rafmacalaba/datause-encoder-data huggingface dataset:
Hortaçsu, A. and C. Syverson. 2004. “Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds.” _The Quarterly Journal of Economics_ 119(2): 403-456.

Iyengar, S. and M. Lepper. 2000. “When Choice is Demotivating: Can One Desire Too Much of a Good Thing?” _Journal of Personality and Social Psychology_ 79(6): 995-1006.

Karlan, D., M. Morten and J. Zinman. 2016. “A Personal Touch in Text Messaging Can Improve Loan Repayment.” _Behavioral Science and Policy_ , 1(2): 25-31.

Krozner, R. 2007. Creating More Effective Consumer Disclosures. _At the George Washington University School of Business, Financial Services Research Program Policy Forum,_ Washington, D.C.

Loewenstein, G., C. Sunstein and R. Golman. 2014. “Disclosure: Psychology Changes Everything”. _Annual Review of Economics_ 6: 391-419.

Luca, M. and J. Smith. 2013. “Salience in quality disclosure: evidence from the U.S. News college rankings.” _Journal of Economics and Management Strategy_ 22(1): 58–77.

Lusardi, A. and O. Mitchell. 2011. “Financial literacy and planning: implications for retirement wellbeing.” in A. Lusardi and O. Mitchell (eds), “Financial Literacy. Implications for Retirement Security and the Financial Marketplace,” Oxford University Press, 2011, pp. 17-39.

Lusardi, A. and O. Mitchell. 2014. “The Economic Importance of Financial Literacy: Theory and Evidence.” _Journal of Economic Literature_ 52(1): 5–44.

Newell R. G., A. B. Jaffe and R. N. Stavins. 1999. “The induced innovation hypothesis and energy-saving technological change.” _Quarterly Journal of Economics_ 114(3): 941– 75.

Ponce, A., E. Seira and G. Zamarripa. 2017. "Borrowing on the Wrong Credit Card? Evidence from Mexico." _American Economic Review_ , 107(4): 1335-61.

Schwartz, B. 2004. The Paradox of Choice. New York: Harper Collins.

Shu, S. B. and C. Townsend. 2010. “When and How Aesthetics Influences Financial Decisions.” _Journal of Consumer Psychology_ 20(3): 452-458.

Stango, V. and J. Zinman. 2016. “Borrowing High vs. Borrowing Higher: Price Dispersion and Shopping Behavior in the U.S. Credit Card Market.” _Review of Financial Studies_ 29(4): 979-1006.

25
the same program in 2001. Table 2 provides information on socioeconomic and environmental conditions in the four cities, as well as polluting emissions in 2001. Wuxi has the largest population as well as the highest GDP per capita, while Huaian is the poorest. Wuxi and Yangzhou have the lowest readings for air quality, measured by SO2 (sulfur dioxide) and NO2 (nitrogen dioxide), and water quality measured by TSS (total suspended solids) and regulatory compliance percentage.

The dataset includes detailed information on the firms’ characteristics, pollution, and environmental performance ratings. We obtained this information from the municipal environmental protection bureaus of the four cities. Their pollution monitoring, inspection and environmental information systems are well-developed and well-managed, primarily because of their long-standing experience with pollution registration requirements and China’s pollution charge system.<sup>2</sup> Table 3 shows that 36.7% of the firms in the sample were rated by Green Watch. The majority of rated firms were assigned blue (60.38%) and yellow (22.37%); only a few earned the best (green) rating (2.96 %) or the worst (black - 2.96%). The distributions are similar across cities, with the majority of firms rated blue and yellow, and very few green and black.

# **5. Multivariate analysis**

Our pollution data are sufficiently detailed to permit assessment of Green Watch for both water and air pollution, measured by intensity and effluent concentration. Pollution intensity is total emissions divided by the gross value of output. We use total suspended solids (TSS), chemical oxygen demand (COD) and generated waste water to measure water pollution, and sulfur dioxide (SO2), waste gas, and dust and smoke to measure air pollution.

> 2 For discussion of firm-level pollution data in China, see Wang and Wheeler (2006).
we have inequality estimates) and 2010 increased by more than 16 times.<sup>9</sup> The figure shows the large difference between the IERs calculated with subsistence only, and under the assumption that the social minimum increases as the mean income goes up. Under the first assumption, the IER in England/UK oscillated around 60 percent throughout 18<sup>th</sup> and 19<sup>th</sup> century. But if we assume that the social minimum had risen in proportion with real income (b=0.5), the IERs attained 80 percent. The numbers for the United States are very similar.

The political implications of b=0 and b=0.5 are very different. If the elites had to acquiesce to a rise of the minimum living standard for the masses, then an 80% inequality extraction ratio shows that they were able or willing to push overall inequality rather close to the maximum. If, on the other hand, we believe that there was no social pressure to increase the social minimum, then the elites’ appetites may be thought to have been relatively modest.

In both countries, it is only in the 20<sup>th</sup> century, as illustrated more clearly in Figure 5, that the IER, calculated under the more stringent conditions of a changing social minimum, began its downward slide, to be arrested and reversed in the last quarter of the 20<sup>th</sup> century and early 21<sup>st</sup> . But because real incomes have by then risen to very high levels (exceeding, in the United States, 100 times the subsistence minimum), the inequality possibility frontier was close to 1 whether we use b=0.5 or b=0 assumption, and the inequality extraction ratio was similar under both scenarios.<sup>10</sup> Today, actual inequality in the UK reaches the level of some 40 percent of the maximum feasible inequality while in the United States it is just over 50 percent. This is a significant improvement compared to the situation some 150 years ago when the IER ratios in both countries were around 80 percent.

While the changes in the IER are more dramatic and probably more informative when calculated over longer periods of time, the results can be very interesting for the contemporary period as well particularly when we are dealing with countries that register

> 9 GDP per capita data, expressed in 1990 Geary-Khamis PPPs, are from Maddison (2007). The physiological subsistence minimum in the same prices is assumed to be $PPP 300 (for discussion see MLW 2011, p. 262).

> 10 The maximum feasible Gini is bounded from above (at 1), and the gap between the maximum feasible Ginis calculated for different b’s is decreasing in α, as can be seen in Figure 3. To see this, define, using expression (3),  the “gap function” between the two maximum Ginis for two different b’s as, G* (b1,α) – G*(b0, α) where b1>b0 and differentiate it with respect to α. It can be easily verified that the “gap function” is decreasing in α.

10
Pyramids (CP) to assess whether the historical predictors of rural non-farm employment retain their relevance with more recent data.

The Consumer Pyramids is a large household level survey conducted by the Centre for Monitoring the Indian Economy (CMIE). The surveys were initiated in 2014 and covers 170,000 households. Unlike the NSS, the surveys are of the form of a panel with each household being visited once every four months, aggregating to three surveys per year. The data includes individual characteristics such as age, gender, years of education, and nature of employment. Unlike the NSS which provides detailed employment sectors, the CP data only provides broad occupational categories among non-farm workers. We consider the year 2015 for our analysis since a number of significant macro-economic policy interventions were initiated since 2016 (starting with the “demonetization” episode in November 2016), which can affect non-farm employment.

# **2.4 Descriptive Trends**

Basic summary statistics corresponding to the three surveys are shown in Tables 1A, 1B and 1C. We discuss some key descriptive trends using the NSS data in this section prior to a more rigorous empirical analysis.

## **2.4.1 Rural Employment**

Figure 1 shows the broad trends in labor force participation and employment in the farm sector for rural individuals over this period, disaggregated by gender. Although rural males have significantly higher levels of LFP than rural females, there is a marginal decline in male LFP over this period. This decline is significantly accentuated for rural females, whose LFP decline from 47 percent in 1999-00, to 37 percent in 2011-12. Conditional on participation in the labor force, there has also been a steady decline in employment in farm activities. Thus, while 70 (85) of rural male (female) workers were engaged in farm work in 1999-00, this reduced to 58 (74) percent in 2011-12.

Figure 2 shows the fraction of rural workers engaged in non-farm activities by occupation type (left panel) and sector (right panel). While there has been a steady increase across all occupational categories over time, the most substantial increase has been in the fraction of non-farm casual workers, irrespective of gender. Thus, 16 (9) percent of rural men (women) were engaged as casual workers in 2011-12, relative to 8 (3) percent in 1999-00. The increase in the fraction of salaried workers – the most well-paying occupations – is a lot more modest: from 7 (3) percent of rural men in 1999-00 to 9 (5) percent in 2011-12. In terms of sectors, the largest increase has been in the construction (manufacturing) sector for men (women).

9
A few studies have analyzed how providing information influence beliefs. Lybbert et al. (2003) provide computer-based climate forecast to pastoralists in southern Ethiopia and northern Kenya and observe their ex-post beliefs about the rain season. They find that the beliefs of pastoralist receiving and believing computer-based forecasts are consistent with updating in response to forecast information, albeit with a systematic bias toward optimism. Nguyen (2008) uses a randomized intervention to provide various types of information about the return to schooling (mean earnings by education, the story of a role model, or both) to students in Madagascar. Her results suggest that students and their parents are able to process new information. She finds that informing students about the mean earnings reduce the gap between perceived and actual returns, and improve test score. A role model story alone or combined with information about the mean earnings has a smaller effect. Delavande and Kohler (2008b) investigate the response of an individual’s subjective expectations about being infected with HIV to information about his/her HIV this issue. They use a randomized design for HIV testing (Thornton, forthcoming) and instrumental variable technique to overcome potential selection associated with the self-selection of respondents into learning their HIV status. They find that learning about one’s HIV-positive status has no impact on medium-term subjective beliefs about one’s own HIV infection. They also show that learning one’s HIV-negative status results in higher subsequent beliefs about one’s own infection, as well as larger prediction errors about one’s HIV status.

# _3.5. Does the forecasting horizon matter?_

Another aspect that may affect the accuracy of the beliefs is the horizon of the event. Table 3 presents some supporting evidence using a sample of 232 boatowners from coastal Tamilnadu, India<sup>12</sup> . During a survey in the months of November 2005 to January 2006, boatowners were asked to predict daily catches for the second week of April 2006 as well as for the following week. Columns 1 through 4 in Panel A of Table 3 regress the rupee value of average daily catches during the second week of April 2006 against the mean of the elicited subjective distribution and a constant (column 1), the mean of the elicited subjective distribution alone (column 2), the mean of the subjective distribution and the catches obtained the day when

> 12 See Giné and Klonner (2007) for further details.

- 18 -
Table A.2. Impacts of PEP on Medical Access

||Medical<br>Access<br>Index|Covered by<br>the healthcare<br>system|Received medical<br>attention<br>when was ill|Paid<br>out-of-pocket<br>the medical costs|Received the<br>required<br>medicine|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|1[_RAMVijk_ = 1]|0.282|0.495|0.102|0.039|0.056|
||(0.013)|(0.018)|(0.022)|(0.026)|(0.030)|
|R-squared|0.215|0.317|0.046|0.077|0.071|
|Observations|2,618|2,592|1,694|1,370|1,412|
|Mean values(Non-RAMV)|0.291|0.0293|0.752|0.161|0.503|

_Notes:_ Dependent variables: (i) Medical access index is the average of the variables in columns (ii)-(v); (ii) Covered by the healthcare system is an indicator variable [=1] if the person reported to have subsidized or contributory healthcare system; (iii) Received medical attention is an indicator variable [=1] if the respondent or someone in their household received medical attention the last time they needed; (iv) Paid out-of-pocket the medical costs is an indicator variable [=1] if the respondent or someone in their household had to pay for the last time they went to the hospital; (v) Received the required medicine is an indicator variable [=1] if the respondent has a family member that takes any medicine or requires treatment regularly, and had the access to buy that medicine in Colombia. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander), wave, sampling-city fixed effects, and controls for the interaction between wave dummies and the set of individual controls listed in Table A.5. Robust standard errors are reported in parentheses.

Table A.3. Impacts of PEP on COVID-19 Prevention and Detection

||Household head<br>had Covid-19<br>symptoms<br>(1)|Household<br>members that<br>had Covid-19<br>(2)|Household head or<br>the partner<br>are vaccinated<br>(3)|
|---|---|---|---|
|1[_RAMVijk_ = 1]|0.021|0.268|0.085|
||(0.013)|(0.100)|(0.020)|
|R-squared|0.017|0.045|0.051|
|Observations|2,640|1,005|2,640|
|Mean values(Non-RAMV)|0.085|0.605|0.241|

_Notes:_ Dependent Variables: (i) Household head had symptoms is an indicator variable [=1] if the respondent reported to have COVID-19 symptoms, this variable is available for wave 1; (ii) Household members that had COVID-19 is the total number of people in the household who reported to have COVID-19 disease; (iii) Household head or the partner are vaccinated is an indicator variable [=1] if the respondent reported that him or his partner is vaccinated with at least the first dose of the COVID-19 vaccine, this variable is available for wave 2. All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander), sampling-city fixed effects, and the individual controls listed in Table A.5. Robust standard errors are reported in parentheses.

9
<!-- Start of picture text --> 25<br>—E=o)© 20<br>ssooFo 1<br>£5c=oqgs<br>Sg 10<br>=9<br>co2 5<br>a<br>0<br>2030 2035 2040 2045 2050<br>— Base — Build age — Income — Joint<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>Bo)©— 15<br>BsQo20<br>=O©roqge 10-<br>25<br>ee F<br>co32 0<br>a<br>5<br>2030 2035 2040 2045 2050<br>— White — Black — Hispanic<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>go 20<br>ha* 5<br>ges o 10<br>ES3cd<br>oo<br>£R<br>£2<br>oe<br>D2<br>2S 4<br>oe<br>-20<br>2030 2035 2040 2045 2050<br>— Days below 60 — Days above 70<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>g<br>83<br>of<br>2 2<br>Bo3<br>= sot88<br>Bo28en 5 |<br>2o<br>ce<br>os rb<br>oe —_ '<br>Bs I I<br>55 0 +<br>Ss59gooF<br>a<br>5<br>Below 40 740-50 50-60 760-70 170-80 780-90 Above 90<br>Joint adaption Base<br><!-- End of picture text -->
been to reduce state shareholding in all PSUs, except defense, atomic energy and railways, to 26% or lower. However, progress on this front has been extremely slow, largely owing to political opposition (see Gupta 2005). By March 2001, the federal government had sold shares in 41 out of 258 PSUs, of which in 39 cases the state still retained majority shareholding; the median “disinvestment” was only 16% of shares. The disinvestments have taken various forms including sale of shares to domestic and foreign institutional investors and the public through open auctions, public offerings, and global depository receipts in international stock markets. In most cases, the partial or complete privatization has involved listing of PSUs on major exchanges and active trading in these shares.<sup>26</sup> Next, we briefly examine standard corporate governance mechanisms in the listed sector. In the years since independence, with less developed stock markets, three national level development finance institutions together with the state financial corporations have been the main providers of long-term finance to companies. Along with the government owned mutual fund company, the Unit Trust of India, they also held large blocks of shares in the companies that they lent to and invariably had board representations. In this respect, the corporate governance system resembled the bankbased German model. The institutions were traditionally evaluated on the quantity rather than quality of their lending and thus had little incentive for effective monitoring. Their nominee directors routinely served as rubber-stamps of the management of the day. With their support, promoters of businesses in India could frequently enjoy managerial control with very little equity investment of their own. This makes it relatively easy for the promoters and other insiders to recoup their investment in a short period by self-dealing and other strategies.<sup>27</sup>

> 26 The Bombay Stock Exchange has a separate index, the BSE PSU index, consisting of 47 partially privatized public sector undertakings. On March 31, 2006, these 47 companies had a combined market capitalization of over 166 billion US dollars, accounting for just over 25% of the total Bombay Stock Exchange market capitalization.

> 27 There is some evidence on the “tunneling” of funds from one group company to another.  Bertrand _et al_ (2002) estimate that an industry shock leads to a 30% lower earnings increase for business group firms compared to stand-alone

22
food consumption score and HFIAS having higher coefficients both at the mean and across the distribution of weight-for-age.

**Table 2: Weight-for-age Z-score (Bangladesh)**

|||**Model**|**1 - Without C**|**ontrols**||
|---|---|---|---|---|---|
|VARIABLES||U|nconditionalq|uantile regress|ion|
|||||||
|||10th|25th|50th|75th|
|HFIAS|-0.162***|-0.121***|-0.136***|-0.153***|-0.176***|
||(0.009)|(0.013)|(0.011)|(0.011)|(0.014)|
|CDDS|-0.244***|-0.051***|-0.100***|-0.211***|-0.402***|
||(0.010)|(0.010)|(0.009)|(0.010)|(0.016)|
|FCS|0.166***|0.121***|0.125***|0.142***|0.183***|
||(0.010)|(0.013)|(0.011)|(0.012)|(0.015)|
|MDDS|0.098***|0.078***|0.094***|0.081***|0.087***|
||(0.011)|(0.014)|(0.012)|(0.012)|(0.016)|

|||**Mod**<br>|**el 2 - With Co**<br>|**ntrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|U<br>|nconditionalq <br>|uantile regress<br>|ion<br>|
|||10th|25th|50th|75th|
|HFIAS|-0.034***|-0.028*|-0.038***|-0.028**|-0.037**|
||(0.011)|(0.017)|(0.014)|(0.014)|(0.016)|
|CDDS|-0.036***|0.040**|0.043***|-0.015|-0.087***|
||(0.014)|(0.020)|(0.017)|(0.016)|(0.021)|
|FCS|0.034***|0.035**|0.023|0.010|0.037**|
||(0.011)|(0.016)|(0.015)|(0.014)|(0.018)|
|MDDS|0.034***|0.020|0.038***|0.020|0.028*|
||(0.010)|(0.015)|(0.013)|(0.012)|(0.016)|

Controlling for confounding factors in Model 2, all food security measures are statistically significant in the OLS estimation, with all four food security indicators having similar quantitative effects on weight-for-age. However, at the lower end of the distribution, child DDS has the highest association with weight-for-age.

Contrary to our expectation, the coefficient on child DDS has a negative sign in the parsimonious models for both height-for-age and weight-for-age, in the OLS estimation of weight-for-age, and in the higher end of UQR in Model 2. A negative coefficient on child DDS implies that a higher child DDS is associated with lower height-for-age (or weight-for-age), which is counterintuitive except perhaps in the case of children at the higher end of the weight distribution. One explanation for this could be the way child DDS is constructed and the CDDS values assigned to children. In our analysis we consider children 18
<!-- Start of picture text --> Avg EICV3<br>Avg 2011<br>Avg EICV3<br>Avg 2011 (9)<br>iY ercv3 = 1.90 * 365 * 246.8344 * 0.99<br>igicv3 < IPLAGG grcv3 = 1.90 * 365 * 246.8344 * 0.99 = 169,468<br>iEICV3<br>Asy ercva = $1.90 * 365 * PPP «149g, (10)<br>Avg EICV4<br>Avg 2011<br>Avg EICV4<br>Avg 2011<br>iercva < [PLASY eicva = 1.90 * 365 * 246.8344 * 1.14 = 195,145<br>iEICV4<br><!-- End of picture text -->
Below, we do not argue that the above explanations are wrong. Rather, we argue that the above explanations are unnecessary. We propose three alternative explanations for the general difficulty of detecting any growth effect of remittances. Regardless of whether or not previous explanations are correct, there may be little hope of reliably measuring growth effects of remittances in cross-country data at this time. Each of the next three sections explains one of these reasons.

# **3. Remittance growth may be greatly overstated**

Growth impacts of remittances would be unexpectedly low if recent estimates overstate the true increase in remittance flows over time. Suppose that rises in measured remittances (ρ) overstate ∂ρ� rises in true, unobserved remittances (ρ�) so that = φ, where 0 < φ< 1. The chain rule ∂ρ requires Overstating a change in remittance flows, then, means that the true effect of that flow on GDP is smaller than would be expected without mis-measurement—and smaller in the same proportion. This requires no assumptions about _Kρ_ or the functional form of _F_ . We offer two types of evidence on the extent to which current estimates overstate changes in remittances over time: evidence from macro data and evidence from micro data. We find that roughly 80% of recent rises in measured remittances likely arise from changes in measurement rather than true rises in remittances. Even with extreme assumptions this fraction is no less than two-thirds.

## **3.1. Evidence from macro data**

Macro data on remittances, such as that shown in Figure 1, come from balance of payments data provided by each country to the IMF. Until 2005, the Balance of Payments Manual 5 (BPM5) had three categories related to transfers from migrants: _workers’ remittances_ , which are current transfers by migrant workers, where migrants are defined as individuals who come to work for at least a year; _compensation of employees_ which covers income earned by temporary workers; and 9