rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3 <!-- Start of picture text --> Average percentage loan loss as a function of project risk<br>4.0%<br>3.5%<br>3.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0<br>Project risk (standard deviation)<br><!-- End of picture text --> Figure 4 Risk premium incorporating country and project default risks <!-- Start of picture text --> 7.0%-<br>6.0%<br>5.0%<br>4.0% - p=10o%<br>320% -<br>2.0%<br>1.0%<br>20.0 22.0 24.0 26.0 28.0 30.0 32.0 34.0 36.0<br>Project nsk (standard deviation)<br><!-- End of picture text --> 16
- 7 -

In Antananarivo, currently, there is no formal public transportation that is operational. The vast majority of passenger traffic is carried by “informal” public transport, mostly, minibus, called taxi-be. There are no official statistics of minibus ridership, however, based on the 2016 household survey in Antananarivo, it is estimated that about 38 percent of the residents use minibus services (World Bank 2019). The total ridership in the sector is estimated at about 1.3 million rides per day.

Minibuses are loosely regulated by the central and local governments, depending on their service areas. While within-city services are registered at the Antananarivo Urban Commune (CUA), the Ministry of Transport is responsible for licensing other suburban operations that operate across districts and communes ( **Table 1** ). Although all operating routes, stops and schedules are in theory supposed to be registered, their actual implementation is not necessarily supervised. Operators only pay a license fee of Ar35,000 (US$10) and a parking fee of Ar120,000 (US$35) per year.

**<u>Table 1. Minibus Market Structure in Greater Antananarivo</u>**

||Suburban|Within city|
|---|---|---|
|Operating area|Across communes|CUA|
|Regulating authority|Ministry of Transport|CUA|
|Operating entity|31 cooperatives|52 cooperatives|
|Bus routes|64|73|
|Average distance of routes (km)|15.9|12.3|
|No. of buses registered|1,472|5,578|
|Average fleet age (year)|23.6|...|
|Average rotation (times per bus-day)|3 to 5|4|
|Average capacity (seats)|20|20|
|Average fares (Ar.)|888|500|

Source: Updated based on World Bank (2019).

Over 6,000 minibuses are currently operating along about 130 routes. Since there is no formal supply and demand matching mechanism in Antananarivo, many operators choose to offer their services where the passenger demand is concentrated, i.e., along the major national roads (e.g., RN1, RN2, RN3, RN4 and RN7). Other less populated areas or remote suburban areas are left unserved. Because of the lack of coordination between the two regulatory authorities, within-city and suburban bus services are duplicated geographically,
# 1. Introduction

Throughout the world laws and regulations constrain the activity of economic agents and have substantial impact on the economic performance of different countries. Economic, social and political reasons are used to justify them, although very rarely any empirical evidence exists to back these up. However, the degree of compliance with regulation is likely to be as important for its impact as the details of the particular rule being enforced, although it is usually not explicitly considered. Economists study both the effect of regulation on social and economic outcomes (e.g., Heckman and Pages, 2003, Besley and Burgess, 2003), and the effect of enforcement on compliance with regulation (e.g., Levitt, 1997, 2004) in a variety of settings, but the two problems are not analyzed together. These two issues can and should be studied simultaneously: if variation in enforcement causes changes in compliance, then it will also have an effect on economic performance because the degree with which a given law is applied will vary.

In this paper we look to labor markets in Brazil and we examine two important issues on which our current empirical knowledge is quite limited: 1) what is the impact of enforcement on the employment of informal labor?; 2) what is the impact of increased labor fl exibility on fi rm performance and total employment? In particular, we analyze how labor inspections and fi nes affect the demand for informal (illegal) labor by fi rms, and how access to informal ( fl exible) labor affects fi rm performance. Our empirical work integrates these two problems using insights from the literature on regulation, informality, crime and labor demand.<sup>1</sup> Our work focuses on Brazil because it has one of the most regulated labor markets in the world (e.g., Botero, Djankov, La Porta, Lopez-de-Silanes and Shleifer, 2003).<sup>2</sup> Furthermore, enforcement of the law is apparently weak since approximately 40% of total employment is

> 1We do not explicitly examine the damage illegal behavior in fl icts on institutions and social protection. However, on the latter point the available evidence is not clear. Even though labor market institutions are usually used to explain differences in inequality across developed countries (e.g., Blau and Khan, 1996), it has been repeatedly shown that regulation can actually be detrimental to poverty and inequality in developing countries (e.g., Besley and Burgess, 2003, and the essays in Heckman and Pages, 2003).

> 2According to the Doing Business data set collected by the World Bank, Brazil has the third strictest labor regulation in the world. Labor market regulations changed signi fi cantly in 1988 with the new constitution, increasing the degree of worker’s protection. A brief description of the current state of labor market regulation in Brazil is presented in table A1.

2
_97_

# Budget DOeficit()/GDP EXGNFS more than 25% of GOP

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# <u>Table 1:  Variable Definitions and Mean Statistics</u>

|Variable Name|Definition|Mean|
|---|---|---|
||**_Measures of Trade Credit_**<br>||
|AR_d|Dummy (0/1), =1 if the firm offers credit to its customers (i.e.<br>accountsreceivable),=0ifthefirmdoesnot offertrade credit|0.39|
|AR_per|The percent of monthly sales sold oncredit|14.02|
|AR_days|Average number of days customers are allowed to use the credit<br>before thefirm imposes penalties|60.15|
|AR_discount|Dummy (0/1), =1 if the firm offers a pre-payment discount on<br>credit toits customers,=0 otherwise (and=.if AR_dis=0)|0.20|
||Dummy variable =1 if the difference between the number of days<br>offered to customers less the number of days until receivable||
|AR_gap|payments are received from customers is greater than zero (i.e.<br>customers prepay their receivables); =0 if the difference equals<br>zero (i.e. customers pay on time); and =-1 if the difference is less<br>than zero (i.e. the customers payslate)|0.08|
|AP_d|Dummy (0/1), =1 if the firm uses supplier credit (i.e. accounts<br>payable) to purchaseinputs,=0 otherwise|0.45|
|AP_per|The percent of inputs purchased on credit (based on period<br>averages),=0ifthefirmdoesnot use trade credit|9.58|
|AP_days|Average number of days the firm is allowed to use the credit<br>beforeits suppliersimposes penalties|31.67|
|AP_discount|Dummy (0/1), =1 if the firm received a pre-payment discount on<br>creditfrom its suppliers,=0 otherwise (and=.if AR_dis=0)|0.07|
|AP_gap|Dummy variable =1 if the difference between the number of days<br>offered to the firm by its suppliers less the number of days until<br>the firm pays its suppliers is greater than zero (i.e. the firm prepays<br>its payables); =0 if the difference equals zero (i.e. the firm paysits<br>suppliers on time); and =-1 if the difference is less than zero (i.e.<br>thefirms paysits supplierslate)|0.09|
||**_General Firm Characteristics_**||
|L_Age|Lognumberofyears (+1) since thefirmwas established|2.57|
|L_Emp|Log average number of total employees (including contractual<br>employees)|4.94|
|D_Foreign|Dummy (0/1), =1 if the percentage of the firm owned by foreign<br>individuals, foreign institutional investors, foreign firms, and<br>foreignbanksis greaterthan50,=0 otherwise|0.07|
|D_State|Dummy (0/1), =1 if the percentage of the firm owned by the<br>government (federal, state, local, and collective/cooperative<br>enterprises)is greaterthan50,=0 otherwise|0.23|
|D_Export|Dummy (0/1),=1 ifthefirm is exporting,=0 otherwise|0.09|

36
# Global Inequality:

The Implications of Thomas Piketty’s _Capital in the 21st Century_ _JEL classification_ : D31.

_Keywords_ : Income distribution, inequality, global inequality, globalization, top incomes

> * Development Economics Research Group, World Bank. I am very grateful to Espen Prydz and Matthew Wai-Poi for help with the data, and Heather Boushey, Francisco Ferreira, La-Bhus Fah Jirasavetakul, Branko Milanovic and Carmen Ye for very helpful comments. Prepared for _After Piketty: the Inequality Agenda post-Capital in the 21st Century_ , edited by B. DeLong, H. Boushey and M. Steinbaum, Harvard University Press, 2017.
WPS3427

# **Shelter Strategies for the Urban Poor:**

# **Idiosyncratic and Successful, but Hardly**

# **Mysterious**

## Robert M. Buckley

## and

## Jerry Kalarickal

### World Bank Policy Research Working Paper 3427, October 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The data set used in the analysis was prepared by Matthew Ramsdell. Kathryn Owens and Matthew Ramsdell also provided excellent research assistance. The support of the Dutch Low Income Housing Trust Fund supported the work. Helpful comments on an earlier draft were made by Solly Angel, Alain Bertaud, Billy Cobbett, Bruce Ferguson, James Fitz Ford, Roy Gilbert, Paula Jiron, Christine Kessides, Jay-Hyung Kim, Emmanuel Jimenez, David Le Blanc, Steve Malpezzi, Peter Marcuse, Maryvonne Plessis-Fraissard, Carol Racelis, David Satterthwaite and Laura Vecvegare.
to data from Indian states, using physical indicators of infrastructure assets in transport and power, and find that infrastructure development accounted for almost half of the observed TFP growth. Duggal, Saltzman and Klein (1999) allow for nonlinear production technologies. Using aggregate U.S. data, they find that public infrastructure capital is an important determinant of TFP. Duggal, Saltzman and Klein (2007) extend the framework to include also privatelysupplied IT infrastructure, which contributes to production both as a standard input and as a driver of TFP. Both types of infrastructure are found to have a significant positive effect on productivity.

A related line of research, pioneered by Berndt and Hansson (1991), takes a dual approach and focuses on the estimation of cost and/or profit functions augmented by either infrastructure or public capital stock measures. The empirical finding in most cases is that infrastructure reduces production costs or increases profits – see Demetriades and Mamuneas (2000) on OECD crosscountry data, and Cohen and Morrison Paul (2004) on U.S. state data.

A different strand of literature evaluates the long-term growth impact of infrastructure, typically using a reduced-form growth-regression framework relating long-run growth to suitable indicators of infrastructure, public capital or public investment, often in conjunction with standard control variables from the empirical growth literature. Measures of infrastructure and conditioning variables differ across studies, so they are not easy to compare. However, those papers using monetary measures of public capital stocks or public investment yield mixed results – e.g. Holtz-Eakin and Schwartz (1995) and Crihfield and Panggabean (1995) find no significant growth effects of infrastructure across U.S. states and metropolitan areas. In turn, Easterly and Rebelo (1993) find that public investment in transport and communications significantly raises growth across countries. Devarajan et al. (1996) find a negative relationship between the share of infrastructure in total public expenditure and economic growth in panel data for developing countries, while Gupta et al (2005) find the opposite result in a different cross-country panel data set.

In contrast, growth regressions using physical indicators of infrastructure stocks almost invariably find significant growth effects. In many cases, they use the number of telephone lines to proxy for infrastructure (e.g. Easterly 2001). In others, they use synthetic indicators capturing physical stocks in multiple infrastructure sectors – transport, power, and telecommunications.

8
# **Table 5: Some Characteristics of the Six South American Countries**

|Country|GDP per<br>capita, PPP<br>(current int.<br>$, 2016)|WGI:<br>Rule of<br>Law,<br>2016|WJP:<br>Civil<br>Justice<br>Index,<br>2017|Doing Business<br>DTF: Enforcing<br>Agreements<br> 2017 (higher is<br>better)|Interpersonal<br>trust<br> (% very or<br>somewhat<br>trustworthy)|F<br>Ethnic|ractionaliza<br>Language|tion<br>Religion|
|---|---|---|---|---|---|---|---|---|
|Argentina|19,939|-0.35|0.58|55.6|71.6%|25.5%|6.2%|22.4%|
|Bolivia|7,234|-1.20|0.34|54.6|46.0%|74.0%|22.4%|20.8%|
|Ecuador|11,242|-0.69|0.46|56.0|55.2%|65.5%|13.1%|14.2%|
|Paraguay|9,567|-0.67||59.7|69.0%|16.9%|59.8%|21.2%|
|Peru|13,018|-0.49|0.44|60.7|46.7%|65.7%|33.6%|19.9%|
|Uruguay|21,619|0.63|0.74|54.4|75.9%|25.0%|8.2%|35.5%|
|Latin America & Caribbean|15,210|-0.06|0.53|54.0|65.7%|40.5%|18.9%|44.4%|
|Europe & Central Asia|31,361|0.53|0.63|65.7||32.7%|31.2%|40.0%|
|East Asia & Pacific|17,021|0.21|0.58|56.4||29.6%|39.3%|52.0%|
|World<br>Notes:|16,214|0.00|0.56|55.3|67.4%|43.9%|38.6%|43.7%|

WGI = World Governance Indicators; WJP = World Justice Project; Doing Business DTF = the distance to the frontier measure of Doing Business. Countries included in regional averages vary by the respective data availability (e.g. the interpersonal trust world average includes only Canada and the United States of America in addition to the Latin American and Caribbean countries).

The WGI rule of law index captures perceptions of the extent to which agents have confidence in and abide by the society's rules.

The WJP Civil Justice Index measures whether disputes can be resolved peacefully and effectively through the civil justice system.

The DB distance to frontier score is measured on a scale of 0 to 100, where 100 is best practice on enforcing agreements and 0 represents the lowest performance. Interpersonal trust is based on Americas Barometer and shows the percentage answering 'Very trustworthy' or 'Somewhat trustworthy' to the following question: "And speaking of the people from around here, would you say that people in this community are very trustworthy, somewhat trustworthy, not very trustworthy or untrustworthy?" The data on fractionalization is from Alesina et al. (2003) and is available for different countries in different years, ranging from 1981 to 1998. The data measures 100 times the probability that a random member of the population is not from the same group.

35
**Table 9: long-term impacts of the 4P program on empowerment - decision-making - ITT**

||Fraction of<br>domains for<br>which<br>respondent<br>makes the<br>decision|Fraction of<br>domains in which<br>respondent's<br>opinion is heard<br>|Fraction of<br>domains in<br>which<br>respondent<br>cares about<br>making the<br>decision<br>|Dummy for<br>dictator<br>decision<br>making<br>Wom|Dummy for<br>contributor<br>decision making<br>en|Dummy for<br>separate<br>spheres<br>decision<br>making|Dummy for<br>norms-based<br>decision<br>making|Dummy for<br>most-<br>knowledge<br>decision making|
|---|---|---|---|---|---|---|---|---|
|ITT|0.019|0.036*|0.041**|-0.034|-0.002|0.024|-0.051|0.011|
||(0.45)|(0.08)|(0.03)|(0.35)|(0.96)|(0.65)|(0.18)|(0.78)|
|Observations|437|437|437|437|437|437|437|437|
|Adj. R2|0.05|0.02|0.04|0.01|0.01|-0.00|0.05|0.06|
|Control mean|0.627|0.728|0.762|0.241|0.286|0.641|0.236|0.327|
|||||Men|||||
|ITT|-0.004|-0.017|-0.007|-0.029|-0.046|-0.075|0.052|0.042|
||(0.90)|(0.52)|(0.74)|(0.50)|(0.24)|(0.19)|(0.22)|(0.39)|
|Observations|435|435|435|435|435|435|435|435|
|Adj. R2|0.03|0.03|0.03|0.02|0.01|-0.00|0.04|0.02|
|Control mean|0.513|0.649|0.703|0.268|0.336|0.668|0.250|0.368|

P-values in parentheses: * p<0.1 ** p<0.05 *** p<0.01 32
1000 fi rms in the subregion leads to a reduction of 12.5% in value added per employee (which means that the elasticity of productivity with respect to enforcement is approximately 0.1).

The absence of discernible effects on total employment is rather surprising. When faced with the possibility of hiring informal workers fi rms decide not to change the size of their labor force, in spite of lower labor costs. Instead, they increase their investment and change their technology (the technological change changes the demand for labor, and in our case it seems to reduce it). They just substitute away from formal workers and towards informal workers. In this instance, increased labor market fl exibility does not produce strong effects on employment, but in spite of that (this section shows that) labor market fl exibility can have a substantial impact on the economy. In fact, our fi nding is somewhat consistent with Barros and Corseuil (2001) who fi nd very small effects on employment of tighter labor market regulation due to the 1988 constitutional change (although they do not examine other fi rm outcomes).<sup>32</sup> It is quite possible that there are small effects on employment and large effects on other fi rm outcomes.<sup>33</sup> Our fi ndings illustrate the importance of looking at multiple outcomes of the rm.

# 4.2. Robustness and Endogeneity

Our estimates are quite robust to several changes in the speci fi cation. Table 3 reports the least squares estimates of equation (4.1) using as dependent variable the proportion of informal employment. In column (1) we include only enforcement in the regression. The effect of enforcement on informal employment is strongly negative.<sup>34</sup> Our estimates of β in column (1) will be an underestimate of the true effect if more enforcement is caused by more informality (reverse causality), and it will be an overestimate if strong enforcement and low informality are caused by a third factor, such as strong institutions and strong civic culture

> 32Gruber (1997) also fi nds small effects of social security provisions on employment using data from Chile.

> 33Even though the effect of enforcement on employment is not large, enforcement has an effect on the reported desire of the fi rm to change its labor force. Firms are asked whether they would change the size of their labor force if they could hire and fi re with no restrictions. In results available on request we fi nd that an increase in our measure of enforcement in 1 unit leads to an increase of 4% in the probability that the fi rm reports a desire to change the labor force in the absence of hiring or fi ring constraints.

> 34Using labor inspections as an enforcement measure does not produce such a strong negative correlation as using fi nes (available on request).

19
# Voice Lessons'

## _Introduction_

Questions about improving the quality of government are more than just academic in Indonesia today; they are pressing, practical questions. Indonesia has long been considered a classic example of a "developmental authoritarian" state- one that fostered economic success and delivered concrete material benefits as a claim to political legitimacy while simultaneously creating institutions through which popular participation in politics was structured, channeled, and thereby marginalized. With a radical

> decentralization of responsibilities to regional (district) governments underway, continuing economic turmoil, and frequent shifts in national leadership, Indonesia is in

> the midst of economic, social, and political change. From the national to the local level, the structures and behaviors taken for granted during the Soeharto/New Order era are being challenged and, in many cases, overturned. This paper focuses on the role of villagers' social activities in creating more participatory and accountable local governments, and aims to contribute an empirically grounded analysis to inform discussions of the reforms of local governance.

> Putnam (1992)2 argued that, even in a "modem" and "developed" country like

> Italy, the nature and type of social relationships were the most important determinant of

I We would like to thank many people who helped in the long course of the LLI2 study and this particular paper: Scott Guggenheim, Pieter Evers, Kamala Chandrakirana, Robert Chase, Christiaan Grootaert, Michael Woolcock, Sandy Jencks, Jeffrey Hammer, Deon Filmer, Menno Pradhan, and Chitra Buchori provided valuable comments and input during the course of this research. Leni Dharmawan, Erwin Fahmi, R. Yando Zakaria and their respective regional teams shaped and collected the data. Financial support from the World Bank's Indonesia Country Team, the Research Support Budget, the Norwegian Trust Fund for Environmentally and Socially Sustainable Development, and the ASEM Trust Fund is gratefully acknowledged. **2** Although it should be noted that the book is "with" Robert Leonardi and Raffaella Nanetti.

2