rafmacalaba/datause-encoder-data huggingface dataset:
# _4.2 Stove Satisfaction_

During the satisfaction survey respondents were asked to rank the new stove using a five point Likert scale for each of the attributes listed in Appendix C. In contrast to our CCT, the satisfaction survey is based on reported rather than measured data and we cannot be sure that all respondents answered objectively, accurately and truthfully. The results below are therefore best viewed in broad rather than precise terms. For example, if only 15% of respondents said they liked a particular attribute, we can be confident the attribute is not popular, but after purging biases the popularity may be greater or less than 15%.

With these caveats, as shown in Table 5 we find that virtually 100% rate the _Mirt_ stove as good or very good and over 80% of respondents give the _Mirt_ stove the most positive ranking possible (very good). The same is found to be true for several attributes, but especially reduction of smoke (85% ranking very good and 14% good) and convenience to use compared with the traditional stove (83% very good and 16% good). No more than 10% of respondents ranked any attribute below good, suggesting that in virtually all respects respondents say they like and prefer the _Mirt_ stove.

Respondents were also asked if they would take the new stove if they needed to buy it at the current market price, which is between ETB150 and 200 ($8.00 - $12.00). Perhaps surprisingly, about 90% of sample households said that they would buy it even if they had to pay full price.

Respondents were also asked if they would recommend that their neighbors buy the _Mirt_ stove. As shown in Table 6, about 75% said they would advise neighbors to buy the stove at full price. Substantial minorities would also suggest that neighbors take the stove if they were given freely (23%) or subsidized (27%). Only 4% said they would not recommend the _Mirt_ stove to neighbors. There appears to be significant discussion of the _Mirt_ stoves, because about 90% of respondents said they had given others advice regarding the use of the new stove and 42% had received advice.

To gauge the strength of preferences we asked respondents to rank their top three attributes. As shown in Table 7, reduced fuelwood consumption and less smoke (indoor air pollution) turn out to be the attributes most preferred by households. Among attributes that households _<u>least</u>_ valued, the top choice by a wide margin (69% of respondents) was 15 | P a g e
(questions relating to the perception of teachers’ expectations of their students and meeting student needs, as well as the morale, enthusiasm, pride and valuation of academic achievement) to construct an index of teacher motivation. Column (2) of Table 3 shows that, consistent with the theory, the teacher motivation index is 0 _._ 591 standard deviations _higher_ among private schools than public schools, significant at the 1 percent level. Using the people management index also yields a consistent relationship, shown in Column (5). The coefficient suggests a one standard deviation higher people management score is associated with 0 _._ 238 _higher_ teacher motivation score, also significant at the 1 percent level.

**Teacher effort.** The expected effort level of a teacher hired to a high management private school is higher than the expected effort level of a teacher hired to an intermediate management public school (via teacher selection, extrinsic teacher incentives, and augmentation of teacher intrinsic motivation on-the-job)

We explore this prediction by using the school climate section of the school principal questionnaire (questions relating to how often teachers are absent, late and/or unprepared) to construct an index of teacher effort. Column (3) of Table 3 shows that, consistent with the theory, the teacher effort index is 0 _._ 792 standard deviations _higher_ among private schools than public schools, significant at the 1 percent level. Column (6) reports the same specification for the people management index, suggesting that a one standard deviation increase in the management score is associated with 0 _._ 074 _higher_ teacher effort, significant at the 5 percent level.. For completeness, Figure 8 plots the coefficients for country-level regressions using the same specifications reported in Columns (4) to (6) of Table 3. While there is some variation across countries, the results broadly hold.

# **3.4 The impact of good operations management**

In this subsection, we use the theoretical framework to give a possible explanation for why schools with good operations management may produce better student outcomes. In Section 3.4.1 we 21
and obtain an advantage over its foreign competitor, resulting, once again, in a rise in skillintensive R&D spending and a rise in income inequality.<sup>3</sup> A related argument is developed by Thoenig and Verdier (2003) in a model of leapfrogging R&D. Note that these R&D-based theories are fully consistent with North-North trade, and complement approaches such as Heckscher-Ohlin and Feenstra-Hanson that are based on North-South trade.

Now we turn to more recent developments in the theory.

# 2.2 Heterogeneous ...rms and bargaining.

An important element was introduced to trade theory by Melitz (2003), who incorporated heterogeneous-...rms monopolistic competition, following an approach pioneered in Hopenhayn (1992), into a model of international trade. The approach has had considerable in‡uence on a wide scope of trade topics, and income inequality e¤ects are no exception.

To explore the e¤ect of heterogeneous ...rms on trade and inequality, we ...rst should review the features of the basic model. That model can be summarized as follows. Consumers have constant-elasticity-of-substitution preferences over a continuum of potential products. Anyone can choose to become an entrepreneur by incurring a ...xed cost fe, which can be interpreted as the cost of developing a new product. Once this has been done, the entrepreneur can produce the output, with a production function given by: where l is the labor employed per period and q is the output produced per period, f is a ...xed labor requirement per period, and � is the marginal product of labor. The ...xed cost f is a constant of known value across ...rms and time, but the productivity parameter � is a random variable, constant across time for any one ...rm but taking di¤erent values from one ...rm to

> 3Intriguing evidence for this mechanism for Brazil is presented by Nelson (2008).

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the selection bias in OP methods. Meanwhile, the current method of LP (such as the levpet command in Stata) is developed for one proxy variable (material) and one endogenous variable (capital) when using revenue as the dependent variable, and does not allow the addition of innovation as another endogenous variable; (it is possible, however, to manually code this method to have an additional endogenous variable). For these reasons, OP and the current method of LP are not the most suitable methods for analyzing innovation effects for INS data; thus, I used GMM instead of these two semi-parametric methods.

As mentioned above, the OLS estimate would be biased upward, and the fixed effects (FE) estimate of coefficients would be biased downward.<sup>14</sup> However, those estimation results are still useful since they provide an upper and lower range of the estimated results. Also, the current LP estimation is still closer to true estimation than OLS and FE, since it disregards simultaneity issues of other factors inputs, such as capital, in estimating the production function, even though it does not remove the simultaneity issue in my interest variable, innovation. Therefore, I will report the results from OLS, FE, and the current LP methods to ensure my estimated results using GMM are within the credible range.

# _In case the innovation variables are censored_

If there are costs to seeking out innovation activities, and innovation activities require a certain scope to be materialized, firms would invest in innovation activities only when their investment amount is above a certain threshold. A number of firms that are not investing in innovation would have invested in innovation without these threshold constraints. In the INS data, about 40 percent of firms reported zero innovation investment, therefore, the innovation variable might be censored at zero.

Therefore, I consider a firm’s decisions to innovate as two-fold: first, whether to invest in innovation at all, and if so whether to invest in internal or external innovation (or both), and second, how much to invest. I used the Type II Tobit model (selection in censored data) with three-stage equations. The first equation of the model explains the propensity to invest in

> 14 . The explanation on how FE provides an estimate with a downward bias is described well in Van Beeren 2012.

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# **References**

Adams, P., S. Hunt, C. Palmer and R. Zaliauskas. 2016. “Attention, Search and Switching: Evidence on Mandated Disclosure from the Savings Market”. FCA Occasional Paper 19.

Agarwal, S., J. Driscoll, X. Gabaix and D. Laibson. 2013. “Learning in the Credit Card Market.” Mimeo. Available at http://dx.doi.org/10.2139/ssrn.1091623.

Agarwal, S., S. Chomsisengphet, N. Mahoney and J. Stroebel. 2015. “Regulating Consumer Financial Products: Evidence from Credit Cards.” _The Quarterly Journal of Economics_ 130(1): 111-164.

Anagol, S. and H. H. Kim. 2012. "The Impact of Shrouded Fees: Evidence from a Natural Experiment in the Indian Mutual Funds Market." _American Economic Review_ 102(1): 576-93.

Bhargava, S. and D. Manoli. 2015. “Psychological Frictions and the Incomplete Take-Up of Social Benefits: Evidence from an IRS Field Experiment.” _American Economic Review_ 105(11): 3489-3529” Bertrand, M., D. Karlan, S. Mullainathan, E. Shafir and J. Zinman. 2010. “What's Advertising Content Worth? Evidence from a Consumer Credit Marketing Field Experiment.” _The Quarterly Journal of Economics_ 125(1): 263–306.

Bertrand, M. and A. Morse. 2011. “Information disclosure, cognitive biases, and payday borrowing.” _The Journal of Finance_ 66(6), 1865-1893.

Ben-Shahar, O. and C. E. Schneider. 2011. "The Failure of Mandated Discourse." _University of Pennsylvania Law Review_ 159: 647-749.

Beshears J., J. J. Choi, D. Laibson and B. C. Madrian. 2013. “Simplification and saving.” _Journal of Economic Behavior & Organization_ 95:130–45 Bettinger E. P., B. T. Long, P. Oreopoulos and L. Sanbonmatsu. 2012. “The role of application assistance and information in college decisions: results from the H&R Block FAFSA experiment.” _The Quarterly Journal of Economics_ 127(3): 1–38.

Bruhn, M., G. L. Ibarra and D. McKenzie. 2014. “The minimal impact of a large-scale financial education program in Mexico City.” _Journal of Development Economics_ 108: 184-189, 23
39 as a whole: thus, the actions of an Insolvent minority may harm the interests of more creditworthy individuals with a good repayment record (Rello,1987:84). The bank may feel that defaulters are more likely to pay up If faced with pressure from angry peers who have been cut off from credit. However, experience in various countries demonstrates that effective pressure Is only brought to bear when the group Is small and tightly knit (Adams & Vogel,1986).

85. in this respoet, the ejldo Is often an Inappropriate unit for credit alocation purposes. The members of an ejido do not in most cases work together on a cooperative basis, either for production or marketing purposes. The frequent renting out of parcels to outsiders and the high absenteelsm of ejidatarlos (owing to temporary migration) help to undermine solidarity between ejido members. Moreover, group solidarity Is likely to be diluted by the sheer size of most ejido coumunitles and the cilentelist policies often pursued by the ejido leadership.

88. This helps to explain the lack of credit discipline in state-bank lending to 3Jidatars. The only sanction that Banrural may bring to bear on ejidatarios who fal to repay their ioans is to deny them further credit: they cannot seize their assets and In many cases there Is no crop to seize either. This Increases the likelihood of Individual default. Glven that these Individual cases of default make It likely that the whole ejido will be cut of f from credit In the next crop cycle, there Is ittle incentive for the solvent to repay their ioans. A recent survey of rural financial markets concludes that "the most kmportant factors In determining whether a loan was likely to be delinquent were those related to the borrower's assessment of the probability of obtaining a new, larger loan In the future on a thiely basis" (Adams & Vogel,1986:483); even the most creditworthy ejidatario is likely to have low expectations in this respect.
either treat or refer patients as appropriate. Sub-centers are supposed to be staffed with qualified nurses with doctors visiting on a fixed rotation. Most doctors hold a Bachelor of Medicine and Bachelor of Surgery (MBBS) degree, the rough equivalent of an MD in the US, and receive a fixed salary from the government, with no variable compensation based on either patient load or quality of care.<sup>9</sup> Consultations in public clinics are free or nominally priced. Patients are also supposed to receive free medication, if available. Although a federally-funded insurance program for inpatient hospital care was introduced in 2007, at the time of our study, the tax-funded public system of care was the only source of (implicit) public insurance in the system at the time of this study. Although public facilities are theoretically accountable to administrative norms and procedures (documented in the _Civil Service Codes_ for each state), both the perceptions of staff members and process measures of effort suggest severe deficiencies. Nationwide, doctor absences averaged 43 percent on any given day in 2003 and 40 percent in 2010 (CPR, 2011; Muralidharan et al., 2011). These absences do not occur on predictable days or hours (Banerjee, Deaton and Duflo, 2004) and they are not easy to address at a system-level (Banerjee, Duflo and Glennerster, 2008; Hanna and Dhaliwal, 2015). When asked about adherence to administrative rules, more than 80 percent of public sector doctors agree that the rules and norms are frequently flouted and that appropriate ‘payments’ can allow providers to circumvent disciplinary proceedings, even due to grave negligence (La Forgia and Nagpal, 2014).

While official policy documents of the Government mainly focus on improving the public system of primary healthcare (Planning Commission of India, 2013), data from household surveys consistently show that the fee-charging private sector accounts for over 70 percent of primary care visits (DHS, 2007; Selvaraj and Karan, 2009; CPR, 2011). Barriers to entry for private healthcare providers are low. Provider qualifications range from MBBS degrees to no medical training at all, and clinics can range from well-equipped structures to small one-room shops, the provider’s residence, or the patients’ home for providers that make home visits. Providers operate on a fee-for-service basis, and prices often include the cost of medicines. While providers operating without a medical license are not legal and face the threat of an occasional raid, they have come to be the dominant source of care in these

> 9India also recognizes medical degrees from alternative schools of medicine including the BAMS (Bachelors in Ayurvedic Medical Sciences), the BHMS (Bachelor of Homeopathic Medical Sciences) and the BUMS (Bachelor of Unani Medical Sciences). However, providers with these qualifications are only licensed to prescribe medication in line with their training and are not given prescription rights on allopathic medicine. They also are not typically posted in the frontline healthcare system of PHCs, CHCs, and district hospitals that prescribe allopathic medicine.

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the OLS and the IV results for teleworkable jobs (originally reported in column 1 in Table 6, and columns 3 and 6 in Table 7) in columns 1 to 3 for ease of comparison. Interestingly, while there is a strong positive impact of migration on the expansion of telework jobs, the effect is not significant for essential occupations. This is likely to be related to the different nature of these jobs. While telework jobs tend to be concentrated among the highly educated and highly paid information technology and service jobs, a large share of essential jobs is found in low-paid occupations in agriculture, transport, and domestic work. While there are high-skilled occupations such as medical professionals among the essential jobs, they do not necessarily dominate the list. Thus, we find there is a positive but mostly insignificant impact of immigration on income-safe jobs.

The impact of immigration on the share of native workers in essential jobs does not show much variation across education categories. As seen in Table 12, the impact of immigrants of high or low education levels on natives with the same education levels is not significant in terms of essential jobs. However, we see that highly educated natives tend to move out of essential jobs, in the presence of more immigrants with low levels of education (columns 3 and 4, Table 12). In terms of combined effects, as it was the case with the previous table, there is no significant impact on movement toward or away from income-safe jobs for natives in either education category.

Another source of exposure to COVID-19 risk that has become salient with the onset of the pandemic is the potential exposure to health risks. These tend to arise from the necessity to have face-to-face interactions with other people as workers perform the duties required by their jobs. As previously mentioned, we define jobs that place one’s health at risk as those that are essential (continuing even during the peak of the pandemic) but cannot be performed from home; these jobs instead require frequent in-person contact with customers, suppliers or other colleagues. Around 17% of the jobs in our sample fall into the high health-risk category. As Table 13 shows, the impact of migration on the share of health-safe jobs among natives is positive and highly significant. In other words, migrants encourage and enable natives to move into safer jobs both in terms of income and health exposure. A 1 percentage point increase in the share of immigrants in the labor force leads to an almost 0.5 percentage point increase in the share of health-safe jobs among natives. By education group, this positive impact is again concentrated among tertiary-educated natives in locations where the impact of immigration of workers with low levels of education is especially 28
# **IV. Empirical Results**

Data on air-transport costs comes from the U.S. Imports of Merchandise Database put together by the U.S. Department of Commerce. The level of data aggregation is HS four-digit, and the period covered is 1990 – 2001. Our dependent variable, air-transport costs, is the variable _imports charges_ (by unit of weight), which is defined by the U.S. Bureau of Census as: " _..._ the aggregate cost of all freight, insurance, and other charges (excluding U.S. import duties) incurred in bringing the merchandise from alongside the carrier at the port of exportation—in the country of exportation—and placing it alongside the carrier at the first port of entry in the United States.”<sup>24</sup> Our explanatory variables are HS four-digit aggregated, and they were obtained from different sources. In the case of value of imports, volume of imports and directional unbalance,<sup>25</sup> the source is the U.S. Imports of Merchandise Database gathered by the U.S. Department of Commerce, 1990 - 2001. Population and GDP data were obtained from the World Bank’s World Development Indicators (2002). Most of the country-specific variables (coordinates for the calculation of distances, number of airports, etc.) were taken from the CIA’s World Factbook.<sup>26</sup> Finally, the information for regulatory quality and government efficiency were taken from Kaufmann et al. (2003). Details on the definition of the variables are provided in Appendix A.

# _Cross Sectional Results_

Cross Sectional results are reported in Table 1 and Table 2. The main difference between these tables is that in Table 2 we control for country development level using a dummy variable for developed countries.<sup>27</sup> Both tables report the results for the year

> 23  For this reason, our short period of time is perfect to estimate the effect of open skies.

> 24 To avoid a measurement error, in our empirical exercises we only use countries that have 50 or more observations in our sample. Our results are robust to the inclusion of those countries with less than 50 observations.

> 25 This variable was only available for the year 2000.

> 26 We obtained the number of airports from  the CIA  World Fact Book, 1990 – 2001.

> 27  To control for this country specific characteristic, we constructed a dummy variable that takes the value of 1 if the country is classified by the World Bank Income Classification (2002) as a high income country, and zero in all other cases.

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<!-- Start of picture text --> What share ofthe use of different remote learning strategies are being monitored? (%)<br>online wv radio paper<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>HIC UMC LMC uc HIC UMC Lc LIC HIC UMC LMC LIC HIC UMC LMC uc<br>What is the average share (%) of children/youth accessing each remote learning system?<br>80 online wv radio paper<br>73 73<br>70<br>60<br>60 57 58<br>50<br>40 37 41 35 38 38<br>30 29<br>23<br>20 19<br>10 7<br>0 0 | 0<br>HIC UMC LMC uc HIC UMC LMC LIC HIC UMC LMC LIC HIC UMC uMc LIC<br><!-- End of picture text -->
economics and how recent developments in economics can actually offer new instruments to model forced displacement. This is done from the perspective of utility theory and modeling choice. Next, we discuss data issues that are currently preventing economists from working on forced displacement. Section 5 will provide a concrete example of how economics research can serve policy makers and improve the lives of refugees, IDPs and their hosts. Section 6 concludes.

# 2. Some facts

The United Nations High Commissioner for Refugees (UNHCR) reports that at the end of 2015 there were 65.3 million forcibly displaced people worldwide.<sup>5</sup> This number includes 21.3 m. refugees and about 10 m. stateless people with the rest mostly being accounted for by Internally Displaced Persons (IDPs). The forcibly displaced population accounts therefore for about 0.9 percent of the world population estimated at 7.3 bn people in 2015. The latest estimate of forcibly displaced people is the largest on record, since the creation of the UNHCR in 1950. The growth in these numbers is evidently associated with episodes of conflict and violence. The Somalia, South‐Sudan and Syrian conflicts have been responsible for the major outflows of refugees and movements of IDPs over the past decade, with the Syrian conflict being responsible for the latest spike (Figure 1).

_Figure 1 ‐ Number of Forcibly Displaced Persons (1951‐2015)_ <!-- Start of picture text --> 70000000<br>60000000<br>50000000<br>40000000<br>30000000<br>20000000<br>10000000<br>0<br>1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Source: Constructed from UNHCR population data <u>(http://popstats.unhcr.org/en/time_series). Note:</u> 2015 data are mid‐year and lower than end of year data.

> 5 http://www.unhcr.org/en‐us/figures‐at‐a‐glance.html.

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