rafmacalaba/datause-encoder-data huggingface dataset:
Roumasset (1976) to show that the “law of supply” may fail under conditions of uncertainty even where producers have low levels of risk aversion.

# **III. Empirical Literature**

Ultimately, the degree of supply responsiveness is an empirical question. The empirical literature on supply response in the mining sector is very sparse. Following a thorough, though not exhaustive search, we were able to find only one empirical study focused on estimating the short- and long-run price elasticities of supply for iron ore. Williams and Frazer (1985) used a log linear model with a Nerlove stock adjustment mechanism to estimate both short- and longrun iron ore price elasticity of supply for Western Australia. Their model utilized time-series production and price data covering the period from 1961/62 to 1981/82 and was estimated using Ordinary Least Squares (OLS) techniques. They found that supply was inelastic (0.61) in the short run but quite elastic (4.36) in the long run. Their estimate of the speed of adjustment from desired supply to actual supply implied from the coefficient of lagged output was 0.86. This suggests that more than three-quarters of the gap between desired and actual production was closed each year.

Similar supply response studies of other mining products making use of the partial adjustment mechanism have also been popular in the literature. For example, Owen (1980) used a partial adjustment framework and applied least squares techniques to time-series data for 1960-1978 to obtain estimates of short- and long-run price elasticity of supply of 0.15 and 0.71, respectively, for US uranium. He also obtained an estimate for the speed of adjustment from desired supply and actual supply of 21 percent. This suggests that less than one-quarter of the gap between desired and actual production was closed each year.

However, some economists have argued that the partial adjustment framework and the adaptive expectations framework, particularly where they are based on price-only models, may not be appropriate for estimating the price elasticity of supply for commodities. For example, Lecraw (1979) suggests that the partial adjustment adaptive expectations model may not be useful where bottlenecks prevent actual change in output from being equal to desired change and the price expectations have not been realized. Hojman (1980, 1984) also argues against the use of price-only Koyck-Nerlove models of demand and supply in non-ferrous metals. He argues that these models are incomplete specifications of demand and supply and suggest that the respective estimates of the price impact and the gap between long-and short-term effects are both biased. Furthermore, he suggests that for non-ferrous metals, a more realistic approach should look at least to the influence of general industrial activity, prices and substitutes, and production costs.

9
|Media|$9.5M|
|---|---|
|Industrial<br>Goods<br>&<br>Services|$7.1M|
|Financial Services|$30.9M|
|**Ponemon Institute, 2017**<br>Industrial|$29.4M|
|Energy& Utilities|$24.8M|
|Transportation|$24.3M|
|Technology & Software|$23.6M|
|Health care<br>Pharmaceuticals|$19M<br>$18.2M|
|Consumer Products|$17.6M|
|Communications|$16.7M|
|Public Sector|$14.5M|
|Retail|$11.5M|
|Education & Research|$9.8M|
|Media<br>_Note:Figures have been converted from euros to U.S. dollars using XE’s cur_|$7.7M<br>_rency converter._|

_<u>Note: Figures have been converted from euros to U.S. dollars using XE’s currency converter.</u>_ Salaries for privacy compliance personnel form a major part of privacy-related expenditures. A study by DataGrail surveyed 301 professionals involved in the GDPR decision-making process at companies with 50 or more employees in 2019 and found that 67% of companies engaged at least 25 employees when preparing for the GDPR, 44% of companies had at least 50 employees.<sup>82</sup> Findings from the IAPP’s survey report that privacy staffing, like total privacy spending on GDPR compliance, reportedly leveled off in 2019, demonstrated by only 30% of organizations surveyed in 2019 expecting an increase in privacy staff, 66% expecting no changes, and 4% expecting a decrease.<sup>83</sup> An average mean of 7.1 employees work on privacy related matters full-time while a mean of 15.7 do so parttime.<sup>84</sup> Blended companies that engage in both business to business and business to consumer activities report the highest numbers in staffing along with privacy professional responsibilities, IAPP attributes the increase to the complexities such organizations face in role defining, negotiations, and contractdrafting due to their blurred role as data processors and controllers.<sup>85</sup>

|**Research Entity**|**Staff**<br>**Related**<br>**Expenditure**|**Cost**|
|---|---|---|
|**IAPP-EY, 2019**|Privacy team salaries and<br>benefits (2019)|$397,100 (avg.)|

> 82 DataGrail, _The Age of Privacy: The Cost of Continuous Compliance_ (DataGrail 2020).

> 83 J. Trevor Hughes & Angela Saverice-Rohan, _Annual Privacy Governance Report_ (IAPP-EY 2019).

> 84 _Id._

> 85 _Id._ at xiv.

15
ratio – the share of income or expenditure allocated to a specific good or service. Figure 2 illustrates the affordability ratio of electricity for all income quintiles from 2003 to 2008.

As indicated in Figure 2, electricity is a normal good in the sense that its budget share declines as household income rises. This is consistent with observations on energy consumption patterns in other developing countries (World Bank, 2004 and 2009). Additionally, expenditures on electricity have been a moderate component of the total budget of the Turkish households even after the significant price increase in 2008. For an average income household, they represented 2.9 percent of the household’s disposable income in 2007 and increased to 3.5 percent in 2008. For the lowest quintile households, they rose to 6.3 percent in 2008 from 5.4 percent in 2007, which is still below the 10-percent benchmark affordability level (Lee, 2007).

# **V. Results**

# _Demand Parameters_

Table 3 presents two sets of regression results. The first set presents electricity demand regressions based on 2008 HBS data (Columns (1) – (5)). The second set of regressions was performed based on the combined 2008 and 2009 datasets. Comparison of the results suggests that all parameter estimates enjoy a high degree of stability across the two datasets.

In addition, parameter estimates are largely insensitive to the choice of model. Columns (1) and (6) report results from estimating determinants of the log of household monthly electricity consumption identified in Equation (1) via OLS model. Columns (2) and (7) report estimation from the Heckman selection model (maximum likelihood). Both Wald and Likelihood Ratio tests strongly support the contention that the unobserved variables influencing the reporting or nonreporting decision are statistically independent of unobserved factors of the electricity demand. Table 4 reports the first stage regression of the Heckman selection model. The estimated correlation coefficient ( is statistically indistinguishable from zero. Therefore, the selection process can be ignored conditional on observable household characteristics.

Coefficients of the log of price and the log of income measure the price and income elasticities of electricity demand. These elasticity estimates correspond to the percentage change in a household’s monthly electricity consumption resulting from a 1-percent increase in electricity price or annual household disposable income, holding all else fixed. As expected, the consumption declines in response to price increases and increases when income rises. Estimates of income elasticity suggest that a 10 percent increase in annual household disposable income will cause electricity consumption to increase by about 1 percent. Overall, electricity is price and income inelastic in the short-run in the residential sector of Turkey. Table 8 summarizes short-run price and income elasticities estimated in other studies and on other countries. The estimates of income and price elasticities of the study are in the range of elasticities suggested in the literature.

11
**Table 6. Parental Matchmaking and Wife Submissiveness**

||**Wife Sub**|**missiveness**|**Woman C**<br>**Imp**|**areer Not**<br>**ortant**|**No Good M**|**ale Friend**|**Cannot R**|**eject Sex**|
|---|---|---|---|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|
|Rural|-0.215|-0.534*|-0.028|-0.260**|0.003|-0.120|-0.161*|-0.123|
||(0.150)|(0.304)|(0.054)|(0.102)|(0.085)|(0.163)|(0.089)|(0.145)|
|Parent matched|0.061|2.509***|0.028|0.071|0.035|1.478***|-0.002|0.925**|
||(0.037)|(0.733)|(0.018)|(0.185)|(0.024)|(0.460)|(0.023)|(0.402)|
|Parent matched|0.002|-0.456|-0.028|0.693***|0.021|-0.430|0.005|-0.665|
|* rural|(0.055)|(0.847)|(0.028)|(0.235)|(0.034)|(0.512)|(0.029)|(0.426)|
|Other controls as in|||||||||
|column (1) of<br>Table 5|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Religion and ethnic<br>dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|N obs.|6,256|6,256|6,334|6,334|6,298|6,298|6,268|6,268|
|Adjusted R2|0.133|-0.984|0.055|-0.493|0.083|-0.959|0.040|-0.178|
|Cragg-Donald Wald<br>F stat||28.1||29.8||28.9||27.9|
|Kleibergen-Paap<br>Walk rk F stat||16.2||16.9||16.1||16.3|

Note. *, **, and ***: statistically significant at the 10, 5, and 1 percent level. White-corrected standard errors clustered at the township level in parentheses.

41
# **Table 3a: Industry traits for unorganized manufacturing**

|||Male esta<br>cou|blishment<br>nts|Female est<br>cou|ablishment<br>nts|count<br>Female est|share<br>ablishment|share, emp<br>Female est|loyee wtd.<br>ablishment|
|---|---|---|---|---|---|---|---|---|---|
|Indu|stry|2000|2005|2000|2005|2000|2005|2000|2005|
|15|Food products and beverages|2,465,014|1,943,115|307,284|380,881|0.11|0.16|0.09|0.11|
|16|Tobacco products|707,281|760,128|1,346,440|1,967,726|0.66|0.72|0.52|0.63|
|17|Textiles|1,319,150|1,190,366|647,578|831,089|0.33|0.41|0.19|0.23|
|18|Wearing apparel; dressing and dyeing of fur|1,812,817|1,632,443|887,051|1,427,978|0.33|0.47|0.22|0.35|
|19|Leather tanning; luggage, handbags, footwear|159,303|122,752|5,931|11,731|0.04|0.09|0.04|0.06|
|20|Wood and wood products; straw and plating articles|2,160,422|1,396,976|432,969|347,713|0.17|0.20|0.13|0.19|
|21|Paper and paper products|46,768|34,381|36,140|124,301|0.44|0.78|0.27|0.62|
|22|Publishing, printing and media reproduction|120,983|94,498|8,558|9,217|0.07|0.09|0.07|0.08|
|23|Coke, refined petroleum and nuclear fuel|6,458|2,146|94|96|0.01|0.04|0.02|0.09|
|24|Chemicals and chemical products|43,856|64,077|154,997|316,288|0.78|0.83|0.49|0.63|
|25|Rubber and plastic products|59,118|47,792|19,631|12,659|0.25|0.21|0.13|0.14|
|26|Other non-metallic mineral products|717,221|532,770|32,448|30,443|0.04|0.05|0.04|0.04|
|27|Basic metals|33,516|28,749|496|1,293|0.01|0.04|0.03|0.07|
|28|Fabricated metal products, except machinery|586,980|568,258|10,783|11,000|0.02|0.02|0.03|0.03|
|29|Machinery and equipment, n.e.c.|147,769|155,131|3,222|3,646|0.02|0.02|0.03|0.03|
|31|Electrical machinery and apparatus, n.e.c.|55,978|95,954|2,966|4,257|0.05|0.04|0.04|0.06|
|32|Radio, television, and comm. equipment|5,406|3,973|262|493|0.05|0.11|0.05|0.26|
|33|Medical, precision and optical instruments, watches|7,151|8,597|147|406|0.02|0.05|0.02|0.06|
|34|Motor vehicles, trailers and semi-trailers|18,357|12,382|314|195|0.02|0.02|0.02|0.03|
|35|Other transport equipment|14,778|15,522|114|7,918|0.01|0.34|0.01|0.49|
|36|Furniture, manufacturing n.e.c.|1,016,010|865,217|158,410|150,557|0.13|0.15|0.10|0.09|
||**Totals and weighted averages**|**11,504,336**|**9,575,226**|**4,055,836**|**5,639,889**|**0.26**|**0.37**|**0.17**|**0.25**|
||**Unweighted averages**|**547,826**|**455,963**|**193,135**|**268,566**|**0.17**|**0.23**|**0.12**|**0.20**|

Notes: Descriptive statistics taken from National Sample Statistics.
_Reslts_ _11_ Table 4. Age-standardized mortality rates (per 100,000) for major and specific causes, by region, 1985

|_Cause_|_World_|_Devel-_<br>_oped_|_Devel-_<br>_oping_|_Indus-_<br>_trial_|<br>_Non-_<br>_market_|_LAC_|_Sub-Sa_<br>_haran_<br>_Afr*a_|_Middle_<br>_East_<br>_- and_<br>_Noith_<br>_Afr*a_|_Asia_|
|---|---|---|---|---|---|---|---|---|---|
|AlU causes<br>|1046<br>|692|1147|<br>578|894|901|<br>1621|<br>1243|1077|
|Infectious, parasitic|308|77|386|43|126|199|<br>658|422|350|
|Diarrhea|62|2|78|1|5|48|221|114|47|
|Tuberculosls<br>|18|3|25|1|5|14|<br>33|24|25|
|Acute respiratory<br>|116<br>|29|141|20|40|72|258|173|120|
|Measles<br>|9|0|10|0|0|4|31|17|5|
|COPD<br>|41|18|54|18|16|33|54|49|56|
|Polio<br>|1|0|1|0|0|0|0|0|1|
|Yellow fever|0|0|0|0|0|0|0|0|0|
|Malaria|3|0|4|0|0|1|3|4|5|
|Schistosomiasis|0|0|0|0|0|0|0|0|0|
|Intestinal parasltes|3|0|3|0|1|2|7|4|3|
|Other infectious|56|24|69|3|59|26|50|35|88|
|Neoplasms<br>|103|117|92|125|97|91|86|92|93|
|Clculatory<br>|280|298|255|260|381|272|328|297|237|
|Ischemicheart<br>|84|119|55|99|164|69|85|82|46|
|Cerebrovascular<br>|81<br>|74|84|59|106|57|74|68|91|
|Other cardiovascular<br>|<br>78|78|73|75|86|97|119|103|59|
|Diabetes|11|8|12|9|5|20|20|18|10|
|Certain degenerative|26|19|30|17|21|29|30|26|30|
|Pregnancy|5|1|6|0|2|3|11|7|5|
|Perinatal|69|20|77|12|32|48|89|76|79|
|Injury, poisoning|77|56|83|51|67|64|71|61|94|
|Other|205|123|248|87|189|223|377|288|219|
|Mental disorders|2|2|2|2|2|2|2|1|2|
|Oralhealth diseases|0|0|0|0|0|0|0|0|0|
|Mlcronutrient|5|3|6|2|4|7|12|9|5|
|Malnutrition<br>|8|3|9|0|6|6|11|8|9|
|Unspecified|190|115|231|83|177|209|352|269|202|

Note: Rates are standardized using the 1985 world age structure.

acute respiratory infections and diarrhea combined account for three-fourths of deaths under five.

Among circulatory system diseases, ischemic heart disease, cerebrovascular disease, and other cardiovascular diseases are nearly equal in importance for the world as a whole. Regionally, ischemic heart disease is responsible for the largest share of circulatory system disease deaths in developed regions; cerebrovascular disease has the largest share in Asia; and other cardiovascular diseases have the largest share in the remaining developing regions. All these categories are important everywhere, however: each always accounts for a fifth or more of the deaths under the major category. Diabetes is of lesser importance in these estimates, with less than 5 percent of deaths under the major category, and nephritis, cirrhosis of the liver, and ulcers of the stomach and duodenum account for roughly twice this proportion.
16 actuarially neutral. Penalties for continued work beyond the normal retirement age occur when a plan offers benefit enhancements which do not offse **t** he now-shorter retirement period. This is quite common among private sector plans in the United States, where continued work after age 65, and sometimes even after age 60, Is often penalized (Luzadis and Mitchell 1991). In contrast, social security retirement pension rules in the United States are roughly actuarially neutral for those working beyond age 65, while in Japan, work between the ages of 65 and 70 yields benefits higher by 88 percent instead of the more actuarially neutral 40 percent (Myers 1991). These retirement system Incentives influence workers' decisions about when to retire, and must be a central focus of those seeking to alter retirement pattems and trends.

It should also be noted that in many countries private and public pension systems interact In such a way that benefit formulas include cascading or cumulative effects on retirement incentives."' Furthermore, as has been i-c m'xnized in a few recent studies of retirement trends in Westem Europe, the availability of other, non-pension income transfer programs also interacts closely with the penslon schemes to influence the relative appeal of retirement at young ages. For example, as will be discussed below, unemployment relief and disability benefit systems sometimes offer altemative routes out of the workplace for persons seeking altematives to the job market. Though existing studies are thus far mainly descriptive, they tend to support the view that these other transfer programs have generated new pathways to retirement which to some extent work outside the regular public and private pension systems.

Researchers are currently examining whether the long term trend toward early retirement can be explained adequately by analyzing changes over time In pension plan characteristics and formula-driven benefit reduction or increase factors. There is some evidence that these have, in fact, played a powerful role. Incentives to retire early have strengthened In many nations over the last four decades, and these benefit increase **s** eem to "track" declining labor supply pattems quite closely. In a study using US aggregate data, Levine (1993) found that average social security retirement benefits predicted men's labor force participation fairly well, and certainly much better than a simple time trend
# A Simple Method to Quantify the _ex-ante_ Effects of “Deep” Trade Liberalization and “Hard” Trade Protection<sup>_∗_</sup>

Mario Larch Shawn W. Tan University of Bayreuth World Bank Yoto V. Yotov<sup>_⋄_</sup> Drexel University

## **JEL Classification Codes:** F1, F10, F13, F14.

**Keywords:** Trade Costs, Trade Policy, Structural Gravity, CEFTA, EU.

> _∗_ Acknowledgments and disclaimers to be added later. The views expressed herein are those of the authors and should not be attributed to the World Bank, their Executive Boards, or management.

> _⋄_ Contact information: Larch—Faculty of Law, Business Management and Economics, University of Bayreuth, CESifo, CEPII, GEP, and Ifo Institute. Universitatsstrase 30, 95447 Bayreuth, Germany. Phone: +49 (0) 921 55 6240. Email: mario.larch@uni-bayreuth.de; Tan– World Bank, 1818 H St NW, Washington, DC 20433. Phone: (202) 473-7621. Email: swtan@worldbank.org; Yotov—School of Economics, LeBow College of Business, Drexel University, CESifo, Gerri. C. LeBow Hall, 1020, 3220 Market Street, Philadelphia, PA 19104, USA. Phone: (215) 895-2572. Email: yotov@drexel.edu.
us numerous valuable descriptions of what happened in the past and numerous instances of causes in the past (provided of course that one is willing to accept the determinist axiom). On the other hand, what they show is very limited. This is because when they show that it was the switch from y to x that caused the switch from b to a, what they are saying is that this was true under certain historical conditions (z), but they cannot tell you what those historical conditions are. RCT discoveries never graduate from something “was a cause” of something else to “is a cause.” RCTs give us no insight into universal causality because it cannot tell us what is it that was being held constant (z in the above example), when we switched some intervention b to a. For Bengal in a certain period, electing a woman leader of the local government caused water provisioning to be better. This is no guide to the future, since we do not fully know what Bengal in a certain period is like. Henceforth, a reference to causality without a qualifying epithet should be taken to be a reference to universal causality, since for policy purposes that is what is of essence.

One clarification is called for here. When we hold the z constant and change y to x, in the above example, why are we saying that it is not known what is being held constant and that we simply know that _something_ is being held constant? In the Chattopadhyay and Duflo (2004) example, for instance, that this is true for Bengal of a certain period. Why is this not a useful description? The reason is that this is a ‘dated description’—one in which the date or period of the experiment is a part of the description of what is being held constant. Since a date never recurs, we can never use this description in the future. So we will never be able to say that the same conditions now hold and so the same result is to be expected (recall the determinist axiom). What is minimally be needed is a description of what is being held constant when a particular experiment is being run without reference to a date. RCTs do not give this to us and this is what makes the findings of RCTs non-portable.

Cartwright (2010) is right in her broad reservations about RCTs. However, contrary to Cartwright’s claims, I am arguing that RCTs do provide insight into circumstantial causality and is the gold standard for _describing_ large populations 11
Table 3: People management and intermediate outcomes, public and private schools in Latin America

||(1)<br>z-teacher<br>shortage|(2)<br>z-teacher<br>motivation|(3)<br>z-teacher<br>efort|(4)<br>z-teacher<br>shortage|(5)<br>z-teacher<br>motivation|(6)<br>z-teacher<br>efort|
|---|---|---|---|---|---|---|
|Private School|-0.535<br>(0.122)<br>[0.000]|0.591<br>(0.139)<br>[0.000]|0.792<br>(0.128)<br>[0.000]||||
|People Index||||-0.062|0.238|0.074|
|||||(0.035)<br>[0.077]|(0.040)<br>[0.000]|(0.033)<br>[0.026]|
|_R-squared_|_0.152_|_0.142_|_0.154_|_0.139_|_0.169_|_0.123_|
|Observations|3035|3043|3043|3035|3043|3043|
|School controls|Y|Y|Y|Y|Y|Y|
|Country FE|Y|Y|Y|Y|Y|Y|

Notes: The first row reports the coefficients from regressions of a binary indicator (coded to 1 if the school is a private school, 0 otherwise) on the standardized index of three intermediate school outcomes: teacher shortage, teacher motivation and teacher effort. The second row reports coefficients from regressions of the standardized people management index on each of the intermediate school outcomes. The people management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include PISA _school final weights_ and country fixed effects. School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources, and average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added to the specifications.

31