rafmacalaba/datause-encoder-data huggingface dataset:
Duflo, E., 2012. `Women Empowerment and Economic Development’, _Journal of Economic Literature_ , 50(4), 1051–1079.

Duflo, E., P. Dupas and M. Kremer. 2015. ‘Education, HIV, and Early Fertility: Experimental Evidence from Kenya’, _American Economic Review_ , 105(9), 2757–2797 _._ Ferreira Vaz, R., D. Leite Maia Monteiro and N. Pinheiro Rodrigues. 2015. ‘Tendências da gravidez na adolescência no Brasil, 2000-2011’, _Revista da Associação Médica Brasileira,_ 62(4).

Foureaux Koppensteiner, M. 2014. ‘Automatic Grade Promotion and Student Performance: Evidence from Brazil’, _Journal of Development Economics_ , 107, 277–290.

Foureaux Koppensteiner, M. and J. Matheson, 2019. “Secondary school enrolment and teenage childbearing: Evidence from Brazilian municipalities”, IZA discussion paper no. 12504.

Fort, M., N. Schneeweis and R. Winter-Ebmer, 2016. ‘Is Education Always Reducing Fertility? Evidence from compulsory schooling reforms’, _Economic Journal_ , 126, 1823–1855.

<mark>Glewwe, P. and A. Kassouf. 2012. ‘The Impact of the Bolsa Escola/Família Conditional Cash Transfer Program on Enrolment, Dropout Rates and Grade Promotion in Brazil’,</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 97(2), 505-517.</mark> Guimarães de Castro, M.H. and S. Tiezzi. 2004. ‘The Reform of Secondary Education’, in: _The Challenges of Education in Brazil_ . Ed.: C. Brook and S. Schwartzmann. Cambridge University Press, Cambridge.

Geruso, M. and H. Royer, 2018. ‘The Impact of Education on Family Formation: Quasiexperimental evidence from the UK’, NBER working paper 24332.

IBGE. 2011. _Evolução da Divisão Territorial do Brasil 1872–2010_ . Available at <u>http://www.ibge.gov.br/home/geociencias/geografia/default_evolucao.shtm.</u> IBGE. 2012. _Estatícas do Sécolo XX._ Database Appendix. Rio de Janeiro.

INEP. 2003. _Sinopse Estatística da Educação Básica 1991_ – _1995_ . Ministry of Education, Brasília.

INEP. 2011. _Sinopse Estatística da Educação Básica 2010_ . Ministry of Education, Brasília.

JBIC (Japan Bank for International Cooperation). 2005. ‘Sector Study for Education in Brazil’, _JBIC Sector Study Serie_ s 2004–No.2.

James, J. and S. Vujic. 2019. ‘From High School to High Chair: Education and Fertility Timing’, _Economics of Education Review_ , 69, 1–24.

LDB (Lei de Diretrizes e Bases da Educação Nacional). 1996. Senate of the Federal Republic of Brazil. http://www6.senado.gov.br/legislacao/ListaPublicacoes.action?id=102480.

Marchelli, P.S. 2010. ‘Expansão e Qualidade de Educação Básica no Brasil’, _Cadernos de Pesquisa_ 40, 561–585.

Monstad, K., C. Propper and K.G. Salvanes. 2008. ‘Education and Fertility: Evidence from a natural experiment’, _Scandinavian Journal of Economics,_ 110, 827–852.

Monteiro de Barros Araújo, F. and A. Mendes Najjar. 2017 ‘Apontamentos Sobre o Programa de Melhoria e Expensão do Ensino Médio’, in: Najjar, J. and M Morcarvel (eds.) Política Públicas em Educação, Curitiba.

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**8**

The share of TVPs i1i the output of specific industries or product groups **varies greatly, although they were active** in virtually all broad industrial lines by 1985. Considering only TVCEs (there are no corresponding data for all TVPs) shows that in 1985 they accounted for 26 percent of the gross value of industrial production in machine building, 19 percent in construction materials, and 13 percent in textiles. They produced some 80 percent of all bricks, almost 20 percent of cement, and nearly 25 percent of paper and cardboard made in China.

# III. Markets and Administrative Structure.

# **A.** The Market Environment of the TVPs

Does the rise of the TVP sector represent a strengthening of market forces? Or do TVPs, like their state counterparts, operate largely according to mandatory or "guidance" plans? Insofar as they are market based, are their markets fragmented by "local protectionism" policies implemented by local governments, or are their markets regional or national?

The answers to these questions differ for product and factor markets. Surveys provide ample evidence that planned output as a share of total output is minimal for most TVPs. Furthermore, they are "outward oriented" with respect to their home communities, as might be expected given the very small size of their "home markets. " For example, in Wuxi only 4 p--cent of sales of industrial TVCEs occurred within the home township in 1985. Large majorities of sampled firms in all four counties sold at least 40 percent of their output outside the home province. In any case, communal governments have little scope for protecting their enterprises from outside competition. The surveyed TVPs
-  12  -

**Table 4:** _Total trade turnover, exports and imports: EU-15 and Rest of World in 1992-2003 (in millions of US dollars and percent)_

||1992|1993|1994<br>|1995<br>(totaltr|1996<br>ade turn|1997<br>over in|1998<br>millions|1999<br> ofUS d|2000<br>ollars)|2001|2002|<br>2003|Index<br>1995<br>1992=<br>100|Index,<br>2003<br>2000=<br>100|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|EU-15|2,648|2,699|3,472|4,392|4,010|3,723|3,765|4,361|5,073|5,652|6,735|7,719|166|152|
|ROW|2,511|3,965|4,408|5,548|6,239|5,482|4,264|4,439|4,907|5,772|6,078|7,266|221|148|
|Total|5,159|6,664|7,880|9,940|10,250|9,205|8,030|8,799|9,980|11,424|12,813|14,985|193|150|
||||||(inperce|nt)|||||||||
|EU-15|51|41|44|44|39|40|47|50|51|49|53|52|86|101|
|ROW|49|59|56|56|61|60|53|50|49|51|47|48|115|99|
|||||(e<br>|xport sh<br>|aresin<br>|percent<br>|)<br>|||||||
|EU-15|45|47|47|46|39|40|45|51|54|52|55|56|103|104|
|ROW|55|53|53|54|61|60|55|49|46|48|45|44|98|96|
|Total (million|||||||||||||||
|ofUS $)|2,051|2,444|2,319|3,400|5,220|4,781|4,314|4,150|3,755|4,760|5,062|5,631|166|150|
|||||(i|mport sh|aresin|percent|)|||||||
|EU-15|56|37|43|43|40|41|49|48|49|48|51|49|78|100|
|ROW|44|63|57|57|60|59|51|52|51|52|49|51|128|100|
|Total (million|||||||||||||||
|of US$)|3,107|4,220|5,561|6,540|5,030|4,424|3,716|4,649|6,225|6,664|7,751|9,354|210|150|

Source: IMF Direction of Trade Statistics.

**Figure 2:** _Bulgaria’s non-EU and EU-15 oriented exports in 1992-2003 (in millions of US dollars)_

<!-- Start of picture text --> 8,000<br>7,000 EU-oriented<br>exports<br>6,000<br>5,000 Other<br>exports<br>4,000<br>of w hich:<br>3,000<br>Belarus<br>2,000<br>Total<br>1,000<br>exports<br>0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: Source: IMF Direction of Trade Statistics.

In fact, it appears from the IMF Direction of Trade statistics that exports to Belarus shaped Bulgarian exports over 1994-99 displaying an unusual pattern of magnitude and volatility. They soared from US$ 570 million in 1994 to US$ 2 billion in 1995, accounting for 81 percent of the annual increase in the value of total exports (Table 5). They fell rather precipitously in 1996 to a still respectable level of US$ 1.2 billion and their value over 1997-98 was still above US$ 1billion. The size of contraction (US$ 831 million) was almost twice as large as the contraction in the value of total exports Bulgarian National Bank’s trade statistics do not back up the claim about unique Belarus’ role in Bulgarian export. These statistics, available only since 1995, portray a different image. First, total Bulgarian exports to CIS and Baltic states combined were lower according to BNB
We define star firms as firms that realize high returns for their investors. We begin by using a standard definition of Return on Invested Capital (ROIC) as our measure of returns, where ROIC for firm _i_ in year _t_ is defined as: where _EBIT_ is Earnings before Interest and Taxes (Compustat item EBIT) and _AM_ is Amortization of Intangible Assets (Compustat item AM). ROIC, as used in the Council of Economic Advisors [2016] report and Ben-David et al. [2013], among many others, computes the earnings that a corporation realizes over a period, as a fraction of capital that investors have invested into the corporation. The advantage of ROIC is that it measures investment capital as more than physical capital (fixed asset investment) which Doidge et al. [2018] show to be a declining portion of total assets over time in the US.

We adopt a relatively conservative definition for Invested Capital as the amount of net assets a company needs to run its business: where _PPENT_ is Net Property, Plant, and Equipment, _ACT_ is Current Assets, _INTAN_ is Total Intangible Assets, _LCT_ is Current Liabilities, _GDWL_ is Goodwill that represents the excess cost over equity of an acquired company, _CHE_ is Cash and Short-term Investments, and _SALE_ is net sales. All these variable labels are the corresponding items in Compustat.<sup>11</sup> The intangible assets as registered in Compustat, _INTAN_ , include externally purchased assets like blueprints, copyrights, patents, licenses etc. and goodwill but do not include internal intangible assets like R&D and SG&A. We exclude Goodwill, which are the intangible assets arising out of M&A transactions when one company acquires another for a premium over fair market value, in the computation of invested capital in equation 2. Thus, our measure is not distorted by price premiums paid for in acquisitions, allowing for an even comparison of operating performance across

> 11We replace missing values of AM and GDWL with 0.

8
Market Size, Sunk Costs of Entry, and Transport Costs: An Empirical Evaluation of the Impact of Demand‐Side Factors versus Supply‐Side Factors on Manufacturing Productivity Patricia Jones Economics Department SUNY New Paltz Emmanuel Lartey World Bank Taye Mengistae World Bank Albert Zeufack World Bank JEL Codes: L6, 014, 012.

Key Words: Industry Studies-Manufacturing; Manufacturing and Service Industries; Industrialization; Microeconomic Analyses of Development.
In this framework, the variable H, which is an index, is constructed using Barro and Lee’s (1994) methodology where Where is actual number employed, is average number of years of education per worker; and is a parameter that increases the returns to education. In this paper, we assumed that is 10% There are no theoretical reasons as to why the coefficients α and β should be constant over time or be the same between sectors/industries or between the firm level and macroeconomic level. Remember that the nature of the machinery and other capital goods (the ) differs between time‐periods and according to what is being produced. So do the skills of labor (the ). For example, consider two sectors which have the exact same Cobb‐Douglas technologies: α β α β if, for sector 1, _Y_ 1 = _AK_ 1 _H_ 1 and, for sector 2, _Y_ 2 = _AK_ 2 _H_ 2 , That, in general, does _not_ imply that _Y_ 1 + _Y_ 2 = _A_ ( _K_ 1 + K2)<sup>α</sup> ( _H_ 1 + _H_ 2)<sup>β</sup> This holds only if H1 / H2 = _K_ 1 / _K_ 2 and α+β = 1, i.e. for constant returns to scale technology.

It is thus a mathematical mistake to assume that just because the Cobb‐Douglas function applies at the micro‐level, it also applies at the macro‐level. Similarly, there is no reason that an aggregate Cobb‐ Douglas should apply at the disaggregated level. The results presented below confirm the mathematical propositions.

# 2. **Data Description**

This section uses data gathered by the World Bank’s Enterprise Surveys (www.enterprisesurveys.org) over 2002‐2005 (standard format data). Out of 71,789 firm survey records, we selected those from 97 countries that have full data availability for firm level productivity analysis (those with firm level “value added”, “physical capital” and “workers (adjusted for years of schooling)" available). Out of these 97 countries, our macro level data constraint will further limit us to 45 out of them (4645 firms in total), since we want to have countries (and firms in those countries) with full data availability for productivity analysis on both national and firm level. [Note: Our productivity analyses are based on those 45 countries (for both firm and macro level), however, the growth contribution study is further limited in the number of observations, as the survey on firm level may not have historical data for us to calculate the growth for “value added”, “physical capital” or “workers (adjusted for education)”.] The 45 countries included in our analysis are: Argentina, Bangladesh, Bolivia ,Brazil, Cameroon, Chile, China, Colombia, Costa Rica, Dominican Republic, Egypt, Arab Rep., El Salvador, Ethiopia, Greece, Guatemala, Guyana, Honduras, India, Indonesia, Ireland, Jordan, Kenya, Madagascar, Malawi, Mali, Mauritius, Mexico, Morocco, Nicaragua, 8
to migration and at the destination, as well as the present occupation for return migrants (As examples, see Schaffner, 2000; for examples in practice see Carletto and Kilic, 2011; Mendola and Carletto, 2012). A survey that includes oversampling of migrants may be particularly useful for statistical power, especially as the number of job types increases. There are numerous studies on the impact of emigration on local labor market outcomes in origin countries. In Albania, Miluka et al. (2010) find that international migration reduces overall labor inputs into farming. Similarly, in the Kayes area of Mali, Azam and Gubert (2006) report that individuals left behind by international migrants can exhibit shirking behavior in terms of working in agriculture. Within China, de Brauw et al. (2008) find that, as internal migration increased, overall agricultural labor inputs decreased, but the average share of agricultural labor inputs provided by women did not change. Migration can also affect child labor: on one hand, households with out-migrants may become wealthier, reducing the need for child labor; on the other hand, migration could lead to labor shortages on the farm, potentially increasing the need for child labor. Migration need not only affect the households that migrants leave; at the source, emigration can have general equilibrium effects on wages (Hanson, 2007; Mishra, 2007). The macro impact of immigration in source and destination countries can be analyzed using aggregate data as well as labor force survey data. The big debate (Borjas, 1988, 1989, 1994 and Card, 2001, 2009) on how migration affects native workers is concerned deeply with properly identifying endogenous location choices. Migrant workers are attracted to geographic areas and sectors where there are strong labor demand and promising future prospects. On the other hand, arrival of migrants is likely to cause native workers to move to other sectors and areas, causing the effect to ripple 31
# **_Length of the school day and the quality of the textbooks_**

The fact that the state-managed network could cover as much school content as the locallymanaged network that remained open is associated with the longer school hours per day (5.3 hours versus 4.9 hours). This difference is equivalent to an addition of three to four school days in the whole school year compared to locally-managed schools (Table A.4).<sup>29</sup> However, the additional time in the classroom due to the longer duration of the classes in the state-managed schools is still inferior to the lost time in the classroom due to the period of the shutdowns. We did not find any source of information that could suggest that the state government extended the length of the school year to compensate for the days the schools were closed. The length of the school year was five days longer in the locally-managed network (Table A.4). These statistics also suggest that teachers of the state-managed network had to rush to cover the school curriculum, probably making it challenging for the students to keep up.

A glance at the descriptive statistics presented in Table A.3 also highlights the challenges faced by the municipal schools affected by the shutdowns. One of the issues that stands out is that these schools could not cover the school curriculum in a shorter time frame. While 55% of municipal schools in municipalities that did not extend the winter break covered more than 80% of the school curriculum, only 45% did so in municipalities that extended the winter break.

Data from _Prova Brasil_ questionnaire show that teachers faced many barriers to successfully covering the entire school curriculum. For instance, students in treated schools had fewer hours of classes per day (4.8 hours _×_ 5 hours). The 12-minute daily difference resulted in children from the affected schools having two days less of school content between the reopening of the schools and the proficiency test.<sup>30</sup> Also, we do not find any source of information of an extension of school days later in the year to account for the missing days during the winter break extension.

> 29There are between 44 and 53 business days between the reopening of the schools (August 17, 2009) and the proficiency test (which took place between October 19 and October 31, 2009). Therefore, the 24-minute difference would result in 1,056 to 1,272 additional minutes of class, equivalent to 17.6 to 21.2 hours. If the average number of class hours in the state-managed network in _G_ = 1 is 5.3, the estimate in terms of school days is between three and four.

> 30There are between 44 and 53 business days between the reopening of the schools (August 17, 2009) and the proficiency test (which took place between October 19 and October 31, 2009). Therefore, the 12-minute difference would result in 528 to 636 fewer minutes of class, equivalent to 8.8 to 10.6 hours. If the average number of class hours in the affected network is 4.8, the estimate in terms of school days is roughly two.

26
would violate the exclusion restriction. In our first-stage regression, we account for year fixed effects, which control for common shocks. That said, to be sure that we get rid of commonshock influences, we orthogonalize other countries’ equity value, or issuances, by removing any correlation between other countries’ equity value or issuances with the equity value or issuances in the subject country.

Even before employing our instrumenting, one could argue that firm-level differences in the magnitude of the connection between capital inflows and issuances are indicative of a supply-side story. If demand-side influences are largely common across firms of different sizes, then greater issuance responses to capital inflows for large firms likely reflect supply-side differences resulting from differential access to international investors. From that perspective, even our firm-level OLS analysis, which controls for firm and year fixed effects, and which displays a stronger association between equity inflows and issuance for large firms, is suggestive of supply-side influences.<sup>14</sup> Furthermore, the relation between inflows and issuances also applies to lagged inflows. A positive relation between lagged inflows and issuances suggests a supply-side influence whereby equity inflows put upward pressure on stock prices, which reduces the cost of equity capital, encouraging destination countries’ firms to issue equity, a decision that occurs with a lag.

Although differential effects for large and small firms and observed connections between lagged inflows and subsequent issuances are both suggestive of supply-side causation, these facts by themselves are not conclusive. It is conceivable that demand-side influences differ across firms of different sizes. And serial correlation in demand shocks could produce

> 14 In a prior version of this paper (available as NBER Working Paper 24433), we sorted firms by size according to the average size of their issuances over the sample period and obtained similar results.

16
were issued as an instrument, this result offers some confirmation as the result is similar using a different instrument.

When we add covariates to the regression (columns 2-5), the estimated coefficient on migrant share remains negative; however, it is no longer significantly different from zero. Point estimates are slightly smaller in magnitude than the point estimates found using the quartic in years since IDs were issued, but they are consistent with estimates in Tables 6 and 7. Apart from levels of significance, the Anderson-Rubin test statistics indicate that we cannot reject the joint hypothesis that the instrument does not affect the migrant share and that the migrant share does not affect school enrollment. When we use the quartic in years-since-IDs were issued, this test is consistently rejected.

We can be somewhat more sanguine about results using contemporaneous city GDP growth in potential destinations (Appendix Table A.10). Estimated coefficients on the share of the village population working as migrants range from -2.16 to -2.28, and the Anderson-Rubin test statistics suggest that while weak, the instruments are sufficiently strong and yield results consistent with the years-since-IDs instruments. As these results provide no evidence against the years-since-IDs instrument, we view them as offering confirmation. Yet, as they identify effects of migration over short distances, we view this instrument as identifying the effects of relatively local migration. We prefer the years-since-IDs instrument used in the main text as it identifies the network outside the village without strong assumptions about the work locations of current and past migrant residents from the village.

# **A.3. Evidence on Returns to Education among Migrants from RCRE Villages**

To understand how returns in the urban labor market might in influence decisions about enrollment in high school, we use a module from the 2003 round of the RCRE survey that was designed to study the returns to migrant employment. For individuals who had out-migrated from RCRE households in 2003, the RCRE survey collected information on earnings, the cost of migration and the number of days individuals worked as migrants. Using a sample of all adult children of the household head and spouse who were between 15 and 50 years old, we estimate the net returns to education for migrants using a Heckman selection model. Our objective is to assess whether the pattern of returns to education are consistent with the observed decline in high school enrollment in those villages where it became easier to migrate. For the selection equation, we use household land per capita and demographic characteristics (e.g. household size, number of laborers, number of elderly in the household, the household 54