rafmacalaba/datause-encoder-data huggingface dataset:
Buses, houses or cash? Socio-economic, Spatial and Environmental Consequences of Reforming Public Transport Subsidies in Buenos Aires Paolo Avner<sup>(a,b,c)</sup> , Shomik Raj Mehndiratta<sup>(a)</sup> , Vincent Viguié<sup>(b)</sup> , Stephane Hallegatte<sup>(a)1</sup> (a) The World Bank, Washington DC, USA (b) Center for International Research on the Environment and Development (CIRED) (c) Association pour la Promotion de la Recherche sur l’Economie du Carbone (APREC) JEL codes: H21, H23, O18, R14, R48, R52, C63 Keywords: Transit subsidy, Lump sum transfers, Construction subsidies, Land use – transport model, Urban form, Household welfare 1 The authors would like to thank Cynthia Goytia and Guadalupe Dorna for their time, patience and fruitful discussions as well as for accepting to share precious data and insights. Andrés Gartner was a great guide of Buenos Aires, may he be thanked for his insights as well. The crucial employment and household location data was shared by German Freiberg and Wagner Colombini Martins, we are very grateful to them. We also thank Camila Rodriguez and Santiago Ezequiel Arias for the information they provided us with on the history of subsidies in Buenos Aires and Tatiana Peralta-Quiròz for the GTFS data. Harris Selod, Daniel Rodriguez, Augustin Maria, Bernardo Alvim, Nancy Lozano Gracia as well as participants in the 2015 NARSC conference provided excellent comments on previous drafts of this paper.
The template should be completed for each public operator (whether an SOE, parastatal, special fund, or PPP/utility), so as to cover each of the institutions for which quantitative data will be collected. Where one enterprise covers more than one subsector (for example, electricity and water supply), the template should be completed for each subsector, if possible.

Sources of information include interviews with officials (ministry of finance, line ministries, regulators) and with providers (state-owned enterprises, parastatals, special funds).

This guide covers only institutional development indicators classified as _governance variables_ collected at the utility or operator level following institutional data template 7. Template 7 has to be filled for each operator for each of the infrastructure sectors covered (template 1).

When filling template 7, the column for comments should not be overlooked. Even when a clear answer such as ―yes‖ or ―no‖ can be provided, any areas of doubt or ambiguity should always be recorded in the comments column. And in the case of the option ―Other,‖ the comments column needs to be filled in with any necessary explanations and the name of the operator/institution.

# **Governance of public operators (template 7)**

There are 33 governance variables that are laid out in the institutional data template 7, which has been adapted from Vagliasindi (2009). The data are organized along five dimensions: autonomy, transparency, accountability, tools, and universal service obligations.

Governance variables refer to the implementation of (i) measures inside a given enterprise (such as strengthening shareholder voice and supervision, board and management autonomy, and mechanisms for accounting and disclosure) and (ii) measures aimed at improving the external environment in which the enterprise operates (including outsourcing to the private sector and introducing discipline in a competitive labor and capital market).

Governance variables should be collected both from sector regulators and from the central government entity (line ministry), against which data from the given SOE can be verified.

30
|Herfindahl-Hirschman<br>Index (HHI)|-11.004|-11.442|-4.669|-5.691|-6.664*|-6.700*|
|---|---|---|---|---|---|---|
||(6.920)|(7.321)|(4.247)|(4.356)|(3.627)|(3.655)|
|Age of Firm (logs)|-0.874**|-0.842*|-0.164|-0.151|-0.864**|-0.870**|
||(0.437)|(0.435)|(0.436)|(0.433)|(0.361)|(0.362)|
|Foreign Ownership|2.175*|2.309*|1.698|1.707|0.071|0.055|
|(proportion)|||||||
||(1.204)|(1.193)|(1.118)|(1.117)|(0.959)|(0.960)|
|Female Ownership<br>Y:1 N:0|5.267***|5.206***|5.457***|5.443***|5.945***|5.937***|
||(0.771)|(0.776)|(0.720)|(0.717)|(0.650)|(0.652)|
|Skills Obstacle (0-4)|0.398|0.405|0.502|0.498|0.658**|0.657**|
||(0.387)|(0.386)|(0.349)|(0.348)|(0.269)|(0.269)|
|How Much Of An<br>Obstacle: Transport?|-0.254|-0.251|-0.051|-0.040|-0.013|-0.009|
||(0.300)|(0.299)|(0.274)|(0.276)|(0.209)|(0.209)|
|Firm Purchased Fixed<br>Assets Y:1 N:0|-0.520|-0.482|-0.650|-0.619|-1.046*|-1.033*|
||(0.819)|(0.818)|(0.671)|(0.673)|(0.557)|(0.558)|
|Power Outages (days)|-0.205*|-0.192*|-0.074|-0.072|-0.153**|-0.156**|
||(0.105)|(0.106)|(0.082)|(0.083)|(0.068)|(0.068)|
|Crime Losses Y:1 N:0|-1.432|-1.378|-1.285*|-1.299*|-0.670|-0.679|
||(0.910)|(0.908)|(0.733)|(0.734)|(0.668)|(0.668)|
|Firm Inspected Y:1|-0.564|-0.552|-0.190|-0.188|-0.875|-0.879|
|N:0|||||||
||(0.849)|(0.850)|(0.754)|(0.752)|(0.605)|(0.605)|
|How Much Of An<br>Obstacle: Tax Rates|-0.415|-0.426|-0.448|-0.438|-0.254|-0.252|
||(0.321)|(0.322)|(0.276)|(0.275)|(0.229)|(0.229)|
|How Much Of An<br>Obstacle: Labor|-0.193|-0.173|-0.082|-0.076|0.041|0.051|
|Regulations?|||||||
||(0.401)|(0.399)|(0.360)|(0.359)|(0.300)|(0.300)|
|GDP per capita (logs)|-38.168***|-42.889***|-20.342**|-21.450***|6.526|5.296|
||(9.297)|(10.036)|(8.271)|(8.223)|(5.760)|(5.807)|
|Population (logs)|-46.729|-57.415|-100.948***|-94.482***|-37.758***|-37.641***|
||(40.569)|(40.654)|(36.755)|(36.360)|(10.909)|(10.849)|
|Urbanization (%)|-1.699|-1.623|-1.582**|-1.486**|1.709***|1.723***|
||(1.360)|(1.355)|(0.745)|(0.736)|(0.423)|(0.427)|
|WBL Index|-5.633|-0.854|17.887|17.764|16.988*|17.157**|
||(12.997)|(13.437)|(11.050)|(10.931)|(8.755)|(8.677)|
|Women can work the<br>same night hours as<br>men|-26.481***|-24.642***|1.877|1.469|2.962|2.967|

48
# _Economic model_

The underlying economic model used in the analysis will simply follow Mincer’s (1974) human capital earnings function extended to control for a number of other variables that relate to location. In particular, we apply a semi-logarithmic framework that has the form: ln yi = φ(xi, zi) + ui (1) where ln yi is the log of earnings or wages for an individual, i; xi is a measure of a number of personal characteristics including human capital variables, etc.; and zi represents location specific variables. The functional form is left unspecified in equation (1). The empirical work makes extensive use of dummy variables in order to catch nonlinearities in returns to years of schooling, tenure, and other quantitative variables. The last component, ui, is a random disturbance term that captures unobserved characteristics.

# _Quantile regressions_

Labor market studies usually make use of conditional mean regression estimators, such as OLS. This technique is subject to criticism because of several, usually, heroic assumptions underlying the approach. One is the assumption of homoskedasticity in the distribution of error terms. If the sample is not completely homogenous, this approach, by forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information.

The method applied in this paper is quantile regression. The idea is that one can choose any quantile and thus obtain many different parameter estimates on the same variable. In this manner, the entire conditional distribution can be explored. By testing, whether coefficients for a given variable across different quantiles are significantly different, one implicitly also tests for conditional heteroskedasticity across the wage distribution. This is particularly interesting for developing countries such as Argentina where wage disparities are huge and returns to, for example, human capital may vary across the distribution.

The method has many other virtues apart from being robust to heteroskedasticity. When the error term is nonnormal, for instance, quantile regression estimators may be more efficient than least square estimators. Furthermore, since the quantile regression objective function is a weighted sum of absolute deviations, one obtains a robust measure of location in the distribution and, as a consequence the estimated coefficient vector is not sensitive to outlier observations on the dependent variable.<sup>8</sup>

> 8 That is, if _y i_ − _x i_ ′β ˆ θ > 0 then yi can be increasing towards + ∞, or if _y i_ − _x i_ ′β ˆ θ < 0 , yi can be decreasing

> towards -∞, without altering the solution β<sup>ˆ</sup> θ . In other words, it is not the _magnitude_ of the dependent variable that matters but on which _side_ of the estimated hyperplane the observation is. This is most easily

8
Rather than cover all of CEE. we take an in-depth look at the most spectacular example, Poland'2 The first author conducted a series of firm-level interviews over 1991 and 1992 while at the World Bank -office in Warsaw. The resulting panel data set sheds a fascinating light on this ouestion3;-.the answers have, in our view. great significance for those countries that are now where Poland was three years ago.

Pinto et alii (1993) provide survey results indicating that hard budgets and import competition can spur ste firms to adjust even when privatizaEion lags behind. Our discussion goes to the key question of why mianagers instigated such adjustments as we examine the micro-underpinnings of the Polish reform. We. examine the influence of corporate ownership and control on SOE behavior as illuminated by four key results from the survey evidence First, contrarv to expectations, SOEs<sup>did take</sup> painful adjustnent measures: second. we present both econometric evidence and direct survey results indicatinz that the excess wage tax restained wage setting behavior. Third. we show that banks played a powerful role in disciplining enterprises, but only did so after their own governance<sup>was reformed in</sup> the banking reforms of end-1991. Finally we present intriguing and encouraging evidence on the incentive effects for managers of fumtre privatization plans. Shleifer (1993) questioned whether the budget constraint imposed on Polish enterprises was indeed hard. This paper provides importan insighLs into the hardening of the budget constraint and the credibility of the re'orms as seen through the eyes of state firm managers.

In what follows we first document the adjustment that has taken place in the SOE sector in Poland drawing on the survev evidence (section.2). In section _3_ we provide econometric evidence on the extent to which the various claim holders (the government. banks and suppliers) have exercised governance. Section 4 directlv addresses the issue of management incentives: was there more than direct.pressure from claim holders that led managers to restructure firms and focus on efficiencv? And Section.5 concludes.

# 2 State Enterurise Adjustment in Poland

In this section. we first present a sketch of the background against which the SOEs functioned (Section 2.1)- the second part of the section documents the adjustment that has taken place in the SOEs drawing on our survey evidence (Section 2.2).

An early paper on incentive issues in the Polish context is Frvdman and Wellisz 1:991). **Other** relevant referencesare Dabrowski et alii (1991): Pinto et alii (1991).Schaffer (1992). Fan and Schaffer (1993) and<sup>in</sup> particular Gomulka (1993).

See Pinto et alii (1993) for a detailed description of the survey and'a preliminary analysis: a short description<sup>of</sup> ffie enterprises covered is given in Annex 1 to this paper.
4

# TABLE 2.1

# Froduct Tax Rates on Major Product Groups

# May 1988

|Product Group|Rat|es|
|---|---|---|
|Tobacco Products|35Z|<br>60Z|
|Beers, Wines and Spirits|10%|- 50Z|
|Rubber Products|10%|- 202|
|Building Materials|3%|- _1zZ_|
|Minerals|**32**|-<br>12%|
|Electric Power|5Z|-<br>252|
|Gas|3%|-<br>15%|
|Gasoline||4|
|Diesel||10o|
|Kerosene||25%|
|Other Petroleum Products|3%|-<br>35Z|
|Chemicals|3Z|- 25z|
|Metals|3%|-<br>15X|
|Tea|102|- 25X|
|Luxury Foods|5%|- 357|
|Timber, Bamboo and Lacquer|5b|-<br>15Z|
|Meat||3Z|

Source: General Taxation Bureau In principle, the VAT should credit enterprises for both the product tax and the VAT that has been paid on purchased intermediate inputs. However, in practice, the credit for inputs that have Daid Product Tax is made at a composite rate of 14 percent. For some enterprises this composite rate is extended to include all intermediate inputs, including those that have paid VAT. This practice is administratively attractive when firms purchase a range of inputs that have paid VAT at different rates. The operation of the Chinese VAT is therefore one of "presumptive" crediting and thus differs from the standard invoice method.

VAT in China does not allow credit for tax paid on purchases of fixed assets. Such discrimination against investment goods makes the tax income-based in contrast to the consumption-type VAT that appears on the statute books of many countries.
Figure 5 completes the descriptive figures by showing differences in mean total amount remitted in the past year by source country. This incorporates those who remit zero. There is also considerable heterogeneity in the raw data across countries in the amount remitted. Migrants from Ghana, Nigeria and Senegal each sent home more than $2,500 on average, while those from Algeria, Ethiopia, and South Africa remit less than $500.

**Figure 5**

<!-- Start of picture text --> Total Remittances by Country of Birth<br>Africa<br>Rest of World<br>North Africa<br>Sub-Saharan Africa<br>Nigeria<br>Senegal<br>Ghana<br>Egypt, Arab Rep.<br>Congo, Dem. Rep.<br>Morocco<br>Somalia<br>Ethiopia<br>Algeria<br>South Africa<br>0 1,000 2,000 3,000 4,000<br>Average total remittances ($ p.a)<br><!-- End of picture text -->

# **4. Which characteristics explain the remitting behavior of Africans?**

The differences observed in the raw data reflect differences in which destination country surveys emigrants from different origin countries are in, as well as differences in the characteristics of the emigrants from different countries. We therefore next examine whether these country of origin differences persist once we control for these other factors, as well as investigating which characteristics describe the remitting behavior of African migrants at the micro level. To do this we estimate the following regression for individual _i_ from country _j_ now living in country _k_ : _Remitting Behaviori,j,k = α + β’Xi,j,k + γ’Origini,j,k + δ’Destinationi,j,k + εi,j,k_ 11
# Health Data Book Washington, DC: CDF, 1988.

Creedy, **BS** & Polgar, S "Returning for Yearly Cleck-ups: A Study of 22,000 Family Planning Clients." New York: Planned Parenthood-World Population, 1963.

Cumper, G.E. "Jamaica: A case study in health development." Social Science & Medicine 17(24) 1983:1983-1993.

Datta, SP; Srinivas, DK; Kale, RV & Ranjgaswamy, R. "Evaluation of Maternal and Infant Care in a Rural Area., Indian J Medical Sciences 27(2) 1973:120128.

Dressekie, Dorrell. "Primary Health Care in Riral Jamaica." JRSH 6 1988:213.

Folta, Eleanor. personal communication 9/27/1988.

Fonaroff, Arlene "Cultural Perceptions and Nutritional Disorders: A Jamaican Case Study." Bulletin of the PAHO 9(2) 1975:112-123.

Fortney, JA. "The importance of Family Planning in Reducing Maternal Mortality." Studies in Family Planninm 18(2) 1987:109-115.

Giugliani, Elsa R.J; Lovel, Hermione; & Ebrahim, G.J. "Attitudes, Practices and Knowleoge of Health Professionals on Breastfeeding in Kingston, Jamaica." J of Tropical Pediatrics 34(4) 1988:169-173.

Goujon, H; Papiernik, E; & Maine, D; "The prevention of preterm delivery through prenatal care: an intervention study in Martinique." Intl J of Gynaecology & Obstetrics 22(5) 1984:339-344.

Granthamx-l.grs3or, S.M; Powell, C; Stewart, M; & Schofield, W.N. "Longitudinal Study of Growth and Development of Young Jamaican Children Recovermn fran Severe Protein-energy Malnutrition." Developmental Medicine & Child Neurology 24(3) 1982:321-331.

Hall, MF "Field Effectiveness of the Oral and Intrauterine Methods of Contraception." Milbank Mem Fund Ouart 47 1969:55.

Hay, David M. & Boyd, John J. "A Study of the Obstetric Performance of the Adolescent Jamaican Primigravida." American J of Obstetrics & Gyneco1rv 116(1) 1973:34-38.

Hayes, Janice S; Dreher, Melanie C; & Mugent, J Kevin. "Newborn (Oxtccies with Maternal Marijuana Use in Jamaican Women." Pediatric Nursinq 14(2) 1988:107-110.

Herz, B & Measham, AR. "Tmhe Safe MotUerhood Initiative: Frpposals for Action." World Bank Discussion Papers, #9. Wasiington, D.C., 1987. Hogg, Donald W. "M4agic and Science in Jamaica." (Paper read at Annual 24
# Table of Contents

|1 INTRODUCTION ...................................................... 1|
|---|
|2FLOW OF FUNDS DATA .<br>...........................................<br>4<br>2.1 The elements of the flow of funds. ........ ....................<br>4<br>2.2 Financial assets ............................................<br>9<br>2.3 Data pitfalls ....................-.-.------------------------<br>12|
|3 ANALYTICAL ISSUES .15<br>|
|3.1 Sectors and instruments ....----------........................-<br>15<br>3.2 Intersectoral<br>flows: theoretical framework ....................6<br>3.2.1 Interactions<br>between the savings behavior of the domestic<br>sectors ...........<br>16<br>3.2.2 The fluidity of international<br>flows<br>................<br>7<br>3.3 Instruments:<br>sharing of risks<br>..<br>18<br>3.3.1 Types of sharing .........................................<br>19<br>3.3.2 Ability of instruments to shs.re<br>..........................<br>21|
|4 FINANCIAL ASSETSAND FLOWS IN DEVELOPING COUNTRIES<br>..........<br>31<br>4.1 Mainfeatures of the data ..............................<br>.......<br>31|
|4.2 Exogenous determinants<br>...............................<br>.......<br>35<br>4.3 Pulling and pushing sectors ..........................<br>..........<br>39<br>4.4 Cyclical factors in individual countries<br>......................<br>44<br>4.5 The message from the principal components .....................<br>48|
|5 INSTRUMENTS IN DEVELOPING COUNTRY FLOWS ............................<br>51.|
|6 SUMMARY AND CONCLUDING REMARKS ................................. 56|

This is a background paper prepared for the World Development Report 1989. We are indebted to Bela Balassa, Jerry Caprio, Warren Coats, Alan Gelb and Gerhard Pohl for comments on earlier drafts.
There has been debate within the literature on whether high sensitivities of firms’ investment to their cash flows should be interpreted as indicators of financial constraints. The standard identification strategy is based on the pioneering work of Fazzari, Hubbard, and Petersen (henceforth FHP) (1988, 2000) who argue that one should be able to gauge the impact of credit frictions on corporate spending by comparing the sensitivity of investment to cash flow across samples of firms. Firms are typically split into sub-samples based on a priori criteria indicating the size of the wedge between the internal and external cost of funds. An implicit assumption underlying these empirical tests is that investment-cash flow sensitivities increase monotonically with the severity of financial constraints. However, Kaplan and Zingales (1997, 2000) provide theoretical reasons why a monotonic relationship between sensitivities and constraints need not hold. They report evidence that firms that paid no dividends (hence classified by FHP (1988) as financially constrained) and that had high investment-cash flow sensitivities did not suffer from financial constraints based on their management’s statements of firm liquidity.

While we acknowledge the controversy in the literature, and see merit in both sides of the argument depending on the context and sample, we argue that the current Chinese institutional context is especially suitable for applying the FHP (1988) methodology to study financial constraints.<sup>9</sup> To begin, the criteria by which we classify firms as being financially constrained a priori is based on the known institutional context in China. There is by now a well-established literature demonstrating that government ownership of the dominant banks in China has resulted in privileged access to bank credit, the primary source of external financing in China, for

> 9  Indeed, the studies of China cited above have used those techniques.

9
||Table 4: E|ndogeneity|checks|||
|---|---|---|---|---|---|
|Equation number|4-1|4-2|4-3|4-4|4-5|
|variation|Obs. from|Obs. with|WVS 1995-|Initial|Final|
||wave 5 only<br>dropped|mean survey<br>year > 2001<br>dropped|1998 only|WVS|WVS|
|No secondary|-1.603**|-3.841*|-4.410***|-2.260***|-1.947***|
|schooling (%)|(-2.23)|(-1.84)|(-2.75)|(-4.13)|(-2.73)|
|Morality|0.005|0.005|0.046***|0.012|0.015**|
||(0.60)|(0.52)|(3.22)|(1.41)|(2.00)|
|Morality-ability correlation|4.797***|4.908***|2.597**|3.432***|3.010***|
||(4.61)|(3.08)|(2.16)|(3.73)|(3.01)|
|Distance from|0.008|0.005|0.007|0.010*|0.012**|
|Equator|(1.53)|(0.48)|(0.85)|(1.98)|(2.26)|
|Trade share of|0.010|-0.004|-0.205|0.178|0.144|
|GDP (log)|(0.06)|(-0.02)|(-1.27)|(1.21)|(0.97)|
|Rule of law index|0.479***|0.408***|0.439***|0.467***|0.484***|
||(4.45)|(3.12)|(5.19)|(4.93)|(5.17)|
|Constant|8.154|8.453|9.694|7.399|7.526|
||(10.46)|(8.00)|(16.94)|(9.55)|(9.56)|
|R<sup>2</sup>|.75|.73|.73|.77|.76|
|Observations|55|42|45|74|74|
|Mean dep. var.|9.32|9.31|9.41|9.14|9.14|
|Dependent variable is lo<br>standard errors) are in pa<br>and .01 levels for two-tai|g of 2008 GDP<br>rentheses. A *,*<br>led tests.|per capita (PPP-<br>* and *** respec|adjusted). Test s<br>tively indicate si|tatistics (from<br>gnificance at .|robust<br>10, .05|

39
council. In contrast, variation at the state level implies differences in exposure to both women leaders as well as women members of the councils at all levels of local government.

# **4. Political Representation and Women’s Labor Market Outcomes**

# **4.1. Women’s Labor Force Participation**

We report our results from estimating equation 1 for men and women in Table 3, for both overall LFP and specific labor force activities. Part (A) of the table shows the impact on women’s LFP, while part (B) depicts the effects on men’s LFP. Column (1) shows the impact of women PRs on overall female LFP. We find that an additional year of exposure to women PRs across all levels of local government increases women’s LFP by 17.5% (row 1); in addition, having a woman leader at the district level improves this outcome by 8.8% (row 2). Both findings are statistically significant at the 10% level. In contrast, women PRs have little impact on men’s LFP.

In terms of individual types of work, district women leaders seem to significantly increase women’s self-employment (worker in OAE) opportunities by 17% while the broader impact of having women PRs is seen in an 18.7% increase in wage and salaried work. At both levels of analysis, women PRs have positive effects of fairly large magnitude on female public employment, although these effects are not statistically significant. They also lower the percentage of women exclusively involved in domestic work.

In order to examine these effects in greater detail, it is worth understanding the factors that constrain women’s LFP in the Indian context, and discuss potential channels through which women PRs can change these constraints directly or indirectly. To do so, we draw on existing research.

# **4.2 How Women Political Representatives Could Affect Female LFP: Possible Channels**

What are the factors that affect a woman’s decision to seek work outside the home, in the Indian context – and what are the big barriers she faces? Recent field surveys in India (Khera-Nayak 2009, Narayanan 2007, Krishnaraj et al 2004) highlight some key factors relevant to our analysis: Most household duties are entirely a woman’s responsibility, hence proximity of the 12