rafmacalaba/datause-encoder-data huggingface dataset:
reported in Section S1 of the appendix.

For agricultural investment, a summary variable is reported constructed employing six variables: area sown, labor invested in days, value of fertilizer employed, value of seeds employed, investment in animals, and investment in tools. Fertilizer, seeds and agricultural investment are reported as expenditure in yuan, and deflated using a summary price index for agricultural inputs published by the China National Bureau of Statistics. Principal-component analysis is conducted on these six variables, and the first component is employed as the summary index.

Agricultural income is calculated valuing all agricultural production (including livestock, fish, and forestry products) at the market price observed in each village-year cell; the market price of each crop or product is calculated as a sales-weighted average of unit prices reported by households. This income measure is then deflated using a weighted Laspeyres price index constructed from the same prices, employing 1993 as the base year. (Note this calculation abstracts from any variation introduced by quota policy itself, as all production is valued at the market price rather than the quota price.) To analyze non-agricultural production, results are again reported for variables capturing non-agricultural investment and income. For non-agricultural investment, a summary outcome measure is constructed using four variables: a dummy variable equal to one if the household reports any new cash investment in a non-agricultural business, a dummy variable equal to one if the household reports any labor invested in non-agricultural machinery, and the amount of labor and cash investment reported in non-agricultural businesses. It is important to note that both agricultural and non-agricultural investment as constructed here are flow measures: they capture new investments in the year of interest. Again, the summary index is constructed via principal component analysis, retaining the first component as the index of interest. Non-agricultural income is the sum of income from non-agricultural household businesses and wage labor.

In order to deflate non-agricultural investment and income to constant prices, an index of ex-factory prices for industrial products published by the National Bureau of Statistics 18
Appendix A

|**Category**|**Specification**||||**Seri**|**es and Coding**||||**0-1 dummies**<br>**(1:yes)**|
|---|---|---|---|---|---|---|---|---|---|---|
|||**1981**|**1985**|**1988**|**1990**|**1993**|**1995**|**1997**|**1998**||
|**Assigned**<br>**workbook**<br>**(Carteira)**||**v0506**|**v0506**|**v0506**|**v0506**|**v09042**|**v09042**|**v9042**|**v9042**||
||yes|2|2|2|2|2|2|2|2|cart|
||no|4|4|4|4|4|4|4|4||
||NA|9|9|9|9|9|9|9|9||
|**Tenure**|||||||||||
||Less than 1 year||||**v3011**|**v090612**|**v090612**|**v9612**|**v9612**|ten_0|
||More than 1 year||||**v3001**|**v090611**|**v090611**|**v9611**|**v9611**|ten_1|
||1 to 3 years||||v3001|v090611|v090611|v9611|v9611|ten1_3|
||3 to 5 years||||v3001|v090611|v090611|v9611|v9611|ten3_5|
||more than 5 years||||v3001|v090611|v090611|v9611|v9611|ten_5|
|**Income Variables**|||||||||||
|**Valor de todas as**<br>**fontes**||**v0601**|**v0601**|**v0601**|**v0601**|**v0905313**|**v0905313**|**v4720**|**v4720**||
|**Valor do**<br>**rendimento**<br>**mensal domicilar**||||||**v0905314**|**v0905314**|**v4721**|**v4721**||
|**Valor do**<br>**rendimento**<br>**mensal familiar**||**v5010**|**v5010**|**v5010**|**v5010**|**v0905316**|**v0905316**|**v4726**|**v4726**||

48
|**9: Selection to missing Household E. coli measurement**<br>**Dependent Variable: Missing Household E. coli measurement**||
|---|---|
|HH practices appropriate water treatment (=1)|0.128**|
||(0.051)|
|Log of Child Age|0.029|
||(0.047)|
|Log of Child Age Squared|‐0.003|
||(0.009)|
|Female Child (=1)|0.021|
||(0.023)|
|Child was ever breastfed (=1)|‐0.084|
||(0.072)|
|**Mother's Education**||
|_Mother Completed Primary Education_|‐0.030|
||(0.028)|
|_Mother Completed Secondary Education_|‐0.030|
||(0.041)|
|_(baseline: No education)_||
|Improved Sanitation Ratio within Cluster|‐0.012|
||(0.067)|
|**Sanitation Status**||
|_Improved Sanitation, Shared_|0.013|
||(0.036)|
|_Improved Sanitation, Private_|0.015|
||(0.038)|
|_(baseline: Unimproved Sanitation)_||
|Log Household Size|‐0.020|
||(0.036)|
|Household in Urban Areas (=1)|0.258***|
||(0.029)|
|**Wealth Index (Excluding Floor Material)**||
|_Second Quintile_|0.080*|
||(0.046)|
|_Third Quintile_|0.037|
||(0.047)|
|_Fourth Quintile_|0.073|
||(0.052)|
|_Richest Quintile_|0.151**|
||(0.068)|
|_(baseline: First Quintile)_||
|Household's Floor is made of Earth, Dung (=1)|‐0.153***|
||(0.057)|
|Functioning Handwashing Station (=1)|‐0.018|
||(0.030)|
|Unsafe Disposal of Child Feces (=1)|‐0.051|
||(0.042)|
|**Observations**|1,292|
|**Pseudo R2**|0.304|
|Standard errors in parentheses||
|*** p<0.01, ** p<0.05, * p<0.1||

# **Annex** **<mark>9: Selection to missing Household E. coli measurement</mark>**

29
<!-- Start of picture text --> Figure 5:Quantile Returns to Schooling by<br>Country (LAC)<br>20<br>18<br>16<br>14 Argentina<br>Chile<br>12<br>Colombia<br>10 Guatemala<br>Venezuela<br>8<br>Mexico<br>6 Bolivia<br>Brazil<br>4<br>2<br>0<br>Q10 Q25 Q50 Q75 Q90<br><!-- End of picture text --> Source: Patrinos, Ridao-Cano and Sakellariou 2009 Educational attainment in the East Asian countries examined, and employed in the formal sector, ranges from 7 to 11 years of schooling for males with an average of 9.8 years, and between 5 and 13 years with an average of 9.9 years for females. In Latin America, the averages are 9.0 and 10.2 years of schooling for men and women. The typical rate of return – or average from OLS – for another year of schooling for the average person for males in East Asia is comparable to the average for Latin American countries, at 10.2 to 11.6 percent. Generally speaking, wage effects are considerable for all countries, especially Thailand and Brazil. In only three cases are the 14
The parameters can be estimated by the Maximum Likelihood Method, using an iterative nonlinear optimization technique such as the Newton-Raphson Method. These estimates are CAN (Consistent and Asymptotically Normal) under standard regularity conditions (McFadden 1999).

Note that farmers can choose more than one species of livestock among the five animals in our study. That is, there are many combinations of animals that the farmer could choose. In this analysis, we examine only the primary animal chosen by each farmer. The primary animal is the single animal that generates the highest total net revenue in the farm (Train 2003). Given these assumptions, the set of choices are mutually exclusive.

# **3. Data**

The data for this study came from a World Bank project to study climate change impacts on agriculture in Latin America. The project used economic surveys to collect farm level data from the following seven countries: Argentina, Brazil, Chile, Columbia, Ecuador, Uruguay, and Venezuela. The countries were selected to represent the wide range of climate throughout South America and include representatives from both the Southern Cone and Andean regions. Districts within each country were selected to provide as much within country climate variation as possible. The original survey interviewed over 2000 farmers of which 1278 raised livestock.

The data includes information on livestock production and transactions, livestock products, and relevant costs. The five major types of livestock in Latin America are beef cattle, dairy cattle, pigs, sheep and chickens.

Climate data came from two sources: US Defense Department satellites and weather station observations. We relied on satellite temperature observations and interpolated 5
# 1. Introduction

In many low-income countries, children learn little from attending school (World Bank 2018). For example, four out of five students in Mozambique and Nigeria cannot read a simple word of Portuguese and English, respectively, after more than three years of compulsory language learning. In India, only one in four fourth grade students manages tasks—such as basic subtraction—that are part of the curriculum for the second grade. In Uganda, roughly half of the students cannot order numbers between 0 and 100 after three years of mathematics teaching.<sup>1</sup> A growing body of evidence—based on teacher value-added and experimental studies—suggests that teacher quality, broadly defined, is a key determinant of student learning.<sup>2</sup> Less is known, however, about what specific dimensions of teacher quality matter. In this paper we focus on teacher subject content knowledge, noting that there is a dearth of well-identified studies on the impact of this knowledge on learning outcomes in low- and middle-income countries (see Glewwe and Muralidharan 2015).

In particular, we investigate the role of teachers’ knowledge of the subject they teach, using unique cross-sectional data collected from nationally representative surveys of schools in Kenya, Nigeria, Mozambique, Senegal, Tanzania, Togo, and Uganda (these countries, together, represent close to 40 percent of the region’s total population). The data quantify teacher quality along three core quality dimensions: time spent teaching, teachers’ knowledge of the subject they are teaching, and teachers’ pedagogical skills (see Bold et al. 2017). Unlike many other large-scale data collection efforts, which assess students and teachers using multiple choice items and thus introduce additional chance variation in test scores, the data we use are collected using one-on-one tests for students, and test scores for teachers that are derived from mock student tests marked by the teachers.

Our first contribution is methodological. We show that data on test scores of the student’s current and the previous year’s teachers, and knowledge of the correlation structure of teacher knowledge across time and grades, allow us to estimate two structural parameters of interest: the contemporaneous effect of teacher content knowledge, and the extent of fade out of teacher impacts in earlier grades.

> 1 The estimates for Mozambique, Nigeria, and Uganda are derived using the data we present in this paper. The estimate for India is from ASER (2013).

> 2 For a review of the experimental literature, see Kremer, Brannen, and Glennerster (2013), Murnane and Ganimian (2014), Glewwe and Muralidharan (2015), and Evans and Popova (2016).

2
It is also inteesting to note that the relationship between these sector-by-sector results and the earlier across-the-board results. Taking simple sumns of the welfare gains in Table 3 indicates aggregate gains of 0.8% from removing export subsidies, _minus_ 0.14% from removing tariffs, 0.7% from removing foreign distortions, 6.5% from marginally reducing export subsidies, and 0.05% from marginally reducing tariffs. The first three of these numbers compare with the actual welfare gains of across-the-board reform of 1.0%, _minus_ 0.12%, and 1.2%, respectively. This indicates that there are significant interaction effects from liberalizing foreign distortions on a multi-sectoral basis rather than implied by the sum of the individual sectoral reforms: the welfare gain is roughly doubled.

# 3.4 Harmonization with the European Communities

# Background

Turkey has long held aspirations of becoming a member of the EC. As part of negotiations with the EC on this matter Turkey has adopted a policy of harmonizing it's tariff structure with the Common External Tariff (CET) of the EC. There has been some dispute as to exactly what this means, however.

Turkey's 'effective (nominal) tariff consists of several components, as discussed in Appendix B. First there is a statutory customs duty. Then there are a series of import surcharges, such as stamp taxes and wharf charges. The customs duty varies across sectors, but the surcharges are generally unif,orm. We refer to the sum of customs duty and these surcharges as the 'total duty' to be applied to dutiable imports.

However, Turkey exempts a significant portion of imports from duties through a number of mechanisms including 'duty drawback" and the investment code. The imr.mediatr **e** sult of these exemptions for our purposes is that the share of imports that is dutiable varies greatly across sectors. Hence the effective (nominal) tariff is the product of the total duty and the share of imports that is dutiable in each sector. It is this effective duty which is the best measure of the nominal protection that the sector is receiving.

These distinctions become crucial when one attempts to implement a political commitment to "harmonize the tariff structure". Turkey initially interpreted harmonization to mean that it would levy a zero _customn duty_ on imports from the EC but continue to levy certain of the import surcharges on those imports, albeit at A; reduced rate.

-24-
# Minority Status and Labor Market Outcomes: Does India Have Minority Enclaves?

_Markets for labor, land and money are easy to distinguish; but it is not so easy to distinguish those parts of a culture the nucleus of which is formed by human beings, their natural surroundings, and productive organizations, respectively._ _Karl Polanyi, 1944:162_

**I.  Introduction and Motivation** :  Building on previous work, this paper uses data from the 61<sup>st</sup> Round of the National Sample Survey to understand the employment outcomes for two sets of minorities in India – Dalits and Muslims. The first is a caste minority and the second a religious minority, and despite many differences, they are similar in many respects as this paper argues later.  Almost 18 percent of our sample of working age individuals is Dalit and about 13 percent is Muslim.  We ask the question – do minority groups build “enclave labor markets” if they have the requisite wherewithal?

The idea of “ethnic enclaves” or “ethnic labor markets” was first developed in the US context by Alejandro Portes and his colleagues (Wilson and Portes, 1980; Portes and Jensen, 1989; 1992; Wilson and Martin, 2001) who maintain that ethnic minorities who enter the US labor market are discriminated against because they are unfamiliar with the language and culture and are obviously distinct from the mainstream. But they do not necessarily enter at a disadvantage if they have the human capital. If they do, they build “ethnic enclaves” of self-employment and do well in these ventures. This paper tries to apply this conceptual model to the Indian context by testing it for two minorities – each historically discriminated against in different ways. Unfortunately, lack of earnings data does not allow us to directly test whether self-employment has higher rewards than regular salaried work and therefore whether these ventures are successful.

The context for this paper is a heightened awareness in India that formal jobs are on the decline and that in a growing economy, self-employment is at least a next best option – if not the first option at the highest levels. Simultaneously there has been increasing political attention to the issue of labor market outcomes of minorities. Some events in recent years serve to place this in perspective. First, there is an ongoing and often polarized national debate around the issue of extending reservation of seats in public employment to the private sector. Related to this is the national debate around the issue of the “creamy layer” or the fact that second and third generation beneficiaries of quotas form an entrenched elite and should be excluded from the benefits. Second, the Prime Minister set up a high level commission to study the socioeconomic status of Muslims. This Commission, known more popularly as the Sachar 1
# Figure 3: Predicted TFR, 1985 for Different Levels of Female Education & FP Services

## TFR

87

| _ TFR with Fem. Ed. g TFR with FP Services

65- 4- 3- 20 0 10 20 30 40 50 60 70 80 90 100 Secondary Enrollment Rate, 1975 & levels of FP Service, 1982 Note: calculated from Table 1 holding all other variables constant at their mean value. (Means: Female Sec. Enroll. (1975, 19.1%, Family Planning Service Score (1982). 25%, TFR (1985) 5.3).
**Figure 22: Average project experience at start of new project by types of investors.**

<!-- Start of picture text --> Average project<br>experience<br>Outside experience,<br>public sector<br>40<br>30<br>Outside experience,<br>private firms<br>20<br>Regional<br>experience,<br>public sector<br>Regional unilateral<br>10<br>experience<br>Regional experience, private firms<br>0<br>2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Notes: Average project experience shows the number of projects in regions other than ECA,or within ECA for the public sector and for private firms in the indicated year.

75
##

# **Account penetration among the poorest**

Adults living on less than $2 a day by whether with or without a formal account (as % of all adults) <!-- Start of picture text --> 100<br>80<br>Below $2 a day<br>WITH<br>60 ACCOUNT<br>40<br>WITHOUT<br>ACCOUNT<br>20<br>0<br>HIGH-INCOME EUROPE & LATIN AMERICA EAST ASIA MIDDLE EAST SOUTH SUB-SAHARAN<br>ECONOMIES CENTRAL ASIA & CARIBBEAN & PACIFIC & NORTH AFRICA ASIA AFRICA<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012; Gallup World Poll, 2011.

Consider Ghana and Benin. Both have a GDP per capita of about $560.<sup>5</sup> But while 29 percent of adults in Ghana report having a formal account, only 10 percent in Benin do. Thus even among economies with similar income levels and in the same region there can be significant differences in account penetration.

Indeed, when the analysis is restricted to the bottom 50 percent of economies by income level, GDP per capita explains only 22 percent of the variation in account penetration among economies. This suggests that the variation across economies is not determined solely by national income as proxied by GDP per capita.

At the individual level, household income—both absolute and relative—plays an important part in explaining the variation in account penetration. The role of absolute household income can be assessed by looking at the share of adults living on less than $2 a day who have a formal account (figure 1.3).<sup>6</sup> Worldwide, only 23 percent of adults in this income category report having an account at a formal financial institution. Economies in South Asia and in East Asia and the Pacific have been most successful in expanding financial services to this group. In these regions about 27 percent of those living on less than $2 a day have an account. In the Middle East and North Africa only 6 percent do.

Comparing account penetration across within-economy income quintiles sheds light on the role of relative income (figure 1.4). Account penetration in the _poorest_ quintile in high-income economies is 37 percent higher on average than in the _richest_ quintile in developing economies. Within developing economies, adults in the richest income quintile are on average more than twice as likely to have an account as those in the poorest. While average account penetration in the poorest quintile varies widely across regions, the average in the richest quintile clusters around 55 percent—except in East Asia and the Pacific (with the highest, at 76 percent) and the Middle East and North Africa (with the lowest, at 25 percent).

**13**

MEASURING FINANCIAL INCLUSION
Figure 5: Probability Distribution of Debt Service in Year 2005 _Riskfactor: Total Revenue_

# **Histogram of Monte Carlo Simulation, 1000 iterations**

<!-- Start of picture text --> 180- Mean: 1.36<br>160 Median: 1.37<br>140 Probability <1.25: 41.3%<br>L 120,<br>80-<br>i 160M<br>40-<br>20- L<br>Debt Service Coverage, 2005<br>I 1 0 _ _ _ _ _ _ _ _<br><!-- End of picture text --> Figure 6: Probability Distrbution of Interest Service in Year 2005 <!-- Start of picture text --> Risk factor: Total Revenue<br>* ~~Histogram of Monte Carlo Simulation, 1000 iterations<br>180<br>160- Mean: 2.07<br>140-~ Median: 2 08<br>'120~ Probability < 1.5 25.4%<br>0 80-<br>U- 60'<br>404<br>MC)o ,Dm m CD t O C'. CO Om L~UO NLfo<br>Interest Coverage, 2005<br><!-- End of picture text --> 12
# **2.4 Post Basic Schooling and Working Age**

## **2.4.1 Tertiary Education**

**The prevalence of tertiary education varies dramatically across countries** . As shown in Figure V, tertiary educated workers are virtually absent in poor countries, while they represent half or more of the working age population in the richest countries. The rise of upper tail knowledge is thought to have played an important role in the historical development process of currently rich countries, by facilitating innovation and the adoption of new technologies (Mokyr, 2002; Squicciarini and Voigtl ̈ander, 2015). Today, in many countries inventors, entrepreneurs and executives in leading firms are educated disproportionately in a small set of universities (Bell et al., 2018; Levine and Rubinstein, 2016; Martellini et al., 2022).

Figure V: Tertiary Education and Development <!-- Start of picture text --> 80<br>RUS<br>60 USA<br>CAN<br>ARM ISRJPN ARESGP<br>UKR KORTWNDNK IRL<br>40 MDA LTUCYPEST ESPFRAFINGBRBELAUS SWE HKGNLD NOR CHE LUX<br>200 BDICAFCOD MWI NERLBRMOZ SLEHTITGOUGAMLIRWAGMBTZAYEMNPLZWESENBENZMBTJKCMRLSOKHMKENBGDSDNMRTCIVPAKCOGMMRKGZHNDNICVENGHAINDSYRVNMBLZLAOPHLBOLSLVMARGTMJAMSWZIDNJORIRQMNGTUNNAM GUY ECUALBPERPRYLKADZAEGYBRBCHNZAFFJIIRNCOLBRADOMBWAGABSRBTHACRIBGRMDVMEXMUSURYARGROUHRVCHL MYS KAZLVAPANHUNTURGRCPOLSVKPRTTTOSVNCZE MLT ITANZL ISLDEUBHRAUTSAUKWT MACQAT<br>7 8 9 10 11 12<br>Log GDP per worker in PPP (2015)<br>Share with Tertiary Education (%, 2015)<br><!-- End of picture text --> _Notes_ : The figure plots the share of the 25-64 population with some tertiary education against log GDP per worker (in PPP terms) in 2010. Data on educational attainment is taken from Barro and Lee (2013), while GDP per worker is taken from version 10.0 of the Penn World Tables (Feenstra et al., 2015).

**Recent work shows that the quality of tertiary educated workers also varies substantially across countries.** Martellini et al. (2022) use data on the earnings of migrants across many host and origin countries to show that those educated in universities in developing countries earn less than their peers from rich countries, everything else equal. Moreover, migration reinforces cross-country differences in the quality of the tertiary educated labor force, as migrants from poor to rich countries are positively selected in human capital. These results reinforce the conclusion (already discussed for primary and secondary schooling in Section 2.3.1) that closing educational gaps between countries requires more than increasing enrollment in the developing world.

10