rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6431

# **Abstract**

This paper estimates the impact of informality on firm profits using a new firm-level survey designed specifically for this study. The survey was administered to about 1,200 firms with 50 employees or less in Ecuador’s two largest cities, Quito and Guayaquil, plus two main centers of economic activity near the northern and southern borders. The paper’s results confirm that the extent of firms’ compliance with a set of regulatory requirements is linked to the perceived costs and benefits of informality, such as the probability of detection by the authorities and the likelihood of being fined. Nonetheless, taking into account the non-random placement of firms along the formality-informality spectrum and controlling for a large set of firm, owner, and location characteristics, the paper finds that more formal firms tend to be more profitable and have higher output per worker. This impact operates, inter alia, through more formal firms’ ability to obtain improved access to credit and achieve higher sales by issuing receipts to clients.

This paper is a product of the Poverty Reduction and Economic Management, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at dmedvedev@worldbank.org@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,
**Table 9.  Estimates of Tobit regression**

||**Dep. var**<br>**clust**<br>|**.:****_Qit_ = CPIA**<br>**ers A & B**<br>|**Dep. var.**<br>**Freedo**<br>|**: Economic**<br>**m Index**<br>|
|---|---|---|---|---|
|**Independent variables**|**Coeff.**|**Robust s.e.**|**Coeff.**|**Robust**<br>**s.e.**|
|∑���<br>�<br>A�<br>�.�= # policy actions raised to<br>γ= 0.0,cumulated|0.0741|0.0148***|0.9936|0.183***|
|Cumulated length of engagement|‐ 0.0025|0.0008***|‐ 0.0486|0.0130***|
|GDPper capita at PPP|0.000023|6.84e‐06***|0.0001|0.0001|
|Aid/GDP|0.5077|0.1978***|‐18.98|5.73***|
|Political Rights|‐ 0.00214|0.0222|‐ 0.204|‐ 0.302|
|Country&year fixed effects|Yes||Yes||
|N|2280||2518||
|Number of countries|145||154||

The third test of robustness is to examine whether the relationship between policy actions and
- [17] Donaldson, D. (2013). "Railroads of the Raj: Estimating the Impact of Transportation Infrastructure." American Economic Review, forthcoming.

- [18] Eaton, J., Grossman, G. (1986). "Optimal Trade and Industrial Policy under Oligopoly." Quarterly Journal of Economics, 101: 383–406.

- [19] Gibson, P., Wainio, J., Whitley, D. and Bohman, M. (2001). "Pro...les of Tari¤s in Global Agricultural Markets". Agricultural Economic Report No. 796, Market and Trade Economics Division, Economic Research Service, U.S. Department of Agriculture.

- [20] Karp, L. S. and McCalla, A.F. (1983). "Dynamic Games and International Trade: An Application to the World Corn Market." American Journal of Agricultural Economics, 65: 641-56.

- [21] Krishna, K. and Thursby, M. (1992). "Optimal policies and marketing board objectives." Journal of Development Economics, 38, 1-15.

- [22] Kolstad, C. D. and Burris, A.E. (1986). "Imperfectly Competitive Equilibria in International Commodity Markets." American Journal of Agricultural Economics, 68: 25-36.

- [23] Maccini, S. and Yang, D. (2009). "Under the Weather: Health, Schooling, and Economic Consequences of Early-Life Rainfall." American Economic Review, 99(3): 1006–1026.

- [24] Matsuura, K. and Willmott, C.J. (2007). Terrestrial Air Temperature and Precipitation: 1900-2006 Gridded Monthly Time Series, Version 1.01. http://climate.geog.udel.edu/�climate/.

- [25] Gawande, K. and Bandyopadhyay, U. (2000). "Is Protection for Sale? Evidence
# **Cholera Risk in Lusaka: A Geospatial Analysis to Inform Improved Water and**

# **Sanitation Provision**

## **A1 AUTHORS & AFFILIATIONS Peter W. Gething**<sup>**i,ii §**</sup> **Sophie Ayling**<sup>**iii**</sup> **Josses Mugabi**<sup>**iii**</sup> **Odete Duarte Muximpua**<sup>**iii**</sup> **George Joseph**<sup>**iii**</sup> **Solomon Sitinadziwe Kagulura**<sup>**iv**1</sup>

JEL: L95;C21; C19: I18 Key Words: Water, Sanitation, Utilities, Urban, Spatial Modelling, Targeting, Infrastructure, Cholera, 1 i. John Curtin Distinguished Professor, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, Kent St, Bentley, WA 6102, Australia ii. Telethon Kids Institute, Perth Childrens Hospital, Nedlands, WA6009, Australia. iii. Water Global Practice, World Bank Group, 1818 H Street NW, Washington DC, 20433, USA. § Corresponding author The authors gratefully acknowledge Lusaka Water and Sanitation Company for their collaboration namely Jonathan Kampata (Managing Director, LWSC), Kennedy Mayumbelo (Project Manager – LSP), Mwansa Nachula Mukuka (Sanitation Specialist, LSP) Kalikeka Malate (Senior Engineer, Project Planning, LSP), Lusungu Nyirenda (Wastewater Specialist, LSP). The cholera case data was made available with thanks to the Zambian Ministry of Health and Mazyanga Mazaba liwewe, Prof Victor Mukonka, Prof Nathan Kapata and Dr Muzala Kapina from the Zambian National Institute of Public Health (ZNPHI). The household water and sanitation survey data collection was made possible with then Field Coordinators Sensio Banda and Gertrude Namitala; survey firms Fibonacci Engineering and Palm Associates.

From inside the
In practice model (5) will be estimated first by Ordinary Least Squares (OLS) in order to obtain the residuals _u_ ˆ _it_ , which are then used in place of _uit_ in model (8). In the second stage, model (8) is estimated using the pooled Bernoulli quasi-Maximum Likelihood Estimator (QMLE), which corresponds to maximizing the pooled probit log-likelihood.<sup>11</sup> Because of the panel form of the data one may want to allow for any form of serial dependence across _t_ .<sup>12</sup> Finally, the two-stage estimation procedure should correct for biases in the standard errors. Bootstrap methods are used in the empirical application.

The parameter estimates can then be used to perform several specification tests. In particular, rejection of the null hypothesis ρ = 0 will indicate that _yit_ is endogenous; while the joint significance of the ξ parameters will confirm that the observed time-varying explanatory variables and the cluster-specific unobserved effect are correlated.

All parameter estimates are scaled by the same factor which can be calculated as follows:

> where φ( ) . is the density of the normal distribution (Papke and Wooldridge, 2008). This scale factor is unique and corresponds to the scale effect averaged across all time periods and all cross-sections. Hence the average partial effects of the explanatory variables in **x** it (that is, the partial effects averaged across the population) are obtained by multiplying the scale factor from (9) by the corresponding estimated parameters **β**<sup>ˆ</sup> .

# **6. Empirical analysis**

In what follows we describe the estimation of model (8) using data for 405 clusters over four years (1993-94, 1998-99, 2003 and 2008). The dependent variable is the average proportion of girls aged 5-15 attending school in the community/cluster.

> 11 The Bernoulli log-likelihood function is given by _li_ ( _b_ ) ≡ _yi_ log [ _G_ ( **x
higher education is slightly higher, and they are more likely to have a playground, adequate sanitation for preschool, and a fridge. We also observe that quality indicators are fairly homogeneous across municipalities of different size.

## **2.5 Additional data**

To perform validity tests to our empirical strategy, we use additional data from _Instituto de Pesquisa Economia Aplicada_ (IPEA) on a number of time invariant attributes of municipalities. These include the area of the municipality (in squared Km), its altitude, latitude, and longitude, as well as the distances to the state and federal union capitals. In addition, we use IBGE data from the 1991 census on a number of municipal attributes, notably the share of females aged at least 10 years old that were economically active, the share of black population, and income per capita.

# **3 Empirical method**

Here we present the econometric strategy for examining the impact of FPM transfers on the supply of municipal and private preschool in Brazilian municipalities. As shown by Brollo et al. (2013), the allocation system of FPM transfers over the time period considered in this paper makes it possible to apply a (fuzzy) Regression Discontinuity (RD) design. A municipality will receive high versus low federal transfers (the treatment) depending on its population size (the running variable) in a stochastic manner, but the likelihood of being treated conditional on the running variable is known to have sizable discontinuities at multiple thresholds.<sup>8</sup> In the neighborhood of a given population threshold separating two population intervals of the FPM revenue-sharing mechanism, “theoretical” transfers sharply increase from a lower to a higher level. Theoretical transfers are therefore a step function of population (the running variable). Due to imperfect compliance or measurement error, transfers effectively received by municipalities may differ from theoretical transfers. Theoretical transfers can

> 8Litschig and Morrison (2013) use the same Brazilian data set on federal transfers for an earlier period in a
# **Table 9:** **<mark>Proportion of undernourished individuals who fall into the poorest 20% and 40% of the household consumption per capita distribution</mark>**

||Poores<br>|t 20% of househ<br>|olds<br>|Poores<br>|t 40% of househ<br>|olds<br>|
|---|---|---|---|---|---|---|
||Underweight<br>women|Stunted<br>children|Wasted<br>children|Underweight<br>women|Stunted<br>children|Wasted<br>children|
|Burkina Faso||0.222|0.184||0.449|0.420|
|Ethiopia||0.250|0.230||0.463|0.465|
|Ghana|0.297|0.217|0.184|0.467|0.448|0.378|
|Malawi||0.184|0.182||0.414|0.419|
|Nigeria||0.222|0.275||0.424|0.526|
|Tanzania|0.322|0.319|0.284|0.529|0.565|0.442|
|Uganda||0.214|0.265||0.466|0.496|
|Mean|0.318|0.241|0.228|0.519|0.465|0.448|

Note: Data are drawn from LSMS surveys. Means are population weighted. The table lists the proportion of underweight women, stunted children and wasted children who fall in the bottom 20th and 40th percentiles of the consumption per capita distribution.

46
answering the recall module. The latter module was specifically designed to inform the national statistics agency on the transition from diary to recall, which is planned for 2021.

Our methodological innovation is in the use of diary and recall measurements for the _same_ household. Compared to prior work, we derive conditions – building on Hu and Schennach (2008) – to identify nonparametrically any functional of the underlying distribution of usual consumption allowing for errors in both survey measurements. Our strategy also yields identification of survey error distributions for recall and diary measurements without assuming that errors are mutually independent or independent of usual consumption.<sup>3</sup> Moreover, our estimation framework leaves unrestricted the functional forms of usual consumption and survey error distributions. The availability of repeated diary-recall measurements of food spending or acquisition is not unique to our setting, the Consumer Expenditure Surveys in the United States and the Canadian Food Expenditure Survey being notable examples. This substantiates the external validity of our approach in a number of contexts beyond the specific case study.

Among the conditions needed for identification, three play a fundamental role (as shown in Figure 3). First, we assume that household acquisition in the diary week is a random variable centered on household usual consumption. We make the theoretical case for this assumption using a model of consumer demand (as in Meghir and Robin, 1992). This model also sets the reasons for expecting good estimates of consumption _averages_ from diary data, but not of inequality or poverty rates in general.<sup>4</sup> Second, we seek variability in survey measurements induced by a change in usual consumption. Specifically, we build on the IHSES sampling design and assume that household usual consumption covaries with the average acquisition of same-income households located in other census enumeration areas and interviewed the same week. Our key restriction is that the average acquisition of these peers correlates with household diary and recall measurements only through the household’s own consumption. Third, while we allow survey errors in recalled consumption and diary
basic approach we use calculates the household-level incidence of FONCODES benefits with information from the 1996 INEI survey on per capita income, access to educ;.tional infrastructure, and access to school breakfast programs financed by FONCODES. Two (pc ssible) concerns with this approach are measurement error, and the impact of participation in FONCODES programs on household income.

In rural areas in Peru, households are likely to have little choice of primary school. I n the absence of measurement error, one would therefore expect a high degree of consistency in I he answers given by households within a given rural community about the presence of FONCGI)DES-

> 10 Note that it is important to break the analysis down by year, because of changes over time in both targeting performance and the composition of expenditures. For example, most FONCODES projec s in education were funded in the earlier years, whereas most FONCODES projects in water and sanitatic n were funded in the later years. Graphs of the distribution of expenditures by sector for the entire 19921998 period would therefore (misleadingly) suggest that it was easier to target water and sanitation tlan education projects.

9
these three cases, Brazil ranks in the bottom third of the Doing Business 2018 ranking (176th, 170th and 131th, respectively). With regards to operational costs, the performance is even worse for paying taxes, where Brazil occupies the 184th position out of 190 countries (Figure 20).

# **<u>Figure 20. Doing Business in Brazil: rank and distance to frontier</u>**

|**Topics**|**DB 2018 Rank**|**DB 2017 Distance**<br>**to Frontier (%**<br>**points)**|**Change in**<br>**Distance to**<br>**Frontier 2018-**<br>**2017 (% points)**|
|---|---|---|---|
|Overall|125|56.45|0.38|
|Starting a Business|176|65.05|0.01|
|Dealing with Construction Permits|170|49.83|0.04|
|Getting Electricity|45|82.46|1.23|
|Registering Property|131|52.6|0.02|
|Getting Credit|105|45|..|
|Protecting Minority Investors|43|63.33|..|
|Paying Taxes|184|32.97|..|
|Trading across Borders|139|59.78|4.21|
|Enforcing Contracts|47|66|..|
|Resolving Insolvency|80|47.46|1.69|

Source: World Bank Doing Business 2018 dataset.

Note: An economy’s distance to frontier score is indicated on a scale from 0 to 100, where 0 represents the worst performance and 100 the frontier

## **Box 1. Tax structure and resource allocation in Brazil**
<mark>evidence, new research shows</mark> that the domestic value added of China’s exports is higher than previously thought, and rising over time due to trade and FDI liberalization.<sup>48</sup> <mark>One important policy implication from the micro approach to trade is to have targeted redistribution policies in place when countries experience trade shocks, given that trade impacts are often very localized.</mark> For example, new World Bank work finds that globalization increases income inequality significantly across locations rather than across industries. Therefore, labor market policies focusing on industries, such as U.S. “Trade Adjustment Assistance”, can be less effective than location‐based or active labor market policies, such as Denmark’s “Flexicurity.”<sup>49</sup> The World Bank has already taken steps in the direction of helping policy makers design policy based on insights from this ground‐up approach. It has been doing so by developing statistical tools that enable countries to track the heterogeneous effects of existing policies, such as the Exporter Dynamics Database and the Enterprise Surveys. Moreover, it has increasingly showcased the importance of a firm‐level and sectoral‐level approach to macroeconomic variables in its various analytical and research products, such as the latest World Development Report on global value chains.

> 48 Kee, H., Heiwai T, 2016, “Domestic Value Added in Exports: Theory and Firm Evidence from China.” American Economic Review.

> 49 Artuc, E., Lee, E., and Bastos, P., 2019, “Trade, Jobs, and Worker Welfare,” Mimeo, World Bank.

17
electorate. Lack of accountability in the use of subsidies to achieve private goals, such as political dominance over the sector, may be one of the most difficult problems to overcome when designing a new tariff structure in infrastructure. This is why renegotiations of contracts, for instance, so often result in increases in subsidies to investors to protect users from initially scheduled tariff increases. These aspects lead to a lack of credibility to implement screening mechanisms (sophisticated tariff schemes) or the usage of social program databases as a proxy of affordability issues. The difficulty of securing fair and accountable use of subsidies under club-leadership may also lead to difficulties in the use of prices for demand management. Manipulation of subsidies impedes the signaling effect of prices and thus the benefit of markets.

**_Oversight and regulation:_** Political dominance over all institutions tends to be the norm under club leadership in politics, including regulatory institutions initially intended to be independent. The loss of actual control over tariffs can have painful long-term consequences, as prices and investments decisions become disconnected. Public investment in infrastructure may be kept low, unless it serves the interests of the regime (highways for military operations, for example). Information about sector performance and regulatory decisions is likely to be kept confidential or at least blurred to hinder insights into the regime’s performance.

# **3.3. Industry‐friendliness for party revenues and/or personal benefits in the private sector**

Politicians sometimes reveal a very strong industry-friendliness. The need for campaign funding may be one explanation. Another is that some politicians might want to use their time in political office to expand their own or their allies’ opportunities for private sector positions. While the results in terms of industryfriendliness might be similar, the literature suggests that employment patterns with career opportunities for politicians and regulators—often referred to as “the revolving door”—is less of a welfare-challenge than various forms of lobby pressure or strategic influence from the private sector.<sup>33</sup>
**Binswanger, Hans P., and Shahidur R. Khandker (1995).** “The impact of formal finance on the rural economy of India.” _Journal of Development Studies_ 32, no. 2: 234-262.

**Bhaduri, Amit (1973).** “A study in agricultural backwardness under semi-feudalism.” _Economic Journal_ 83.329: 120-137.

**Burgess, Robin, and Rohini Pande (2005).** “Do rural banks matter? Evidence from the Indian social banking experiment.” _American Economic Review_ 95, no. 3 (2005): 780-795.

**Census of India (2011).** “Table B7: Main workers classified by industrial category, educational level and sex – 2011.” Ministry of Home Affairs, Government of India, New Delhi, India.

**Das, Abhiman, Manjusha Senapati, and Joice John (2009).** “Impact of agricultural credit on agriculture production: an empirical analysis in India.” _Reserve Bank of India Occasional Papers_ 30, no. 2: 75-107.

**De Long, J. Bradford (1988).** “Productivity growth, convergence, and welfare: Comment.” _American Economic Review_ 78.5: 1138-1154.

**Du, Kerui (2018).** “Econometric convergence test and club clustering using Stata.” _Stata Journal_ 17.4: 882900.

**Esposti, Roberto (2011).** “Convergence and divergence in regional agricultural productivity growth: Evidence from Italian regions, 1951–2002.” _Agricultural Economics_ , 42.2: 153-169.

**Government of Bihar (2010-2019).** “Bihar economic survey.” _Finance Department,_ Government of Bihar, Patna.

**Islam, Nazrul (1995).** “Growth empirics: A panel data approach.” _Quarterly Journal of Economics_ 110.4: 1127-1170.

**Kishore, Avinash (2004).**
). Immigrants’ effect on native workers: New analysis on longitudinal data. _American Economic Journal: Applied Economics_ , 8(2):1–34.

- Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X. (2010). MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. _Remote Sensing of Environment_ , 114(1):168–182.

45
# **Online Appendix**

## **A1. Data**

Household surveys are retrieved from the SEDLAC database (CEDLAS and the World Bank, 2019). The data repository covers the period from 1974 to 2015. However, for most countries, the surveys are only available since the 1990s.

To add power to the analysis, I pool data from the adjacent year surveys to the reference years whenever possible. Table A1 lists the mapping of survey years to model year for each country.

**Table A1: Surveys constituting model years**

|Model Year||Survey years included||
|---|---|---|---|
||Costa Rica|El Salvador|Panama|
|1995|1995, 1996|1995, 1996|1995, 1997|
|2005|2004, 2005, 2006|2004, 2005, 2006|2004, 2005, 2006|
|2015|2013, 2014, 2015|2013, 2014, 2015|2013, 2014, 2015|

The sample selection of workers is discussed in detail in the main paper. For the sample, I extract the following variables from the survey – sex, age, years of schooling, occupation, hours worked and labor income.

**Generalities:** The occupational taxonomy assigns workers into 10 professions which correspond to the major groups of the International Standard Classification of Occupations, 1988 (ISCO-88) (ILO, 2012). Following Hsieh _et al._ (2019), the workers engaged in armed forces are dropped. Respondents not in the labor force are assumed to be employed in the home sector. Hence, together with the home sector, a worker can choose from a set of 10 occupations. For Costa Rica and Panama, some recoding is needed to map workers to the same occupational taxonomy.

Besides the number of workers in each _cohort-gender-occupation_ category, I also need the years of schooling, labor income, and hours worked associated with them.