rafmacalaba/datause-encoder-data huggingface dataset:
In this paper, we will use an alternative analytical strategy initially proposed in Wang and He (forthcoming) to estimate individual valuation distributions for cancer risk reduction. This new approach is based on the random valuation theory as described in Wang (1997) and Wang and Whittington (2005) and uses a similar data encoding strategy as used by Evans et al (2003). Subjective verbal likelihood responses given by MBDC respondents are first encoded into numerical likelihood data, or SPC data. Then the SPC data are analyzed to estimate individual valuation distributions, as did in Wang and Whittington (2005).

This new WTP estimation strategy suggests that an individual i may not know the exact value of his WTP for risk reduction but he does have some idea about the range of values in which it lies. When the proposed bid price comes out to be sufficiently lower (higher) than the value range, he will be relatively more sure about his positive (negative) answer, but when the bid price is located close or in the value range, he will be more unsure about his choice. So we can express an individual i’s WTP for risk reduction, represented by Vi, as a random variable with a cumulative distribution function F(t). The mean value of Vi is  i, which represents the mean WTP of the individual i for the cancer preventive medicine, and the standard variance is  i . So we can write the WTP model as, where  i is a random term with a mean of zero. When given a price tij for the cancer vaccine, the probability for the person to say “yes” will be, Once Pij, the probabilities for individual i to agree on the price tij, is known to a researcher, either by assigning numerical values to the verbal MBDC data or by directly asking individuals of their numerical likelihood information as did with the SPC approach, equation (7) can be estimated for each individual. The estimation model can be constructed as follows: where the error term λi has a mean of 0 and a standard variance of <sup>2</sup> .  can be constant for a respondent i, but its value will be different for different respondents. Pij is a dependent variable, which is the certainty answer chosen from “definitely yes” to “definitely no” by a respondent i at price j. Pij will take values between 0 and 1, with the value approach to 1 meaning a higher certainty of accepting and the value approach to 0 meaning a lower certainty. These values can be viewed as a continuous variable. tij is an independent variable, which is the bid price proposed in the questionnaire. We can also consider tij as a continuous variable.

Assume a specific functional form for Fi(•), such as of a normal distribution, with a mean  i and a standard variance  i , i.e., _F_ ( _tij_ )  <sup></sup>  _tij_  _i_  _i_ <sup></sup>  , then model (8) can be written as, Assume λi also has a normal distribution. Then, 10
Table 2: Explaining Differences in Relative Prices Across C'ountries

|_pij= A,+ _i3_rentp _<br>_Pi,_is the log price of tra<br>traded prices in country<br>Independent variables ar<br>|_j+ 132sertice_<br>ded good i<br>_j,_minus the<br>e explained i<br>|_pj+ /33_l<br>in country<br>same relat<br>n the text<br>|og_urban,_+/34<br> <sup>j relative to</sup><br>ive<sup>price of g</sup><br>describing<sup>equ</sup><br>|log_densityj_<br><sup>the expenditu</sup><br><sup>ood i in the</sup><br><sup>ation 12.</sup><br>|_+ i35ezrate_<br><sup>re weight</sup><br><sup>United Stat</sup>|_j_<br>_+ i7ij_<br><sup>ed sum of all</sup><br><sup>es.</sup><br>|
|---|---|---|---|---|---|---|
|Good|Coefficient|on the Ind|ependent Vari|able<br>||r-squared|
||_service-p._|_rent-P._|log_densityj_|log_urbanj_|_ex-rate,_||
|Food|0.31|-0.20|0.006|0.037<br>|0.34<br>|0.41|
||(2.1)|(1.1)|(0.4)|(0.6)|(2.0)||
|Tobacco & Beverage|-0.075|0.112|0.008|-0.182|0.16|0.17|
||(0.2)|(0.3)|(0.3)|(1.5)|(0.4)||
|Clothing|0.72|-0.28|-0.08|-0.04|-0.07|0.43|
||(3.0)|(0.9)|(3.0)|(0.3)|(0.2)<br>||
|Fuel|0.70|-0.50|0.106|0.047|0.007|0.16|
||(1.0)|(0.6)|(1.4)|(0.2)|(0.0)||
|Household|0.56|-0.14|0.006|-0.200|0.52|0.16|
||(1.8)|(0.4)|(0.2)|(1.5)|(1.4)||
|Appliance|-0.31|0.54|0.077|-0.218|-0.29|0.24|
||(0.8)|(1.2)|(1.9)|(1.3)|(0.7)||
|Autos|-1.04<br>|2.08<br>|0.034<br>|-0.003<br>|-0.20<br>|0.32|
||(2.1)|(3.2)|(0.7)|(0.0)|(0.4)||
|Auto Parts|-0.644<br>|0.934<br>|0.049<br>|0.153<br>|-0.408<br>|0.16|
||(2.0)|(2.3)|(1.4)|(1.1)|(1.1)||
|Recreate-Durable|-0.50|0.030|-0.074|0.147<br>|-0.190<br>|0.19|
||(1.4)|(0.1)|(1.9)|(0.9)|(1.4)||
|Recreate-Nondur|0.441|-0.224|-0.078|-0.087|0.62|0.14|
||(1.2)|(0.5)|(1.9)|(0.5)|(1.4)||
|Other|-0.517|1.08|-0.021|0.087|0.071|0.19|
||(1.7)|(2.8)|(0.7)|(0.7)|(0.2)||
|Government|0.271|-0.047|-0.012|0.036|-0.018|0.31|
||(1.5)|(0.2)<br>|(0.6)<br>|(0.9)<br>|(0.1)<br>||
|Transportation|-0.598|-0.528|-0.054|0.086|-0.85|0.40|
||(1.6)|(1.2)|(1.4)|(0.6)|(2.0)||
|Machines|-0.137|-0.927|-0.014|0.095|-0.216|0.48|
||(0.6)|(3.3)|(0.6)|(1.0)|(0.8)||
|Agricult. Mach.|-0.215<br>|-0.350<br>|-0.162<br>|-0.152<br>|-1.00<br>|0.40|
||(0.6)|(0.6)|(3.5)|(0.8)|(2.0)||
|Elec. Equipment|-0.953|0.192|0.023|0.286|-0.905|0.28|
||(2.7)|(0.4)|(0.6)|(1.9)|(1.8)|.<br>-|

t-statistics in parentheses Coefficients from estimation on the _restricted_<sup>sample</sup> 16
be able to sponsor and receive compensation for doing so from the beneficiaries (Weinstein, 2002; DeVoretz, 2008; Posner and Weyl, 2018, chapter 4). These schemes are one-sided market mechanisms in that permits do not come at an opportunity cost to their sellers. In our paper, however, supply is driven by domestic workers’ choices to temporarily renounce their right to work in exchange for some financial compensation.<sup>1</sup> An upside of such double-sided market is the property that it is a self-targeting and progressive mechanism, whereby only the most vulnerable citizens would opt in. Tax policy is therefore the government lever to regulate the market for work permits and in doing so trades breadth off against the depth of this market-driven social protection scheme. On the other hand, the market for work permits will be affected by the existence of informal and unregulated labor markets, all the more than formal and informal labor markets are more fully integrated due to weak labor and migration law enforcement.

The rest of the paper is organized as follows. Section 2 presents our theoretical model of a market for work permits and section 3 uses data from the US and Mexico to calibrate it. In section 4, we discuss the results of the calibration under our preferred specification as well as under alternative parameter values. In section 6, we discuss the limits of the model in the presence of imperfect enforcement. Section 7 concludes.

# **2 Theoretical Framework**

To understand the implications of a market for work permits, we consider two countries indexed by _j ∈{o, d}_ , where _j_ = _d_ refers to the destination country and _j_ = _o_ refers to the origin country. Each consists of a continuum of workers of measure _Nj_ . Consumers-workers have utility where **c** is the vector of consumption and _l ∈_ [0 _,_ 1] is labor supplied.

> 1Lokshin and Ravallion (2022) discuss the policy issues further, including those related to implementation.

4
13 based on only 500 households.<sup>14</sup> Clearly, there is a need to extend the scope of these studies to examine the impact of remittances on economic development by using larger, nationally representative samples.

The rest of this section will examine how remittances are spent or used at the household level by drawing upon the results of five recent papers. Each of these papers is based on a large, (usually) nationally-representative sample from a different developing country, and each study finds that international remittances has a positive effect on some aspect of development.

The first, and probably most ambitious, paper uses four linked household surveys from the Philippines to analyze how exchange rate shocks during the 1997 Asian financial crisis affected the expenditure patterns of 1646 Philippine households receiving international remittances (Yang, 2004). Since the paper has panel data from before and after the 1997 crisis, it is able to analyze how different types of exchange rate shocks – positive and negative -- affected changes in the expenditure patterns of remittancereceiving households. This represents a type of “natural experiment,” because the size and direction of exchange rate shocks are probably uncorrelated with other householdlevel shocks. As shown in Table 5, the study finds that positive exchange rate shocks had no statistical effect on the level of expenditures by remittance-receiving households on food. In other words, households receiving more remittance income as a result of favorable exchange rate shocks are not “wasting” such income on increased food consumption. Rather, Table 5 shows that positive exchange rate shocks led to a statistically significant rise in remittance-household expenditures on education, and a reduction in total hours worked by male children. For example, a one-standard deviation increase in the size of the exchange rate shock led to a 0.4 percent increase in remittancehousehold expenditures on education in the Philippines. The paper also finds that favorable exchange rate shocks were associated with increased investment by remittancereceiving households in entrepreneurial activities, specifically transportation, communication and manufacturing enterprises. In all likelihood, households receiving more remittances as a result of positive exchange rate shocks were able to invest more in these relatively capital-intensive enterprises because they no longer faced the credit constraints that had previously hindered such investments.
# References

Alesina, A., and Perotti, R. (1999). Budget Deficits and Budget Institutions. In Fiscal Institutions and Fiscal Performance, 13-36. Cambridge, MA: _National Bureau of Economic Research_ .

- Alesina, A., and Devleeschauwer, A., and Easterly, W., and Kurlat, S., and Wacziarg, R. (2003). "Fractionalization ." _Journal of Economic Growth, 8(2),_ 155-194.

- Alesina, A., F.R. Campante, and G. Tabellini. (2008). Why is Fiscal Policy Often Procyclical?. _Journal of the European Economic Association, 6_ , 1006-1036.

- Aizenman, J., Jinjarak, Y., Nguyen, H. T. K., and Park, D. (2019). Fiscal space and governmentspending and tax-rate cyclicality patterns: A cross-country comparison, 1960–2016. _Journal of Macroeconomics_ , 60, 229-252.

- Ardanaz, M. and A. Izquierdo. (2017).  Current Expenditure Upswings in Good Times and Capital Expenditure Downswings in Bad Times? New Evidence from Developing Countries. _InterAmerican Development Bank, IDB-WP-838_ .

- Arezki, R., and M. Bruckner. 2012. Commodity Windfalls, Polarization, and Net Foreign Assets: Panel Data Evidence on the Voracity Efect. _Journal of International Economics, 86(2),_ 318-326.

- Blume, L., and Voigt, S. (2008). Federalism and Decentralization- A Critical Survey of Frequently Used Indicators _. MAGKS Joint Discussion Paper series in Economics_ , No. 21-2008, Marburg: Philipps-University Marburg.

- Blume, Lorenz, J. Muller, and S. Voigt. (2009). The Economic Effects of Direct Democracy—A First Global Assessment. _Public Choice, 140(3-4),_ 431-461.

- Chinn, M., and H. Ito. (2006). What Matters for Financial Development? Capital Controls, Institutions, and Interactions. _Journal of Development Economics, 81(1)_ 163-192.

- Debrun, X., Moulin, L., Turrini, A., Ayuso-i-Casals, J., & Kumar, M. S. (2008). Tied to the mast? National fiscal rules in the European Union. _Economic Policy_ , 23(54), 298-362.

- Debrun, X., and Kinda, T., and Curisitine, T., and Eyraud, L., and Seiwad, J. (2013). The Functions and Impact of Fiscal Councils. _IMF Policy Paper_ , Washington DC: International Monetary Fund.

- Debrun, X., and Zhang, X., and Lledo, V. (2017). The Fiscal Council Dataset: A Primer to the 2016 Vintage _. IMF Note_ , Washington DC: International Monetary Fund.

- Fatas, A., and I. Mihov. (2013). Policy Volatility, Institutions and Economic Growth. _The Review of Economics and Statistics_ , 95(2),362-375.

- Frankel, J., C. Végh, and G. Vuletin. (2013). On Graduation From Fiscal Procyclicality. _Journal of Development Economics, 100(1),_ 32-47.

- Furman, J. (2018). The fiscal Response to the Great Recession: Steps Taken, Paths Rejected, and Lessons for Next Time. _Mimeo. Brookings Institution_ .

- Gavin, M., and P. Roberto. (1997). Fiscal Policy in Latin America. _NBER Macroeconomics Annual_ , vol. 12, pp 11-72, Cambridge, MA: National Bureau of Economic Research, Inc.

25
Estimates of Equations (1) and (2) have the advantage of shedding light on the long-term effect of the conflict on primary education. However, by focusing only on individuals who are generally old enough to have finished primary schooling (i.e., are aged 14 or older at the time of the 2006 DHS), we introduce the risk that estimates may be biased due to migration. An alternative strategy is to compare the educational attainment of children too young to have migrated away from the district for work or marriage, between 2001 and 2006 using Equation (3). By focusing on children under 14 years old, these estimates may not capture the long-term effect of conflict, but they are unlikely to be much affected by migration patterns. In addition, since we also observe household characteristics at the time of survey, it is possible to gain precision by including time-invariant household covariates.<sup>5</sup>

# Table 6 goes about here

Findings for children aged between 5 and 14 years old at the time of the survey are presented in Table 6. Column (1) of Table 6 indicates that an increase in violence by one standard deviation increases completed years of education by about one quarter of a year, for girls aged 5 to 14 in 2006 compared to girls from the same district who were the same age when surveyed in 2001 before the conflict escalated. However, there is no discernible effect on enrollment, and no effect on either outcome for boys. The combination of improved accumulation of years of education for females without evidence of increased enrollment suggests that the channel through which girls obtained more education is through an increase in the speed at which they accumulate years of education.<sup>6</sup> Results in Table 7 are obtained in the same way as those in Table 6, but for the subgroup of individuals aged 15 to 18 at the time of the survey. This sample is more likely to be selected due to migration patterns, but it is interesting to note that females in heightened conflict areas appear to make educational gains at this more advanced age too.

# Table 7 goes about here

In Table 5, I investigate whether specific aspects of the conflict had different effects on primary schooling completion, and test the hypothesis that Maoist rule over a district may have

> 5 Conclusions are unchanged when these regressors are excluded. Results are available upon request.

> 6 When estimating Equation (3) on a dummy indicating progression to the next grade, for the sample of children enrolled in school at the time of the survey, I find a statistically significant, positive effect of conflict intensity. Results are available upon request.

23
level (we would expect significant difference in one of every 20 measures by chance). Particularly concerning is that there is imbalance on having a savings account (56% in placebo control group; 63% in the pure control group). This is likely to have been driven by differential non‐response at follow‐up, where we find higher non‐response rates in the pure control. We also explore balance across treatment groups for male and female business owners separately (Table 15, Table 16, Table 17 and Table 18) and find similar results.

Table 2 reports the mean characteristics of those who were assigned to a screening event (Column 1) which excludes individuals in the pure control group, and details observable differences of those who attended (Column 2) with those who did not (Column 3). As indicated in Column 4, the selection into screenings is strongly correlated with more educated micro‐entrepreneurs, who are more likely to speak English, enjoy higher access to financial products, and are more likely to keep financial records for their business. This selection process may be explained by the way the screening events were framed: business owners were told that they were invited to a “business development” event and the invitation was in English (see Annex 1 for an example of the invitation). Since a major aim of edutainment is to reach out to the “bottom of the pyramid”, future edutainment activities may want to consider framing the event less as business development and more as entertainment, as well as promoting and designing it in a way that language is not perceived as a barrier to attendance. Overall participation rates are reasonably high (60%) when compared to other financial literacy programs, but it is clear that non‐participants present a target group that potentially has the most marginal added value to participation but are at the same time the most difficult group to entice into these types of events.

Although there is strong evidence of self‐selection into screening events, Table 3 shows that the drivers of this selection across screening events appear to be the same. For those who participated, we see balance across observable characteristics, which is in line with the fact that all screening events were marketed in the same way with the same characteristics. This balance of selection across events supports the possibility of comparing attendees against each other, rather than needing to rely on the intention to treat estimates.

# **_Attrition_**

The attrition rate in this study is 21.1%, which is relatively high compared to other household surveys (e.g. EFInA 2010 had an attrition rate of 6%), but within reason when compared to enterprise surveys. Intensive efforts were made to reach all respondents who were listed at the baseline, but around 12% could not be contacted again, some refused to be re‐interviewed (2.9%), and very few (0.3%) were unable to 11
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35
**Table 3: Balance checks on baseline characteristics**

||Mean [SD]|Diffe<br>an|rence betwee<br>d Controlgro|n [...]<br>up||P-valu|e for differ|ence...|P-values of joint<br>test|
|---|---|---|---|---|---|---|---|---|---|
||in Control|||||Group 1|<br>Group 1|Group 2|<br>group1=group2=|
||Group|Group1|Group2|Group3|N|and 2|and 3|and 3|group3=0|
|_Firm owner characteristics_||||||||||
|Female owner|0.629|0.001|0|-0.002|3600|0.717|0.363|0.414|0.63|
||[0.483]|(0.003)|(0.002)|(0.002)||||||
|Age of the owner|39.2|1.1|0.6|-0.2|3561|0.49|0.069*|0.091*|0.11|
||[10.7]|(0.7)|(0.5)|(0.4)||||||
|Firm owner has at least some|0.707|-0.034|-0.002|0.026|3595|0.374|0.063*|0.16|0.163|
|formal education|[0.456]|(0.032)|(0.02)|(0.018)||||||
|_Firm characteristics_||||||||||
|Trade|0.55|-0.007*|0|0|3600|0.103|0.071*|01|0.315|
||[0.498]|(0.004)|(0.002)|(0.002)||||||
|Services|0.259|0.007|-0.001|0.007|3600|0.761|01|0.586|0.937|
||[0.438]|(0.023)|(0.014)|(0.013)||||||
|Craft|0.166|-0.007|-0.01|-0.008|3600|0.929|0.97|0.921|0.884|
||[0.372]|(0.023)|(0.014)|(0.013)||||||
|Business created less than one year|0.137|-0.008|0.009|0.017|3600|0.527|0.323|0.645|0.596|
|ago|[0.344]|(0.025)|(0.016)|(0.014)||||||
|Firm area in m2|19|6*|-0.2|-2.3|3594|0.076*|0.008***|0.282|0.055*|
||[42.1]|(3.1)|(2)|(1.7)||||||
|Number of employee|1.18|-0.044|-0.021|0.016|3600|0.826|0.519|0.52|0.86|
||[1.681]|(0.092)|(0.057)|(0.051)||||||
|The firm does any form of|0.173|0.013|-0.001|0.015|3598|0.631|0.917|0.306|0.678|
|accounting|[0.378]|(0.027)|(0.017)|(0.015)||||||
|Amount of sales in an average week|60822|75|298|-1049|3600|0.929|0.618|0.337|0.751|
|(FCFA)|[57010]|(2255)|(1402)|(1253)||||||
|Amount of profit in the last month|46195|142|309|1161|3600|0.944|0.633|0.521|0.788|
|(FCFA)|[44825]|(2134)|(1327)|(1186)||||||
|Firm owner owns a bank account|0.22|0.004|0.004**|0.005***|3517|0.987|0.772|0.663|0.016**|
||[0.414]|(0.003)|(0.002)|(0.002)||||||
|Firm pays taxes|0.561|-0.014|-0.016|-0.022|3564|0.94|0.791|0.77|0.668|
||[0.496]|(0.033)|(0.021)|(0.018)||||||
|Amount of taxes paid in the|19451|-3883**|-281|-688|3486|0.097*|0.103|0.735|0.265|
|previous year (FCFA)|[28140]|(1953)|(1210)|(1076)||||||

Notes: sources: listing-baseline survey March 2014. Column 1: Standard deviations presented in brackets. Columns 2-4: coefficients and standard errors (in parentheses) from an OLS regression of the firm owner/firm characteristic on treatment dummies, controlling for strata dummies (dummies for each triplet). ***, **, * indicate statistical significance at 1, 5 and 10%.
either ex ante contracts or spot-market purchases under the government regulations and market conditions facing processors in AP. Specifically, the model shows that the system enables the transfer of rents from sugarcane farmers to privately held sugar processors by inducing farmers to compete for permits by increasing the quality of their cane, a benefit for which they are not compensated under India’s sugar cane price floor policy.

The model’s predictions are tested using data from a fall 2008 household survey. The results provide strong support for the prediction that a factory’s choice of ex post permits is more profitable than the available alternative procurement options due to the transfer of rents from farmers to privately held processors. The results also suggest that a permit policy reduces efficiency in the sense that farmers’ incremental costs to raise quality exceed the value of the higher-quality cane to processors.

Despite its importance to the Indian economy, there have been relatively few economic analyses of the sugar sector in India or of India’s complex sugar and cane policies. Studies to date have addressed Maharashtran sugar cooperatives (Banerjee et al. 2001; Lalvani 2008) and compared the technical efficiency of sugar processors across organizational forms, including private factories, cooperatives, and public (state-owned) factories (Ferrantino and Ferrier 1995; Ferrantino, Ferrier, and Linvill 1995; Patlolla, Goodhue, and Sexton 2012). To the authors’ knowledge, no studies have considered the specific cane procurement methods used by private sugar processors in India or investigated the links to product quality and government policy.

This study contributes to understanding the economic impacts of India’s sugar and sugar cane policies and contributes more generally to a growing body of literature on the impacts of alternative procurement mechanisms in developing countries’ agriculture (e.g., Fafchamps, Hill, and Minten 2008; Reardon and Minton 2011; Barrett et al. 2012). Most of these studies on India 3