rafmacalaba/datause-encoder-data huggingface dataset:
WPS4070

# Assessing Job Flows across Countries: The Role of Industry, Firm Size, and * Regulations

John Haltiwanger University of Maryland, U.S. Census Bureau, NBER and IZA Stefano Scarpetta World Bank and IZA Helena Schweiger† European Bank for Reconstruction and Development (EBRD).

## World Bank Policy Research Working Paper 4070, November 2006

_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._

*Updates of this paper can be downloaded at http://www.helenasch.net.

†We are grateful to Robin Burgess, John Shea, John Sutton, Luis Serven, James Tybout and the participants to the World Bank Conference on the ’Microeconomic of Growth’ (Washington, D.C., May 18-19, 2006) and to the Annual Conference of the European Association of Labour Economists (EALE, Prague, September 21-23, 2006) for their insightful comments.
||Mean Adu|lt|Poverty line 40%|lowest<br>|
|---|---|---|---|---|
||_Census_|_HBS_|_Census_|HBS|
||(6801)|(4273)|(0.023)|(0.024)|
|Sheki|253816|248874|0.31|0.34|
||(5653)|(4156)|(0.023)|(0.029)|
|Lanakaran|245929|241499|0.35|0.37|
||(2679)|(3432)|(0.014)|(0.025)|
|Shirvan|255715|252779|0.34|0.335|
||(3761)|(4922)|(0.014)|(0.032)|
|Karabah|271245|258223|0.31|0.39|
||(6814)|(4111)|(0.025)|(0.021)|
|**Urban**|||||
|Nakhchivan|239444|237958|0.44|0.43|
||(4679)|(5993)|(0.020)|(0.040)|
|Absheron-Guba|242869|242368|0.42|0.41|
||(2815)|(4024)|(0.012)|(0.024)|
|Mugan-Salyan|259218|241078|0.30|0.38|
||(10062)|(5398)|(0.046)|(0.038)|
|Ganja-Gazakh|214910|216514|0.55|0.54|
||(2297)|(2766)|(0.015)|(0.027)|
|Sheki|227578|222539|0.44|0.56|
||(8695)|(4516)|(0.065)|(0.037)|
|Lanakaran|233407|232017|0.46|0.45|
||(4238)|(5217)|(0.020)|(0.038)|
|Shirvan|222600|209556|0.50|0.58|
||(4488)|(3843)|(0.027)|(0.041)|
|Karabah|228797|231541|0.48|0.45|
||(3676)|(4218)|(0.019)|(0.031)|
|Baku|261656|253530|0.39|0.42|
||(10782)|(2551)|(0.03)|(0.013)|

Note: Standard errors in parenthesis. Household survey figures are calculated using weights that are the product of household weights and household size. Census-based figures are calculated weighing by household size.

Figures A1-A8 in Appendix A illustrate to what extent the estimates of the three welfare indicators are to be considered reliable. Figure A1 for example ranks urban rayons by the coefficient of variation. Considering the coefficient of variation derived by the survey stratum estimate as a cut off point for the level of error we allow, we can see that for the estimates of the headcount in urban area at rayon level around two thirds of those estimates are above the coefficient of variation obtained from the survey, whereas for estimates of mean adult equivalent monthly consumption those estimates are below or around the coefficient of variation obtained from the survey (Figure A2 in Appendix A). For the two welfare indicators, the rayon estimates for rural areas are below or around the stratum level value suggesting that those estimates are to 39
|Western Cape provincial dummy|0.041|0.099|67.9%|
|---|---|---|---|
|sole proprietor dummy|-0.518|0.117|0.0%|
|close corporation dummy|-0.179|0.088|4.2%|
|Manufacturing|-0.200|0.106|6.0%|
|VAT|0.207|0.121|8.9%|
|PAYE|0.247|0.103|1.6%|
|Adj. R2|0.459|||
|Mean of the dependent variable|9.987|||

**Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+20% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|7.367|0.364|0.0%|
|LN full-time employees|0.084|0.036|1.9%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.191|0.027|0.0%|
|Share of activities completely outsourced|-0.977|0.131|0.0%|
|Share of activities partially outsourced|-0.276|0.127|3.0%|
|Guateng provincial dummy|0.161|0.076|3.5%|
|Western Cape provincial dummy|0.048|0.099|62.4%|
|sole proprietor dummy|-0.522|0.116|0.0%|
|close corporation dummy|-0.186|0.088|3.4%|
|Manufacturing|-0.197|0.105|6.3%|
|VAT|0.211|0.121|8.1%|
|PAYE|0.244|0.102|1.7%|
|Adj. R2|0.456|||
|Mean of the dependent variable|9.948|||

|**Dependent Variable: Sensitivity analysis: LN Total cost of tax**<br>**board)**|**compliance (**|**+10% acros**|**s the**|
|---|---|---|---|
||Coeff.|Std. Error|Sig.|
|(Constant)|7.287|0.363|0.0%|
|LN full-time employees|0.085|0.035|1.7%|
|LN of q55 after imputed values were introduced (imputed "no<br>answer")|0.191|0.027|0.0%|
|Share of activities completely outsourced|-0.918|0.130|0.0%|
|Share of activities partially outsourced|-0.250|0.127|4.9%|
|Guateng provincial dummy|0.164|0.076|3.1%|
|Western Cape provincial dummy|0.056|0.098|56.8%|
|sole proprietor dummy|-0.526|0.116|0.0%|
|close corporation dummy|-0.194|0.087|2.7%|
|Manufacturing|-0.193|0.105|6.7%|
|VAT|0.216|0.120|7.4%|
|PAYE|0.240|0.102|1.9%|
|Adj. R2|0.452|||
|Mean of the dependent variable|9.908|||

42
appendices with information for all states in the Northeast region<sup>5</sup> : Appendix A contains a variable list, deflators and codings; Appendix B presents poverty indexes and general measures of inequality from DHB Tables as well as PNAD data; Appendix C contains poverty profiles; Appendix D contains an analysis of poverty probits in Brazil, the Northeast and individual states in the northeastern States.

# **2. Data and Methodology**

This section explains and discusses the data sets applied in the analysis. Then follows a description of the poverty measures and poverty lines applied in the study.

## **2.1. Data**

The vehicle for the analysis in this paper is both money-metric and non-money-metric welfare indicators constructed from available data. The data applied are from two sources: (1) the _Desenvolvimento Humano no Brasil_ – DHB (1998), a co-production of IPEA, IBGE, FJP, and PNUD constructed from the census figures for 1970, 1980 and 1991; and (2) _Pesquisa Nacional por Amostra de Domicílios_ - PNAD (The Brazilian annual National Household Survey) for the years 1981, 1985, 1988, 1990, 1993, 1995, 1997, 1998 and 1999. The PNAD data is an annual national household survey conducted and performed by IBGE, the Brazilian Census Bureau, in the third quarter of each year and it is derived from interviews of approximately 100,000 households. The survey began at national level in 1971 and underwent major revision between 1990 and 1992. This revision has made it difficult to obtain full compatibility of data between the PNAD concept before and after 1992; and since we do compare data across decades, this is important to keep in mind. The survey contains extensive information on personal characteristics, including information on income, labor force participation, and educational attainment and attendance. Ferreira, Lanjouw and Neri (1999) discuss shortfalls of the PNAD data and find that the PNAD underestimates incomes, and most seriously so in rural areas. Appendix A presents the data series, deflators and codings for the PNAD data and indicates differences over the 1981-98 period in the survey instrument.

> 5 There is far more information in the appendices for other northeastern States than actually included in the text.

5
firms in the estimation (Appendix Table 3). For this regression, we use a different measure of risk. The risk of banked firms is based on real and financial data, while that of unbanked firms is calculated using only real data. To simplify, we report only results with industry and municipality fixed effects. The selection results are also robust to using the ex-ante spread as a simple and direct measure of risk instead of using the predicted default probability from our estimated default probability model (Appendix Table 4).

# **5.3 Dynamic Lockdowns and Program Selection**

The result that firms with both positive and negative variations in sales during the pandemic are more likely to apply and be approved for credit indicates that credit flows across the board regardless of how the pandemic affects firms. To further investigate this issue, we analyze credit distribution to firms in municipalities under lockdown and those in contiguous municipalities with free circulation. To do so, we exploit the dynamically staggered imposition by the sanitary authorities of mandatory lockdowns across municipalities (as explained in Appendix A). These measures aim to contain the coronavirus as it spreads over time across the country and generates a spatial heterogeneity of exposure to social distancing measures. Appendix Figure 1 presents the map of municipalities according to their overall lockdown status and shows substantial geographical variation. Appendix Figure 2 presents the weekly evolution of the cumulative number of municipalities under lockdown.

At the firm level, the lockdowns produce a plausibly exogenous source of variation of sales since the sales of firms in municipalities affected by the sanitary measures decline substantially (Briones et al., 2021). We thus define the treatment event as the week in which a municipality enters a mandatory lockdown. Treated firms are in municipalities that enter a lockdown at any point during May to July of 2020. Control firms are those in adjacent municipalities that remain open during the same period. The underlying assumption is that firms in adjacent municipalities are similar in terms of risk, firm attributes, and exposure to the pandemic and only differ in how they are affected by an exogenous decision to lock down the community.

To estimate selection into public policies by firms affected by the lockdown measures, we run the following regression: _Program Useit_ = _βLockdowni_ + _γPostit_ + _δLockdowni × Postit_ + _αl_ + _αt_ + _uit._ (3) _Program Useit_ is equal to one if firm _i_ participates in a public program in week _t_ and is zero otherwise. _Lockdowni_ is a dummy equal to one if firm _i_ is in a municipality subject to a lockdown and is zero otherwise. _Postit_ is a dummy equal to one after the firm’s municipality 17
An important caveat is that self-reported barriers to account opening might be inflated by government efforts to offer JDY accounts to the unbanked, who may also have lower levels of financial capability, including poor adults and those living in rural areas, who might have the greatest difficulties accessing bank branches, documentation, etc. In other words, although JDY accounts might have simpler opening requirements, adults opening JDY accounts might report greater difficulties, relative to adults that opened traditional accounts.

**Box 1: Efficiency of Application Process**

|Travel Time|Adults who report it tookmore than 1 hour to get to the required location<br>to apply for an account|
|---|---|
|Transport cost|Adults who report thattransportation coststo get to the place to apply for<br>an account wereexpensive|
|Necessary documents|Adults who report that theprocess of getting all necessary documentsto<br>apply for an account wasdifficult|
|Minimum deposit|Adults who report that they wererequired to deposit a minimum amount<br>of money to apply for an account|
|Presents/Money|Adults who reportgiving presents or additional money to apply for<br>an account|

# **_5.1 The efficiency of opening an account (among applicants in the past five years)_**

In response to the question, “Was the whole process of applying for an account easy or difficult?”, 15 percent of adults who applied for an account within the past five years found the overall process difficult (Figure 5). The survey investigated further and asked questions on different aspects of cost and ease of application to identify specific challenges to the overall efficiency of the process. Figure 6 summarizes, by step of the application process, the percentage of adults that report the process of applying for a new bank account (within the past five years) to be expensive or difficult.<sup>20</sup>

> 20 Summary statistics by state are shown in Appendix 2.

16
# **TABLE A12:  Gallup International (GAL, GMS)**

## **A12: Gallup International**

http://www.gallup-international.com Gallup International was founded in May 1947, is registered in Zurich, Switzerland, and has 55 members around the world governed by the same Code of Statutes to ensure technical competence and quality standards.

The Gallup International Millennium Survey polled 57,000 adults in 60 different countries of the world between August and October, 1999. The survey covered a wide range of topics of an ethical, political and religious nature, focusing specifically on issues related to democracy, the United Nations, human rights, women's rights, environment, religion, crime and basic values. This source asks several questions which also appeared in the Gallup 50th Anniversary Survey which we use for 1998.y ( ) The survey interviews citizens all around the world and helps understand the opinion of today's world population on issues like the environment, terrorism, global issues, governance and democracy. In 2004, in conjunction with Transparency International Gallup initiated the Global Barometer survey, interviewing citiznes on corruption issues.

In the table below we list the variables included in each of the governance indicators. In this paper, we use data from the 2002-2005 Voice of the People Surveys, the 2004-2005 Global Barometer Surveys, the 2000 Gallup Millenium Survey and the 1997 50th Anniversary Survey.

|**Table A12: Gallup International Citizens Surveys (69 devel**|**oped and**|**devel**|**oping**|**coun**|**tries**|**)**||
|---|---|---|---|---|---|---|---|
||2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||
|Trust in National Government|..|..|..|X|..|..|..|
|Trust in the Parliament|..|..|..|X|..|..|..|
|Percent who believe the country is governed by the will of the people|..|..|X|..|X|..|..|
|Percent who believe elections are free and fair|X|X|X|..|X|..|..|
|Percent who believe the government is accountable|..|..|..|..|X|..|..|
|Freedom of speech|..|..|..|..|X|..|..|
|Human Rights|..|X|..|..|..|..|..|
|Satisfaction with Democracy|X|..|..|..|..|..|..|
|**Political Stability**||||||||
|Terrorism / crime|..|X|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Percent who believe the government is efficient|..|..|..|..|X|..|..|
|**Regulatory Quality**||||||||
|NA|..|..|..|..|..|..|..|
|**Rule of Law**||||||||
|Trust in the Legal System|..|..|..|X|..|..|..|
|Concern with level of crime|..|..|..|..|X|..|..|
|**Control of Corruption**||||||||
|Percent who believe the government is corrupt|..|..|..|..|X|..|..|
|Frequency of corruption|X|X|..|..|..|X|..|
|Frequency of household bribery|X|X|..|..|..|..|..|
|Extent of grand corruption|..|X|..|..|..|..|..|
|Extent of petty corruption|..|X|..|..|..|..|..|
|_Country coverage:_|_69_|_62_|49|_46_|_60_|_44_|..|

60
19 TabLe 6

|_______________<br>_<br>RDISTRI|BUTION<br>OF UNEMPLOYE|DBYEDUCATION LEVEL, 1988|____<br>,___l|
|---|---|---|---|
||Me|n|Women<br>l|
|Years of school|Standard<br>Definition|Atternative<br>Definition|Standard<br>Definition|
|0|2.44|<br>4.40|1.65|
|1-5 years|9.72|<br>12.53|<br>8.56|
|6|15.99|<br>19.94|<br>14.56|
|7-8|12.92|<br>12.83|8.61|
|9|20.37|<br>18.81|18.90|
|10-12|20.17|<br>18.06|35.61|
|13andmore|18.39|<br>13.43|12.12|
Wage Structure: Evidence from Brazil.” _World Bank Economic Review_ 18(3): 319–44. Paz, L. S. 2014. “The Impacts of Trade Liberalization on Informal Labor Markets: A Theoretical and Empirical Evaluation of the Brazilian Case.” _Journal of International Economics_ 92(2): 330–48.

- Perry, G. E., O. Arias, P. Fajnzylber, W. F. Maloney, A. Mason, and J. Saavedra-Chanduvi. 2007. _Informality: Exit and Exclusion_ . Washington, DC, Unites States: World Bank.

- Pierce, J., and P. Schott. 2016. “The Surprisingly Swift Decline of US Manufacturing Employment.” _American Economic Review_ 106(7): 1632–62.

- Revenga, A. 1997. “Employment and Wage Effects of Trade Liberalization: The Case of Mexican Manufacturing.” _Journal of Labor Economics_ 15(S3): S20–S43.

- Roberts, M., and J. R. Tybout. 1996. “Industrial Evolution in Developing Countries: A Preview.” In: M. Roberts and J. R. Tybout, Editors. _Industrial Evolution in Developing Countries_ . Washington, DC, United States: World Bank.

- Robertson, R. 2004. “Relative Prices and Wage Inequality: Evidence from Mexico.” _Journal of International Economics_ 64(2): 387–409.

- Rodr ́ıguez-Castel ́an, C., L. L ́opez-Calva, N. Lustig, and D. Valderrama. 2016. “Understanding the Dynamics of Labor Income Inequality in Latin America.” Policy Research Working Paper 7795. Washington, DC, United States: World Bank.

- Song, J., D. Price, F. Guvenen, N. Bloom, and T. Von Wachter. 2018. “Firming Up Inequality.” _Quarterly Journal of Economics_ 134(1): 1–50.

- Tavares, P., and N. Menezes-Filho. 2011. “Human Capital and the Recent Fall of Earnings Inequality in Brazil.” _Brazilian Review of Econometrics, Sociedade Brasileira de Econometria_ 31(2).

- Topalova, P. 2007. “Trade Liberalization, Poverty and Inequality: Evidence from Indian Districts.” In: A. Harrison, editor. _Globalization and Poverty_ . Chicago, United States: University Chicago Press.

- Topalova, P. 2010. “Factor Immobility and Regional Impacts of Trade Liberalization: Evidence on Poverty from India.” _American Economic Journal: Applied Economics_ 2(4): 1–41.

- Tornarolli, L., D. Battist ́on, L. Gasparini, and P. Gluzmann. 2014. “Exploring Trends in Labor Informality in Latin America, 1990-2010.” Documento de Trabajo 159. La Plata, Argentina: Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS).

- Verhoogen, E. 2008. “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector.” _Quarterly Journal of Economics_ 123(2): 489–530.

- World Bank. 2016. “World Bank Development Report 2016: Digital Dividends.” Washington, DC, United States: World Bank.

43
- 23 -

# Tablo 7: STATIC. LONr-C4U SOLUTIONS OF TE UNRESTRICTED ERROR IECTION NOVEL

33 Commodity Index (sample 1952-88) LnlOP33 * 4.906 -5.549 TRBt (0.084) (1.576) Agricultural Commodities (sample 1952-88) LnlCPAG a 4.832 -5.189 TRBt (0.127) (2,140) Minerals and Metals (sample 1954-88) LICPW4 * 5.035 - 5.826 TROt (0.129) (2.169) Agricultural Foods (sample 1952-88) LnICPAGF a 4.737 - 7.279 TRB, + 0.075 L#.POIL (0.196) (2.309) (0.079) Agricultural Non-Food (sample 1952-88) LnICPAGWF 4.893 -6.424 'R3t (0.271) (3.641) Note: Standard errors are In paranthoses.
<!-- Start of picture text --> 2014 2017<br>ancqualiy |Ds ancquality |<br>Wealth |is wealth |<br>education |] Hl education |] i!<br>ancMedicallyTrained 7 Hill anc4plus 7 ii!<br>anc4plus 7 Hi ancMedicallyTrained |] Hl<br>amenities_basic |] Hl ancPrivate 7 Hi<br>mediaAcess 7 Hl mediaAcess 7 il<br>labtests 7 Hl amenities_basic 7 i<br>ageMarriage 7 ageMarriage 4 Il<br>division 7 Hi division 7 I<br>ie) 10 20 30 ie) 10 20 30<br>Relative importance Relative importance<br><!-- End of picture text -->

<!-- Start of picture text --> 2014 2017<br>ancPublic |is ancPublic 5<br>ancquality |] ancquality] i’<br>amenities_basic] Hi readiness_delivery 7] Hill<br>equipment_basic 7 Hil ancPrivate7 Hil<br>wealth | Hill division] Hill<br>ageMarriage 7 Hl equipment_basic 7 Hi<br>division] staff_delivery] Hl<br>labtests 7 Hl age<br>staff_delivery7 Hi education] Hl<br>visitedByNgo 7 Hf amenities_basic] Hl<br>fe} 10 20 30 40 fe) 10 20 30<br>Relative importance Relative importance<br><!-- End of picture text -->
# Assessing Urban Policies Using a Simulation Model with Formal and Informal Housing: Application to Cape Town, South Africa<sup>_∗_</sup>

Basile Pfeiffer<sup>a</sup> , Claus Rabe<sup>b</sup> , Harris Selod<sup>c</sup> , Vincent Viguié<sup>a</sup>

> _aCIRED, Nogent-sur-Marne, France_

> _bIndependent Consultant, Cape Town, South Africa_

> _cThe World Bank, Washington D.C., USA_

**Keywords** : LUTI model, urban planning, informal settlements, backyarding, Cape Town **JEL classification** : R14, R31, R52

> _∗_ We thank the City of Cape Town for its support and facilitation of access to the data used in this paper. We are especially grateful to Peter Ahmad, Hugh Cole, Jaco Petzer, Eloise Rousseau, John Spotten and participants to the City of Cape Town’s workshop “Economics of Human Settlements” (August 27, 2018) where preliminary simulations were presented. We are also indebted to Roger Behrens, Edward Beukes, Jan Brueckner, Rob McGaffin, Danie du Plessis, Hazvinei Tamuka Moyo, Ivan Turok, Tony Yezer and Mark Zuidgeest for useful discussions and remarks, as well as to participants to the World Bank/GWU/IGC Urbanization and Poverty Reduction Research Conference (September 7, 2018), the World Bank’s seminar “Backyarding in South Africa” (October 18, 2018) for valuable feedback. Funding from DFID under the World Bank’s Strategic Research Program is gratefully acknowledged.
# **Contents**

|1.|Introduction...................................................................................1|
|---|---|
|2.|The Quest for the Right Balance...........................................................1|
|3.|Leading Change: Developing a Vision and a Strategy.................................5|
|4.|Realigning the authorizing environment to a focus on societal goals................7|
||a. Clarifying Roles of Various Governments in Public Service Delivery ...........8|
||b. Taxing Choices: Determining Who Taxes What, At What Rate and How?.....17|
||c. Designing Fiscal Transfers: Dividing the spoils or creating an enabling|
||framework for innovative and competitive service delivery.........................21|
||d. Facilitating Responsible Credit Market Access ....................................27|
||e. Oversight of Local Governments: Freedom and Responsibility within|
||Boundaries...............................................................................29|
||f. Ensuring a fair suck of the sauce bottle: institutional considerations.............30|
|5.|Aligning Operational Capacity with the Authorizing Environment.................34|
|6.|Adapting to a changing world with learning from each ..............................35|
|Refere|nces...........................................................................................40|
|**Tables**||
|Table 1|: Governance Structure—20th Versus 21st Century....................................4|
|Table 2|. Decentralization Indicators for Developing and Transition Economies...........15|
|Table 3|: Central Involvement in Local Functions Remains Extensive.......................16|
|**Figure**|**s**|
|Figure|1: Public Sector Institutional Environment in Transition and Developing<br>Countries...................................................................................7|
|Figure|2: Sub-national Expenditures as % of GDP.............................................12|
|Figure|3: Sub-national Expenditures as % of Consolidated Government<br>Expenditures.............................................................................12|