rafmacalaba/datause-encoder-data huggingface dataset:
domestic market, and perhaps only then if the domestic market is protected from imports by trade costs or tariffs. The success of industrial policy in relatively less productive sectors depends on dynamic effects (say, significant improvements in efficiency or quality, or reductions in input costs). For this reason, industrial policy in the sectors with the highest levels of comparative advantage can be considered low risk, whereas interventions in sectors with lower levels of comparative advantage can be considered high risk.

The measurement of comparative advantage from export data, which is widely practiced and rests on the assumptions of the Ricardian trade model, warrants some discussion. The most widely used measure of comparative advantage is the share of national exports in a sector divided by the share of world exports in that sector. This measure is called Balassa-revealed comparative advantage, after Balassa (1965). In 2019 in Costa Rica, for example, 16 percent of exports were business services, while for the global economy, only 2 percent of exports were business services. In this example, the statistic is greater than one (16/2 = 8), indicating that Costa Rica is more specialized in exports of business services compared to world exports. One infers from this that Costa Rica is relatively more productive in business services, compared to other sectors in which the country does not specialize.

Despite the widespread use of Balassa-revealed comparative advantage as a proxy for relative productivity, the measure has received significant critical scrutiny in the international trade literature; indeed, French (2017, p. 83) argues that Balassa-revealed comparative advantage is “not generally useful.” What are the issues, and what do they imply for how and whether the measure is used to target policy?

Balassa-revealed comparative advantage is frequently used to make statements like “this country is more productive in product _k_ ” in an unqualified sense, perhaps implying “relative to the world” and “relative to other products.” In a two-country world, where there is a home country and all other countries are grouped together as “rest of world,” this statement makes sense. Industries can be ranked within a country by the average Balassa-revealed comparative advantage of their products, and those with a higher ordinal ranking are more productive relative to those with lower values.

It is more complicated to compare values of Balassa-revealed comparative advantage across countries since the definition of “rest of world” changes across countries, and data availability varies. To address these issues, an approach is to double-index revealed comparative advantage 9
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to school by the children in the family and "distance to work" if the school<sup>distance data</sup> was not available. The hypothesized negative impact on non-farm participation<sup>was</sup> significant in the 1992 equation but not in the 1988 equation. Also, the distance<sup>variable</sup> is negative and significant in the agriculture equation for 1987-88, indicating<sup>that this</sup> variable is important for all of rural economy. These results confirm the<sup>now perceived</sup> wisdom that price incentives alone are not adequate to revise and<sup>rejuvenate the rural</sup> economy-markets and other infrastructure are necessary.

We expected non-labor income to be an important determinant of non-farm participation as hypothesized by others and since small amounts of capital are<sup>probably</sup> needed to start self-employment enterprises. Along those lines, some analysts<sup>argue that</sup> remittances are used to help households start new, non-farm activities. In this estimation, non-labor income is the sum of remittances, rental income, and "other" incomes<sup>listed</sup> (measured in 100,000 cedis). The results show that non-labor income is a positive<sup>and</sup> significant determinant of non-farm activity in both periods. The effect of non-labor income on agriculture is positive but not significant for the first two models for the 1987 survey, while it is negative and significant for the 1992 survey. Several explanations could be offered, one being that families with non-labor income moved out of<sup>agriculture</sup> in the latter period. It could also, by itself, indicate that agriculture families relied less on other sources of income than on the proceeds from agriculture. The first explanation<sup>is,</sup> however, more in keeping with the evidence on declining agricultural incomes, and the evidence on the growth of importance of non-labor income in non-farm participation in 1991/92.

24
# **5.  Predictive Performance**

We assess the empirical implications of our results by predicting air pollution measures for the monitoring sources and comparing their performance. In the following tables, we present summary statistics for CAMS-3 measures and predicted values, along with sample sizes for each case.

Our exercise in matching, estimation and prediction has produced widely-varying sample sizes. To cite one example, Table 6 reports statistics for the period 1 PM - 2 PM that include daily observations for Sentinel-5P and hourly observations for CAMS-3 and Google Traffic. For daily matching, we have computed means for CAMS-3 and Google Traffic observations at 1 and 2 PM. The result of this necessary matching exercise is great disparity in available daily observations. For the Sentinel-5P regression, there are 362 CAMS-3 observations, along with predictions from 434 and 406 observations for the S5P aerosol index and tropospheric NO2 measure. For the Google Traffic (GT) regressions, which use GT data for two periods in 2019, CAMS-3 provides observations from 1 PM, 2 PM or both for 34 days. Predictions are produced from 93 days during which GT data are available for 1 PM, 2 PM or both.

**Table 6:  CAMS-3 PM2.5 vs predictions from Sentinel-5P and Google Traffic**

Units: ug/m<sup>3</sup> Collection period (Dhaka time): 1 PM - 2 PM

|||Sentinel-5P||Google|Traffic|
|---|---|---|---|---|---|
||CAMS-3|Aerosol<br>Prediction|NO2<br>Prediction|CAMS-3|Prediction|
|N|362|434|406|34|93|
|min|11|13|14|14|21|
|p10|22|25|25|23|23|
|p25|35|31|31|31|26|
|p50|55|43|44|84|36|
|p75|87|70|70|125|116|
|p90|132|100|102|176|152|
|p95|151|115|113|196|160|
|p99|210|137|130|297|210|
|max|306|190|173|297|210|

Tables 6 to 8 present summary statistics for CAMS-3 air pollution measures and predictions from the global technologies for the same times and weather conditions. The common pattern is clear: Increases from minimum to maximum measures for the global technologies translate to increases from safe to hazardous levels in local pollution. At the high end, another pattern emerges: Beyond the 90th percentile, observed values are generally greater than predicted values. This implies that large positive outliers are more common when predicted pollution is higher, but the predicted values are already quite hazardous. To illustrate, consider the maximum Google Traffic prediction of PM 2.5 in Table 6: 210 ug/m<sup>3</sup> . This far exceeds the WHO health standard (WHO 2005) and matches the 99th-percentile measure for CAMS-3. However, a few large positive outliers drive the 14
participation), market entry might be driven by the attributes of individuals rather than by their interaction with peers inside the classroom.

To deal with this concern, we test whether students without trading background in courses with more (above-median) and less (below-median) precourse trading experience display significant differences in any characteristics. We also run the same stratification test, splitting the sample in courses where experienced students have returns above and below the median. The raw results of all six characteristic variables in our data set are presented in Table 2. Only gender is significant, although the difference is small in magnitude; courses with below-median experienced classmates have 3.3% more women than men. Other characteristics, such as age, students’ earnings, and education are similar for the two groups. Also, both groups have instructors with similar teaching experience and trading returns.

Another potential concern with our class setting is that friends or acquaintances might register together for a CSE course. If an inexperienced student enrolls in a course with a friend who has a trading background, interactions about trading strategies and past performance might occur outside the classroom before the start of the course. According to our electronic survey, while 22% of students reported registering for a stock trading class with a friend, among experienced students, only 5% said they had taken the course with an acquaintance. In other words, most experienced students did not know their classmates before the program began.

# **4. EMPIRICAL METHODOLOGY**

To analyze peer effects and the role of selective communication in stock market participation, we estimate several variations of the following baseline empirical model: E (3) ii,cc,tt cc cc tt cc ii tt pp ll ii,cc yy = αα+ ββEThe subscriptEEiiEEaaccEE RRaaRREE _i_ refers to an individual, + γγPPE EE RREER _c_ indexes each course,EEaass + QQ′Ω + MM′Ψ _l_ + ZZ′Γ indexes the location (city), + μμ + δδ + γγ + εε _t_ is the month when the course started, and _p_ indexes courses with the same syllabus. In our first set of empirical exercises, the dependent variable is a binary variable that represents market entry. The variable is ii,cc,tt yy 21
# **Table 1: The socio-economic determinants of mortality in 1994 in Rwanda**

# **Dependent variable: Died in 1994**

# **A. Education is measured by comparing primary education with no education**

|Male<br>0.013<br>[0.005]***|
|---|
|Sister with at least some primary education<br>0.017<br>[0.008]**|
|Sister was living in an rural area in 1994<br>-0.035<br>[0.008]***|
|N =<br>44568|
|**B. Education is measured by comparing secondary education with low**<br>Male<br>0.013|
|[0.005]***|
|Sister with some secondary education<br>0.045<br>[0.016]***|
|Sister was living in an rural area in 1994<br>-0.032<br>[0.008]***|
|N =<br>44568<br>**C. Education is measured by the years of education**|
|Male<br>0.013<br>[0.005]***|
|Sister’s years of education<br>0.003<br>[0.001]***|
|Sister was living in an rural area in 1994<br>-0.032<br>[0.008]***|
|N =<br>44522|

# **B. Education is measured by comparing secondary education with lower schooling**

Marginal effects of logit regressions, dummies for age in 1994 included. The data is taken from the siblings' mortality data of the Demographic and Health Survey, 2000, Rwanda. The data are weighted as recommended by the data provider, and adjusted to account for unequal probability of selection into the sample by a factor of 1/number of surviving females age 15-49 in each family. When more than one respondent in the same household reports on the same sibship, only one respondent is randomly retained. The background of the family is derived from the characteristics of the sister who answered the survey. For sisters who never moved or moved for the last time before 1994, the actual location is recorded. Sisters who moved after 1994 are assumed to have been living in 1994 in the same type of location as the one reported, in 2000, as the previous one. Clustered standard errors between square brackets. ***, **, *: significant at the 1, 5 and, 10 percent confidence interval, respectively.

18
take place so as to save high-cost labor. However, the introduction of large-scale mechanization is difficult if farm size is constrained by some factors, such as total land endowment, population density and a relatively high value of non-agricultural land use, which all prevail in Java. All the above factors generate an advantage to large farmers. If expansion of farm size is easy for larger farmers, it may create a divergence in production efficiency and profitability between large and small farmers.

This paper shows evidence from Indonesia to support the proposition that wage growth in recent years led to an introduction of labor saving practices among relatively large farmers. That is, large farmers tend to acquire more land by renting in land when real wages increase, and they install machines if agricultural real wages increase. In contrast, small farmers seemed not to change their behavior. The empirical findings also show that land and machines are complementary among relatively large landholders. The above trend also divides the dynamics of farm production between Java and off-Java as land endowments are relatively large outside Java and small landholders are concentrated in Java.

As Otsuka (2013) elaborates in his paper, increasing real wages (and transformation of occupational structures in labor markets) challenge Asian agriculture in which the majority of farmers are smallholders, because of the increasing need (i) to reduce the labor force in agriculture (as the opportunity cost of farming increases), (ii) to increase the average farm size (to reduce labor use by introducing labor-saving production methods) and (iii) to generate enough income to retain parity with non-agricultural workers. If land markets and/or institutional mechanisms are imperfect, major inefficiencies in the allocation of farm land will be bound to arise. Otsuka, Liu and Yamauchi (2013) present evidence consistent with the above conjectures using cross-country panel data. Foster and Rosenzweig (2010, 2011) also show some evidence to support the second point in India.

Indonesia provides an interesting setting in which relatively abundant and scarce land endowments coexist. Land is immobile across islands, while labor is mobile. Demarcation between macro-regional 3
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Callen, Michael, Saad Gulzar, Ali Hasanain, and Yasir Khan. 2014. “The Political Economy of Public Employee Absence: Experimental Evidence from Pakistan” Unpublished, Harvard Kennedy School.

Cammett, M., 2014. _Compassionate communalism: Welfare and sectarianism in Lebanon_ . Cornell University Press.

Campante, Filipe, and Daniel Hojman. 2013. “Media and Polarization: Evidence from the Introduction of Broadcast TV in the US.” _Journal of Public Economics_ , 100: 79–92.

Cantoni, D., Chen, Y., Yang, D.Y., Yuchtman, N. and Zhang, Y.J., 2017. Curriculum and Ideology. _Journal of Political Economy_ , _125_ (2), pp.338-392.

Cantoni, D. and Yuchtman, N., 2013. The political economy of educational content and development: Lessons from history. _Journal of Development Economics_ , _104_ , pp.233-244.

Caplan, Bryan. 2007. _The Myth of the Rational Voter: Why Democracies Choose Bad Policies_ . Princeton, NJ: Princeton University Press.

Casey, Katherine. 2015. “Crossing Party Lines: The Effects of Information on Redistributive Politics.” _American Economic Review_ 105 (8): 2410–48.

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Clements, Benedict J., David Coady, Stefania Fabrizio, Sanjeev Gupta, Trevor Alleyne, and Carlo Sdralevich, eds. 2013. _Energy Subsidy Reform: Lessons and Implications_ . Washington, DC: International Monetary Fund.

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Dal Bó, Ernesto, Pedro Dal Bó, and Erik Eyster. 2013. “The Demand for Bad Policy When Voters Underappreciate Equilibrium Effects.” Working Paper.

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49
Policy Research Working Paper 9069

# **Abstract**

This paper examines the effect of Basel III implementation on the access to finance of small and medium-size enterprises in 32 emerging markets and developing economies. Analyzing rich, repeated cross-sectional data and a panel of matched firm-bank data in a difference-in-differences setting with sample selection adjustment, the authors find a short-term, moderately negative effect of Basel III on small and medium-size enterprises’ access to financing. The results suggest that firms with access to bank credit prior to Basel III implementation could have been affected less than firms that were initially on the fringes of financial inclusion—firms with only a bank account. The paper fails to find any additional heterogeneous effects across firm size or age, bank capitalization or liquidity, or across countries that transitioned to Basel III from Basel II versus Basel 2.5. Overall, the initial conditions of the banking system as well as of complementary business and financial regulation can co-determine the size of short-term costs from the newly implemented global financial regulation in emerging markets and developing economies.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmelecky@worldbank.org, boris.fisera@euba.sk, and roman.horvath@fsv.cuni.cz.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table A.9: Impact of expected dowry on fertility and sex ratio, REDS data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable:**||**Numb**|**er of bir**|**ths**||
|_Firstborn girl_ * _Expected dowry_||0.085**|0.054*|0.055|0.044|
|||[0.031]|[0.029]|[0.042]|[0.045]|
|_Firstborn girl_|0.302***|0.080|0.123|0.119|0.132|
||[0.060]|[0.126]|[0.124]|[0.161]|[0.156]|
|_Expected dowry_||-0.060|-0.022|||
|||[0.041]|[0.074]|||
|N|3,078|3,078|3,078|3,078|2,846|
|Dep var mean for Firstborn boy|2.285|2.285|2.285|2.285|2.285|
|**Dependent variable:**|**Fr**|**action s**|**ons (par**|**ity** _≥_**2)**||
|_Firstborn girl_ * _Expected dowry_||-0.004|-0.007|-0.008|-0.011|
|||[0.010]|[0.015]|[0.019]|[0.024]|
|_Firstborn girl_|0.073***|0.084*|0.101|0.102|0.113|
||[0.021]|[0.043])|[0.059]|[0.073]|[0.079]|
|_Expected dowry_||0.009|-0.002|||
|||[0.007]|[0.029]|||
|N|2,342|2,342|2,342|2,342|2,174|
|Dep var mean for Firstborn boy|0.535|0.535|0.535|0.535|0.535|
|Caste FE + YOB FE + State FE|||x|x|x|
|State*YOB FE|||x|x|x|
|Caste*YOB FE|||x|x|x|
|Caste*State FE|||x|x|x|
|Caste*State*YOB FE||||x|x|
|**X**_i_|||||x|

NOTES: This table reports the coefficients corresponding to specification (3). Each column is a separate regression. The sample is restricted to households where all children have the same mother. The dependent variable is the proportion of male births among second and higher parity births. _Firstborn girl_ indicates that the firstborn child of the household is female. Expected dowry (in INR 10,000) for a female (male) child is defined as the average net dowry paid (received) by brides (grooms) from the same caste and state as the child and who married during the year of the child’s birth or the prior four years. Dep var mean is the mean of the dependent variable for firstborn-boy households. YOB refers to the year of birth of the firstborn child. Standard errors in brackets are clustered by state. *** 1%, ** 5%, * 10%.

52
of impact. However, it is equally true that the knowledge gained from impact evaluations is a necessary complement to these other kinds of analyses (Savedoff and others 2005).

While policymakers are keen to know the likely impacts on student academic achievement of various policy interventions, retrospective studies offer only limited guidance. Even the best retrospective studies suffer from serious estimation problems, the most serious being omitted variable bias with respect to school and teacher characteristics, unobserved child and household characteristics that are correlated with observed school and teacher variables, and measurement error in school and teacher data. This has turned attention in recent years to many studies based on natural experiments and randomized trials.

Evidence from recent natural experiments in middle-income countries suggests that increases in school resources (as measured by the student-teacher ratio) raise academic achievement on reading tests (but not math tests) among black students in South Africa (Case and Deaton 1999). Case and Deaton (1999) examined education outcomes in South Africa using data collected in 1993, when government funding for schools was highly centralized and blacks had virtually no political representation of any kind. The authors argue that blacks did not control the funds provided to their children’s schools and that tight migration controls limited their ability to migrate to areas with better schools. Studies using Israeli data indicate that reducing class size raises reading scores and (less often) math scores (Angrist and Lavy 1999) and that providing computers has no effect on academic performance (Angrist and Lavy 2002).

Randomized trials offer evidence from some relatively poorer developing countries as well. In Nicaragua, workbooks and radio instruction had significant impacts on pupils' math 23