rafmacalaba/datause-encoder-data huggingface dataset:
A middle ground between individual household modeling and the RHG approach is offered by Hertel, Ivanic, Preckel, and Cranfield (2004). Their approach is geared towards shedding light on the impacts of global trade liberalization across a wide range of developing countries. This multi-country approach is inevitably less detailed for any given country, which is why they seek a middle ground between the full complexity of household-level micro-simulation, and the RHG approach.

These authors begin by stratifying households into seven groups, based on earnings specialization. They identify households that receive 95% or more of income from agricultural selfemployment, non-agricultural self-employment, wage labor (rural vs. urban) and transfer payments.<sup>11</sup> All other households are considered diversified, and are broken into rural and urban groups. Up to this point, this sounds like the RHG approach, but within each of the seven strata the authors aggregate the survey data into twenty vingtiles, based on initial income per capita. This results in 140 distinct households per country, and permits them to analyze changes in income distribution _within_ strata, as well as _between_ strata.

The authors estimate a common demand system for all households and use this to characterize consumption behavior as well as establishing a poverty level of utility. The micro-simulation model in each country consists of each of the 140 households maximizing utility subject to a budget constraint determined by their endowments and the commodity and factor prices obtained from the CGE model. They explore two different factor market closures, the first (limited factor mobility) is one in which only wage labor is mobile across sectors, and the second (full factor mobility) is one in which selfemployed labor and capital are also mobile. (Agricultural land is sector-specific in both cases.) In their analysis of the Indonesian poverty impacts of multilateral trade liberalization, the authors find that in the short run (limited factor mobility), the _between stratum_ earnings differences dominate, while the limited _within stratum_ differences derive mainly from consumption effects.

More specifically, Hertel et al. (2004b) find that multilateral trade liberalization boosts food prices, relative to non-food prices in Indonesia. This hurts the poor within each stratum more than the rich, since the poor spend a much larger share of their income on food products. On the earnings side, wages rise, particularly for unskilled workers. So, in the short run, with limited factor mobility,

> 11 Sensitivity analysis by the authors indicates that the classification of households is robust to variation in the cut-off point between 90% and 99% specialization.

21
# **Table S17. Relationship between the Environmental Performance Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.467***|-0.027|-0.024|||||
||(0.04)|(0.04)|(0.04)|||||
|Open Data Barometer<br>||||0.464***|0.000|0.000||
|Score||||||||
|||||(0.04)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.747|
||||||||(0.06)|
|Trade (% of GDP)|||0.028|||0.000||
||||(0.02)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.008|||0.000||
||||(0.15)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||-0.297|||0.000||
||||(0.21)|||(0.00)||
|School Enrollment,|||0046|||0000||
|Primary (% gross)|||-.|||.||
||||(0.07)|||(0.00)||
|Year 2014|||||0.000|0.000||
||||||(0.00)|(0.00)||
|Year 2015|||||0.000|0.000||
||||||(000)|(000)||
|Year 2016|||||.<br>0.000|.<br>0.000||
||||||(0.00)|(0.00)||
|Year 2017||0.017|-0.092||0.000|0.000||
|||(0.03)|(0.12)||(0.00)|(0.00)||
|Year 2018||0.142|-0.076|||||
|||(0.21)|(0.30)|||||
|Year 2019||0.142|-0.150|||||
|||(0.21)|(0.37)|||||
|Year 2020||-8.864***|-8.906***|||||
|||(0.66)|(0.66)|||||
|Year 2021||-8.864***|-9.078***|||||
|||(0.66)|(0.68)|||||
|Year 2022||-12.648***<br>(0.75)|-13.080***<br>(0.84)|||||
|Constant|29.690***|||43.746***|||22.852***|
||(1.92)|||(1.67)|||(2.03)|
|Sigmau||8.86|5.49||8.93|6.41||
|_<br>Sigmae||6.41|6.35||0|0||
|_<br>R2|0.275|0.927|0.927|0.535|1.000|1.000|0.579|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|156|156|156|102|102|102|93|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1059<br>05, *** p<0.0<br>(WDI), Open|1059<br>1. Standard erro<br>Data Watch (OD|1059<br>rs are clustered<br>IN), Global Da|361<br>at the country le<br>ta Barometer (G|361<br>vel. Data fro<br>DB), and Op|361<br>m the Worl<br>en Data Bar|93<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

66
In this study, for several sectors the list of restrictions and weights in our STRIs are identical to those of the Australian authors. In particular, the restrictions and weights are the same as those of: McGuire, Schuele and Smith (2000) in maritime services; McGuire and Schuele (2000) in banking services; Kalirajan (2000) in distribution services; and Nguyen-Hong (2000) in professional services. In the absence of explicit matrices of restrictions for the other sectors, it was necessary to develop STRIs with weights and restrictions based on those of the Australian authors. In particular, in air transport, rail transport and road transport, they were developed by Kimura et al., (2004a), based on categories of restrictions on maritime services in McGuire Schuele, and Smith (2000). In insurance services, Kimura et al., (2004b) developed the restriction list and weights based on banking services in McGuire and Schuele (2000). In fixed and mobile line services, Kimura et al., (2004c) developed a list based on categories in Warren (2000a) in telecommunications supplemented by categories in financial sectors and transport sectors in McGuire and Schuele (2000) and McGuire, Schuele and Smith (2000).

Finally, but crucially, for our STRIs we define a mapping from the World Bank database to the list of restrictions (or matrices) of the Australian authors. In the case of the European Union countries, a case by case assessment was required to assess the notes on the intra-EU policies in the World Bank database. In this manner, we score the regulatory regimes of the 11 sectors in the 103 countries according to the Australian authors’ criteria, i.e., we calculate services trade restrictiveness indices according to the methodology of the Australian authors.

There is considerable overlap in the categories of services restrictiveness in the World Bank database and the Australian authors scoring matrices, but the mapping is not one to one. Defining the mapping from the World Bank database to the criteria of the various Australian authors involved judgments in many cases. When there was not a clear one to one mapping, we used the closest proxy from the World Bank database. We attempted to use all the information in the World Bank database, but in a few cases, no reasonable proxy was available. In these cases, we deleted the category from the matrix of the Australian authors and rescaled the remaining categories so that the sum of the category weights equaled one. The mapping for the banking sector is included as table 1 to illustrate the procedure. The detailed mappings for all sectors are 8
Table 3: IV results

||Inversions in frst two layers|
|---|---|
|Mean PM2_._5|-0.0075<sup>_∗∗_</sup><br>(0.0033)|
|N|6,142|
|**Relative Efects:**||
|1SD_↑_PM2_._5_→_“X”SD∆GDP change|-0.41|
|**Kleibergen-Paap rk Wald F statistic:**|5.92|
|**Weather controls:**||
|Temperature|Y|
|Precipitation|Y|
|Relative humidity|Y|
|Surface pressure|Y|
|Wind speed|Y|
|Wind direction|Y|
|**Fixed efects:**||
|Fiscal year|Y|

Notes: The outcome is the first difference within a district of the log of annual GDP measured at the district level. We instrument for annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population with inversions and inversion strength. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of days with an inversion. Inversion strength is the average difference in temperature when there is an inversion. All inversion data and weather controls are population weighted averages across districts. The unit of observation is the district year. In all columns we control for the first difference of the number of days in one of fifteen precipitation bins, the number of days the mean temperature is in one of twenty-five bins, a second order polynomial of relative humidity and surface pressure, four wind speed bins, ten wind direction bins, and interactions of relative humidity and the square of relative humidity with all of our temperature bins. All columns include fiscal year fixed effects. In all columns we cluster standard errors at the district level. We winsorize GDP growth at 1%. (* p _<_ .10 ** p _<_ .05 *** p _<_ .01).

20
# **Table 2: Growth rates**

||1981|Brazil<br>1993|1981|1981|China<br>1993|1981|1981|India<br>1993|1981|
|---|---|---|---|---|---|---|---|---|---|
|% peryear|-1993|-2005|-2005|-1993|-2005|-2005|-1993|-2005|-2005|
|GDP per capita|0.2|1.3|0.8|8.9|8.7|8.8|2.9|4.8|3.9|
|<br>PCE per capita|0.0|1.4|0.7|8.1|6.4|7.3|1.7|3.6|2.7|
|Survey mean|2.2|0.7|1.4|5.5|7.0|6.3|1.0|1.1|1.1|
|<br>Mixed mean|0.5|1.7|1.1|3.8|6.1|5.0|1.1|1.5|1.3|
|Headcount index ($1.25)|-2.3|-4.2|-3.2|-3.7|-9.5|-6.6|-1.6|-1.4|-1.5|
|Headcount index; mixed<br>method ($1.25)|0.2|-5.1|-2.5|-4.0|-10.4|-7.2|-2.7|-3.3|-3.0|
|Headcount index ($2.00)|-1.9|-2.5|-2.2|-1.8|-6.1|-4.0|-0.5|-0.6|-0.6|
|Headcount index; mixed<br>method ($2.00)|-0.1|-3.3|-1.7|-1.6|-6.7|-4.2|-1.0|-1.5|-1.2|
|Population|1.9|1.5|1.7|1.4|0.9|1.1|2.1|1.7|1.9|
|Notes<br>:The compound annual<br>similar results to the annualize|growth rate<br>d log diffe|between<br>rence,<br><br>ln(|year 0 and<br>_T_<br>_y_<br>_yT_<br>/)<br>/<br>0|year_T_is<br><br>.)|/<br>(<br><br>_T_<br>_y_<br>_g_|1<br>)<br>/<br>1<br>0<br><br>_T_<br>_y_|. (This gi|ves very||
|Source<br>:Table 1.||||||||||

# **Table 3: Rates of poverty reduction under all combinations of growth rates and elasticities**

|Rate of pov|erty reduction (%||Growth rate||
|---|---|---|---|---|
|per year; $1|.25 a day)|(GDP<br>Brazil|per capita; % pe<br>China|r year)<br>India|
|||(1.3)|(8.8)|(4.8)|
|Elasticity|Brazil (-3.2)|-4.2|-28.2|-15.4|
|of poverty|China (-0.8)|-1.0|-6.6|-3.8|
|reduction|India:||||
|to GDP|survey mean (-0.3)|-0.4|-2.6|-1.5|
|growth|mixed mean(-0.7)|-0.9|-6.2|-3.4|

<u>Note: The time periods are 1993-2005 for Brazil and India, and 1981-2005 for China</u> (corresponding to their reform periods.) The numbers in parentheses are the elasticities (left) and growth rates (top).

<u>Source: Table 2.</u> 32
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- Laryea, A. and S. Akuoni (2012). An Overview of Trade Policies and Development in Ghana. In C. Ackah and E. Aryeetey (Eds.), _Globalization, Trade and Poverty in Ghana_ , Chapter 2, pp. 10–32. Sub-Saharan Publishers.

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30
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50 Table 11: Using Other Covariates for Vehicle Thefts

||Basic<br>Model|OtherCo|variatesA|ddedOne|ataTime|AllOther<br>Covariates|
|---|---|---|---|---|---|---|
|**Inequality**In**PoliceStation**|-0.02<br>(0.10)|0.71<br>(0.12)**|0.07<br>(0.11)|-0.10<br>(0.10)|-0.10<br>(0.10)|0.48<br>(0.13)**|
|**MeanExpenditureInPolice Station**|1.67<br>(0.07)**|1.38<br>(0.07)**|1.70<br>(0.07)**|1.43<br>(0.07)**|1.81<br>(0.07)**|1.39<br>(0.08)**|
|UnemploymentInCriminal|-0.21|-0.15|-0.20|-0.06|-0.22|-0.03|
|<br>CatchmentArea|(0.09)*|(0.09)|(0Q09)*|(0.09)|(009)*|(0.09)|
|RichestJurisdictioninCriminal|0.09|0.18|0.09|0.17|0.09|0.21|
|CatchmentArea|(0.10)|(0.09)|(0.10)|(0.09)|(0.10)|(0.09)*|
|Population Density||0.25<br>(0.03)**||||0.14<br>(0.03)**|
|Female-headedhouseholds|||(0.151)*|||(0.511)**|
|Percentageaged21-40||||1.88<br>(0.18)**||1.70<br>(0.22)**|
|**PercentageAfrican**|||||0.23<br>(0.05)**|0.05<br>(0.05)|
|**Population**|0.97<br>(0.03)**|0.75<br>(0.04)**|0.95<br>(0.03)**|0.98<br>(0.03)**|0.96<br>(0.03)**|0.81<br>(0.04)**|
|**Constant**<br>Constant|-18.31<br>(0.50)**|-14.62<br>(0.59)**|-17.85<br>(0.54)**|-14.54<br>(0.60)**|-18.62<br>(0.50)**|-12.08<br>(0.71)**|
|ProvinceDummies|YES|YES|YES|YES|YES|YES|
|Pseudo**R**<sup>**2**</sup>|0.170|0.179|0.171|0.180|0.172|0.186|
|Observations|1066|1066|1066|1066|1064|1064|

Standard errors in parentheses. * significant at 5%; ** significant at 1%
# **Growth Spillover Effects and Regional Development Patterns: The Case of Chinese Provinces**

## **Xubei Luo**<sup>*****</sup>

The Chinese economy has witnessed an unprecedented growth of more than 8% per annum since its reforms in 1978. However, the rapid take-off of the coastal provinces has widened the regional development gap. As shown in various studies, such as Naughton (1999) and Chen and Fleisher (1996), the dynamics of the coastal provinces and the relative stagnation of the inland provinces configure a center-periphery regional growth structure. Remoteness to the major economic centers limits the market accessibility of the inland (in particular, western) provinces, and impedes their growth performance (Luo, 2004). The growing disparity in income and standard of living may entail high social and economic cost and undermine long-term sustainability. Combining growth with a more balanced distribution of income has become one of the guiding tenets of the development plans of the Government of China. Since the late 1990s, encouraging the catching-up of the inland provinces – “develop the West (1999)”, “develop the NorthEast (2003)”, and “develop the Central (2005)” – has been a focus of regional development strategies of the Chinese government.

This paper studies the effectiveness of the regional growth pattern in China and examines ways to attain regional balanced growth without too much compromising this effectiveness through geographic spillover effects. It argues the importance of sequencing development aid to optimize growth spillover effects and prioritizing the development of some inland hubs in achieving the regional development goal.

By measuring the “neighbor performance” of each province and by simulating the aggregate growth spillover effects of each province on the entire country and on different

> * The author is grateful to David Dollar, Shahrokh Fardoust, Alan Gelb, Henri-François Henner, Bert Hofman, Louis Kuijs, Russell Pittman, Mark Sundberg, and Nong Zhu, for fruitful discussions and pertinent comments. The views expressed are those of the author, who is responsible for all remaining errors.

2
Given the program was oversubscribed, half of the applicants were randomly assigned to the entrepreneurship track (“treatment group”) and the other half were assigned to continue with the standard curriculum (“control group”). Randomized assignment was conducted at the project level, stratified by gender and by the subject students were reading for (divided into 14 groups<sup>6</sup> ). The study assigned 757 projects to the treatment group (658 individual projects, and 99 projects in pairs), and 742 projects to the comparison group (652 individual projects; 97 projects in pairs). Students participated in the entrepreneurship track between February and June 2010, and graduation took place in June 2010. Two-thirds of the beneficiaries of the entrepreneurship track who graduated are women.

A first round of follow-up data was collected through face-to-face interviews between April and June 2011, approximately nine to twelve months after the end of the academic year. Despite the high mobility of the population of graduates, thorough tracking procedures led to low nonresponse rates at follow-up: 92.8 percent of the 1,702 applicants were tracked. Attrition was balanced and uncorrelated with treatment status.

A second round of follow-up data was collected through face-to-face interviews between March and June 2014, approximately four years after graduation. The instrument included similar questions as the first follow-up survey. Additional modules were introduced, including on employment history since graduation, information on the process taken by individuals to set up projects, and on constraints faced by women.

The population of university graduates is highly mobile, and the endline survey took place three years after the midline survey, and nearly four years after students’ graduation. Therefore, extensive efforts were undertaken to track individuals. The non-response rate at endline survey was 14.75 percent: 1,451 of the 1,702 applicants were tracked and interviewed. Attrition was balanced and uncorrelated with treatment status.<sup>7</sup> The short-term study documented good balance between the full treatment and control group at baseline, as well as between the groups of treatment and control students effectively interviewed

> 6 The 14 groups of subjects were: Economics and Finance; Accounting; Business Administration; Marketing; Humanities; Languages; Science; Technical; Telecommunications; Civil Engineering; Informatics; Sports and Tourism; Food/Agriculture, and Other.

> 7 Regressing attrition on treatment status yielded a coefficient of (-) 0.018 with a standard error of 0.017 (pvalue=0.285, N=1699).

9
<!-- Start of picture text --> Ingroup and Outgroup Giving<br>Sw<br>gs<br>gs<br>°<br>Partner Identity<br><!-- End of picture text --> <!-- Start of picture text --> Ingroup and Outgroup Giving<br>6 ire}<br>3<br>g<br>”<br>Partner Identity<br><!-- End of picture text --> <!-- Start of picture text --> Optimism - Outreach to Baseline<br>Optimism<br>-.15 -.1 -.05 0 05 1<br>® Life Optimism © Economic Optimism<br><!-- End of picture text -->
4 Table 1 The Export Quots Allocation System In Korea: S-loectd Cases as of 1984

|||II _ _<br>Quota<br>Allo|cation<br>Criteria/V|
|---|---|---|---|
|Co_modity|Country|BasicQuota|Open Quota|
|Toxtilos|IU.S.A.,<br>Canada, <br>EC,Swoden<br> <br>I<br>|I* last<br>year's<br>export<br>volum<br>I* lost<br>year's<br>average<br>unit<br>Iexrport<br>pric|* unit *xport<br>price in L/C<br>* lost<br>year's<br>average<br>unit<br>exportprice<br>* last<br>year's<br>export<br>volume<br>Itononrestricted<br>countries|
|Silk Fabric|IJapan|* lost<br>year's<br>export<br>volume|*lost year's<br>average<br>unit<br>export price|
|Cotton<br>Yarn|IJapan<br>_<br>_<br>_<br>_<br>_<br>_|elost<br>year's<br>export<br>volume<br>_<br>__export|* lost year's<br>average<br>unit<br>prie|
|Athletic<br>Leathe<br>Footwear|r U.S.A.<br> <br>j<br>I<br> <br>|I* last<br>year's<br>export<br>volume<br>I<br>I<br>I<br>____________________________|* unit export<br>price In L/C<br>I*lost year'e average unit<br>Iexport price<br>I<br>|* last year's<br>export<br>volum to<br>Inonrestricted<br>countries|
|Footwear|j Great Britain<br>|j last year's<br>export<br>volume<br> <br>|* unit export<br>price in L/C<br>Ilost year's<br>average<br>unit<br>I<br>export<br>price<br>|
|Footwoor||Ireland<br>|!last<br>year's<br>export<br>volume|!in the order<br>of application|
|Stainless<br>Steel<br>Flatware|IU.S.A.,<br>Benolux<br>IGreatBritain, <br>IWestGermany|,I<br>* last<br>year's<br>export<br>volume<br>I*last year's<br>export<br>value<br>I|* basic quota<br>allocation<br>e last year's<br>export<br>volume<br>to<br>nonrestricted<br>countries|
|8/W TV|Great Britain'<br>I<br>I<br> <br>I<br>|*last<br>year's<br>oxport<br>volume<br>Ia las-t<br>year's<br>average<br>unit<br>xI<br>xport price<br>I<br>I<br>|* basic quota allocation<br>I* lost<br>year's<br>avorage<br>unit<br>I<br>export<br>price<br>a<br>*last year's export volume to<br>I<br>nonrestricted<br>countries<br>|
|Carbon<br>and<br>Certain Alloy<br>Steel Products|!U.S.A.<br>I<br> I<br>I||*last<br>year's<br>export<br>volume<br>I<br>I<br>I|!**e**xport volume<br>of nowcommodity<br>I*smail and now firms<br>j*last<br>year's<br>export<br>volume<br>to<br>Ilnonrestricted<br>countries|

Notes: L/C local currency 1/ Specific weights given to each criterion vary across commodities. t/ For all items, some portion of the basic quota licenses are transferable while the open quota licenses **are** not transferable.

Source Rh-e (1984).