rafmacalaba/datause-encoder-data huggingface dataset:
women who are particularly likely to experience harassment, and these women drive demand for the reserved space as well as the benefits from using it to avoid men. It is possible that they are more likely to recognize or record an incident of harassment; however, note that the questions in the rider experiment use neutral terminology to elicit incidents of harassment (Section 6.1). Regardless, these users are effectively more _vulnerable_ to harassment and more affected by its prevalance in making decisions about their commute.

# **7 Complying with Norms**

We now bring the results from the revealed preference exercise and the randomized experiment together to examine whether riders place a value on riding in the reserved space above and beyond the protection the space offers against harassment. This is relevant especially as _de facto_ compliance to the reservation rule is largely imperfect.

Figure 4 divides the sample by the extent to which the reservation rule is followed: the difference between the presence of men on the reserved versus the public space. The figure shows how both takeup of the reserved space and the impact of the random assignment to the reserved space on physical harassment vary across the distribution of compliance to the reservation rule. As the presence of men equalizes across spaces, the protective value of the reserved space against harassment vanishes. In the bottom two quintiles, i.e. 40 percent of rides, riding the reserved space does not reduce harassment because the reservation rule is not well followed. Surprisingly, however, riders’ willingness to pay to ride in the reserved space does not go to zero. This suggests that, while they value the protective nature of the reserved space, women riders also place a large positive fixed-amenity value on riding in the reserved space.

While participants’ willingness to pay correspondingly decreases with the presence of men, take-up of the reserved space under positive opportunity cost remains positive. The results in Figure 4 show that participants’ willingness to pay for the reserved space in the 27
<!-- Start of picture text --> 1<br>RS)<br>Oo<br>‘5 7<br><!-- End of picture text --> <!-- Start of picture text --> Bs<br>3<br>5 A<br>=<br>Rs<br>2<br>a 2<br><!-- End of picture text --> <!-- Start of picture text --> 0 au amigos -<br>Selection Performance Detection Attrition Reporting Sample size<br>Bias type<br>[~~] Developed [5] Developing<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>9<br>a<br>1, ee<br><!-- End of picture text --> <!-- Start of picture text --> Bs<br>‘ow<br>54<br>=<br>83<br>2<br>a2<br><!-- End of picture text --> <!-- Start of picture text --> at<br>SSS — ———SS SS SSS<br>Selection Performance Detection Attrition Reporting Sample size<br>Bias type<br>[~~] Developed [=] Developing<br><!-- End of picture text -->
# Table 5: Results by Type of Restriction

|Dep Var TFP Index|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|<br>Foreign equity restrictions|<br>-0.975*<br>(0.497)||||<br>-0.384<br>(2.152)|
|Screening and prior approval requirements||-21.64**<br>(8.706)|||-6.043<br>(23.24)|
|Rules for hiring key personnel|||-6.410<br>(4.705)||12.27<br>(17.82)|
|Other restrictions on the operations||||-19.27*<br>(11.24)|<br>-35.96<br>(30.99)|
|Policy Restrictiveness in Manufacturing FDI|-0.242|-0.206|-0.222|<br>-0.250|<br>-0.264|
||(0.762)|(0.748)|(0.723)|(0.717)|(0.726)|
|Tariffs on Inputs (Effective)|<br>-0.00713|<br>-0.00821|<br>-0.00710|<br>-0.00705|<br>-0.00724|
||(0.00501)|(0.00516)|(0.00503)|(0.00504)|(0.00520)|
|% of Output by Foreign Firms in the Sector|<br>-0.000151|<br>-0.000154|<br>-0.000236|<br>-0.000215|<br>-0.000176|
||(0.000394)|(0.000394)|(0.000404)|(0.000403)|(0.000373)|
|Output Tariffs (Effective)|<br>-0.000775|<br>-0.000366|<br>-0.00105|<br>-0.000919|<br>-0.000540|
||(0.00339)|(0.00367)|(0.00340)|(0.00342)|(0.00368)|
|Constant|<br>0.383***|<br>0.314***|<br>0.393***|<br>0.419***|<br>0.386***|
||(0.0448)|(0.0309)|(0.0624)|(0.0665)|(0.0753)|
|Year Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Firm Fixed Effects|Yes|Yes|Yes|Yes|Yes|
|Standard Errors|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|Clustered<br>ISIC2/Year|
|Observations|40,208|40,208|40,208|40,208|40,208|
|R-squared|0.029|0.029|0.029|0.030|0.030|
|Number of id|25,425|25,425|25,425|25,425|25,425|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||||||

28
Kume, H., G. Piani and C. Bráz de Souza (2003); “A política brasileira de importação no período 1987-98: descrição e avaliação,” in Corseuil, C. and H Kume (eds.) _A abertura comercial brasileira nos anos 1990: impactos sobre emprego e salário._ Rio de Janeiro: IPEA.

Levin, A., C. Lin and C. Chu (2002); “Unit Root Tests in Panel Data: Asymptotic and Finite-. Sample Properties,” _Journal of Econometrics_ , 108, 1-24.

Levy, S. (2006); “¿Productividad, Crecimiento y Pobreza en México: Qué sigue después de Progresa-Oportunidades?,” mimeo, The World Bank.

Lobato, L. (2000); “Reorganizing the Health Care System in Brazil,” in _Reshaping health care in Latin America. A Comparative Analysis of Health Care Reform in Argentina, Brazil, and Mexico_ , edited by Fleury, S., S. Belmartino and E. Baris, IDRC.

Lobato, L. and L. Butlandy (2000); “The Context and Process of Health Care Reform in Brazil,” in _Reshaping health care in Latin America. A Comparative Analysis of Health Care Reform in Argentina, Brazil, and Mexico_ , edited by Fleury, S., S. Belmartino and E. Baris, IDRC.

Lucas, R.E. (1978); “On the Size Distribution of Business Firms,” _Bell Journal of Economics_ , 9(2):508-523.

Maloney, W. F. (1999); “Does Informality Imply Segmentation in Urban Labor Markets? Evidence from Sectoral Transitions in Mexico,” _The World Bank Economic Review_ , 13(2) 275-302.

Maloney, W. F. (2001); ‘‘Self-Employment and Labor Turnover in Developing Countries: Cross-Country Evidence.’’ In S. Devarajan, F. Halsey Rogers, and L. Squire, eds., World Bank Economists Forum. Washington, D.C.: World Bank.

Maloney, W. F. (2004); “Informality revisited,” _World Development_ 32(7):1159-1178.

Martinez, J. (1999); “Brazil health briefing paper,” IHSD.

Mortensen, D. and Ch. Pissarides (1994); "Job Creation and Job Destruction in the Theory of Unemployment," _Review of Economic Studies_ , 61(3):397-415.

Mortensen, D. and Ch. Pissarides (1999a); "Job Reallocation, Employment Fluctuations and Unemployment," CEP Discussion Papers 0421, Centre for Economic Performance, LSE.

Mortensen, D. and Ch. Pissarides (1999b); "New Developments in Models of Search in the Labour Market," CEPR Discussion Papers 2053.

29
26 The third information variable that is used is a dummy variable capturing whether countries reported data on the prices of exports and imports in the International Financial Statistics25 Countries that are lax or reluctant about reporting such price data are more likely to be the same countries in which voters are less well-informed. In fact, in countries that do not report these data, newspaper independence is rated 2.9 on average, relative to 3.4 for countries that do report.

Institutional variables The Database of Political Institutions (DPI), version 2 (Beck, et al., 2000) provides useful measures of the political institutions that the foregoing discussion has identified as important. All of the political variables used below are three year averages, starting with the first year of crisis (reported by Honohan and Klingebiel), and extending back to two years prior to crisis (or the average of values in years ¢, t-1, t-2, t=first year of crisis).

The number of veto players is captured by the variable Checks2a from that data set (hereinafter, checks). This variable is built up from several other variables collected in the data set. Two of these are che legislative and executive indices of electoral competitiveness (EJEC and LIEC in DP]), scaled one to seven, that indicate the competitiveness of elections. EJEC is also used to establish the validity of the underlying assumptions in the information literature, that the effect of imperfectly informed voters depends on the existence of competitive elections.26 25 Educational attainment was also tried, but was insignificant in all specifications.

26 Where there are no elections, countries receive a one; the scores rise to seven when there are multiple candidates and multiple parties, and no single party or candidate receives more than 75 percent of the vote. If the legislative index of electoral competitiveness is less than five (where five indicates that multiple parties can legally be established, but where only one party wins any seats in the legislature), checks is one. This reflects the notion that legislatures that are not competitively elected are less likely to exercise decision making authority independent of the executive. Otherwise, coding of this variable depends on whether countries are presidential or parliamentary.

In presidential systems, checks is the sum of one (if EJEC is greater than four), one (for the president),
Figure 1 <!-- Start of picture text --> 350<br><!-- End of picture text --> <!-- Start of picture text --> PIL Cases in the Indian Supreme Court<br>Number of Cases Instituted per Year<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Year<br>No. of Cases<br><!-- End of picture text --> Figure 2 <!-- Start of picture text --> 25<br><!-- End of picture text --> <!-- Start of picture text --> Fundamental Rights cases<br>Number of Cases in Which Complainants Belong to Advantaged<br>Classes<br>25<br>20<br>15<br>10<br>5<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year<br>Advantaged Classes All<br>Number of Cases<br><!-- End of picture text --> 17
and _H i_ in the reduced form are quadratics in educational attainment and age, measures of health status of the respondent (ADL z-scores, described below), and the average educational attainment of a spouse and adult children of the household. As some older workers may find that their time is best utilized in provision of care to relatives and that this may influence employment decisions, we also include numbers of grandchildren and living parents, respectively, of the household head and spouse.

The three data sources used to examine employment outcomes in China (CHARLS 2008), Indonesia (IFLS 2007) and Korea (KLOSA 2006) provide roughly comparable information on labor supply, health status and education of both the respondent and spouse, and information on parents and children regardless of current residence in the household.<sup>24</sup> We estimate the reduced form labor supply model in each country: where labor supply,<sup>_L_</sup> _Si_<sup>, is a binary indicator of whether individualworked for one hour or</sup> more during the previous week. We expect that higher values of educational attainment of elderly,<sup>_E_</sup> _i_<sup>, will be associated with higher wealth and savings and, as leisure is a normal good,</sup> may be negatively related to elderly labor supply.<sup>25</sup> Similarly, we expect that access to own pension income, _Peni_ , and pension income of a spouse, _Pen_  _i_ , will be negatively related to employment.

The health of older workers and elderly are measured using two z-scores calculated from responses to a set of activities of daily living questions, **ADL** _i_ .<sup>26</sup> For each of the three data

> 24An important benefit of knowing the numbers of grandchildren and living parents independent of whether they are members of the household is that we are able to control for potential demand for a family care-provider without introducing biases related to endogenous household composition, as would occur if relying on information about household members alone.

> 25Of course, an individual with more education may also be able to earn significantly higher returns, and so the coefficient on education will reflect the net effect of returns and accumulated wealth on employment.

> 26Bound (1991) cautions that general health status questions are likely to be correlated with unobservable individual characteristics, and further, that they may suffer from justification bias. Several studies (e.g. Bound, 1999; Dwyer and Mitchell, 1999) have suggested that proxies constructed from ADLs do not suffer from such serious bias. Bound et al (2010) cautions that financial wealth may affect ADL outcomes, and that even proxies developed from ADLs may lead us to underestimate the negative effects of poor health on labor supply. As we do

16
olaJ 1S: l.ndle cf rdl eMlrl. In the Public Sector: Ganral Mdai.trative Scale 11975 - 100)

|**Scale**|<br>**Poet**|Jan.67|hNy71|July 75|Jan 79|Aug. 80|Jan. 82|Jan. 83|'Mv. 83|Oct. 85|July 86|
|---|---|---|---|---|---|---|---|---|---|---|---|
|53|Under-Secretar'|135|132|100|60|60|59|5i|45|29|22|
|S7|£,iatant<br>Director|137|124|100|60|62|53|46|41|27|21|
|S12|**Univ. Grad.**(batry)|121|106|100|58|63|53|46|41|29|23|
|S13|Dipim<br>Entry|119|IOS|100|59|67|57|S0|44|31|25|
|'21|lowest Pa148binned<br>rlo.es|95|99|100|71|89|89|87|83|70|58|
||Unulled<br>aon-<br>eatabliahed|**n.m.**|96|100|73|85|90|91|88|75|63|

o ss eG 1916b. Table 1.9.. and text, table 14.
# **Sources of Welfare Disparities across and within Regions of Brazil: Evidence from the 2002-03 Household Budget Survey**

Emmanuel Skoufias Roy Katayama The World Bank (MC4-415) 1818 H Street NW Washington DC, 20433 _JEL classification:_ O15, O18, R10, R23, R58, I31 _Keywords:_ Brazil, Leading and Lagging Regions, Welfare, Poverty, Oaxaca-Blinder decompositions.

_Corresponding author_ : Emmanuel Skoufias, The World Bank, 1818 H Street NW, Washington DC 20433-USA. tel: (202)458-7539. fax: (202) 522-3134. e-mail: <u>eskoufias@worldbank.org.</u> This paper is part of a regional World Bank study in the LAC region financed by the LAC Chief Economist office and the PREM Poverty anchor unit. The findings, interpretations, and conclusions in this paper are entirely those of the authors and they do not necessarily reflect the view of the World Bank.
- start dates (i.e. date when the business was started) were identical across the two waves; or

- the field of the business was the same across the two waves, and the start date of the business reported in the follow-up round was a date preceding the interview date of the baseline wave; or

- at least one of the business owners (person(s) indicated as being responsible for the business) was the same across the two waves, and the start date of the business reported in the follow-up round was a date preceding the interview date of the baseline wave.

We coded the business reported at baseline as closed, if:

- no business was reported in a subsequent round.

Otherwise, we coded survival/closure as missing.

We coded a new firm start, if:

- the start date of the business reported in the follow-up round was a date after the interview date of the baseline wave.

We coded the variable _newfirmstart_ to zero, if:

- the business reported in the baseline round survived; or

- no business was reported in the follow-up round and the business of the baseline round was coded as closed; or

- the start date of the business reported in the follow-up round was a date preceding the interview date of the baseline wave and no business was reported in the baseline wave.

Otherwise, the variable _newfirmstart_ was coded as missing.

We used these three types of business characteristics (start date / age of the business, ownership, and business sector) together, because start dates turned out not to be very accurate, and it is possible that ownership of a household business changes over time. Even so, there remained a number of inconsistencies and cases which could not be matched, so that in a final step, we inspected the characteristics of these businesses in all three consecutive survey waves visually and coded survival on a case-by-case basis, again, taking into account information on firm age, ownership and the business sector reported in the three survey waves. For these remaining observations, we decided to exclude firms from the sample if information on firm age was missing, since this information is important in determining _survival_ and _newfirmstart_ .

Given that the non-farm business module of the IFLS allowed for more than one owner to be reported, and ownership could change from one round to another, we also had to decide on which of the owners to include in case we identified multiple owners for a business, as we were interested in assessing also owner characteristics in our study. For firms for which we had identified more than one owner, we decided to include the owner, who was reported as the owner in all of the waves in which we observed the firm. If this was the case for both owners, we included the owner,

- who had the lowest household roster number (this chose the household head as the owner if he or she was among the owners), if no other business was reported for at least one of the owners over the whole observation period; or

- who had already been identified as the owner of a different business, observed during one or more different waves over the whole observation period;

40
**Table 4. The macroeconomic effects of a 5 percent climate-induced shock to capital stock with reconstruction financed by public debt issuance**

||Year 0|Year 1|Year 2|Year 3|Year 4|Year 5|Year 10|Year 20|Year 30|
|---|---|---|---|---|---|---|---|---|---|
|**Economic Activity**||||||||||
|<br>GDP|0.0|-0.2|-0.0|0.2|0.4|0.6|-1.3|-1.0|-0.3|
|Private consumption|0.0|-1.6|-1.3|-0.7|-0.2|0.4|-0.2|-1.0|-0.5|
|<br>Government consumption|0.0|-0.2|-0.5|-0.7|-0.7|-0.8|-1.7|-1.7|-0.5|
|Total investment|0.0|3.6|3.8|3.9|4.0|4.3|-1.3|0.2|0.2|
|Exports|0.0|-0.3|-0.7|-1.2|-1.7|-2.2|-2.3|-0.6|0.1|
|Imports|0.0|-0.1|0.3|1.0|1.6|2.4|1.9|0.8|-0.1|
|Current account balance<br>(pp of GDP)|0.0|0.0|-0.0|-0.1|-0.1|-0.2|-0.3|-0.4|-0.1|
|**Prices**||||||||||
|Producer prices|0.0|0.1|0.3|0.6|0.9|1.1|0.7|-0.1|-0.2|
|Consumer prices|0.0|0.1|0.2|0.3|0.4|0.4|-0.1|-0.2|-0.1|
|Real exchange rate|0.0|0.3|0.9|1.5|2.2|2.8|3.0|0.8|-0.2|
|Import prices|0.0|-0.1|-0.3|-0.6|-0.9|-1.3|-2.4|-0.9|0.0|
|Export prices|0.0|0.2|0.4|0.6|0.9|1.0|0.5|-0.1|-0.1|
|<br>**Fiscal**||||||||||
|Revenues|0.0|-0.1|0.3|0.8|1.3|1.8|-0.6|-1.2|-0.5|
|Expenditures|0.0|13.3|14.5|15.7|16.9|18.0|3.4|1.1|0.9|
|<br>Deficit (pp of GDP)|0.0|-2.0|-2.1|-2.2|-2.2|-2.3|-0.6|-0.3|-0.2|
|<br>Debt (pp of GDP)|0.0|2.0|3.7|5.2|6.7|8.1|9.1|5.9|3.4|
|<br>**Supply Side**||||||||||
|<br>Potential GDP|0.0|-3.0|-2.5|-2.0|-1.7|-1.2|-0.5|-0.4|-0.1|
|Capital stock|-5.0|-4.2|-3.5|-2.8|-2.1|-1.5|-1.0|-0.6|-0.2|

_Sources:_ MFMod, World Bank; Authors’ calculations. _Note:_ Table shows deviations as percent from a baseline scenario.

25
# **Housing expenditure**

_Housing expenditures._ In the CMte d'Ivoire surveys, as in other surveys, statistical techniques are used to impute housing expenditures for the households that do not pay rent. Using the sample of households that do pay rent, regression analysis was used to estimate a hedonic rental equations relating the amount of rent paid to different household characteristics, with regional dummy variables added to capture spatial variation in rental prices. The small sample size was a problem in 1995; only 9 households in West Forest and 12 households in the Savannah rented lodging in **1995.22** In the initial processing of the data, housing expenditures were estimated separated for the 1993 and 1995 using different specifications of the hedonic rental equation. Real housing expenditures increased substantially in rural areas. The increase in the Savannah of 47 percent was particularly suspect. There is little reason to think the imputed value of housing would have increased significantly, especially since the housing costs rose less rapidly than other goods after the devaluation.

Another approach to deriving imputed housing costs is to pool the 1993 and 1995 data and use the same equation to estimate housing costs, with dummy variables for each region allowed to vary between 1993 and 1995. This improves comparability but does not eliminate entirely the problem caused by the small sample size in 1995. With the pooled sample, real housing expenditures fall in all regions but the Savannah, where they remain unchanged. The two methods yield different estimations of the headcount index of poverty in 1995 (table 23). While recognizing that housing expenditures in the Savannah may be overestimated even with the pooled estimate, it is the better of the two estimates.

Table 23 Headcount Index of Poverty Using Different Estimates of Housing **Expenditure**

|_(poverty line_|_= 75,000 C_|_FAF in 1985_|_CFAF)_||
|---|---|---|---|---|
||_Separate E_<br>_1993 a_|_quations for_<br>_nd 1995_|_Pooled E_<br>_1993 a_|_quation for_<br>_nd 1995_|
||_1993_|_1995_|_1993_|_1995_|
|Abidjan|.048|.183|.051|.202|
|Other Cities|.313|.285|.312|.286|
|East Forest|.373|.396|.389|.410|
|West Forest|.356|.460|.382|.501|
|Savannah|.485|.417|.494|.494|
|All|.313|.339|.323|.386|

**22** See Appendix A for a discussion of the estimation of housing expenditure.

25
assumptions. This is important because the quality of the predictions is directly linked to the quality of the underlying welfare model used under this approach.

# **3. Data and empirical approach**

This paper uses harmonized microdata for several countries to validate and estimate economic mobility using LASSO-PMM. We first employ 36 panels from four selected countries to validate the method, performing a battery of sensitivity and robustness exercises in a controlled environment where we can observe household income in both periods. Then, we proceed to apply the method to harmonized cross-section data from 43 countries around the world, located in six broad regions.

# _3.1 Validation with harmonized panel data_

For the validation of the LASSO-PMM method, we employ panel data from four Latin American countries: Argentina, Chile, Peru, and Nicaragua. These data are part of the SEDLAC and LABLAC projects.<sup>14</sup> The selection of these countries depends mainly on the availability of comparable panel data over time, but we highlight that these sources contain information for small and large economies; lower-middle, upper-middle, and high income Latin-American countries, and encompass short and long panels.

We construct 36 different panels for the four selected countries, which vary in the length of time in which households are followed. There are 23 one-year panels, 7 two-year panels, 5 three-year panels, and one four-year panel. To the best of our knowledge, this is the first paper that

> 14 The SEDLAC project consists of more than 400 household surveys in more than 25 LAC countries to provide statistics on poverty and other distributional and social variables. The LABLAC project is a complementary source to SEDLAC, which monitors trends in labor market variables with the highest frequency information available in 13 LAC countries. Both projects are a joint venture between the World Bank and and the Center for Distributive, Labor, and Social Studies (CEDLAS, for its acronym in Spanish) at the _Universidad Nacional de La Plata_ in Argentina. See Bourguignon (2015) for a detailed description of the SEDLAC project. Data for Argentina, Peru, and Nicaragua are part of the SEDLAC project, while data for Chile are obtained from LABLAC project.

15