rafmacalaba/datause-encoder-data huggingface dataset:
Champaign. Two treatment arms provided encouragement to major in economics. A “prosocial” treatment provided information emphasizing the wide variety of career options and personal benefits associated with the major, while an “earnings” treatment provided information on financial returns. We evaluate the effects of the two treatments on subsequent choices to take another economics course and declaration of the economics major by the end of the student’s junior year using student-level matched administrative data. In addition to examining the overall effects of these treatments, we examine whether treatment effects are mediated by the gap between introductory-course performance and expected grade and having a female teaching assistant (TA).

Our primary aim is to evaluate whether women can be “nudged” into a major with lowcost, theoretically grounded, encouragement/information interventions. In addition to contributing to the college major literature, our work speaks to the broader literature on gender differences in behavior, including whether women are more prosocially motivated than men and gender differences in responsiveness to small nudges and framing.

We find that the average male student receiving either treatment is more likely to take at least one more economics course after the focal course, but there is little evidence of increased majoring. The average woman appears unresponsive to either treatment. We next consider mediating factors suggested by the prior literature, finding that the introductory course grade is an important mediator for women, while TA gender is an important mediator for men. Women with better-than-expected grades can be nudged to take another economics course, but there is no effect on majoring. Men with a female TA in the focal course are more likely to take another economics course in response to treatment, with no effect on majoring.

# **2. Experimental Design and Analysis**

# 2.1 Setting

The study took place at the University of Illinois at Urbana-Champaign, a large elite public university. The economics program is housed in the College of Liberal Arts and Sciences. When students register for courses, they sign up for both a lecture with a faculty instructor and one of many recitation sections led by TAs, who are graduate students in the economics Ph.D. program.

In addition to being a popular major, introductory economics courses often satisfy requirements for students pursuing other majors (most notably business and engineering), and introductory-level economics classes also provide general studies credits, which are required of all 3
4 Implications of credibility based models and of models that rely on the Idea cf strategic government behavior.

# 11.1 IL Inflation Tax

Table 1 contains data on Inflation and on seignorage for 52 devaloping countries for four subperiods between 1963 and 1987: 1963-73, 1973-13, 197883 and 1983-87. For each subperiod we have presented data on the averaL rate of inflation -- that is the average rate of change In the consumption price index -- as well as the average revenue from the Inflation tax, expressed as a per_entage of GDP. For every year this revenue was computed as: R NM vzm(1) where w is the inflation rate, m is the monetary base and y is GDP.<sup>3</sup> All the data were obtained from the most recent IFS tape.<sup>4</sup> These figures strikingly illustrate a wide variabtlity in the inflation tax, both across countries and across time. First, In almost every case there Is an important increase in the rate of the inflation tytY in the late 1970s and early 1980s. Second, the croa country variability is remarkable, both regarding the rates _as_ well as revenues. For the period 1963-73 the ratio of higher to lower rate of the inflation tax was 41 times For 1983-

## 2The countries have been grouped geographically.

3In the actual computations we used nominal m and y. If real base and GDP are used the results would be the same, **as** long as we use the CDP deflator to compute the real stock of monetary base. 4A i..roblem with the raw IFS data is that while the price level is _a_ yearly average the monetary figures are wend of year*. This was tackled by "centering* the monetary variables and, thus, constructing yearly waverage" figures for the monetary variables.
# **IV. International redistribution through aid**

To evaluate the static impact that actual aid flows have on international income inequality, we conducted a simple exercise, treating grant aid flows and the grant element of concessional loans as _direct income transfers_ from donor countries to developing countries. In doing so, we also assumed that all individuals within the country benefit equally from this transfer in the same way they were assumed to share equally the GNI. Since gross national income includes aid transfers as an accounting concept, the exercise consisted of subtracting out aid flows received by the beneficiary countries and adding the value of those flows back to donors’ GNI. The OECD/DAC database (DAC 2004) allowed us to compile a dataset of different types of aid flows by donor-recipient pairs.

The most straightforward type of aid is bilateral grants, which are given by a donor country to a recipient country with no expectations of repayment.<sup>9</sup> To these flows, we added the grant-equivalents of concessional lending, received by developing countries from bilateral donors. We calculated the grant-equivalents of bilateral net concessional lending using each donor’s average grant element of ODA loans over the 1995-2002 period, based on annual grant element data from OECD’s Development Cooperation Reports (OECD 1998-2002).<sup>10</sup> To take into account bilateral contributions to multilateral agencies as well as multilateral agencies’ distribution of aid to developing countries, we used the data from

> 9  For each of the flows (bilateral grants, bilateral loans, and imputed multilateral contributions), we have distributed “unspecified” and “unallocated” aid (listed as a regional aggregate) back to the region’s members, using a pro rata calculation based on specified donor-recipient flows.

> 10 Some DAC donors (Denmark and Switzerland), which had concessional loans in 2002, did not have the data on concessionality of these loans in the Development Cooperation Reports.  For these donors, we used the average of DAC donors’ grant element of ODA loans.  Non-DAC bilateral donors (Czech Republic, Hungary, Iceland, South Korea, Latvia, Lithuania, Poland, Slovak Republic, and Turkey) do not report their loans individually, so OECD/DAC cannot calculate the concessionality of these loans.  Since non-DAC bilateral donors were only recently emerging-market economies themselves, we assume that their lending is least concessional, and we assign a zero grant element to their concessional loans.  We thank Ms. Yasmin Ahmad in OECD/DAC who helped us to gather the data on grant elements and explained to us the intricacies behind these numbers.

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microcredit programs over a long period fared better than intermittent participants in terms of income and consumption gains and poverty reduction (Khandker and Samad 2013). A macro study, using cross-country data, reinforces the positive impacts of microcredit (Imai et al. 2012).

The second strand of studies, using randomized control trials (RCTs), finds that microcredit has mixed effects on poverty reduction.<sup>3</sup> Various studies show the positive effects of microfinance (Coleman 1999, 2006; Karlan and Zinman 2009; McKenzie and Woodruff 2008; de Mel, McKenzie, and Woodruff 2008), while others find no evidence for income or consumption gains (Augsburg et al. 2011; Attanasio et al. 2012; Banerjee et al. 2010; Karlan and Zinman 2011; Crépon et al. 2011). Summarizing the findings of several RCT studies, a critic states that microfinance does not end poverty despite all the hype (Roodman 2012).<sup>4</sup> Microfinance is also under attack for charging “exorbitant” interest rates (e.g., the nominal on-lending rate of Grameen Bank in Bangladesh is 20 percent, 7 percent higher than the commercial bank rate), which goes against its stated mission of poverty alleviation. Using anecdotal evidence, many critics cite microcredit’s high interest rates and inadequate benefits relative to the cost of borrowing as reasons for rising indebtedness among borrowers. Their argument is simple: If microcredit programs were so helpful, indebtedness should not have increased over time relative to asset accumulation; thus, microcredit is not a “miracle,” as its proponents would have us believe. However, one study observes that, while the interest rates of microfinance institutions (MFIs) are

> 3 RCT studies have their own methodological weaknesses and thus are not always necessarily better than non-RCT studies (Deaton 2010; Rodrik 2008; Ravallion 2012).  Because microcredit operations are small in scale, the timeframe for realizing their impacts may exceed 1 or 2 years, which is the typical timeframe for most RCT studies in order to minimize spillover effects and contamination (Hermes and Lensink 2007).  In fact, the seminal study of Pitt and Khandker (1998), which uses an innovative quasi-experimental design, relies on data collected on borrowing over a 5- year period from areas where microcredit had been in operation for at least 3 years.

> 4 Roodman and Morduch have been critical of the underlying methodology of Pitt and Khandker (1998), the most cited study in the literature on microcredit impacts.  Roodman and Morduch (2011) is the most recent in a series of papers and postings that seek to refute the findings of Pitt and Khandker.  In 2012, Pitt and Khandker revisited the claims of Roodman and Morduch, observing a lack of due diligence in their replication of the Pitt and Khandker study and confirming that none of their statistical claims invalidate Pitt and Khandker’s findings on the substantial positive effects of microcredit, especially among women borrowers (Pitt and Khandker 2012).

3
the use of lagged migration rates as an instrumental variable is that increased enrollment in time _t_ cannot influence the migration rates at time _t−_ 1. However, it is possible to imagine that rational agents will be able to foresee changes in human capital the following year, for example, in an economic downturn, it may be expected that, as the job market shrinks, the opportunity cost of higher education is reduced and hence more people are likely to enroll the following year. If this is the case then the lagged migration rates, employed by Beine et al (2007), may not entirely eradicate the endogeneity problems.

Since a failure to account for some potential reverse causality is likely to result in biased estimates this paper utilizes the natural experiment of EU accession and the exogenous impact this has on the parameter _p_ (the probability of successful migration). Furthermore, to avoid the data issues that have stymied much of the previous research, this paper has an altogether narrower focus: does an increase in migration opportunity lead to an increase in investment in human capital? The analysis that follows abstracts from measurements of the brain drain—the relocation of skilled workers—focusing instead on the incentivization effect of migration. In order to do this I look at the impact of an exogenous increase in p (the probability that intended migration will succeed) on levels of human capital in the origin country.

# **3 Analytical Methods and Main Results**

Accession to the EU provides a natural experiment to test theories of migration fuelled increases in human capital. This is because, as a member of the EU, a countries citizens benefit from the free movement of persons<sup>16</sup> (Article 39 of the EC Treaty). In terms of the brain gain model outlined in section 1.4, such a policy change represents a significant increase in the parameter p (the possibility of successful migration)<sup>17</sup> . And, whilst the available data limits analysis of

> 16The relevant rights are complemented by a system for the co-ordination of social security schemes and by a system to ensure the mutual recognition of diplomas.

> 17Note that freedom of movement does not increase p to the level that _p_ = 1. If it were to do so there would be no possibility of a brain gain because all those who invest in education

13
# **Table A.6 Summary Statistics of Outcomes: With Attrition and Without Weights N = 5,275 (Continued)**

|||**Base**<br>|**line**<br>|||**Midlin**<br>|**e**<br>||
|---|---|---|---|---|---|---|---|---|
||**Control**<br>**Mean/SD**<br>**N = 1,605**|**Arm1**<br>**Mean/SD**<br>**N = 1,208**|**Arm2**<br>**Mean/SD**<br>**N = 1,219**|**Arm3**<br>**Mean/SD**<br>**N =1,243**|**Control**<br>**Mean/SD**<br>**N = 1,605**|**Arm1**<br>**Mean/SD**<br>**N = 1,208**|**Arm2**<br>**Mean/SD**<br>**N = 1,219**|**Arm3**<br>**Mean/SD**<br>**N = 1,243**|
|Treatment of drinking water|0.1439<br>(0.3511)|0.1573<br>(0.3642)|0.1526<br>(0.3597)|0.1585<br>(0.3653)|0.1570<br>(0.3639)|0.1747<br>(0.3798)|0.2067<br>(0.4051)|0.1866<br>(0.3898)|
|Safe disposal of feces|0.6779|0.7168|0.7814|0.7382|0.6472|0.6569|0.6318|0.5702|
||(0.4684)|(0.4519)|(0.4144)|(0.4408)|(0.4786)|(0.4757)|(0.4834)|(0.4961)|
|Soap at home|0.9913<br>(0.0930)|0.9868<br>(0.1144)|0.9951<br>(0.0700)|0.9944<br>(0.0749)|0.9919<br>(0.0897)|0.9934<br>(0.0811)|0.9959<br>(0.0639)|0.9936<br>(0.0800)|
|**Hygiene awareness and behavior (school-**|**going children)**||||||||
|Handwashing knowledge before eating|0.8333<br>(0.3729)|0.8088<br>(0.3936)|0.8109<br>(0.3919)|0.8025<br>(0.3985)|0.8732<br>(0.3330)|0.9522<br>(0.2135)|0.9582<br>(0.2004)|0.9484<br>(0.2214)|
|Handwashing knowledge after|0.8077|0.8203|0.8164|0.7972|0.8732|0.9331|0.9364|0.9359|
|defecation|(0.3944)|(0.3843)|(0.3875)|(0.4025)|(0.3330)|(0.2501)|(0.2443)|(0.2451)|
|Need for using soap correctly identified|1.0000|0.9962|0.9927|0.9964|0.9986|0.9981|0.9873|1.0000|
||(0.0000)|(0.0618)|(0.0850)|(0.0596)|(0.0377)|(0.0437)|(0.1122)|(0.0000)|
|Handwashing with soap better|0.9729|0.9866|0.9873|0.9698|0.9915|0.9866|0.9873|0.9929|
||(0.1624)|(0.1150)|(0.1122)|(0.1714)|(0.0921)|(0.1150)|(0.1122)|(0.0841)|
|Soap at home|0.9957|1.0000|0.9964|0.9982|0.9943|0.9943|0.9945|0.9982|
||(0.0653)|(0.0000)|(0.0602)|(0.0422)|(0.0753)|(0.0756)|(0.0737)|(0.0422)|
|Soap used before eating|0.8803|0.8298|0.8218|0.8256|0.9145|0.9120|0.9200|0.9004|
||(0.3248)|(0.3761)|(0.3830)|(0.3798)|(0.2798)|(0.2835)|(0.2715)|(0.2998)|
|Soap used after defecation|0.6054|0.5182|0.5636|0.5125|0.6909|0.7419|0.7818|0.7349|
||(0.4891)|(0.5001)|(0.4964)|(0.5003)|(0.4625)|(0.4380)|(0.4134)|(0.4418)|

50
The change in welfare, ΔW, resulting from a climate change from CA to CB can be measured as follows.

# **3. Data and Background**

Farm surveys were pretested and then finalized<sup>3</sup> . Each survey was translated to Spanish or Portuguese depending on the country. Farm surveys were collected by country teams from seven countries in Latin America<sup>4</sup> . The seven countries include: Argentina, Brazil, Chile, Colombia, Ecuador, Uruguay, and Venezuela. Random samples of districts were selected to observe a set of farms over a wide range of climates within each country. In each country, 15-30 clusters were selected and 20-30 households were interviewed in each cluster. Cluster sampling was done to control the cost of the survey. The farm surveys ask questions about farming activities, including crop and livestock production and costs. The survey was conducted from July 2003 to June 2004. Surveys also record the climate and weather related perceptions of the farmers. Altogether, a total of 2003

> 3 Survey forms are available from the authors.

4 We wish to thank Flavio Avila for managing the 7 country data collection process. We also wish to thank the team leaders of the collection process in each country: A. Albin, R. Bruno, J. Gonzalez, P. Granados, L. Irias, P. Jativo, J. Lozanoff, R. Pacheco.

9
17 respond, it would have been desirable that fiscal policy had been more responsive to the shocks.

By October 2002, the characterization of the economy had changed in several respects: (1) the run on mutual funds had been contained; (2) the presidential candidates had already agreed on sound fiscal policy principles; (3) the primary surplus reversed its decreasing trend and rose to unsurpassed levels. Clearly, the factors that originated the “wrong” expectations were not present any more. The central bank was then free to raise interest rates and quickly moved in this direction, bringing about the expected traditional results of the currency appreciating in response to tighter monetary policy as described in Figure 15. Control of the economy was gradually regained and consolidated after the first quarter of 2003.

Figure 15 Short-term policy interest rate (Selic) and exchange rate in Brazil 2000 – 2003 <!-- Start of picture text --> 4.4 28<br>4.0 26<br>Exchange rate<br>3.6 24<br>3.2 22<br>2.8 20<br>2.4 18<br>Selic<br>(right scale)<br>2.0 16<br>1.6 14<br>2000 2001 2002 2003<br><!-- End of picture text --> Source: Bacen

# **_III. Procyclical Fiscal Policy in Brazil_**

The vicious circle of procyclical fiscal policy, volatility and limited creditworthiness has been amply documented for Latin America (Gavin, Hausmann, Perotti and Talvi, 1996). Pro-cyclical fiscal policy is explained by the following factors: a) limited access to international credit markets during a shock implies that countries are unable to follow a tax-smoothing approach and have to tighten fiscal policy; b) tax structures that are heavily dependant on cyclical-sensitive income, such as indirect taxes (Gavin and Perotti, 1997); and c) weak institutional structures that do not allow generation of large enough primary surpluses in good times and lead to increased spending during expansionary phases (Talvi and Vegh, 2000). Several authors have attempted to documented the procyclical nature of Brazil’s fiscal policy ( IMF, WEO, 2002) but results are not very robust.

To examine the relationship between the primary balance and economic activity in the short and in the long run, we adopted the Autoregressive Distributed Lag (ARDL)
consumption, labor earnings and capital income taxes performing simultaneously. However, the distortionary effect of a transaction tax goes beyond the combination of traditional taxes. In contrast to conventional taxes, a TT distorts further allocations by treating depreciation allowances as taxable income (SYS.2 ́) and by driving a wedge of inefficiency between rental prices and marginal productivities ((SYS.6) and (SYS.7)) thus affecting primary factor pricing. The size of the wedge reflects the expanded tax burden associated with the degree of cascading. Is, then, transaction taxation more distortionary? It is not possible to provide an answer just by examining how a TT distorts different margins in a general equilibrium system. In the following, this paper provides a quantitative answer.

# **3. CALIBRATION, COMPUTATION AND WELFARE CALCULATION**

This section describes the procedure used to select parameter values, the solution method to compute dynamic allocations of an infinite-horizon general equilibrium model and the metric employed for welfare comparisons among economies with alternative tax regimes.

# **A. Parameter Calibration**

The benchmark parameterization is based on data from Brazil, which is the largest economy in the region with a bank debit tax currently in effect. Sensitivity analysis is also performed to check for robustness to alternative parameterizations built on data from other countries that have resorted to this type of unconventional tax.

The parameterization procedure picks parameter values so as to equate Brazilian average aggregate variables to equivalent magnitudes for the steady state of the model economy. Table 1 details the Brazilian information required to calibrate all parameter values. In general, aggregate target variables are calculated as averages of time series over the 1972-1993 period, before the introduction of the bank debit tax into the tax code in 1993-94. Thus, benchmark parameter choices imply that the model economy’s steady state and the Brazilian economy prior to the introduction of a transaction tax will share the same capital-GDP ratio, consumption-GDP ratio, capital income tax-GDP ratio, labor income tax-GDP ratio, consumption tax-GDP ratio, real rate of return, growth rate, etc. The model is calibrated so that the length of a model period is one year.

Before proceeding further, notice that an important parameter in the model, n , the number of production sectors which in turn determines the degree of tax pyramidation, is calibrated differently. Using the expression given in footnote 2 where the left-hand side is renamed as TRO - transaction tax revenue to output ratio - yields a nonlinear equation in n if information on τ and T TRO is available: 14
the rural poor in the future. Therefore, the impact of increase in river salinity on the livelihoods of the poor is difficult to assess and warrants location-specific analysis of economic impacts.<sup>46</sup>

# **5. Conclusion**

Even if greenhouse gas emissions were to be stabilized today, it is virtually certain that SLR will continue beyond 2100. Therefore, understanding the potential impacts of SLR and planning adaptation to increased climate risks is essential for the development of countries for which SLR has been identified as a major risk of climate change. With climate change, rising sea levels threaten inundation and extreme weather events in coastal areas of developing countries. These threats have already received some attention in the literature. At present, systematic studies of the implications of climate change for saltwater intrusion and its impacts on livelihoods and adaptation alternatives are scarce. This paper is an attempt to narrow the knowledge gap with a case study of coastal Bangladesh.

In this paper, we have provided an analysis that integrates information on climate change, hydrological models, and geographic overlays to assess the vulnerability of the southwest coastal region of Bangladesh to saltwater intrusion by 2050. In the absence of agreement among scientists about the time and spatial profile of climate change, our paper estimated potential location-specific impacts on river salinity in the southwest coastal region in Bangladesh for 27 alternative scenarios of climate change.

Our study method adopted a five-step process. First, climate in 2050 was approximated by statistical downscaling (daily timescale Bias-Corrected Spatial Disaggregation technique) of temperature and rainfall for the territory of Bangladesh from nine GCMs for three emission scenarios: B1, A1b, and A2. Projections from three GCMs (IPS-CM4L, MIROC3.2, and ECHOG) were selected in order to capture the uncertainty of the extent of climate change. Second, alternative probable estimates of RMSLR for the Bay of Bengal near the coastline of Bangladesh by 2050 were compiled from the available literature on global SLR and land subsidence in the lower Bengal Delta. Third, the Rainfall-Runoff model, the MIKE 11

> 46 This research also developed a River Salinity Information System on location-specific river salinity estimates from December 2049 to May 2050 for user-defined alternative values of climate change parameters over a predefined range.  Annex 2 summarizes expected changes in river salinity by _thana_ - level as an illustrative example.

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