for evaluating broad trends over time. In contrast, the aggregate WGI are often too blunt a tool to be useful in formulating specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. We therefore view the WGI as complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country.

We begin by describing the data used to construct this round of the governance indicators in Section 2. In this current update, we have added two data sources to the WGI. The first is the Institutional Profiles Database, an expert assessment of governance and institutional quality in 85 countries produced by the Treasury and Economic Policy Directorate General of the French Ministry of the Economy, Industry and Employment and the French bilateral aid agency Agence Française de Développement. The second consists of the AmericasBarometer household surveys conducted by the Latin America Public Opinion Project at Vanderbilt University. Details on both new sources are provided below. We have also made numerous minor revisions to the past data from several of our underlying sources in order to make them more fully comparable over time. These revisions have resulted in minor changes to our previous estimates for 1996-2006, and so the entire new dataset described here supersedes previous releases. In Section 3 we briefly describe cross-country differences and changes over time in governance as measured by our aggregate indicators. Section 4 concludes.

6
|Student|2000|Bgrd|Sixth|45,676|3,189|
|---|---|---|---|---|---|
|Student|2001|Bgrd|Third|48,903|3,268|
|Student|2001|Bgrd & Test|Fifth|46,718|3,221|
|Student|2002|Bgrd|Fourth|48,451|3,205|
|Student|2002|Bgrd & Test|Sixth|46,365|3,165|
|Student|1992-2002|Drop|First-Sixth|583,876|82,520|
|Teacher|1998|Bgrd||23,357|3,562|
|Teacher|2001|Bgrd||22,917|3,437|
|Teacher|2002|Bgrd||20,634|3,373|

Source: SEP data.

**<u>Table 4. Secondary School Databases</u>**

|**Respondent**|**Year **|**Type**|**Grade**|**Obs**|**Schools**|
|---|---|---|---|---|---|
|Principal|2000|Bgrd||1,406|1,403|
|Principal|2001|Bgrd||1,184|1,181|
|Principal|2002|Bgrd||860|860|
|Student|2000|Test|Seventh|39,562|1,189|
|Student|2001|Bgrd & Test|Eighth|39,562|1,189|
|Student|2002|Bgrd|Eighth|53,919|1,982|
|Student|2002|Bgrd &Test|Ninth|53,202|1,980|

Source: SEP data.

Information on student test scores came from _Estándares Nacionales_ (EN), an exam that Mexican students took in each year during the period 1998-2002. This test provides information about student ability in Spanish and math. Mexico’s Secretariat of Public Education (SEP) also reports a composite global score based on the student’s Spanish and math ability. SEP standardizes scores to control for changes in exam difficulty between grades and test years. EN gives information on 45,000 students from 3,000 primary schools. The EN sample is relatively small: in 1999, CONAFE gave didactic content support to about 4 million students, so the EN sample includes about 1 percent of all CONAFE students.

EN’s sampling is based on an earlier selection of representative schools. In 1996, SEP selected 3,000 schools to include in its test-score sample.<sup>5</sup> Those schools evenly represented states, socioeconomic backgrounds, student gender, and other demographic factors of the

> 5 In the 1996-1998 period, SEP gave students a different test, the results of which are not comparable with EN.

16
Table 6: Analysis of classroom quality, teacher, and structural characteristics on children’s emotional maturity

||no|DV:Emoti|onal maturity|(Raw score)<br>w/ teacher||
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.141***|0.072|0.063|0.063|0.072|
||(0.048)|(0.051)|(0.051)|(0.051)|(0.062)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.191|0.191|0.183|
||||(0.410)|(0.411)|(0.415)|
|% of teachers w/ post secondary edu.|||0.271<br>(0.429)|0.271<br>(0.431)|0.268<br>(0.434)|
|Mean years of teaching|||0.010|0.010|0.009|
||||(0.008)|(0.008)|(0.008)|
|% of teachers w/ no ECED experience|||0.064<br>(0.103)|0.063<br>(0.101)|0.055<br>(0.100)|
|% of teachers w/ no training|||0.113<br>(0.129)|0.113<br>(0.129)|0.113<br>(0.129)|
|**Structural characteristics**||||||
|Student-to-staff ratio||||0.000<br>(0.007)|0.000<br>(0.007)|
|Hours of operation per week||||0.001<br>(0.013)|0.002<br>(0.013)|
|Constant|5.944***|4.077***|3.817***|3.812***|3.792***|
||(0.137)|(0.256)|(0.490)|(0.502)|(0.504)|
|Observations|7,946|7,946|7,946|7,946|7,946|
|R-squared|0.010|0.116|0.118|0.118|0.117|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stage F-statistic of excluded<br>instrument|||||647.316|
|**Control included:**||||||
|Child characteristics|N|Y|Y|Y|Y|
|Kindergarten dummy|N|Y|Y|Y|Y|
|District dummy|N|Y|Y|Y|Y|

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, agesquared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Model 5 is a two-stage least squares model to correct for measurement error in the ECERS-R. The F-statistic of the first stage is reported.

* p<0.1, ** p<0.05, *** p<0.01

23
Russian firms are very heterogeneous. Of the 978 manufacturing firms surveyed in the five federal districts in Russia, 947 of them have more than 10 non-missing responses that allows for computing a more precise management score (Bloom et al., 2019a).<sup>15</sup> The average management score for manufacturing firms in the surveyed federal districts in 2017 was 0.432 – that is, on an average the surveyed firms adopt 43% of overall structured management practices (Figure 1). The spread is large, however. Looking at the tails of the distribution in figure 1, it is visually clear that the left tail is much thicker than the right – a large share of the surveyed firms are poorly managed, only 3.5% of them adopt more than 75 percent of the structured management practices while a similar share turns away from adopting any (a management score of 0). Nearly 60 percent of the surveyed firms adopted less than 50 percent of overall structured management practices. Recall data from 2012<sup>16</sup> indicates that the average management score was slightly lower at 0.415, suggesting a very small increase of 0.017 in the period 2012-2017.<sup>17</sup>

**_Figure 1: There is large heterogeneity in adoption of structured management practices_**

<!-- Start of picture text --> Distribution of management score<br>0 .25 .5 .75 1<br>2017 2012<br>2012 average:0.415 >0.75:2.7% >0.50:34.9% N:878<br>2017 average:0.432 >0.75:3.5% >0.50:40.2% N:947<br>12<br>10<br>8<br>6<br>Percent<br>4<br>2<br>0<br><!-- End of picture text --> Note: this figure plots the distribution of the management score in our sample. The 2012 data point corresponds to that year or the year the firm started operations, if later than that. Only firms with more than 10 non-missing answers in the Management questionnaire are included.

# _3.2 How do Russian firms compare with other countries?_

Comparing Russian management practices with those in Croatia, Mexico, Pakistan and the United States – countries for which comparable data are available, Table 2 suggests that, _first_ , an average Russian manufacturing firm in the surveyed federal districts is closer to its counterpart in Mexico (average score, 0.42) and Pakistan (average score, 0.44) but farther from structured management practices adopted in

> 15 For the purposes of our paper, we consistently use this set of 947 firms for the analysis henceforth. Nonetheless, Annex Figure A.1 shows the distribution for the full sample of 978 firms.

> 16 For the year 2012, this is a recall variable since the survey asked retrospectively about that year; for new firms which were not operating that year, this data point refers to the year operations started.

> 17 Bloom et al. (2019a) exploit the panel variability in their sample of US firms and show that the quality of recall information correlates with the survey respondent’s tenure in the company. In our work, when restricting the analysis to surveyed firms with respondents working in the firm in both 2012 and 2017, we find that the change in the average management score is 0.006. This further confirms the lack of change in management practices among surveyed firms during this period.

9
Table D6: Robustness of Adjustment of Firm Labor Market Outcomes to Time-Varying Shock Using Alternative Shocks

||Fi<br>si|rm<br>ze|Firm <br>real w|total<br>ages|Fi<br>e|rm<br>xit|Firm sh<br>skilled w|are of<br>orkers|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Lagged frm shock GDP growth export dest.|-0.1288***|-0.0230***|-0.1293***|-0.0193***|-0.0005***|0.0004***|-0.0016***|0.0001*|
||(0.0012)|(0.0005)|(0.0012)|(0.0004)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Lagged frm shock real exchange rate export dest.|-0.0442***|-0.0041***|-0.0483***|-0.0022***|0.0004***|0.0003**|-0.0019***|0.0000|
||(0.0017)|(0.0005)|(0.0016)|(0.0005)|(0.0001)|(0.0001)|(0.0002)|(0.0001)|
|Lagged frm shock real exchange rate import sources|-0.0385***|-0.0047***|-0.0319***|-0.0030***|0.0006*|0.0007***|-0.0006*|0.0000|
||(0.0040)|(0.0012)|(0.0035)|(0.0010)|(0.0003)|(0.0003)|(0.0003)|(0.0001)|
|Observations|467,114|464,907|458,657|456,352|436,714|434,453|332,779|330,622|
|R-squared|0.1341|0.8921|0.1692|0.9066|0.0095|0.2189|0.2141|0.8564|
|Lagged frm shock GDP growth export dest. fxing|-0.0716***|0.0140***|-0.0747***|0.0137***|-0.0005***|-0.0006***|0.0005***|0.0001|
|weights to 2004-2007|(0.0015)|(0.0006)|(0.0014)|(0.0006)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|
|Observations|467,114|464,907|458,657|456,352|436,714|434,453|332,779|330,622|
|R-squared|0.1256|0.8917|0.1584|0.9063|0.0095|0.2189|0.2138|0.8564|
|Lagged frm shock GDP growth export dest.|-0.0916***|-0.0190***|-0.0933***|-0.0163***|-0.0010***|0.0001|-0.0010***|0.0001|
|using weights in t-1 and t-2|(0.0011)|(0.0005)|(0.0011)|(0.0004)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Observations|467,114|464,907|458,657|456,352|436,714|434,453|332,779|330,622|
|R-squared|0.1515|0.8925|0.1867|0.9068|0.0098|0.2189|0.2139|0.8564|
|2-digit industry*year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|No|Yes|No|Yes|No|Yes|No|Yes|

_Note:_ Robust standard errors clustered at the firm level in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. FE stands for 54
to better information allows farmers to strike better price deals within their existing trading relationships and to make better choices in their markets.

Our results are only a first step in understanding how information technologies impact household welfare. We see at least three areas where more research would be helpful. First, using transaction-level data on traders and farmers (with and without mobile phones) would allow us to test whether access to price information increases farmer bargaining power when dealing with traders. Second, it would be valuable to test whether better access to market information has a positive impact on farmers’ crop choices. Indeed, it could potentially allow farmers to diversify their crop mix. Finally, it would be interesting to compare those impacts for farmers with a different product mix ( _e.g.,_ perishable and nonperishable products). In light of the results discussed in this paper, it is possible that farmers producing perishable products ( _i.e.,_ products more likely to experience large day-to-day price variations) will benefit more from mobile phone access than farmers producing nonperishable products. This is left for further research.

15
# Policy Research Working Paper **Serles**

|**Tltle**|Author|Date|ContLct<br>for paper|
|---|---|---|---|
|WPS1019 How Effective are Directed Credit<br>Policies in the United States?<br>A Literature Survey|Anita M. Schwarz|November 1992|M. Raggambi<br>37664|
|WPS1020 Another Look at Population and<br>and Global Warming|Nancy Birdsall|November 1992|S. Rothschild<br>37460|
|WPS1021 Measuring Weifare Changes from<br>Commodity Price Stabilization in<br>Small Open Economies|Jonathan R. Coleman<br>Chris Jones|November 1992|D. Gu'stafson<br>33714|
|WPS1022 A New Approach to Evaluating Trade<br>Policy|James E. Anderson<br>J. Peter Neary|November 1992|M. T. Sanchez<br>33731|
|WPS1023 Tariff Index Theory|James E. Anderson|November 1992|M. T. Sanchez<br>33731|
|WPS1 024 An Exact Approach for Evaluating<br>the Benefits from Technological<br>Change|Will Martin<br>Julian M. Alston|November 1992|D. Gustafson<br>33714|
|WPS1025 Openness and Economic<br>Performance in Sub-Saharan Africa:<br>Evidence from Time-Series Cross-<br>Country Analysis|Kazi M. Matin|November 1992|D. Ballantyne<br>38004|
|WPS1 026 Financial Liberalization and<br>Adjustment in Chile and New Zealand|Paul D. McNelis<br>Klaus Schmidt-Hebbel|November 1992|A. Maranon<br>31450|
|WPS1 027 Lessons from Bank Privatization<br>in Mexico|Guillermo Barnes|November 1992|W. Pitayatonakarn<br>37664|
6 may be capturing the impact of other non trade-related policies. RR argue that the socialist system component is clearly associated to many other policies not related to trade, that the exports marketing board is notably correlated to a country being located in the stagnant African region, and that the exchange rate distortion may be the result of other macroeconomic policies.

The empirical attempts to capture the effect of trade liberalization on economic growth has since then faced the challenge of finding a proxy of trade policy openness that is robust to Rodrik and Rodriguez critiques<sup>2</sup> . Although Rodrik (2000) proposes using average tariffs to represent trade restrictiveness, as argued in Salinas (forthcoming) this indicator completely fails to capture other dimensions of trade restrictiveness<sup>3</sup> and is not even a rigorous representation of the restrictiveness of the tariff structure. In defense of the SW indicator, Warner (2003) provide evidence that the exchange rate distortion component reflects the trade policy stance more than anything else; that only some of the countries in Sub-Saharan Africa trigger the openness dummy on the basis of the export monopoly criterion; and that even after removing the countries that RR claim as misleading there is a positive impact on economic growth of having an open trade regime. Despite these arguments, we know that the Sachs and Warner indicator is far from an accurate measure of openness, as is based on a subjective weighting of imperfect measures of trade restrictiveness such as average tariffs and frequency of nontariff barriers. Berg and Krueger (2003) argue that despite some technical deficiencies, the SW indicator is a fairly accurate indicator of trade restrictiveness, but such weak assertion obviously does not convince the skeptics about any evidence based on this indicator.

Dollar and Kraay (2004) take a different approach and try to measure the increase in trade policy openness (i.e. trade liberalization) by the change in trade flows between two periods in time. However, this indicator was promptly and convincingly criticized in Rodrik (2001) essentially for not recognizing the endogenous relation between trade flows and GDP growth, as Balassa (1978) failed to do more than

> 2 Salinas (forthcoming) surveys the different measures of overall trade restrictiveness that have been proposed before and after RR and how they are all technically questionable or are not available for enough years and countries to be useful in panel regressions.

> 3 For instance, Salinas (forthcoming) shows that since the 1980s there has been a low correlation between available, cross-country measures of unweighted average tariffs and the frequency of non-tariff barriers.
# TABLEOF CONTENTS

|Executive Summary... 12. ec<br>cee<br>eee<br>e center eteseresssscsesess<br>||
|---|
|I. Objectives and strategy .. 0.0... ec<br>cece<br>ce cee etn ee eeetescesesseee<br>DB|
|A. Growth experience of developing countries .............0.ccceececescees<br>4<br><br><br>|
|1. Experience over 1965-88 2.0... .. ccc<br>cece cece ence ete sceseees<br>4<br> <br><br>|
|<br><br><br><br>|
|<br><br> Contribution<br>to knowledge of policy-related issues .................0e2002.<br>6<br><br>|
|C.Empiricalmethodology...........ccccececececreeteceeteeecenssscee<br>9|
|<br><br>D. Relevance<br>of the agenda to policy ........ 0...<br>cece cece ccc ec csecveces<br>10|
|I. Design of the research agenda ....... 0... ccc cece cece tee cece ec ececeveceseees<br>12|
|A.Analyticalframework.......0...cccccccececeeeecereceseescsces<br>12|
|<br><br>1. Policies that affect incentives to capital accumulation .................<br>12|
|2. Policies that affect efficiencyofresource allocation ..................<br>17<br><br><br> <br>|
|B.Syntheticissues2.02...<br>ccc<br>ceceeceeeeeeecenteesecccrescees<br>27|
|<br><br> <br><br>1. Growth<br>andwelfare2.0...occcccccceeecete<br>erececcvee27<br>2.|
|<br> <br> Persistence<br>of growth rates ........<br>cece<br>eee cece cenceseecee<br>32<br><br>|
|C. Empirical methodology ........... 2... ccc cece cee cece ete cesecccces<br>34|
|TIT. Conclusion ... 2... cece cece cee ence twee cess cen cececccecececes<br>42|
|Bibliography<br>.. 2.0.2... ccc<br>ce<br>eee<br>ee eee<br>eee etree eet c ent ee tes essevscese 49|
|Box1:Measurement<br>ofgrowth......0...ccccecececeencetececcceccsv**c**c**c**ec**s**<br>Ll<br>Box <br><br>|
|<br> <br><br> 2: How does growth begin? Why doesgrowth<br>stop? ............cceceeeec e ss<br>16|
|Figure1:Averagegrowthrates,1870-1988;Growthrates,developingcountries,1870-1988;|
|<br>Growth rates, developed countries, 1870-1988 .......... ccc cece cece ccceccee<br>7|
|Figure2: Growth rates of output per capita, 1965 to 1989 2.0.0...<br>cee eee ee eeeee 20|
|Table1:Cross-countryaverages1960-89..0.2...ceecctcc<br>ccccecteesteeccecee<br>3B|
|<br> <br><br>Table2:Growthratesofoutputpercapita,1965to19892.0...<br>ceceeeeceeee<br>5|
|<br> <br><br>Table3:Inequalityandgrowth.......0...cccece<br>cececccceceeeetceseeses|
|<br> <br>Table4:Incomeandsocialindicators........0...ceceeeeeecececccceeceseeserees<br>Sl|
|<br><br>Table5:Persistenceofpoliciesandgrowthperformance................00eeeeeeee+<br>33|
|<br><br>Table6:Summarylistofhypothesestobetested......0...cccceeeeeeeceeeeeeee<br>36|
|<br><br>Table7:Dataforanalysisofpolicyandgrowth...........<br>0.ccccceeecececcevceces<br>ST<br><br> <br>|
|<br> <br><br>Table8:Internationally<br>availabledatasources...........<br>00.0ececececeeeeececeees38<br><br>|
|<br> <br> <br>Table9:Matrixofcorrelationsacrosspolicies........0.0.cececccceecteeecccececee<br>4l|
the former of these relationships. Section 7 concludes.

# 2 Networks and trade with heterogeneous ...rms

To guide our empirical analysis, we present a multiple-destination variant of the Melitz (2003) model featuring market-speci...c ...xed entry costs and idiosyncratic ...rm-destination demand shocks. The version we use draws heavily on Chaney (2008), Lawless (2009), Eaton et al. (2011) and Crozet et al. (2011).

Consider a world composed of multiple asymmetric nations. In each country c, consumer preferences for a di¤erentiated good are characterized by a Dixit-Stiglitz sub-utility function of the form where q(j) denotes quantity of variety j demanded in country c, �c denotes the set of varieties j, and �> 1 the constant elasticity of substitution between any pair of varieties. The ac(j) are ...rm-destination demand shocks, a feature that we borrow from Eaton et al. (2011) and Crozet et al. (2011). We assume that ac(j) represent ...rm j’s network of connections with consumers in country c.

Each ...rm produces a single horizontally-di¤erentiated variety j by means of a Ricardian technology with unit cost <u>w</u><sup>wherewisthewagelevelcommontoall...rmsand</sup> '(j)<sup>,</sup> '(j) is a ...rm-speci...c productivity parameter, randomly drawn from a distribution with cumulative density function G('). To export to market c producers must incur both a ...xed cost, Fc, and a variable cost, � c. The latter is modeled as an "iceberg" transportation cost, where if one unit of a good is shipped to country c only a fraction �1c<sup>arrivestothe</sup> ...nal destination. Fixed costs of exporting can stem from a wide variety of activities, for example establishing business contacts and distribution networks, undertaking marketing e¤orts, or overcoming bureaucratic barriers. The presence of information networks provided by emigrants is likely to reduce several of these entry costs (Rauch and Trindade, 2002). For simplicity, we assume that Fc varies between markets, but not across ...rms in the same market. Hence ac(j) are the sole parameters capturing the presence of ...rmspeci...c social networks in a given destination, while Fc captures networks e¤ects that are common to all ...rms in a market. A valid interpretation is that, by incurring Fc, a ...rm with more contacts in country c � that is, a ...rm with a larger ac(j) � is able to reach a larger number of potential buyers there.

The sub-utility (1) is assumed to enter a Cobb-Douglas full utility, which implies that consumers of country c spend an exogenous total amount, Xc, on the di¤erentiated good. If ...rm j serves market c, its market share in that country is given by: 4