rafmacalaba/datause-encoder-data huggingface dataset:
because of the time required) means that these factors of production may constitute significant barriers to mobility (Catholic University (1988); de Soto (1986)]: "informality" does not mean casual attachment to a particular activity or enterprise. Information on differences in returns to schooling between one sector and another -- when people with the same level of education are feund in both sectors -- may therefore tell us something about the importance of such presumed barriers.

The second argument for analyzing sectors separately depends on the entrepreneurial function exercised by the owners of family businesses. Research on farmers' earnings suggests that education is of little value to them so long as they follow traditional farming practices, where the necessary knowledge has been accumulated over long periods of time and is successfully transmitted outside of any formal education [Schultz (1975)]. Education becomes valuable, in contrast, as soon as farmers take up new crops or methods of production, because schooling makes it possible for them to learn faster how to apply these methods to their particular circumstances and increases their ability to deal with disequilibria and volatility (Figueroa 198C(. To the extent that some family enterprises deal in more traditional activities than others and therefore require less entrepreneurial skill, we may expect that the returns to education will differ among enterprises; and if there are barriers to movement among sectors, these differences will not be eliminated quickly. The "informal" sector certainly includes many traditional activities, but is not limited to them, just as the 'formal" sector is not composed entirely of modern employments.

20
**Table 5 -- Summary statistics on labor market indicators by region in Croatia (2002-04)**

||Active<br>population<br>rate|Participation<br>rate|Employment<br>rate|Unemployment<br>rate|Monthly<br>earnings<br>(kuna)|
|---|---|---|---|---|---|
|Central Region|63.90%|72.30%|60.81%|11.47%|2806|
|Eastern Region|63.00%|61.11%|47.86%|19.93%|2826|
|Zagreb Region|66.09%|63.56%|55.91%|11.84%|3735|
|Adriatic North Region|64.38%|66.06%|58.47%|9.92%|3498|
|Adriatic South Region|62.28%|62.22%|48.94%|20.23%|3524|
|_Croatia_|_64.01%_|_65.17%_|_54.47%_|_14.51%_|_3276_|

Although regions differ from each other, there is significant heterogeneity between counties within regions. The active population rate, the participation rate, the employment rate / unemployment rate, the mean monthly earning, and the mean level of years of school vary across counties within each region to a different extent (table 6).

- On employment rate – three (out of a total of five) counties, including VukovarSirmium,  Slav. Brod-Posavina, and Osijek-Baranja, in the Eastern Region are among the four counties with the lowest employment rate (below 48%),  while the county of Virovitica-Podravina, with an employment rate of 58%, has a higher employment rate than all counties in Zagreb Region and Adriatic South Region. Five (out of a total of seven) counties, including Krapina-Zagorje, Varazdin, Bjelovar-Bilogora, Koprivnica-Krizevci, and Medimurje, in the Central Region are among the six counties with the highest employment rate (above 59%), while the county of Sisak-Moslavina, with an employment rate of 51%, ranks below all counties in the Zagreb Region and the Adriatic North Region and some counties in the Eastern Region.

- On earnings level – all 11 counties with the lowest monthly earnings level are in the Eastern Region and the Central Region. The average monthly earnings of the City of Zagreb is almost 20% higher than the national average.

- On years of school – seven out of eight counties, except the county of Lika-Senj, with the lowest human capital endowment (lower than nine years of school) are in the Eastern Region and the Central Region. All four counties with average education higher than ten years of school are in the Zagreb Region, the Adriatic North Region, and the Adriatic South Region.

- There is a strong correlation (over 0.8) between years of school and level of earnings for the 21 counties.

9
the results presented in Table 4 should be interpreted with caution as the manufacturing samples for bilateral investment with both industrialized and developing countries are small. _Section 4(b). Re-specifying the GDP variable to account for ASEAN Market Size_ As discussed earlier, MNEs also invest in Singapore to re-invest in neighboring developing countries. For instance, MNEs establish regional headquarters in Singapore and re-invest in neighboring countries such as Indonesia. Thus, MNEs investing in Singapore are also likely to take into consideration the entire market size of the ASEAN region. To account for the ASEAN market size, and to establish the robustness of the results using the Singapore market size discussed earlier, we recast the market size variable to include the regional FDI activities. We re-estimate the industrialized country inbound equations separately by using the ASEAN GDP measures to account for regional FDI activities.

Second-stage tobit regression results for aggregate inbound FEI stocks in Singapore from industrialized countries using ASEAN GDP for the period 1984-2003 are presented in the first column of Table 5, while the corresponding results for 1994-2003 and manufacturing FEI from industrialized countries for the same period are presented in columns 2 and 3 respectively. Regarding aggregate inbound FEI from industrialized countries for the period 1984-2003, GDP Sum is insignificant while the coefficient on real GDP difference squared is positive and significant, not the expected outcome under the KK model. Meanwhile, for the aggregate 1994-2003 sample, the GDP Sum is positive and significant while the squared difference in real GDP is positive but insignificant. For inbound manufacturing investment, however, the corresponding coefficients presented in the third column are insignificant.

27
# **Appendix A – Additional Summary Statistics**

Table A1 shows that both the GDP per capita and the percentage of post-high school students over the whole population have grown during our study period; the growth in these variables is likely to increase paper consumption. The composition of employment by sector shows decay in participation for the primary and manufacturing sectors, and a growth in participation for services and construction sectors. This growth in the services sector may increase the consumption of Printing and Writing paper. However, growth in this sector’s participation might increase Internet use, in turn inducing a decrease in the consumption of both Printing and Writing paper and Newsprint paper.

||**T**|**able A1.**<br>**GDP per c**<br>**(In US dol**|**Additio**<br>**apita**<br>**lars)**|**nal Des**<br>|**cripti**<br>|**ve Statis**<br>**Educ**<br>**(In perc**|**tics**<br>**ation**<br>**entage)**||
|---|---|---|---|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Min**|**Max**|**Mean **|**Std. Dev.**|**Min**|**Max**|
|**1999**|19,262|8,327|5,900|38,105|3.4|1.1|1.6|6.1|
|**2000**|20,079|8,557|6,100|39,100|3.6|1.1|1.5|6.4|
|**2001**|20,502|8,589|6,454|39,720|3.7|1.1|1.7|6.6|
|**2002**|20,916|8,555|6,972|40,068|3.9|1.1|2.0|6.7|
|**2003**|21,307|8,475|7,358|40,244|4.1|1.1|2.2|6.7|
|**2004**|22,045|8,599|8,005|41,563|4.3|1.1|2.2|6.7|
|**2005**|22,722|8,624|8,357|42,397|4.3|1.1|2.3|6.7|
|**2006**|23,499|8,556|9,031|42,993|4.4|1.1|2.3|6.6|
|**2007**|24,357|8,600|9,622|43,699|4.4|1.1|2.4|6.6|
|**2008**|24,366|8,421|10,346|43,430|4.5|1.0|2.5|6.6|

|||<br>**Pri**<br>**(In perc**|**mary**<br>**entage)**||<br>|<br>**Manu**<br>**(In per**|**facture**<br>**centage)**||<br>|<br>**Servi**<br>**(In perce**|**ces**<br>**ntage)**||<br> <br>**(**|<br>**Constru**<br>**In perce**|**ction**<br>**ntage)**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Mean **|**Std. Dev.**|**Min**|**Max**|**Mean **|**Std. Dev.**|**Min**|**Max**|**Mean **|**Std. Dev.**|**Min**|**Max **|**Mean **|**Std. Dev. **|**Min**|**Max**|
|**1999**|10.8|8.9|1.9|43.5|19.0|3.9|12.2|27.5|63.8|9.7|32.7|76.2|7.3|1.8|3.7|10.9|
|**2000**|10.3|8.9|1.9|44.3|18.8|4.0|12.4|27.1|64.3|9.6|32.9|76.5|7.4|1.9|3.7|11.8|
|**2001**|10.0|8.8|1.8|43.7|18.6|4.2|12.0|27.7|64.8|9.7|33.3|77.0|7.5|1.9|4.0|11.6|
|**2002**|9.6|7.9|1.8|38.0|18.3|4.4|12.0|27.7|65.3|9.4|36.2|77.5|7.6|1.9|4.5|12.0|
|**2003**|9.2|7.7|1.6|37.2|17.9|4.4|11.0|27.3|65.8|9.5|36.5|77.8|7.9|1.9|4.6|12.2|
|**2004**|8.6|7.0|1.6|33.1|17.7|4.6|11.1|27.1|66.3|9.0|39.3|77.6|8.1|1.9|5.2|12.5|
|**2005**|8.3|6.9|1.7|33.4|17.2|4.6|10.9|27.2|66.8|8.9|39.6|77.5|8.4|2.0|5.1|12.6|
|**2006**|8.0|6.6|1.7|31.8|16.9|4.7|10.4|28.2|67.1|8.7|41.0|77.8|8.7|2.0|5.2|13.1|
|**2007**|7.7|6.3|1.8|30.6|16.6|4.9|9.8|28.6|67.3|8.8|40.9|78.7|9.1|2.2|5.6|13.4|
|**2008**|7.3|6.1|1.9|29.9|16.4|5.0|9.8|28.6|68.0|8.7|41.5|79.7|9.0|2.0|5.4|12.2|

Source: International Monetary Fund (IMF), United Nations Educational, Scientific, and Cultural Organization (UNESCO), and International Labor Organization (ILO).

27
# **Parents-grandparents**

In 2002 CHIP rural data, there is a specific module providing the information about the parents of household heads and spouses. Furthermore, this is the module for the complete parental information of the household heads and spouses, including the grandparents coresiding with the family, grandparents not co-residing, and also the grandparents who had passed away. We have identified 14,777 parents-grandparents pairs with the required information on parents’ age and education, grandparents’ age, education and grandparents. In our sample, most of the parents (95 percent of fathers and 92 percent of mothers) completed their schooling before the economic reform in 1978. Also, 59 percent of fathers’ education and 64 percent of mothers’ education were affected by cultural revolution.

# **(6) Empirical Results**

Table 1A presents the summary statistics for a set of economic and demographic characteristics of six different groups of households for the parents-children sample. Table 1B reports the corresponding estimates for four different groups for the grandparents-parents sample. The evidence in Table 1 shows that the households in farm and low education (less than or equal to primary) group ( _Di_<sup>00)arecharacterizedbyunfavorablesocio-economic</sup> characteristics including lowest education levels in both the parents’ and children’s generations, and lowest per capita income (income data are only for the parents-children sample). The non-farm and high education group ( _Di_<sup>11)occupiestheotherextreme,bothparents</sup> and children have highest levels of educational attainment and also per capita household income is highest.

# **(6.1) Parents** ' **Economic Status and Children’s Schooling: Heterogeneous**

In this section, we use a dummy for more than primary schooling for parental education 23
# **2. MAIN DECOMPOSITION ANALYSIS**

# **2.1 Data and Background**

We use historical data on urbanization, and urban and rural fertility and mortality. We first reconstruct the urban growth and urbanization rates for 19 European and North American countries from 1700-1950 (<sup>_∼_</sup> every 40 years), and 116 African, Asian and non-North American countries that were still developing countries in 1960, from 1900-2010 (<sup>_∼_</sup> every 10 years). This allows us to compare the urbanization process of five developing areas: “Industrial Europe” (which includes the United States in our analysis), Africa, Asia, Latin America (LAC) and the Middle-East and North Africa (MENA). Second, we obtain historical demographic data for 42 countries: 7 European countries for the 1700-1950 period, and 35 countries in Africa (10), Asia (12), the LAC region (8) and the MENA region (12) for the 1960-2010 period. For each country-period observation, we obtain the urban and rural crude rates of birth, death and natural increase (per 1,000 people). We recreated the data using historical sources, as well as the _UN Statistical Yearbooks_ and reports of the _Population Census_ , the _Fertility Surveys_ and the _Demographic and Housing Surveys_ of these countries.<sup>6</sup> We also collect the same type of data for 97 countries that were still developing countries in 1960 for the most recent period.

The most advanced civilizations before the 18th century had urbanization rates of around 10%-15% (Bairoch, 1988). Industrialization accompanied dramatic urbanization. Figure 1 (top panels) shows the urbanization rates for Industrial Europe (1700-1950) and four developing areas: Africa, Asia, LAC and MENA (1900-2000). The urbanization rate for Europe was stable (<sup>_∼_</sup> 10%) until 1800 and increased to<sup>_∼_</sup> 40% in 1910. In 1950, countries in Africa and Asia were also predominantly rural (urbanization rate<sup>_∼_</sup> 15%). By 2010, their urbanization rate had increased to<sup>_∼_</sup> 40%. The LAC region had already surpassed the 40% threshold in 1950, while the MENA region did not surpass it until 1970. In our analysis, we focus on the 1800-1910 period for Europe and the 1960-2010 period for Africa and Asia. During these periods, the urbanization rates of the three areas increased from 15% to 40%. Figure 1 (bottom panels) also shows the urban growth rate for Europe (1700-1950) and the four developing areas from 1900-2010. In the 1800-1910 period, the overall urban growth rate in Europe was 2.2% per year, peaking during the Industrial Revolution. Conversely, the urban growth rate has been<sup>_∼_</sup> 3.8% a year in today’s developing world post-1960. A 3.8% growth rate implies that cities double every 18 years, while a 2.0% rate means that cities only double every 35 years. These rates peaked in the 1950s/60s, with the acceleration of rural-to-urban migration and the demographic transition.

> 6See Web Appx Table 1 for details on the data sources used for each country.

4
Table A6. FDI inflows <u>and local school enrollment: by age group and FDI skills intensity</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Panel A:|16-18|Panel B:|19-22|Panel C:|23-25|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|∆Highschool|∆College|
|∆FDI_skills_H|3.197***|0.352*|0.347|1.825***|1.739***|1.480***|
||(0.951)|(0.213)|(0.701)|(0.569)|(0.539)|(0.554)|
|∆FDI_skills_L|1.724***|-0.186|1.206***|0.325|0.891**|0.735***|
||(0.619)|(0.119)|(0.439)|(0.323)|(0.354)|(0.273)|
|Observations|609|609|609|609|609|609|
|R-squared|0.839|0.499|0.846|0.676|0.722|0.589|
|Prov-Year FE|Y|Y|Y|Y|Y|Y|
|Lagged city|||||||
|characteristics|Y|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level school enrollment by student age group. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

Table A7. FDI inflows <u>and local school enrollment: by gender and FDI export intensity</u>

||(1)<br>Panel A: F<br>|(2)<br>emale<br>|(3)<br>Panel B:<br>|(4)<br>Male<br>|
|---|---|---|---|---|
|Dependent var|∆Highschool|∆College|∆Highschool|∆College|
|∆FDI_export_H|1.500***|0.435|1.179***|0.579**|
||(0.417)|(0.284)|(0.440)|(0.286)|
|∆FDI_export_L|1.382**|0.482**|0.889*|0.704**|
||(0.587)|(0.245)|(0.533)|(0.282)|
|Observations|609|609|609|609|
|R-squared|0.827|0.680|0.769|0.651|
|Prov-Year FE|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|
|Excludingmigrants|Y|Y|Y|Y|

Note: This table reports the effect of FDI shock on city level school enrollment by gender and FDI export intensity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

39
economics. Attachments providing supporting evidence for the suggested motivation accompanied the letter.<sup>5</sup> Written communication was delivered in two modes: An official email and a physical letter on university letterhead mailed to a student-provided primary address.

# 2.4 Outcomes

Students are matched to their administrative records to observe the courses they take in subsequent semesters as well as their major and minor declarations. Our last semester of observations is in the Spring semester of 2019.

Treatment may increase retention among those already declared in economics; may cause a switch to economics or the addition of an economics major for students who have declared another major; and undeclared students may respond to treatment by declaring economics. In all cases, course-taking may increase due to an increase in general interest in the subject, including pursuant to changes in major decisions.

# 2.5 Estimation

We estimate the following specification: 1 2 F F 1 ⋅F F F 2 ∗F F F ii ii ii ii ii ii ii ii ii where yy = αα+ ββ is the outcome of student TT1 + ββ TT2 + γγ F _i_ ,<sup>6</sup> + δδ is an indicator of whether student TT1 + δδ TT2 _i_ is randomly assigned + ψψ XX + εε (1) to the Prosocial Treatment Group 1, ii ii indicates that student _i_ is randomly assigned to the yy TT1 Earnings Treatment Group 2, and F F F ii is the female dummy. is a set of individual control TT2 variables, including age when solicited, US citizenship, first-generation college student, race ii ii XX dummies, pre-college academic and math scores indices, declared major when solicited, class standing and term when solicited. Primary effects are estimated using the sample that includes both men and women, while we present the versions of the mediated findings that are estimated separately for men and women. We report robust standard errors in the tables, instead of clustered standard errors because we only have 11 clusters at the term, introductory economic course, and

> 5 Material available at <u>https://rb.gy/mwtgle.</u>

> 6 We also examined treatment effects on the number of economics courses taken over the college career, for those students observed to graduation. None of the treatment coefficients were estimated to differ significantly from zero in this case. These findings are available in the E-appendix.

5
meat. We produce four predictions: with and without fish consumption preference and peak-catch season birth, respectively. Table 12 presents the results.

**Table 11:  Probit results - child illness rate**<sup>**a**</sup>

|**Protein Intake**|**Parameter**<br>**Estimates**|**Survey Month**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Fish**|**-0.066**|**July**|**-0.18**|
||**(2.48)* **||**(1.46)**|
|**Eggs **|**0.005**|**August**|**-0.303**|
||**(0.19)**||**(2.45)* **|
|**Meat**|**-0.042**|**September**|**-0.218**|
||**(1.17)**||**(1.74)**|
|||**October**|**-0.147**|
|**Characteristics**|||**(1.16)**|
|**Female**|**-0.084**|**November**|**-0.283**|
||**(3.86)****||**(2.19)* **|
|**Mother’s Education**|**-0.018**|**December**|**-0.483**|
||**(5.26)****||**(3.53)****|
|**Household Assets**|**-0.016**|||
||**(2.09)* **|**Birth Year**||
|||**2009**|**0.241**|
|**Peak-Catch Birth**|**-0.071**||**(3.82)****|
||**(2.63)****|**2010**|**0.292**|
||||**(7.19)****|
|**Constant**|**0.036**|**2011**|**0.146**|
||**(0.19)**||**(3.65)****|
|**Diarrhea Adjustment**|**-1.27**|**2012**|**0.093**|
||**(41.54)****||**(1.57)**|
|**Fever Adjustment**|**0.084**|**2013**|**0.177**|
||**(3.60)****||**(4.60)****|

**a Grid location results excluded for brevity**

**Observations:  18,008 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

25
# <u>Table A2. Analysis of differential attrition (Panel D)</u>

||<br>N|<br>(1)<br>**Full data**<br>Mean[SD]|N|(2)<br>**No skills measures**<br>Mean[SD]|N|(3)<br>**Full attrition**<br>Mean[SD]|t-test<br>Diff<br>(1)-(2)|t-test<br>Diff<br>(1)-(3)|Total obs.|
|---|---|---|---|---|---|---|---|---|---|
|**Household - 2003**||||||||||
|HH SES in 2003|4382|-0.12|564|-0.02|905|-0.38|-0.09|0.27*|5851|
|||[1.99]||[1.90]||[1.99]|(0.12)|(0.14)||
|Family owns house it is living in|4391|0.95|565|0.96|909|0.90|-0.01|0.05**|5865|
|||[0.21]||[0.18]||[0.30]|(0.01)|(0.02)||
|Number of rooms house|4391|<br>2.50|565|<br>2.53|909|<br>2.45|<br>-0.03|<br>0.04|5865|
|||[1.36]||[1.36]||[1.36]|(0.08)|(0.11)||
|Type of house is permanent|4391|0.68|562|0.67|909|0.59|0.01|0.09***|5862|
|||[0.46]||[0.47]||[0.49]|(0.02)|(0.03)||
|HH has toilets on premises|4390|0.58<br>[0.49]|565|0.60<br>[0.49]|909|0.52<br>[0.50]|-0.02<br>(0.03)|0.06<br>(0.04)|5864|
|HH has hard roof|4391|0.55|565|0.51|909|0.53|0.03|0.01|5865|
|||[0.50]||[0.50]||[0.50]|(0.03)|(0.04)||
|HH has access to electricity|4383|0.88|565|0.88|905|0.84|<br>-0.01|<br>0.04|5853|
|||[0.33]||[0.32]||[0.37]|(0.02)|(0.04)||
|Relative HH wealth compared<br>to rest of village, max is 4|4391|<br>3.37<br>[0.89]|565|<br>3.36<br>[0.87]|909|<br>3.42<br>[1.01]|<br>0.02<br>(0.05)|<br>-0.04<br>(0.07)|5865|
|HH size|4391|8.86|565|8.57|909|8.24|0.28|0.62**|5865|
|||[4.07]||[3.15]||[2.77]|(0.22)|(0.28)||
|HH religion is not Islam|4381|<br>0.02|565|<br>0.01|909|<br>0.03|<br>0.01**|<br>-0.01|5855|
|||[0.15]||[0.09]||[0.17]|(0.01)|(0.01)||
|Male interview language is not Punjabi|4383|<br>0.26|561|<br>0.17|906|<br>0.21|<br>0.10***|<br>0.06*|5850|
|||[0.44]||[0.37]||[0.41]|(0.02)|(0.03)||
|Father is not living in HH|4293|0.12|555|0.16|884|0.21|-0.03*|-0.08***|5732|
|||[0.33]||[0.36]||[0.40]|(0.02)|(0.02)||
|Mother is not living in HH|4298|<br>0.02|555|<br>0.03|884|<br>0.02|<br>-0.01|<br>-0.00|5737|
|||[0.15]||[0.17]||[0.16]|(0.01)|(0.01)||
|**Parents - 2003**||||||||||
|Parent years of schooling|7791|3.13<br>[4.31]|989|2.86<br>[4.05]|1538|2.84<br>[4.08]|0.27<br>(0.18)|0.29<br>(0.23)|10318|
|Parent can read|7944|0.38|1001|0.35|1561|0.36|0.02|0.02|10506|
|||[0.48]||[0.48]||[0.48]|(0.02)|(0.02)||
|Parent number of children|7386|<br>5.37|937|<br>5.50|1421|<br>5.29|<br>-0.13|<br>0.08|9744|

79