rafmacalaba/datause-encoder-data huggingface dataset:
# **_Appendix 3:_** _Calculation of US Outpatient prices_

|_Procedure_|_Representative_<br>_CPT codes_|_Facility_<br>_fee($)_|_Physician_<br>_fee($)_|_Anesthetist_<br>_fee($)_|_Patient_<br>_co-pay ($)_|_Outpatient_<br>_price($)_|_Estimated_<br>_outpatient_<br>_volume_<sup>_40_</sup>|_Source of volume_<br>_data_|
|---|---|---|---|---|---|---|---|---|
|Knee surgery|29882, 29873,<br>27437, 27446,<br>27447|2,569|879|195|499|4,142|60,000|American Association<br>of Orthopedic Surgeons|
|Shoulder<br>Arthroplasty|23470, 23472|5,696|1,443|230|562|7,931|N/a||
|TURP|52601|2,078|696|159|370|3,303|88,064|American Urological<br>Association|
|Tubal Ligation|58670, 58671|2,458|362|177|445|3,442|621,229|CDC|
|Hernia Repair|49650|2,458|388|159|445|3,450|759,447|National Center for<br>HealthStatistics|
|Excision of skin<br>lesions|11426|1,111|238|142|205|1,696|1,588,884|American Society for<br>DermatologicalSurgery|
|Tonsillectomy|42820, 42821|1,232|315|159|225|1,931|102,749|American Academy of<br>Otolaryngology|
|Hysterectomy|58550, 58553|3,505|1,113|177|624|5,420|N/a||
|Haemorrhoidectomy|46255|1,330|349|159|242|2,081|137,213|Ethicon Endosurgery|
|Rhinoplasty|30400|2,120|754|159|384|3,417|N/a||
|Bunionectomy|28290|1,525|448|142|277|2,392|41,507|American Podiatric<br>Medical Association|
|Cataract extraction|66983|1,340|592|142|251|2,325|1,430,785|Market Watch: Optical<br>Sector report2002|
|Varicose vein surgery|37720|1,558|382|159|274|2,373|148,043|American college of<br>Phlebology|
|Glaucoma procedures|66150|1,273|702|159|157|2,292|75,838|Albright et al. (2002)|
|Tympanoplasty|69631|2,120|701|142|384|3,347|149,246|CDC|

_Sources:_ Current procedural Terminology (2004), and Centers for Medicare and Medicaid Services 40 Outpatient volume was calculated as total volume minus the H-CUP inpatient volume for each procedure. An “N/a” indicates that volume was not available.

31
NOT FOR PUBLICATION

# **Contents (Appendix)**

|**A The FAT Survey**|**60**|
|---|---|
|A.1 The Survey<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|60|
|A.1.1<br>The Grid<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|61|
|A.1.2<br>General Business Functions<br>. . . . . . . . . . . . . . . . . . . . . . .|62|
|A.1.3<br>Sector Specifc Business Functions . . . . . . . . . . . . . . . . . . . .|63|
|A.1.4<br>Barriers and Drivers<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .|76|
|A.1.5<br>Financial Statements and Workers<br>. . . . . . . . . . . . . . . . . . .|77|
|A.2 Validation of the Ranking of Technology Sophistication . . . . . . . . . . . .|77|
|A.2.1<br>Comparison between experts’ and ChatGPT’s sophistication rankings|92|
|A.3 Sampling Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|98|
|A.4 Survey Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|101|
|A.5 Measures to Minimize Bias and Measurement Error During Survey Design||
|and Implementation<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|102|
|A.6 Ex-post Checks and Validation Exercises . . . . . . . . . . . . . . . . . . . .|105|
|**B Construction of Measures**|**113**|
|B.1 Technology Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|113|
|B.1.1<br>Exceptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|115|
|**C Additional Figures and Tables**|**118**|
|C.1 Technological Sophistication . . . . . . . . . . . . . . . . . . . . . . . . . . .|118|
|**D Detailed Acknowledgments**|**122**|

59
**Table 9. Odds ratios from logit model of changing formality in subsequent wave by base wave formality, ELMPS 1998-2018**

||**Pooled**<br>**base**<br>**informal**|**Pooled**<br>**base formal**|**2012-2018**<br>**Base**<br>**informal**|**2012-2018**<br>**Base**<br>**formal**|
|---|---|---|---|---|
|**Wave (1998-2006 omit.)**|||||
|06-12|0.514*|1.762|||
||(0.156)|(0.545)|||
|12-18|0.541|1.734|||
||(0.187)|(0.635)|||
|**Ent. size (one worker omit.)**|||||
|2|0.793|1.271|1.453|1.508|
||(0.279)|(0.349)|(1.081)|(0.707)|
|3-4|0.650|0.948|0.178|2.696|
||(0.316)|(0.308)|(0.181)|(1.804)|
|5-24|2.677|0.363|||
||(2.489)|(0.228)|||
|**Industry (wholesale/retail omit.)**|||||
|Manufacturing and related trades|1.534|2.271*|1.768|0.674|
||(0.655)|(0.907)|(1.217)|(0.621)|
|Construction|0.871|304.036***|0.597||
||(0.685)|(354.038)|(0.832)||
|Transportation and storage|1.009|551.995***|0.864||
||(0.806)|(825.188)|(1.012)||
|Accommodation and food service|3.036|0.560|2.276|0.520|
||(2.022)|(0.276)|(4.233)|(0.334)|
|Various professional acts.|0.808|27.048***|1.118|14.497|
||(0.570)|(26.003)|(1.600)|(22.405)|
|Other service|1.520|2.460|0.935|2.534|

61
**Table 3:  NO2 pollution vs. source traffic volume and distance, 2018**

||**Aug_2018**|**Sep_2018**|**Oct_2018**|**Nov_2018**|**Dec_2018**|
|---|---|---|---|---|---|
|**α1**|**-0.068**|**-0.211**|**-0.196**|**-0.143**|**-0.165**|
||**(4.89)****|**(9.54)****|**(10.44)****|**(5.50)****|**(7.03)****|
|**α2**|**0.066**|**0.060**|**0.077**|**0.094**|**0.147**|
||**(19.73)****|**(11.79)****|**(18.02)****|**(13.73)****|**(24.63)****|
|**α3**|**-0.003**|**-0.002**|**-0.005**|**-0.006**|**-0.010**|
||**(12.45)****|**(5.89)****|**(16.45)****|**(11.10)****|**(21.22)****|
|**α4**|**.00004**<br>**(8.08)****|**.000019**<br>**(2.65)****|**.00008**<br>**(14.53)****|**.0001**<br>**(8.74)****|**0.00016**<br>**(16.97)****|
|**α0**|**15.396**|**16.440**|**17.654**|**16.029**|**12.651**|
||**(127.07)****|**(81.96)****|**(103.46)****|**(70.20)****|**(53.84)****|
|**Observations**|**745**|**780**|**797**|**822**|**907**|
|**R-squared**|**0.75**|**0.54**|**0.33**|**0.34**|**0.58**|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%

**Table 4:  NO2 pollution vs. source traffic volume and distance, 2019**

||**Jan_2019**|**Feb_2019**|**Mar_2019**|**Apr_2019**|**May_2019**|
|---|---|---|---|---|---|
|**α1**|**-0.050**|**-0.058**|**-0.062**|**0.151**|**-0.072**|
||**(2.71)****|**(2.99)****|**(2.23)***|**(5.95)****|**(3.65)****|
|**α2**|**0.082**|**0.133**|**0.210**|**-0.003**|**0.066**|
||**(17.24)****|**(26.44)****|**(29.37)****|**(0.38)**|**(11.82)****|
|**α3**|**-0.006**<br>**(15.76)****|**-0.009**<br>**(23.16)****|**-0.014**<br>**(26.66)****|**0.000**<br>**(0.64)**|**-0.002**<br>**(4.50)****|
|**α4**|**0.0001**|**0.0001**|**0.0002**|**-0.000**|**0.0003**|
||**(12.97)****|**(18.78)****|**(21.82)****|**(0.52)**|**(2.17)***|
|**α0**|**10.132**|**7.788**|**10.974**|**11.100**|**6.744**|
||**(54.00)****|**(40.07)****|**(38.29)****|**(62.07)****|**(48.68)****|
|**Observations**|**906**|**909**|**907**|**485**|**464**|
|**R-squared**|**0.45**|**0.65**|**0.72**|**0.23**|**0.82**|

Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

15
5 projected declines in birth rates.

Motives for saving would prc.a)bly change during the demographic transition. The experience of the idustrial countries suggests that saving for housing, consumer durables, and education become more important during the transition. Let us consider briefly some of these influences on saving, which act along side the life-cycle motives for saving. (See Gersovitz 1987 for a survey.j The direction of causation of the demographic transition and nonlife-cycle savings motives is not clear and is probably not in a single direction. Furthermore, the nature of households change during the transition.

Many people in developing countries today live in extended households that last beyond the lives of those in them, and probably most adults in them expect to rely mainly on their children for support when they reach old age. Thus actual saving for life-.-icle motives would be lower today than in our simulations. With non-negative rates of population growth and interest, lifecycle saving rates for extended households would be lower than for the nuclear, mortal households (Gersovitz 1987). Parents who could expect to share in the earnings of their children would save less for their old age than parents who could not. In Gary Becker style home economics, parents who invest more in children as earning assets would invest less in financial assets. The Bank's assumption that all countries will make the transition to stable populations with low rates of birth and death seems to imply an accompanying transition to nuclear households, since that is what has predominated in the industrial economies that have already made the transition to stable populations. Furthermore, if average net reproductive rates sink to 1.0, a large fraction of the adults would face the substantial probability, if
(Valodia, 2006). For example most of the auto manufacturers import parts that are used to assemble vehicles and the finished product is exported back to the key market, in most cases, Europe.

South Africa is also the largest food exporter in Africa and in 2007 the export value of its goods was $ 4.2 billion (UNCTAD 2009) approximately 2% of GDP. Given its disadvantaged location and a few other inefficiencies in the manufacturing system, logistics is one of the few key areas were the country developed as one of the best in the world<sup>26</sup> therefore improving the country’s competitiveness.

Another major observation at the Durban Port is that most of the cargo going through the port is for the domestic market dominated by large companies as cargo owners but also cargo operators. Transit or over border cargo as they call it, account for a very small share of the total cargo traffic. In most coastal countries in Africa, these account for a larger proportion of import cargo and most of these countries a significant proportion of the transiting goods experience problems relating to documentation and abandoning of the cargo. Most of the big operators in South Africa also have service level agreements with TPT and they prequalify their clients to ensure legitimacy and to avoid taking clients with a low or non compliance record.

In the case of South Africa, any form of delay at the port is a major irritation for the manufacturing industry but even more importantly as expressed by someone in the automotive industry, they also have huge financial implications for those operating in a competitive environment as their businesses have to absorb any cost inefficiencies wherever they occur.

For most shippers, hours count. The Citrus Growers‘ Association in South Africa estimates that delays at the Port of Durban cost its growers US$10.5 million per season (on approximately US$400 million of exports), based on an average delay per load of 12 hours for each of the 20,000 citrus laden trucks that enter the port during peak season (World Bank, 2011).

It is no surprise that import and export traders have very little tolerance for poor performance by a state run freight company, neither does the Department of Trade and Industry which has the responsibility of attracting investments into the country.

In what looks like the key dynamic of the private sector in SA, a Barloworld survey of businesses involved in manufacturing and logistics, the industry expresses a sense of ownership to the logistics process. While recognizing that public sector is key to this, they feel that the onus is on them to agitate and drive the process to see transformation at Transnet (Barloworld Logistics, 2010). The strategy seems to have contributed to reducing delays at some of the ports in South Africa. Facing internal pressure

> 26 Logistics Performance Index, 2010.

21
included in estimating the share equation. While including state effects in the regression has little effect on the estimated coefficients it does increase the R<sup>2</sup> quite a bit, from 0.10 to 0.27. This is interesting in the context of recent work which relates productivity growth in the postliberalization period to state-level regulations (Aghion et al., 2003). We tried replicating those results by checking if changes in state-level labor regulations in the late 1980s predicted skill upgrading in the 1990s using measures from Besley and Burgess (2002).<sup>22</sup> The answer is negative. Skill-upgrading in India was not restricted to a few states which followed particular policies. It appears to be a more a general phenomenon.

In unreported results we have attempted to investigate the effects of one state-specific factor: electricity generated within the state. That variable also has no explanatory power. Further exploration of skill-upgrading in India may do well to focus on state-specific factors such as differences in infrastructure investment and regulation,<sup>23</sup> and in employment protection legislation in the 1990s.<sup>24</sup>

# **5. CONCLUSIONS**

Indian manufacturing was unusual in the 1980s in the falling proportion of non-manual (relatively skilled) workers in employment, bucking a worldwide trend which has been associated with skillbiased technological change. The 1990s were different, though. The proportion of non-manual workers in employment increased, as did the non-manual share of the wage-bill. All this took place against the backdrop of various economic reforms instituted in the early 1990s, and acceleration of investment and productivity.

Is the increased use of skilled workers in Indian manufacturing in the 1990s due to SBTC? The answer is uncertain. On the one hand, most of the skill upgrading occurred _within_ 4 digit 29
In the top panel of the table we start with the base regression estimate of .209 for the _Law_ coefficient taken from Table 7, column 3. Column (2) shows the estimated _Law_ coefficient as controls are added one by one while column (1) shows the estimated coefficients of the controls. The only significant control (apart from _LAC_ dummy and _Si,1990_ ) is the decadal change in _Roads_ but this result does not survive when we add all the controls simultaneously.

The bottom panel of Table 8 shows the impact of including the _Skill_ measure in the regressions. Data on _Skill_ is available for 68 countries in the sample. For this sample of 68 countries, the base regression (equation 3) estimate of the _Law_ coefficient is .174 (first row in the lower panel of Table 8). Starting with this base regression we next added two measures of human capital: the initial stock of human capital ( _Skill_ in 1985) and the decadal change in human capital (Δ _Skill_ ).<sup>23</sup> These variables are insignificant themselves and have a minimal impact on the estimated _Law_ coefficient as the Table shows.

Summarizing our results in this section, we found that _Law_ has a positive and a statistically significant impact on the growth performance of the service sector. Overall the level and growth regressions suggest that a service led development strategy is unlikely to be successful without significant improvements in the quality of institutions associated with the enforcement of contracts or what is broadly called the Rule of Law.

# **Conclusion**

The present paper looks at the role that institutions play in the development of the service sector. Our findings show a strong and positive effect of institutions in this

> 23 Δ _Skill_ is the change in _Skill_ value over 1985-1995 period.

29
<!-- Start of picture text --> Figure 5. Synchronisation of recessions and financial downturns<br>(in percent)<br>A. Fraction of Countries in Recessions<br>100<br>80<br>60<br>40<br>20<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>B. Fraction of Countries in Equity Price Downturns<br>100<br>80<br>60<br>40<br>20<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>C. Fraction of Countries in House Price Downturns<br>100<br>80<br>60<br>40<br>20<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Note: Each bar represents the share of countries experiencing recessions or respective financial downturns. The<br>figures include complete as well as ongoing episodes. The sample contains the quarterly data for advanced<br>economies. Global recession years (1975, 1982, 1991 and 2009) are shaded in gray. House price data start in 1970.<br><!-- End of picture text --> 56
occurring effects. First, a change in any policy affects the price of fuel, which in turn results in a change in the quantity of fuel demanded; this is the shift along the fuel demand curve and is represented by the term _f_ '. Second, a change in the fuel price _PF_ – combined with a change in the consumer price for hydrous ethanol – alters the price gap which affects flex cars owners’ purchasing decision and so the demand curves for fuel and hydrous ethanol shift in opposite directions. The magnitude of the shift is represented by the term _dX/dθ._

**Figure 8. Logistic Curves for an Endogenous Demand Shift under Various Scenarios**

<!-- Start of picture text --> 13<br>X(θ) Scenario 3<br>10<br>7<br>Scenario 1<br>4<br>Scenario 2<br>1<br>-3 -1 1 3 5 7 9 11 θ 13<br>-2<br>-5<br>Benchmark<br>-8<br>-11<br>bil. GEEL<br><!-- End of picture text --> <!-- Start of picture text --> $/GEEL<br><!-- End of picture text --> The first term on the right side of identity (12) is unambiguously negative while the second term is negative only if α >φ _A_ φ _H_ . Using the observed parameters values for 2010/11 and 2011/12 marketing years, the second term is negative only for α > 0.96; this means the Brazilian ethanol mandate would have to be at least 96 percent for the expression (12) to be negative. We therefore do not consider this possibility further.

The probability of expression (12) being negative increases with a higher elasticity of the fuel demand, higher blend mandate, and smaller sensitivity of flex car owners to the change in 21
# **_1. Introduction_**

China has been the most rapidly growing economy in the world over the past 25 years. This growth has led to an extraordinary increase in real living standards and to an unprecedented decline in poverty. The World Bank estimates that more than 60% of the population was living under its $1 per day (PPP) poverty line at the beginning of economic reform. That poverty headcount ratio had declined to 10% by 2004, indicating that about 500 million people have been lifted out of poverty in a generation.

At the same time, the phenomenal rate of change has brought with it different kinds of stresses. China faces serious natural resource scarcity and environmental degradation. It has also seen growing disparities of different kinds as people in different parts of the country and with different characteristics have benefited from the growth at different rates. Growing disparities, and policy measures that might mitigate them, are the main focus of this paper. In the next section I briefly review the main elements of China’s economic reform and its impact on per capita income and the poverty level.

Section 3 then documents different kinds of disparities that have emerged. Starting from the pre-reform situation, some increase in income inequality was inevitable, as coastal urban locations benefited first from the opening policy and as the small stock of educated people found new opportunities. The main point of the section, however, is that particular features of Chinese policy may have exacerbated rather than mitigated growing disparities. The household registration ( _hukou)_ system kept rural-urban migration below what it otherwise would have been, and contributed to the development of one of the largest rural-urban income divides in the world. Weak tenure over rural land also limited the ability of peasants to benefit from their primary asset.

Aside from income inequality, there has also been an increase in inequality of educational outcomes and health status. These developments are partly the result of China’s uniquely decentralized fiscal system, in which local government has been primarily responsible for funding basic health and education. Poor localities have not been able to fund these services, and poor households have not been able to afford the high private cost of basic education and healthcare.

Section 4 argues that the large trade surplus that has emerged in China exacerbates these inequalities and makes them harder to address. The trade surplus 2
Table 3: Gender differences in risk appetite and performance

||Leverage|Profts/<br>Revenue|TFP|Log(Sales/<br>Worker)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_A. All countries_|||||
|Female|0.005|0.144|0.002|-0.112|
||[0.049]|[0.048]***|[0.017]|[0.035]***|
|Obs|21,411|21,411|21,411|21,411|
|R2|0.026|0.034|0.865|0.165|
|_B. By social perceptions about women_|||||
|Female in less traditional countries|0.02|0.141|-0.019|-0.148|
||[0.069]|[0.069]**|[0.025]|[0.042]***|
|Female in more traditional countries|-0.01|0.147|0.023|-0.076|
||[0.064]|[0.064]**|[0.021]|[0.058]|
|Obs|21,411|21,411|21,411|21411|
|R2|0.026|0.034|0.865|0.166|

Sources: World Bank Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports, whether it has financial statements certified by external auditors, the percentage of the firm that is privately owned, and industry, country, and year fixed effects. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

Despite female-managed firms being comparably risky and productive, and in fact more profitable than their male counterparts, they operate with lower credit levels, indicating potential sub-optimal credit allocation. The reasons for this could stem from either demand-side constraints, where women-managed firms request lower credit amounts, or supply-side barriers, such as explicit or implicit discrimination by loan officers in allocating credit to these firms. While our data does not allow for precise identification of the source of sub-optimal credit allocation,<sup>21</sup> it suggests a potential misallocation of capital, particularly when considering the higher profitability and no significant differences in TFP for females compared to male-managed firms. Consequently, we now examine the relative efficiency of capital utilization in female-managed firms.

# **3.2 Capital Misallocation**

Drawing on extant literature, we present firm-level empirical indicators of capital misallocation. Notably, these indicators are at the firm-level, in contrast to widely known misallocation metrics (e.g. Restuccia and Rogerson, 2017; Hsieh and Klenow, 2009; Duranton et al., 2016, 2015b) that are aggregated following the same concept. Specifically, misallocation indicators at the level of a unit of analysis can be aggregated to construct meaningful misallocation metrics that allow for benchmarking allocative inefficiency relative, for instance, by gender, location, or sector. Our conceptual discussion of misallocation indicators at the firm-level is followed by a presentation of estimation results on gender-based differences in misallocation.

> 21The WBES survey instrument does not have information on the differences between the requested versus actual approved credit amount.

13
**Chart 6: Share of Oil in World Imports, 1986-2007**

<!-- Start of picture text --> 14%<br>13%<br>12%<br>11%<br>10%<br>9%<br>8%<br>7%<br>6%<br>5%<br>4%<br>1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: IMF’s WEO database 27