rafmacalaba/datause-encoder-data huggingface dataset:
## **Platform observations**

We generate a measure of the typical conditions a rider would face on a trip at a given location and time, independent of their choice of space. Through the app we recruited and tasked men commuters to collect data on crowding and enforcement of the gender reservation policy from the platform across the entire SuperVia network. For this purpose, we divide all SuperVia lines into segments of several stations and designate half-hour blocks of the rush hour periods (6-9AM, 5-8PM). We then offered tasks such that, over a period of about three months, our platform observers collected at least three observations from each such (halfhour _×_ line segment) combination in the direction of rush-hour traffic (i.e., in-bound in the morning, out-bound in the evening). Each task specifies where and when to collect the data. Observers stand on the platform at the designated time and place and record the percentage of male riders in both public and reserved spaces as well as the share of commuters who can sit. We impute the mean observation for each of these station-time period bins for each ride in our data.

We take two additional steps to validate these data. First, we confirm that data collected by the platform observation team are strongly correlated with what riders themselves observe on their rides (Table A3). Second, we obtain administrative data from the SuperVia as an alternative measure of crowding.<sup>15</sup> Figure A2 shows crowding reports from our platform observations are highly correlated with SuperVia administrative records.

# **3.2 Social Norm Survey and Implicit Association Tests**

To measure the attitudes women face while traveling in the public space, we administer a social norm survey and IATs on a random sample of men and women commuters on the train platform. The survey includes questions on commuting behavior, stated preferences and willingness to pay to use the reserved space, perceptions about harassment and attitudes

> 15The estimates are generated by SuperVia transport planners, based on simulations and data from the station fare gates.

12
Saharan Africa do not support the notion that fever and poverty are closely related. There is some support for the idea that levels of wealth in the community might affect the incidence of fever in Eastern and Southern Africa. The results suggest that poverty affects the type of treatment sought as a result of an episode of fever, although the patterns differ between the two broad regions analyzed here. Moreover, the results suggest that it is levels of wealth in the community in which a household lives that influences treatment the most, rather that wealth of the household itself.

# References

The word "processed" describes informally reproduced works that may not be commonly available through library systems.

- Ahorlu, Collins K., Samuel K. Dunyo, Edwin A. Afari, Kwadwo A. Koram and Francis Nkrumah. 1997. "Malaria-Related Beliefs and Behaviour in Southern Ghana: Implications for Treatment, Prevention, and Control." _Tropical Medicine and International Health 2(5)._

- Bonilla-Chacin, Maria, and Jeffrey S. Hammer, 1999. "Life and Death among the Poorest." The Johns Hopkins University and Development Research Group, World Bank. Processed.

- Brinkmann, Uwe, and Agnes Brinkmann. 1991. "Malaria and Health in Africa: The Present Situation and Epidemiological Trends." _Tropical Medicine and Parasitology_ 42(3).

- Butler, J. S., Richard V. Burkhauser, Jean M. Mitchell, and Theodore P. Pincus. 1987. "Measurement Error in Self-Reported Health Variables." _The Review of Economics and Statistics 69(4)._

- Deaton, Angus. 1997. _The analysis of household surveys: a microeconometric approach to development policy._ Baltimore, Md. : Johns Hopkins University Press.

- Deolalikar, Anil. 1998. "The Demand for Health Services in a Developing Country: The Role of Prices, Service Quality and Reporting of Illness." In Aman Ullah and David E. A. Giles eds. _,Handbook of Applied Economic Statistics._ New York: Michael Dekker, Inc.

- Dunyo, Samuel K., Kwadwo A. Koram, and Francis K. Nkrumah, 1997. "Correspondence: Fever in Africa and WHO recommendation." _The Lancet_ 350(9090): 1549.

- Einterz, Ellen M., and Myra E. Bates, 1997. "Fever in Africa: do patients know when they are hot?" _The Lancet_ 350(9080): 781.

- Filmer, Deon, and Lant Pritchett, 2001. "Estimating wealth effects without income of expenditure data - or tears: Educational enrollment in India." _Demography_ 38(1).

- Filmer, Deon, and Lant Pritchett, 1999. "The Effect of Household Wealth on Educational Attainment: Evidence from 35 Countries." _Population and Development Review_ 25(1).

_32_
# **Table 1: Descriptive Statistics**

||1993<br>s.e.|1998|s.e.|
|---|---|---|---|
|Nationally Representative Sample||||
|Sample Size|4,799|5,999||
|Population|12,916,989|16,128,3|13|
|Community Observations|150|194||
|In Panel Community*|0.98<br>0.01|0.83|0.03|
|Urban|0.23<br>0.03|0.24|0.03|
|Rice Price|2.59<br>0.03|3.31|0.03|
|Rice Budget Share|0.28<br>0.01|0.23|0.01|
|% Producing Rice|0.70<br>0.03|0.59|0.03|
|Rural, Panel Communes||||
|Sample Size|3,635|3,639||
|Community Observations|115|115||
|Rice Price|2.54<br>0.03|3.32|0.03|
|Rice Budget Share|0.31<br>0.01|0.26|0.01|
|% ProducingRice|0.85<br>0.02|0.79|0.02|

The full sample means are weighted to be nationally representative. The rural panel commune means are not weighted. Standard errors are corrected for sample design. Because of the increase in sample size overall between 1993 and 1998, four additional rural households were added within panel communes (Basic Information 2000). There is not a panel of 3635 households, because attriters were replaced with randomly selected households from within the same commune (using the original household list). *83 percent of households in the 1998 nationally representative survey are in panel communes.
Generalized Additive Models for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005). This framework not only includes a huge variety of potential response distributions, but also allows to link all parameters of those distributions to explanatory variables. This allows for a straightforward way to model heteroscedasticity and skewness simultaneously in one coherent model. Moreover, nonlinear and spatial effects can be integrated into the GAMLSS framework. Although model choice is also a challenging task, it might be a very interesting direction for future research to combine GAMLSS and existing small area approaches, irrespective of the time span between census and survey collection.

17
Charnavoki, V., and Dolado. J. J., (2014), “The Effects of Global Shocks on Small Commodity- Exporting Economies: Lessons from Canada”, American Economic Journal: Macroeconomics Vol. 6, No. 2, pp. 207-37.

Chatterjee, A., (2016), “Globalization and monetary policy comovement: International evidence”, Journal of International Money and Finance, 68, pp.181-202.

Christiano, L. J., Eichenbaum, M., and Evans, C. L., (1999), “Monetary policy shocks: What have we learned and to what end?” Handbook of Macroeconomics, Vol. 1, pp. 65– 148.

Ciccarelli, M., and Mojon, B., (2010), “Global Inflation”, The Review of Economics and Statistics, Vol. 92, No. 3, pp. 524-35.

CGFS (Committee on the Global Financial System), (2019), “Unconventional Monetary Policy Tools: A Cross-Country Analysis,” CGFS Papers No. 63, October. Annex 2.

Claessens, S., and Forbes, K., (2001), “International Financial Contagion: An Overview of the Issues and the Book,” in S. Claessens and K. Forbes, eds., International Financial Contagion, Boston: Kluwer Academic Press.

Claessens, S., and Kose, M. A, (2018), “Frontiers of Macro-Financial Linkages,” BIS Paper No. 95, Bank for International Settlements Publications.

Claessens, Stijn, Kose, M. A, and Terrones, M., (2009), “What Happens During Recessions Crunches and Busts?” Economic Policy. Vol. 24, pp 653-700.

Claessens, S., Kose, M. A, and Terrones, M., (2011), “Financial Cycles: What? How? When?”, NBER International Seminar on Economics. Vol. 11, Available at https://www.journals.uchicago.edu/doi/10.1086/658308 Claessens, S., Kose, M. A, and Terrones, M., (2012), “How Do Business and Financial Cycles Interact?” Journal of International Economics. Vol. 87, pp 178-90.

Clarida, R., Gali, J., and Gertler, M., (1999), “The Science of Monetary Policy: A New Keynesian Perspective”, Journal of Economic Literature, Vol. 27, pp. 1661-707.

Cogley, T., and Sargent, T. J., (2005), “Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US”, Review of Economic Dynamics, Vol. 8, No. 2, pp. 262-302.

Cook, T., and Hahn, T., (1989), “The Effect of Changes in the Federal Funds Rate Target on Market Interest Rates in the 1970s”, Journal of Monetary Economics 24, pp. 331-351.

Crucini, M. J., Kose, M. A., and Otrok, C., (2011), “What are the driving forces of international business cycles?”, Review of Economic Dynamics, Vol. 14, No. 2, pp. 15675.

70
40

||Fu|llsample|||SHI = 1|||SHI =0||
|---|---|---|---|---|---|---|---|---|---|
||Mean|SD|Obs|Mean|SD|Obs|Mean|SD|Obs|
|Diphteria incidence rate|1.31|5.19|415|0.67|2.94|200|1.93|6.61|213|
|Tetanus incidence rate|0.09|0.11|407|0.11|0.12|196|0.07|0.09|210|
|Cancer incidence rate|245.01|147.37|335|318.21|159.21|138|193.72|113.47|197|
|Tuberculosis immunization rate|92.70|11.32|420|93.10|10.39|201|93.24|9.93|213|
|DPT immunization rate|91.59|9.75|420|91.90|7.67|201|91.87|10.31|213|
|Measles immunization rate|91.22|9.80|420|91.57|8.57|201|91.70|9.45|213|
|Polio immunization rate|92.06|8.81|420|91.94|7.76|201|92.82|8.69|213|
|Mumps immunization rate|82.25|22.69|227|88.63|16.39|127|74.59|26.50|99|
|Rubella immunization rate|88.00|19.23|190|90.05|15.52|124|85.29|22.78|65|
|Length of stay (total)|12.75|3.04|398|11.23|2.93|193|14.19|2.39|204|
|Length of stay (acute care)|10.87|2.71|267|9.40|2.37|150|12.78|1.79|116|
|Bed occupancy rate|72.80|14.78|278|74.85|9.82|149|70.36|18.78|128|
|Hospital beds|8.10|2.91|342|6.72|2.59|155|9.28|2.64|186|
|In-patient admissions|16.18|5.99|397|15.51|6.02|194|16.86|5.90|202|
|Acute care admissions|15.16|5.39|277|15.17|5.5|150|15.20|5.26|126|
|Hospital discharges - infectious|826.10|444.11|353|658.64|352.78|170|981.66|464.08|183|
|Hosp discharges - cancers|809.34|588.59|346|1068.85|643.08|163|578.19|417.81|183|
|Hosp discharges - heart|669.00|468.43|343|684.00|425.49|163|655.41|504.97|180|
|Hosp discharges - circulatory|1904.45|1099.73|354|2092.45|1152.72|170|1730.76|1021.10|184|
|Hosp discharges - cerebrov|339.11|240.51|350|394.82|243.94|168|287.68|226.04|182|
|Hosp discharges - respiratory|2088.68|1014.24|351|1737.75|778.42|170|2418.28|1098.08|181|
|Hosp discharges - digestive|1623.59|626.99|354|1544.75|579.86|170|1696.44|660.83|184|
|Hosp discharges - musculo|776.92|508.15|354|809.35|536.77|170|746.96|479.71|184|
|SDR appendicitis|0.30|0.18|347|0.23|0.14|158|0.36|0.19|188|
|SDR hernia & intestinal|2.23|0.75|350|2.34|0.74|161|2.14|0.74|188|
|SDR adverse effects|0.20|0.33|183|0.19|0.33|116|0.19|0.28|66|
|Surgical infection rate|1.09|1.22|74|0.92|0.82|42|1.30|1.59|32|

_Note:_ Mean, standard deviation (SD) and number of observations (Obs) for the full sample and for the sub-samples of observations with the SHI dummy equals to one (SHI=1) and zero (SHI=0).
**Product Quality, Productive Efficiency, and International Technology Diffusion:**

**Evidence from Plant-Level Panel Data**

Aart Kraay The World Bank Isidro Soloaga The World Bank James Tybout Pennsylvania State University and NBER This paper is part of the World Bank-funded research project "Micro Foundations of International Technology Diffusion." We thank Wolfgang Keller, Jim Levinsohn, Marc Melitz and Mark Roberts for useful discussions and absolve them of blame for methodological flaws that remain.
different calls for funds and humanitarian organizations remain under-funded. In these cases, targeting is an obliged choice and optimal and efficient targeting can make the difference between life and death for some of the displaced populations. This justifies refining targeting techniques making the most of the available technology.

In addition to being technically sound, targeting requires tools that make choice and implementation relatively simple for policy makers and field staff. One cannot expect all humanitarian staff working on the field to be knowledgeable in econometrics or statistics techniques. Hence the need for relatively simple visual devices that can be used for making normative targeting choices based on positive criteria without necessarily be cognizant of the econometrics and statistics that lie behind these devices.

This paper has proposed the use of ROC curves and related indices to refine targeting when budgets are constrained and has developed relatively simple graphs that can be used by policy makers to make decisions on coverage, budgets and poverty lines when targeting is based on welfare criteria. We show existing trade-offs between optimal coverage and leakage rates, the optimal use of existing budgets and small adjustments that can result in large gains in terms of poverty reduction. The methods proposed can be applied to food voucher programs as illustrated in this paper or to any other cash program. We also showed that such methods can be applied using existing data collected by the UNHCR in the framework of existing programs such as the WFP food voucher program. In other words, these are tools that can be readily applied and do not necessarily require the collection of new data or the administration of special programs.

It is also clear that the method proposed cannot be applied in all contexts. The Jordan example provided in this paper relies on a set of data (proGres and home visits data) that is atypical in humanitarian contexts. While proGres data are available in most countries where the UNHCR operates, the quality of these data is very variable (the Jordan data are known to be among the best quality data available). Also, home visits data that contain information on income or expenditure are the exception rather than the rule in humanitarian contexts. Therefore, at present, the replicability of the method proposed elsewhere is limited to selected areas and countries. However, all humanitarian operations with budget limitations that use cash or food vouchers as a form of social protection face the same targeting challenges described in this paper. In the absence of quality data on income or consumption, these operations have to rely on alternative and less accurate targeting criteria the outcomes of which (in terms of poverty, coverage and leakage) are non-measurable. This paper has implicitly shown that the collection of income or 20
such as Taiwan and South Korea, have achieved a relatively equal distribution of income when autocratic regimes prevailed, and, ironically, in the post communist countries of East Europe, inequality seems to have actually increased during the course of democratization. These examples call for a more formal empirical look at the coevolution of democratization and inequality across countries.

Indeed, the empirical research of the subject has been vibrant in recent years. The issue being of an inherently interdisciplinary nature, has been addressed by sociologists, political scientists, and economists. The early research conducted in the 60's and the 70's made evident the methodological issues that have to be faced by researchers in the field, such as measurement problems, sample composition, model specification and others. More recently, measurement of some key variables (particularly of inequality, but also of democracy) has been significantly refined, which has allowed for a better reassessment of the evidence.

Our main purpose is to briefly review the theories that have been developed on the relationship between democracy and inequality and, in particular, to summarize the results of the empirical research. Since the early empirical studies of the 60's and the 70's, have been comprehensively surveyed elsewhere (see Sirowy and Inkeles, 1990) the present coverage focuses on the more recent evidence. We also make an attempt to more explicitly relate the findings to existing political economic models.

The survey proceeds in the following order. The next section sketches some theories on the relationship between democracy and inequality. Section 3 then discusses some methodological issues associated with empirical tests of these theories. Section 4 reviews the empirical evidence, while section _5_ pays closer attention to the recent transition process in the post communist countries of East Europe. Finally, concluding remarks are presented in section 6.

4
in the United States under Regulation S and are not SEC-registered. The implications of SEC registration for bond spreads are therefore explored in the empirical analysis.

# **Table 4: Ratings conversion from letter to numeric scale**

|Standard &Poor’sRating|Numeric Grade|
|---|---|
|AAA<br>|1<br>|
|AA+<br>|2<br>|
|AA|3<br>|
|AA-|4|
|A+|5|
|A<br>|6|
|A-|7|
|BBB+|8|
|BBB|9|
|BBB-|10|
|BB+|11|
|BB|12<br>|
|BB-|13|
|B+|14|
|B|15|
|B-|16|
|CCC+<br>|17<br>|
|CCC|18|
|CCC-|19|
|CC|20|
|C|21|

Source: Ratha, De and Mohapatra (2011) 11
having better educated parents (i.e., more than primary or middle schooling), especially the educated nonfarmer parents, are driven by genetic correlations. In contrast, the estimated positive effects for the nonfarmer group within the low educated (primary or less) households by OLS and matching estimators may not reflect any causal impact. The evidence from Lewbel heteroskedasticity based identification confirms that the effects of family background has become much stronger for the sons in the reform era.

# **(2) Related Literature**

This paper contributes to a small but active literature on intergenerational economic mobility in developing countries. Recent contributions in this literature include, among others, Hertz et al. (2007), Binder and Woodruff (2002), Behrman et. al. (2001), Duncan (1996), Lillard and Willis (1995), Lam and Schoeni (1993), Asadullah (2012), Emran and Shilpi (2011, 2012), Bossuroy and Cogneau (2013), Maitra and Sharma (2010)). However, none of the existing studies exploit both parents’ education and occupation to construct a broader measure of a household’s economic status, and thus do not analyze the implications of the expansion of non-farm occupations for educational mobility of children in rural areas where most of the poor households live in developing countries.

Our analysis is obviously closely related to multiple strands of literature on China: educational inequality, economic mobility, rural nonfarm economy and the interactions among them. A large part of the literature on inequality in China focuses on spatial differences between coastal and interior regions, and across rural and urban areas (see, among others, Fleisher et al. (2010), Kanbur and Zhang (2005), Chen and Fleisher (1996), Knight and Song (1999), Park (2008)). For insightful analysis of inequality in post-reform period see, among others, Shi et al. (2013), Benjamin et al. (2008), Gustafsson et al. (2008), Khan et al. (1999), Ravallion and Chen (1999), Griffin and Zhao (1993). Benjamin 6
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|5|**29.98**|5.13|70.02|3.65|
|---|---|---|---|---|
|6|**26.39**|3.59|73.61|3.41|
|7|**23.38**|3.01|76.62|3.32|
|8|**20.97 **|2.41|79.03|3.26|
|9|**18.84 **|2.13|81.16|3.23|
|10|**16.93**|1.91|83.07|3.14|
|11|**15.46**|1.47|84.54|2.92|
|12|**14.21**|1.24|85.79|2.81|

**_Fig 6. Scree plot and explained information in the trade matrix._**

<!-- Start of picture text --> 35 100<br>C 90<br>30<br>o C<br>80<br>n 25 70 u<br>t m<br>60<br>r 20 u component<br>i n 50 l<br>noise<br>b 15 40 a<br>cumulated<br>u t<br>10 30<br>t e<br>20<br>i d<br>5<br>10<br>o<br>0 0<br>1 2 3 4 5 6 7 8 9 10 11 12<br><!-- End of picture text --> In this case, the first two dimensions explain about half of the information in the trade matrix. However, there is no sharp drop in the contribution for the following dimensions. The determination of the bottom of the scree is somewhat arbitrary, in between the third and sixth dimensions. We retain five dimensions, as from the sixth dimension onward the contribution of the component is not distinguishable from the “noise” value.

# **_Latent Variables and their Interpretation_**

The latent variables and factors for countries and products are available in the Excel files available in the online Annex. Unsurprisingly, compared with known variables (section 5), the latent variables perform much better at explaining the export matrix. Unfortunately, the interpretation of the variables is not totally obvious. The correlation between the known and latent variables is limited. This should be expected since section 5 showed that none of the typical variables explains much of the information structure of the actual matrix (10-20%).

The level of development, logistics and connectivity variables are better associated with the first two factors, as well as the variables measuring government effectiveness (the latter more for the second factor). However, the Doing Business variables do not appear as important to explaining the structure of the export matrix. This result probably stems from the fact that trade is carried 22
-24-

the inclusion of an exit option and a new money option in a menu of options fram which the private creditors freely choose can eliminate the wealth transfers due to the debt reduction. It is sufficient to set the buyback price equal to the ex ante status quo price. Any new money call will do the job. In equilibrium, creditors will provide enough new money to stabilize the ex post debt price at a level that leave then indifferent to the exit option. The important effect of increasing the new money call is to reduce the cost of the menu and the extent of debt reduction achieved.

The menu approach we describe does not require assignments of particular choice form the menu to each lenders. Rather, it implements debt reduction through a price system, allowing different creditors to select different portfolio in equilibrium from a common set of options. However, with heterogeneous banks, some transfer of resources will occur when participation in the debt reduction plan is voluntary and the buyback price will generally need to be above the pre-buyback price.

To illustrate some of our results, we analyze the recent Mexican debt agreement. We show how to read through the complex financial acrobatics to estimate the extent of net debt reduction achieved. We show that the funds provided by IFIs benefited both Mexico and its creditors. Mexico retained about 62 percent of that transfer, and the banks 34 percent.

When creditors are heterogeneous and possess private information about the value of debt reduction to them, a mechanism is needed to elicit that information. Important topics for future research include analyses of how a menu can be combined with an auction over new money or exit instruments to induce revelation, and of the efficiency of various possible implementation mechanisms in the presence of market distortions and with heterogeneous creditors.