rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6: Relationship between estimated coefficients for human capital (average years of schooling) and educational quality.<sup>a</sup> <!-- Start of picture text -->
1.4 1.4<br>1.2 1.2<br>1 1<br>CHL CHL<br>0.8 0.8<br>MEX MEX<br>0.6 0.6<br>0.4 PERBRA COLCRI ARGURY 0.4 BRA PERCOL CRI A RG URY<br>0.2 0.2<br>DOM R2 = 0.60187 DOM R2 = 0.67664<br>0 0<br>300 350 400 450 500 300 350 400 450<br>PISA Science Grade PISA Math Grade<br>1.4 1.4<br>1.2 1.2<br>1 1<br>CHL CHL<br>0.8 0.8<br>MEX MEX<br>0.6 0.6<br>PER ARG URY PER ARG URY<br>0.4 BRA COL CRI 0.4 COL CRI BRA<br>0.2 DOM R2 = 0.16245 0.2 DOM R2 = 0.3082<br>0 0<br>350 370 390 410 430 450 0 500 1,000 1,500 2,000 2,500<br>PISA Reading Grade Education Expenditure per capita<br><!-- End of picture text -->

> a The vertical axis show the estimated elasticities between subnational underlying productivity and city human capital derived from individual country-level regressions that follow equation 5. The horizontal axes show the 2015 science, math and reading grade in the Programme for International Student Assessment (PISA) exams. The dotted line is a fitted line. The panel in the low right corner shows educational expenditure per capita, obtained by dividing the current $US expenditure on education by the population of inhabitants younger than 14 years old using data from the United Nations Statistics Division’s Statistical Yearbook, UNESCO’s Institute for Statistics, and United Nations Population Division’s World Population Prospects.

24
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19
**Figure 2. Trends of Income per capita and Percentile Ratios, RLMS 1994-2015**

<!-- Start of picture text --> 9.5 4<br>Total income per capita<br>90/50 ratio<br>50/10 ratio<br>9 3.5<br>8.5 3<br>8 2.5<br>7.5 2<br>Ratio<br>Log of income<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights. The repeated cross sections are used for each year. We remove 170 individuals in 2004 that have extremely low monthly incomes (i.e., less than 300 rubles per capita).

43
# **App. Table 5: Separate analyses of Table 8 by split samples**

||Initial<br>contracts|Initial<br>restricted to<br>repeat users|Subsequent<br>contracts|2008 and<br>prior|2009 and<br>later|High-end<br>contracts|Low-end<br>contracts|Excluding<br>employer<br>searches|Only<br>employer<br>searches|Workers with<br>good English<br>skills|<br>Workers with<br>poor English<br>skills|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
||||Esti|The sample<br>mates include<br>P|is contracts for<br>fixed effects f<br>anel A: DV is l|med with ethni<br>or year x job ty<br>og hourly wag|c Indian comp<br>pe x country o<br>e paid to work|any contacts<br>f company con<br>er|tact|||
|(0,1) indicator that|-0.132***|-0.137**|-0.158***|-0.098**|-0.166***|-0.181***|-0.103**|-0.132***|-0.191***|-0.112***|-0.182***|
|worker is in India|(0.037)|(0.055)|(0.050)|(0.039)|(0.047)|(0.047)|(0.047)|(0.039)|(0.059)|(0.028)|(0.060)|
|Observations|1,687|957|5,953|1,447|6,193|4,180|3,460|4,463|3,177|5,642|1,321|
|Mean of DV|2.103|2.019|1.990|2.200|1.970|2.444|1.492|1.903|2.167|1.989|1.995|
|Relative effect|-0.063|-0.068|-0.079|-0.045|-0.084|-0.074|-0.069|-0.069|-0.088|-0.056|-0.091|
||||Panel|B: DV is perce|ntage different|ial between acc|epted contract|and median pr|oposal|||
|(0,1) indicator that|-0.046***|-0.032*|-0.014|-0.038***|-0.021**|-0.030**|-0.016|-0.046***|0.009|-0.024**|-0.034*|
|worker is in India|(0.016)|(0.019)|(0.010)|(0.014)|(0.011)|(0.012)|(0.015)|(0.012)|(0.012)|(0.012)|(0.017)|
|Observations|1,687|957|5,953|1,447|6,193|4,180|3,460|4,463|3,178|5,642|1,321|
|Mean of DV|-0.004|-0.027|0.000|0.006|-0.020|-0.006|0.007|-0.010|0.013|0.003|-0.012|
|||Panel C|: DV is a (0,1)|"good perfor|mance" indicato|r from public|feedback scor|es (feedback sco|re greater tha|n 4.5/5)||
|(0,1) indicator that|-0.048|-0.053|-0.003|-0.070*|0.006|-0.022|0.005|-0.030|0.010|0.001|-0.067|
|worker is in India|(0.034)|(0.044)|(0.018)|(0.037)|(0.017)|(0.023)|(0.026)|(0.022)|(0.023)|(0.019)|(0.042)|
|Observations|1,344|816|4,591|1,350|4,585|3,247|2,688|3,379|2,556|4,463|1,021|
|Mean of DV|0.529|0.576|0.651|0.442|0.677|0.657|0.583|0.601|0.653|0.612|0.593|
|Relative effect|-0.091|-0.092|-0.005|-0.158|0.009|-0.033|0.009|-0.050|0.015|0.002|-0.113|
||||Pane|l D: DV is a (|0,1) "good perf|ormance" indic|ator from priv|ate post-job su|rvey|||
|(0,1) indicator that|-0.066***|-0.073*|-0.019|-0.059|-0.024|-0.040*|-0.018|-0.053**|-0.011|-0.020|-0.063|
|worker is in India|(0.033)|(0.043)|(0.019)|(0.037)|(0.019)|(0.023)|(0.026)|(0.021)|(0.023)|(0.018)|(0.044)|
|Observations|1,326|810|4,576|1,333|4,567|3,229|2,671|3,362|2,538|4,432|1,016|
|Mean of DV|0.655|0.671|0.683|0.617|0.694|0.699|0.651|0.658|0.703|0.667|0.674|
|Relative effect|-0.101|-0.109|-0.028|-0.096|-0.035|-0.057|-0.028|-0.081|-0.016|-0.030|-0.093|

Notes: See Table 8.
**Table 47. Importance of IPA services: Assistance with operational issues and grievances**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.000110|-0.00707|||0.0105|-0.00605|||
||(0.0545)|(0.0527)|||(0.110)|(0.113)|||
|Export % of sales|0.000343|2.76e-05|0.000419|6.80e-05|0.000272|-2.10e-05|0.000503|0.000148|
||(0.000700)|(0.000674)|(0.000706)|(0.000692)|(0.00143)|(0.00145)|(0.00147)|(0.00151)|
|Share of sourcing via imports|0.00154**|0.00121|0.00159**|0.00134*|0.00346**|0.00250|0.00359**|0.00303*|
||(0.000739)|(0.000744)|(0.000756)|(0.000759)|(0.00153)|(0.00161)|(0.00160)|(0.00167)|
|High-income source country|-0.207***|-0.133*|-0.220***|-0.146*|-0.402**|-0.240|-0.443***|-0.282|
||(0.0734)|(0.0741)|(0.0753)|(0.0745)|(0.157)|(0.170)|(0.164)|(0.173)|
|>250 employees|0.0277|0.0201|0.0165|0.0113|0.0256|0.0246|0.00688|0.00868|
||(0.0653)|(0.0647)|(0.0659)|(0.0654)|(0.132)|(0.137)|(0.135)|(0.141)|
|Cumulative inv. >US$10M|-0.0604|-0.00765|-0.0643|-0.00630|-0.104|-0.0279|-0.115|-0.0221|
||(0.0628)|(0.0599)|(0.0628)|(0.0601)|(0.126)|(0.127)|(0.129)|(0.130)|
|Years in country|-0.00195|-0.00281|-0.00182|-0.00293|-0.00348|-0.00511|-0.00349|-0.00519|
||(0.00258)|(0.00243)|(0.00257)|(0.00240)|(0.00545)|(0.00526)|(0.00554)|(0.00532)|
|Level of foreign ownership (%)|-0.00201*|-0.000202|-0.00204*|-0.000351|-0.00434*|-0.000438|-0.00455*|-0.000815|
||(0.00107)|(0.00109)|(0.00106)|(0.00107)|(0.00236)|(0.00246)|(0.00239)|(0.00247)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.016|0.080|0.037|0.101|||||

Standard errors in parentheses

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

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How important are the following services offered by Investment Promotion Agencies to your company in this country? Please rate each on a scale from 1 to 4 where 1 is not at all important and 4 is critically important. [Assistance with operational issues and grievances]". Higher numbers denote more greater importance. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

64
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24
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42
# Appendix 1. List of countries in the database

Advanced economies (36) Australia Hong Kong SAR, China New Zealand Austria Iceland Norway Belgium Ireland Portugal Canada Israel Singapore Cyprus Italy Slovak Republic Czech Republic Japan Slovenia Denmark Korea, Rep. Spain Estonia Latvia Sweden Finland Lithuania Switzerland France Luxembourg Taiwan, China Germany Malta United Kingdom <u>Greece</u> Netherlands <u>United States</u> Emerging market and developing economies (144) <mark>East Asia and Pacific (22)</mark> Cambodia* Micronesia, Fed. Sts.* Thailand China Mongolia Timor-Leste* Fiji* Myanmar* Tonga* Indonesia Palau* Tuvalu* Kiribati* Papua New Guinea* Vanuatu* Lao PDR* Philippines Vietnam Malaysia Samoa* Marshall Islands* <u>Solomon Islands*</u> <mark>Europe and Central Asia (24)</mark> Albania Hungary Romania Armenia* Kazakhstan Russian Federation Azerbaijan Kosovo* Serbia Belarus Kyrgyz Republic* Tajikistan* Bosnia and Herzegovina Moldova Turkey Bulgaria Montenegro Turkmenistan* Croatia North Macedonia Ukraine <u>Georgia</u> Poland <u>Uzbekistan*</u> 60