rafmacalaba/datause-encoder-data huggingface dataset:
# **The conceptual framework.**

A household production model guides the empirical analysis carried out in this study. In this model, which is illustrated graphically in Chart 1 (below), health and other commodities such as child education are produced, given existing knowledge and technology, by a combination of market goods and time inputs by household members. For example, given the environmental health threats surrounding a household (e.g. the presence of malaria and dengue in a community), the health status of a child can be thought of as the result of a combination of health care services, goods, and mother's time devoted to childcare. Taking into account its budgetary and other constraints, a household is assumed to choose that level of health for its members and that amount of consumption of other commodities that maximize its welfare. The demand for preventive, curative and other health services can then be expressed as a function of household wealth, the price of health services, the environmental health threats, and other exogenous factors such as education, age and culture that either affect the production function, the resource constraint, or the household's utility function. In principle, the same exogenous factors determine the health status of household members.

> To estimate the effect of ethnicity and other exogenous variables, a logit model

> is used for its convenience in the interpretation of the regression estimates. From the household production model, these results are expected:

- Since health is a normal commodity, household wealth is expected to have a positive impact.

- Health insurance increases demand for health care, since it reduces the price of health service use.

- The time (distance) it takes to access a health service reduces the use of that service; it may, however, increase the use of other health services, depending on whether they are substitutes or complements.

- Greater public health threats due to the presence of malaria and dengue as well as lack of potable water are likely to raise demand for preventive and curative services.

- Education is likely to increase demand for health services as it raises the productivity of health production, the household's appreciation of the benefits of better health care, and its potential labor market earnings (Although theoretically education could reduce health service utilization by increasing the opportunity cost of time devoted to health care, most studies show that education have a positive effect, implying that its opportunity cost effect is less than the other putative effects of education.)

If an objective measure of health status were available and used as a dependent variable, it can be shown that the effects of the above-mentioned variables on health status would parallel their effects on health service utilization. For example, increased wealth, insurance coverage and reduced public health threats are expected to decrease morbidity. In this study, however, the illness data are based on the respondent's 6
**–** **<u>Table 4:  Incidence of Questions on Assets, Individual Level LSMS questionnaires</u>**

|**Asset**|**Latin America &**<br>**the Caribbean**|**sub-Saharan**<br>**Africa**|**Asia &**<br>**the Pacific**|**Europe &**<br>**Central Asia**|**Middle East &**<br>**North America**|**Total**|
|---|---|---|---|---|---|---|
||**# % of total**|**# % of total**|**# % of total**|**# % of total**|**# % of total**|**# % of total**|
|Land|718%|2 20%|1 13%|18%|133%|12 17%|
|Livestock|3 8%|0|1 13%|0|0|46%|
|Housing|1026%|0|3 36%|2 15%|0|1521%|
|Business<br>-Non-Agric.<br>-Farm Equip.|3 8%<br>1 3%|3 43%<br>0|1 13%<br>0|3 23%<br>0|0<br>0|10 14%<br>1 1%|
|Financial<br>-Savings|0|1 14%|0|0|0|1 1%|
|Other Physical<br>-Consumer<br>durables<br>-Other (real<br>estate, jewelry)|2 5%<br>0|0<br>0|0<br>1 13%|0<br>0|0<br>0|2 3%<br>1 1%|
|Total|38 100%|7 100%|8 100%|13 100%|3 100%|72 100%|

Sources: See Table A2 for countries included. Authors’ calculations based on surveys reported in references.

22
**Figure 2c.  Growth in Technology and Capital share in Value Added (firm level)**

<!-- Start of picture text --> Growth in TFP Vs. Capital share of value added output, macro, 2002-2005<br>-20 0 20 40 60<br>Capital share of value added output<br>100<br>50<br>0<br>Growth in TFP<br>-50<br>-100<br><!-- End of picture text --> Note: we limit the range of growth or the share to be within -100 to 100; the slope in the above graph is -1.31, sig at 0.01 levels.

Another interesting perspective is to take a closer look into within-industry productivity for India and Brazil, using the same Enterprise Survey data. Both OLS regressions confirm the findings on firm and national level.

**Table 3: India Firm Level Estimations by Industry**

||Textiles|Leather|Garments|Food|Metals/Machinery|Electronics|Chemicals/Pharm.|
|---|---|---|---|---|---|---|---|
|lcapital_n|0.33***|0.63***|0.36**|0.62***|0.47***|0.46***|0.52***|
||(2.77)|(4.41)|(2.58)|(5.42)|(6.24)|(5.74)|(10.26)|
|lworker_edu|0.66***|0.07|0.59***|0.34*|0.55***|0.54***|0.52***|
||(3.78)|(0.38)|(4.18)|(1.90)|(5.50)|(4.33)|(7.31)|
|Constant|3.00***|2.75***|2.96***|1.94***|2.26***|2.34***|2.01***|
||(6.19)|(5.20)|(4.07)|(3.43)|(5.52)|(6.21)|(7.17)|
|Observations|132|48|164|115|110|188|234|
|R-squared|0.58|0.65|0.53|0.63|0.72|0.68|0.75|

_Note 1: Robust t‐statistics in parentheses, *** p<0.01, ** p<0.05, * p<0.1; note 2: the above results excludes outliers with absolute value for growth in capital or output larger than 200)_ note 3. Excluding outliers with absolute value for growth in capital or output larger than 200) 14
result, valuable investment opportunities go unfunded.

Another source of friction is aversion to risk. While worthwhile on average, education is a risky investment. Debt, which is by far the most widespread credit instrument to finance education, does not transfer much risk away from the student, so students with loans take a substantial amount of risk. As a result, risk-averse individuals will be less likely to take loans. Friedman (1955) proposed an alternative to debt, analogous to the equity investments capitalists make in risky projects. Palacios (2004) analyzes this alternative, which he calls Human Capital Contracts (HCCs). During the last 25 years an increasing amount of income contingent loans, which are equivalent to a HCC with a cap in the payments students make, have been made available by governments and in some few instances, by the market. These instruments transfer risk away from students and thus partially aleviate the problem that students financing their education with loans face.

Debt aversion may be another source of friction in the higher education financing market. If students dislike debt, they might simply not invest as much in their education as they would have if the contract was framed differently. This underinvestment of education will imply lower wages over the rest of their career. Evidence supporting the existence of debt aversion stems from surveys that suggest certain demographic groups are unwilling to finance their education using debt (see Rasmussen (2006) and references therein). Experimental data offers contradictory evidence: Eckel et. al. (2007) find no evidence of debt aversion among Canadian citizens while Field (2009) provides empirical evidence from a field experiment that contracts framed as loans are less attractive for students than a convertible grant (a grant that converts into a loan) even though the financial payoffs are identical. The study of borrowing constraints from the perspective of debt aversion is, to our knowledge, only present in the labor economics literature.

Our work is similar in spirit to that of Eckel et. al. (2007) and Field (2009). In the case of Field (2009) the choices are made in a real transaction, while in Eckel et. al. (2007) the choices are made in a survey under which respondents enter a lottery whose payoffs are related to their answers. We offer equivalent options to students, and observe their choices in a survey, contributing to the literature in two directions. First, our design allows us to disentangle framing effects and labeling effects, which sheds new lights into the nature of debt aversion. Second, our studied population comprises of individuals who typically apply for financial aid to continue their studies in developing countries, where the underinvestment of education is of particular importance.

5
Figure A3: Economic relevance of firms mentioning green technologies in ECTs by sector, 20122021

# A. By WIPO Green sector

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>B. By NAICS sector<br>80<br>Manufacturing<br>Information<br>70 Professional, Scientific, and Technical Services<br>Utilities<br>Other Sectors<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of sales by firms with ECTs mentioning green keywords<br>in each sector<br>% of sales by firms with ECTs mentioning green keywords<br><!-- End of picture text --> Notes: Panel A depicts the share of total sales of firms mentioning green technologies in ECTs by WIPO green sector over the period 2012-2021. Panel B depicts the share of total sales of firms mentioning green technologies in ECTs by NAICS sector over the period 2012-2021.

35
To that effect, we now interact the relevant gravity equation variables with a dummy indicating population‐wide internet use in the origin country, and business‐to‐consumer (B2C) internet use in the destination country. We then estimate a version of equation (3) augmented with the interacted terms, both each by itself, as well as different combinations of them.

The estimation results appear in Table 4, in which, for the sake of clarity, once again we do not report the gravity equation control variables. In Table 4(a) we present estimates in which each of the gravity variables of interest — bilateral distance, common border, common language, and colonial relationship — is interacted with the _product_ of origin‐country internet use and destination B2C internet use. Each of the interacted terms is first introduced by itself in columns (2) to (5), and then all of them are entered together in column (6). The gravity baseline results in Column (1), covering the years 2013, 2015, and 2017, are in line with the results in Table 2, which cover the 2004‐2017 period. Except for the colonial relationship‐ interacted term, all the estimated coefficients are significant and have the hypothesized sign. A wider use of digital platforms in either of the two or in both countries, proxied by internet use, mitigates the negative impact of distance, increasing the demand for faraway destinations relative to the baseline. Moreover, sharing a border and speaking the same language become less important determinants of the demand for tourism services when internet use is widespread. The colonial relationship interaction is _positive_ and significant when it is entered by itself (column 5), but it is not significantly different from zero when all other variables are introduced (column 6).

Table 4(b) presents coefficient estimates of an alternative specification in which the gravity variables are interacted with the internet use measures for the origin and destination countries separately — i.e., not as the product of the two measures. Panel B presents coefficient estimates for the interactions with internet use in the origin country, while Panel C presents estimates for interaction with business internet use in the destination country. Once again, we find that greater internet use in both the origin and destination countries offsets the negative impact of distance on tourism flows and reduces the role of sharing a common border or speaking the same language in facilitating bilateral travel. Once again, however, the colonial relationship interaction does not have the expected sign and remains significant in the case of the origin country even in the fuller specification in column (11).

# **4.3 Estimation results: Google Trends data**

As a robustness check, we now turn to the use of our proxy constructed with Google Trends data. We can now explore a longer time horizon, spanning the 2004 to 2017 period, with a measure that is more directly Page **19** of **34**
in Georgia. However, as compared to decreasing the tobacco-related medical expenses, reducing the productive YLL yields much lower welfare gains for all households.

Figure 5. Effect of reducing the years of productive life lost

# Panel A. 25% Price increase Panel B. 50% Price increase

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Medium-bound Elasticity Lower-bound Elasticity Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.15 .25<br>.2<br>.1<br>.15<br>.1<br>.05<br>Inconme Gains (%) Inconme Gains (%)<br>.05<br>0 0<br>.5<br>.4<br>.3<br>.2<br>Inconme Gains (%)<br>.1<br>0<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. Data on smoking-attributable death events is taken from the GBD (2017). _Notes:_ Deciles are created based on per capita household total consumption.

## Net income effect

The net income effect of increasing taxes on cigarettes in Georgia (Figure 6) is calculated by adding the individual cost and benefit components (A) to (C), according to Equation 1. Across all price simulations, the net distributional effect of increasing these taxes is highly progressive. Lower-income households consistency receive higher benefits, relative to their income, than wealthier peers.

Moreover, increasing taxes on tobacco could be simultaneously pro-poor and growth-enhancing. Under all price and elasticity scenarios, the benefits outweigh the costs, resulting in net positive effects for significantly large sectors of the population, and most importantly, for the poor. Even under the most 20
<!-- Start of picture text --> 100, 7 : 7<br>87 88<br>82 83<br>80<br>65<br>60 56 ss<br>2<br>2 43<br>40<br>20<br>0<br>AFG BGD BTN IND MDV NPL PAK LKA<br>Country<br><!-- End of picture text --> <!-- Start of picture text --> 100 7 :<br>86<br>80<br>74<br>74<br>67<br>64 63<br>60<br>z 51<br>a<br>40 36<br>20<br>0<br>AFG BGD BTN IND MDV NPL PAK LKA<br>Country<br><!-- End of picture text --> <!-- Start of picture text --> 100 : :<br>80<br>72 73<br>60<br>2 47<br>2<br>40<br>28<br>2 22 22<br>20 15<br>0<br>AFG BGD BIN IND MDV NPL PAK LKA<br>Country<br><!-- End of picture text --> <!-- Start of picture text --> 100 -<br>80<br>62<br>60 58<br>=<br>@<br>40 36<br>23<br>20 16 15<br>12 10<br>(e)<br>AFG BGD BTN IND MDV NPL PAK LKA<br>Country<br><!-- End of picture text -->
Table 4: Economics of scope and scale-Bitran-Dunlop study

|Output|Cost of ea<br>With AN Beds|ch output alone<br>Only IP beds|MC|Cost<br>Elasticity|AIC|S|y|yOMC|
|---|---|---|---|---|---|---|---|---|
|Inpatient days|883532|883532|2.582|0.073|2.534|0.981|28410|73361|
|Outpatient visits|548476|268063|-12.226|40.310|-19.542|n.a.|25520|-311995|
|Deliveries|564877|231544|169.047|0.171|155.407|0.919|1016|171752|
|Surgical procedures|478934|478934|3.230|0.006|3.221|0.997|1758|5678|
|Lab tests|680054|332371|7.678|0.356|6.459|0.841|46691|358472|
|Sum|3155873|2527777||0.295||||297268|
|Projected cobt of avg. beds|1006231||||||||
|Economies of scope (all beds)|2.136||||||||
|Econs. of scope (only IP beds)|1.512||||||||
|Ray econs of scale (SR)|3.385||||||||
|Elasticity of cost wrt beds<br>Ray economies of scale (LR)|0.716<br>0.962||||||||
# **5 Data**

The data come from the Rural Permanent Sites Survey conducted by the Ministry of Agriculture of China in 1986–1991, 1993, and 1995–2000.<sup>9</sup> Because surveys in 1986– 1991 do not contain information on landline phones, we restrict our analysis to the 1993 and 1995–2000 surveys. The survey sites were randomly sampled from six provinces (i.e., Gansu, Guangdong, Hubei, Liaoning, Shandong, and Yunnan provinces). As shown in Figure 2, the sample provinces (in red) are spread out across China, ranging from coastal to inland areas and covering northern, southern, western and eastern China; they also feature diverse levels of economic development, climate, natural endowment, and infrastructure. To capture province heterogeneity, we control in the regressions for province-year dummies so that our identification relies on variations across villages within a province-year cell.

We have a total of 67 villages in 1993. Six villages were deleted because they changed location codes over time, for which we cannot trace. Among the remaining 61 villages, 37 had landline phones in 1993, and they are used as the control group; 21 installed landline phones during the sample period, and they are used as the treatment group); three had no landline phones installed even at the end of our sample period, and they are used in a placebo test.

Table 2 reports the summary statistics of our key variables. During the sample period (1993, 1995–2000), the overall ratio of out-province migrant workers is 3.7 percent, and the overall ratio of out-village, within-county migrant workers is 5.2 percent. Interestingly, the ratio of immigrant workers (i.e., those who came to the village to work from other places) is quite large, around 12.9 percent. Meanwhile, our sample villages are quite poor with an average annual income per capita of 2119 yuan or US$340, small (i.e.,

> 9 Surveys were not conducted in 1992 and 1994 because of financial reasons.

16
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28
|**Table 3.**<br>**Dep. Variable: PP**|**Mixed-effe**<br>**P Status (1=**<br>Empty|**ct probit re**<br>**Cancelled P**<br>Basic|**gressions on**<br>**PP contract).**<br>Square|**canceled PPP**<br>**Group varia**<br>LSpline1|**s.**<br>**ble: Country**<br>LSpline2|<br>CSpline|
|---|---|---|---|---|---|---|
|Exp : Country PPP Experience||-0.003<br>***|-0.045<br>**<br>|||-0.054<br>***|
|Exp2 : Exp Square|||0.001<br>*||||
|LS1 : 6 <= Exp < 12||||-0.056<br>***|-0.058<br>***||
|LS2 : 12 <= Exp < 24||||0.026|0.032||
|LS3 : 24 <= Exp < 48||||-0.018<br>**|-0.023<br>**||
|LS4 : 48 <= Exp < 96||||0.006|0.006||
|LS5 : 96 <= Exp||||-0.004<br>***|-0.004<br>***||
|Cubic Spline 2||||||30.690<br>**|
|Cubic Spline 3||||||-44.128<br>*|
|Cubic Spline 4||||||13.679|
|Cubic Spline 5||||||-0.321|
|Cubic Spline 6||||||0.207|
|Total investment|||0.419<br>*||0.391<br>***|0.417<br>***|
|Total Investment Square|||-0.025||-0.044|-0.048|
|Sector<sup>12</sup>|||||||
|Energy|||-1.080<br>***||-0.959<br>***|-0.934<br>***|
|<br>Transport|||-0.777<br>**||-0.689<br>***|-0.617<br>***|
|Water and sewerage|||-0.486||-0.297|-0.254|
|Type of PPP|||||||
|Brownfield project|||-0.038||-0.121|-0.031|
|Greenfieldproject|||-0.358||-0.159|-0.144|
|Country Level|||||||
|GDP per capita®|||-0.025||-0.084|0.018|
|Population(millions)|||0.001<br>**||0.001|0.000<br>*|
|Region|||||||
|AFR|||1.099<br>***||1.016<br>*|0.384|
|EAP|||1.206<br>***||1.235<br>**|0.875<br>***|
|ECA|||1.196<br>*||1.183<br>*|0.237|
|LAC|||1.514<br>***||1.624<br>***|0.751<br>***|
|MENA|||0.892||0.815|0.112|
|Constant|-1.529<br>***|-1.444<br>***|-1.700<br>***|-1.185<br>***|-1.562<br>**|-1.050<br>**|
|_Country level variance_<br>_var(constant)_|_0.327_<br>***|_0.295_<br>***|_0.446_<br>**|_0.261_<br>_***_|_0.283_<br>_**_||
|_Wald chi2()_||_39.45_|_78.40_|_55.41_|_110.77_|_144.63_|
|_Prob > chi2_||_0.000_|_0.024_|_0.000_|_0.000_|_0.000_|
|_LR test vs. probit : chi2(2)_|_84.26_|_63.34_|_40.08_|_59.89_|_21.60_||
|_Prob > chi2(2)_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_||
|_Multilevel Structure_|||||||
|_Intra-class Correlation_|_0.246_<br>**||||||
|_# Obs (PPP projects)_|_3276_|_3276_|_1208_|_3276_|_2980_|_2980_|
|_# Groups (countries)_|_109_|_109_|_98_|_109_|_98_|_98_|
|_Pseudo-R2_|_0.058_|_0.091_|_0.628_|_0.102_|_0.256_|_0.244_|
|* <1 ** <05 *** <01|||||||
|p.; p.; p.<br>Robust Std. Err. adjusted for cluste<br>® Constant 2005 US$000|ring on coun|try|||||

> 12 ICT is the base category in the regression.

12