rafmacalaba/datause-encoder-data huggingface dataset:
Conditional Cash Transfers, Political Participation, and Voting Behavior<sup>_∗_</sup> Javier E. Baez Adriana Camacho Emily Conover Rom ́an A. Z ́arate October 1, 2012

**Abstract**

This paper estimates the effect of enrollment in a large scale anti-poverty program in Colombia, _Familias en Acci ́on_ (FA), on intent to vote, turnout and electoral choice. For identification we use discontinuities in program eligibility and variation in program enrollment across voting booths. We find that in the 2010 presidential elections FA beneficiaries relative to non-beneficiaries are more likely to register to vote (1.6-2.5 pp). A standard deviation increase in the proportion of FA beneficiaries at the voting booth results in approximately a 2 pp increase in the probability of casting a ballot and of voting for the incumbent party under which the program was expanded. These results are driven by women, the direct recipients of the transfer. The magnitude of our results does not explain the final outcome of the presidential elections, but their direction shows that voters respond to targeted transfers and that these transfers can foster support for incumbents.

# **JEL Classification:** O10, D72, P16.

**Keywords:** Conditional Cash Transfers, voting behavior, Colombia.

> _∗_ Contact information: Javier E. Baez, World Bank and IZA, email:jbaez@worldbank.org. Adriana Camacho, Department of Economics and CEDE, Universidad de Los Andes, email: adcamach@uniandes.edu.co. Emily Conover, Department of Economics at Hamilton College, email: econover@hamilton.edu. Rom ́an A. Z ́arate, Universidad de Los Andes, email: ra.zarate22@uniandes.edu.co. Comments welcome. Thanks to Roberto Angulo, Hernando Sanchez and Staff at Acci ́on Social for granting access to the data used in the paper. We also thank comments from Paloma Anos Casero, Louise Cord, Aline Coudouel, Marcela Eslava, Francisco Gallego, Theresa Jones, Luis Felipe Lopez Calva, James Robinson, Carlos Rodriguez, Hernando Sanchez, Dean Scrimgeour, Renos Vakis and participants at the 2011 AEA Meetings, Brown University micro lunch, seminars at Lehigh University, Universidad de los Andes, Vassar College, and the World Bank. All errors are ours. 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, or those of the Executive Directors of the World Bank or the governments they represent. _⃝_ c by Javier B ́aez, Adriana Camacho, Emily Conover and Rom ́an A. Z ́arate. All rights reserved.

1
countries. Obviously, focusing on human capital formation, our analysis abstracts from all other indirect channels though which migration a¤ects the sending country (through return migration, remittances, network e¤ects, etc.) or the domestic return to education (wage rate, skill premium, etc.). Although the perspective of a bene...cial brain drain appears more limited than in previous studies, we still obtained "winning" countries. However, the net e¤ect on the total number of educated workers residing in the developing world becomes negative.

# 5 References

Barro, R.J. and J.W. Lee (1993): International Data on Educational Attainment, Journal of Monetary Economics, 32, 363-394.

Batista, C., A. Lacuesta and P.C. Vicente (2007): Brain Drain or Brain Gain? Micro Evidence from an African Success Story, Discussion paper n. 3055, IZA-Bonn.

Baum, C. F., M. E. Scha¤er and S. Stillman (2002): Instrumental variables and GMM: Estimation and testing, Boston College Economics Working Paper, 545.

Becker, G.S., E.L. Glaeser and K.M. Murphy (1999): Population and economic growth, American Economic Review 89(2): 145-149.

Beine, M., F. Docquier and Hillel Rapoport (2001): Brain Drain and Economic Growth: Theory and Evidence, Journal of Development Economics, 64, 1: 275-89.

Beine, M., F. Docquier and H. Rapoport (2007a): Brain drain and human capital formation in developing countries: winners and losers, Economic Journal, forthcoming.

Beine, M., F. Docquier and H. Rapoport (2007b): Measuring international skilled migration: new estimates controlling for age of entry, World Bank Economic Review 21: 249-254.

Commander, S., R. Chanda, M. Kangasniemi and L.A. Winters (2004): Must skilled 26
digit ISIC sectors. This reflects the relatively small matched sample for this pollutant compared to the other air pollutants. The lower bound intensities for PM10 however, are more robust and exhibit a pattern similar to that of Total Particulates.

Table 5.8: Aix Pollution Intensity for Selected Air Pollutants (Pounds/1987 US$ Million Output Value)

|Come<br>Description<br><br>|et<br>Code<br><br>|=[<br>|=[<br>|=[<br>|= <br>|=<br> <br>|[=<br>|
|---|---|---|---|---|---|---|---|
|| wear provucrs<br>|<br>|sta | <br>|as|<br>|<br>apo | <br>|<br> a |<br>|<br>to <br>|<br>|<br>|<br>|<br>|<br>|
|[| parmy propucrs |<br>|stsz| <br>|ae |<br>|age |<br>|as|<br>||<br>||<br>||<br>|
|VEGETABLES||||||||
||omsamrars<br>|<br><br>|sts|<br><br>|ser|<br>|sco|<br>|so|<br>|as72|<br>|soon|<br>|<br>seis|<br>|
|<br><br>|crammint,prooucrs<br>“|<br><br>|<br><br>suze|<br>|<br>sae|<br>|zea|<br>|sa|<br>|<br>a<br>|<br>g<br> <br>|<br> <br>asi|<br>|
|<br><br>saverpropuers<br>|<br>|<br>sa||<br>a||<br>se||<br> Os|<br>|e|<br>|<br>||<br>as||
|<br><br>i<br>REFINERIES<br>|<br>|<br>|<br>|<br><br>|<br>|<br><br>|<br>|
||cowrscrrowmypaopvcrs<br>|__suo]<br>|a7|<br><br>|of<br>||<br>||<br>||<br>||<br> <br>|
|<br>|roompropucrs,wiz.c.|<br>|<br>saan<br><br>|<br><br>azz|<br>|aso|<br>|a|<br>|az<br>|az <br>|<br>a|<br>|
|<br>[prepareawmmasros<br>|<br><br>sazz<br>|<br>_|<br>|<br>cos|<br>|<br>so|<br>|<br>ze|<br>|<br>se|<br>|<br> <br>aa|<br>|
|<br>[| pusrrutgo sererrs |<br><br>|asa|<br><br>|seer]<br>|<br>asa |<br>|<br>asa<br>|<br> asass| <br>|<br> ao| <br>|<br> as |<br>|
||<br>wine ousmrzs |<br><br>|usz | <br>|ao |<br>|of<br>||<br>|| <br>|o<br>|a |<br>|
|[aursrqoorsawwar<br>[<br><br>||sass{<br><br>|asec|<br>|apo|<br>|aos|<br>|as|<br>|a|<br>|as|<br>|
|<br><br>|<br>rosnccomwuracruss<br>|<br><br>saan<br>|<br>azes|<br>|<br>ree]<br>|<br>soo|<br>|<br>az|<br>|<br> so|<br>|<br>a|<br>|
|<br>e<br>FINISHING TEXTILES<br>||<br>||<br>|<br>|<br>|<br>|
|i<br>APPAREL<br><br>||||||||
||murrrmcmes<br>|<br> <br>|sus|<br>|a|<br>|so|<br>|a|<br>||<br>|||e|<br>|
|<br><br>|canversanomoss<br>|<br>|<br>sue|<br>|<br>ot<br>|<br>oft<br>|<br>of<br> <br>|So<br>|ot<br>|<br>|<br>|
|<br><br>|commase,rovecrwims|<br>|<br>sas|<br>|zor|<br>|seo <br>|<br>|<br>goa|<br>|<br>azn|<br>|<br>on| <br>|<br>_a4|<br>|
|<br>|ems,nec.|<br><br>|<br>aio|<br>|<br>as|<br>|<br>sop|<br>|<br> <br>se <br>|<br> ssze|<br>|<br>to|<br>|<br>as||
|<br>[|weansncappares,<br>||<br>anz0||<br>az}|<br>az|<br>||<br>||<br>lf||

54
**Table 19. Significance of legal and regulatory obstacles: Local sourcing or hiring requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.00614|-0.00158|||0.0197|0.00503|||
||(0.0622)|(0.0612)|||(0.110)|(0.114)|||
|Export % of sales|0.00113|0.000446|0.00107|0.000473|0.00183|0.000669|0.00171|0.000685|
||(0.000827)|(0.000810)|(0.000845)|(0.000817)|(0.00147)|(0.00155)|(0.00155)|(0.00160)|
|Share of sourcing via imports|0.00282***|0.00121|0.00292***|0.00116|0.00500***|0.00254|0.00528***|0.00248|
||(0.000872)|(0.000867)|(0.000911)|(0.000898)|(0.00157)|(0.00164)|(0.00169)|(0.00174)|
|High-income source country|-0.00927|0.0522|-0.00984|0.0601|-0.00308|0.100|-0.0103|0.107|
||(0.0960)|(0.0939)|(0.0946)|(0.0918)|(0.172)|(0.179)|(0.174)|(0.179)|
|>250 employees|-0.00175|0.0533|-0.0103|0.0434|0.0206|0.148|-0.000662|0.131|
||(0.0765)|(0.0748)|(0.0771)|(0.0758)|(0.133)|(0.137)|(0.138)|(0.145)|
|Cumulative inv. >US$10M|0.00699|-0.0412|0.000759|-0.0479|-0.000780|-0.0949|-0.00356|-0.0981|
||(0.0737)|(0.0745)|(0.0725)|(0.0719)|(0.131)|(0.138)|(0.131)|(0.138)|
|Years in country|-0.000384|-0.00128|-0.000565|-0.00160|-0.000295|-0.00259|-0.000681|-0.00330|
||(0.00248)|(0.00254)|(0.00243)|(0.00250)|(0.00448)|(0.00464)|(0.00447)|(0.00463)|
|Level of foreign ownership (%)|-0.00288**|-0.00150|-0.00283**|-0.00122|-0.00500**|-0.00283|-0.00497**|-0.00232|
||(0.00131)|(0.00138)|(0.00131)|(0.00137)|(0.00236)|(0.00265)|(0.00239)|(0.00268)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.013|0.080|0.030|0.096|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to use locally produced inputs or local staff]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level. Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

36
Table 4: Correlates of women’s training choices in ICT and energy

||Chos|e the ICT|or the en|ergy voca|tional trai|ning|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|**Sociodemographic characteristics**|||||||
|Age of the respondent (z-score)|0.018|-0.011|-0.012|-0.013|-0.014|-0.019|
||(0.013)|(0.012)|(0.012)|(0.012)|(0.014)|(0.015)|
|Number of dependent children (z-score)|-0.046**|-0.021|-0.022|-0.021|-0.022|-0.022|
||(0.010)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|Household wealth index by cohort (z-score)|0.036*|0.032*|0.033*|0.029*|0.026|0.028|
||(0.017)|(0.015)|(0.015)|(0.013)|(0.013)|(0.014)|
|**Education and training**|||||||
|Years of education (z-score)||0.142*|0.142*|0.141*|0.130*|0.129**|
|||(0.015)|(0.014)|(0.015)|(0.017)|(0.018)|
|Already received training in EICT in the past||0.139**|0.141**|0.148**|0.133**|0.129**|
|||(0.032)|(0.031)|(0.036)|(0.038)|(0.036)|
|**Employment and revenues**|||||||
|Had a paid work in the last 30d|||0.029|0.026|0.026|0.023|
||||(0.016)|(0.016)|(0.018)|(0.019)|
|Worked in EICT in the last 30d|||-0.010|-0.010|-0.014|-0.011|
||||(0.055)|(0.055)|(0.051)|(0.054)|
|**Network**|||||||
|Network size (z-score)||||0.034*|0.031*|0.034*|
|||||(0.013)|(0.013)|(0.012)|
|Proportion of males in the network (z-score)||||0.005|0.002|0.000|
|||||(0.013)|(0.012)|(0.011)|
|**Role model and support**|||||||
|Male role model|||||0.158*|0.159*|
Table S.8: Skill-proximity interactions (OLS)

|Dependent Variable:||log (Loa|nsji)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|log Skilledij ∗log Distanceij|0.099<sup>∗∗</sup>|0.098<sup>∗∗</sup>|0.090<sup>∗</sup>|0.094<sup>∗∗</sup>|
||(0.047)|(0.048)|(0.047)|(0.046)|
|log Unskilledij ∗log Distanceij|0.003|0.003|0.017|0.006|
||(0.048)|(0.049)|(0.047)|(0.047)|
|log Skilledij ∗Languageij|−0.203<sup>∗</sup>|||−0.137|
||(0.112)|||(0.125)|
|log Unskilledij ∗Languageij|0.076<br>(0.102)|||0.006<br>(0.122)|
|log Skilledij ∗Colonizerij||−0.224||−0.103|
|||(0.147)||(0.156)|
|log Unkilledij ∗Colonizerij||0.083||0.000|
|||(0.128)||(0.151)|
|log Skilledij ∗Legalij|||−0.150|−0.065|
||||(0.101)|(0.102)|
|log Unskilledij ∗Legalij|||0.188<sup>∗∗</sup><br>(0.092)|0.156<br>(0.095)|
|log Skilledij|−0.644|−0.635|−0.550|−0.585|
||(0.399)|(0.403)|(0.397)|(0.389)|
|log Unskilledij|−0.014|−0.001|−0.170|−0.077|
||(0.396)|(0.398)|(0.380)|(0.386)|
|log Distanceij|−1.585<sup>∗∗∗</sup>|−1.535<sup>∗∗∗</sup>|−1.646<sup>∗∗∗</sup>|−1.569<sup>∗∗∗</sup>|
||(0.222)|(0.226)|(0.214)|(0.224)|
|Obs.|1,427|1,427|1,427|1,427|
|No. of Lending Countries|17|17|17|17|
|No. of Borrowing Countries|158|158|158|158|
|Estimator|OLS|OLS|OLS|OLS|

Standard errors are clustered at the borrowing country level. N , L and B denote number of observations, number of lending countries and number of borrowing countries. All columns include country fixed effects, colonial link, language, legal origin and common border as additional controls.

8
_Table 5 Intention‐to‐treat effects of the original intervention on main socio‐economic outcomes_

||(1)<br>ITT<br>(no controls)|(2)<br>ITT<br>(controls)|
|---|---|---|
||_Main outcomes – Socioeconomic_||
|Employment index|0.063|0.067|
||(0.281)|(0.242)|
||[0.375]|[0.323]|
|Consumption index|0.094|0.086|
||(0.120)|(0.153)|
||[0.240]|[0.307]|
|Savings, investment|and debt<br>0.274***|0.271***|
|index|(0.000)|(0.000)|
||[0.001]|[0.001]|
|Business skills index|0.016|0.018|
||(0.747)|(0.735)|
||[0.748]|[0.735]|
|Observations|1,523|1,495|

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. Adjusted Benjamini‐Hochberg p‐values are reported in square brackets. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline._ 28
WPS4150 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 12</mark>

# Extending Health Insurance to the Rural Population: An Impact Evaluation of China’s New Cooperative Medical Scheme

by Adam Wagstaff<sup>a</sup> , Magnus Lindelow<sup>b</sup> , Gao Jun<sup>c</sup> , Xu Ling<sup>c</sup> and Qian Juncheng<sup>c</sup> a Development Research Group, The World Bank, Washington DC, USA b East Asia Human Development, The World Bank, Washington DC, USA c Center for Health Statistics and Information, Ministry of Health, Beijing, China

## **Abstract**

In 2003, China launched a heavily subsidized voluntary health insurance program for rural residents. We analyze factors affecting enrollment and combine differences-in-differences with matching methods to obtain impact estimates. We use data collected from program administrators, health facilities and households. Enrollment is lower among poor households, and higher among households with chronically sick members. The scheme has increased outpatient and inpatient utilization (by 20-30%), but has had no impact on out-of-pocket spending or on utilization among the poor. The program has increased ownership of expensive equipment among central township health centers but has had no impact on cost per case.

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: <u>awagstaff@worldbank.org.</u>

**Keywords:** China; health insurance; cooperative medical scheme; impact evaluation.

**Acknowledgements:** The research reported in the paper began as a background paper for a larger World Bank study of rural health reform in China, details of which are available at <u>http://www.worldbank.org/chinaruralhealth. The</u> larger study was task-managed by L. Richard Meyers, and financially supported by the World Bank and the UK’s Department for International Development. We are grateful to staff from the Ministry of Health in China for support and technical advice, to Shengchao Yu and Helena Chang for their help in designing and fielding the survey and in organizing and analyzing the data, and to Bert Hofman for helpful comments on an earlier version of the paper.

World Bank Policy Research Working Paper 4150, March 2007 _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, or of the Government of China. Policy Research Working Papers are available online at http://econ.worldbank.org._
> <sup>25</sup>

rights. The difference here is that secure property rights are seen as a consequence both of the political checks and balances that enhance the credibility of government policies, as in the literature, but also of the willingness of governments to provide public goods.

Regardless of the underlying dynamics of the link, however, the empirical links in this chain have not been explicitly estimated. Table 3 reports the estimates of equations 3 and 4 in the text above, a simple exercise that does just this.

The measure of property rights follows Keefer and Knack (1995) and is the sum of four variables from the _International Country Risk Guide_ (Political Risk Services): the risk of expropriation, the enforceability of contracts with government, corruption and bureaucratic quality. The earliest value of this variable, 1984, is used here. It is well-known that the security of property rights is a significant determinant of financial sector development. This result is reflected, for the specifications and data used here, in the first and third columns in Table 3. These estimations are based on columns 1 and 2 in Table 1a, with the initial value of the property rights variable substituted for the political variable.

Columns 2 and 4 in Table 3 are based on estimates of the system of equations 3 and 4. From equation 3, one extracts the predicted value of the security of property rights based on the particular political variable in the left-most column of Table 3. In all cases, including the case of competitive elections, the 1975 values of the political variables are significant determinants of the security of property rights in 1984. Equation 4 then estimates the effect of this predicted value of the property rights variable on financial sector development, which is the result reported in the cells of Table 3.

From Table 3, it is immediately clear that all of the political variables, with the exception of competitive elections themselves, have a significant influence on financial sector development that runs precisely through their effect on the security of property rights.
# **Countercyclical Financial Regulation**

## **Haocong Ren**<sup>**1**</sup>

## **JEL Classification:** G21, G28

**Keywords:** Countercyclical, procyclicality, macroprudential, financial regulation

1 The views expressed in this paper are those of the author and do not necessarily reflect the views of the World Bank. The author gratefully acknowledges the valuable discussions with and comments by Cesar Calderon, Deepa Chakrapani, Soon Taek Chang, Martin Cihak, Katia D‘Hulster, Erik Feyen, Michael Fuchs, Jose Gomez de Miguel, Eva Gutierrez, Richard Hands, Anoma Kulathunga, Thomas Losse-Mueller, Xiang Qi, Consolate Rusagara, Valeria Salomao Garcia, David Scott, Kiatchai Sophastienphong, Martin Vazquez Suarez, and Makaio Witte at various stages of the paper, and editorial review by Nancy Morrison. All remaining errors are the author‘s. Comments and questions may be sent to hren@worldbank.org.
unobservable time-invariant characteristic (including the firm’s location and any other persistent factor that may affect the firm-bank relationship), firms that are constrained by informal competition are found to have a significantly lower probability of access to bank loans.

Finally, one may be worried that our measure of access to bank funds collapses credit lines and loans that are issued in different times and that, potentially, can date back to many years in advance. To tackle this issue we restricted loan availability to a shorter time horizon for the issuance (10, 7, 5, 2, or 1 year) obtaining results that are virtually unchanged. Results are provided in Table A3 of the Online Appendix.<sup>13</sup> Table 4: Competition from informal firms and loan availability

|Dependent variable:||Lo|an availability|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Constrained by informal_t−_1|-0.0632***<br>[0.0192]|-0.0714***<br>[0.0193]|-0.0840***<br>[0.0225]|-0.175*<br>[0.0942]|-2.187*<br>[1.163]|
|Account_t−_1||0.0374|0.00199|-0.165*|-0.719|
|||[0.0252]|[0.0277]|[0.0953]|[0.937]|
|No need_t−_1||-0.0448***|-0.0467**|-0.0530|0.404|
|||[0.0165]|[0.0192]|[0.0738]|[0.806]|
|Age_t−_1|0.00293|0.00594|-0.00164|-4.018**|-21.45|
||[0.0135]|[0.0137]|[0.0158]|[2.037]|[19.82]|
|Size_t−_1|0.0383***<br>[0.00645]|0.0344***<br>[0.00672]|0.0374***<br>[0.00802]|0.527<br>[1.074]|24.85<br>[33.83]|
|Export_t−_1|0.00420|0.00916|0.0186|0.143|0.176|
||[0.0197]|[0.0196]|[0.0263]|[0.174]|[1.357]|
|Number of competitors_t−_1|||-0.00550<br>[0.00561]|||
|Model|Logit|Logit|Logit|Within|Cond. logit|
|Time FE|yes|yes|yes|yes|yes|
|Geographic area FE|yes|yes|yes|no|no|
|Firm FE|no|no|no|yes|yes|
|Observations|1982|1931|1379|1398|62|
|Pseudo R2 (R2)|0.181|0.186|0.195|0.139|0.355|

_Notes:_ logit marginal effects, within estimator, and conditional logistic models. Variables are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

# **3.3.2 Loan application**

Table 5 shows the estimates for our model when we use as dependent variable a dummy taking value 1 if the firm makes a loan application and zero otherwise. Results of the logit estimates indicate that the effect of being exposed to informality competition on the probability of a loan application is consistently negative across various econometric specifications and samples. In particular, the effect of _Constrained by informal_ is negative when controlling for whether or not the firm does need a loan and the firm has an account (which may imply that the firm can use the overdraft) (column 2), or if we control for the number of firm’s competitors (column 3). In terms of magnitude of the effect, firms more constrained by competition from

> 13Notice that the dataset also contains information on the size and duration of the most recent loan or credit line, as well as the required collateral and its approximate value. Unfortunately, such measures are not populated and the estimating sample size drops substantially (fewer than 200 observations). With the available data no effect of informality emerges.

12