rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Differences between Natives and Non-Natives

||(1)<br>Average<br>Natives|(2)<br>Average<br>Non-Natives|(3)<br>Difference<br>(SE)|(4)<br>with age and gender<br>Fixed effects|(5)<br>+ department<br>and urban-rural<br>Fixed effects|
|---|---|---|---|---|---|
|Precarious housing|0.152|0.067|0.086***<br>(0.015)|0.083***<br>(0.015)|0.049***<br>(0.008)|
|Ownership land & house|0.652|0.692|-0.040***<br>(0.006)|-0.032***<br>(0.006)|-0.021***<br>(0.006)|
|Years of education [24+)|9.111|9.856|-0.745***<br>(0.134)|-0.984***<br>(0.140)|-0.589***<br>(0.085)|
|Use computer [14+)|0.489|0.517|-0.028**<br>(0.012)|-0.063***<br>(0.014)|-0.031***<br>(0.007)|
|Legally married [18+)|0.520|0.598|-0.078***<br>(0.010)|-0.064***<br>(0.007)|-0.038***<br>(0.004)|
|Economically active [18-65]|0.735|0.750|-0.014*|-0.022***|-0.009**|
||||(0.008)|(0.008)|(0.004)|
|Formal employment [18-65]|0.704|0.744|-0.039***|-0.038***|-0.039***|
||||(0.006)|(0.005)|(0.004)|
|Unemployment [18-65]|0.073|0.061|0.012***|0.012***|0.012***|
||||(0.002)|(0.002)|(0.002)|
|Health insurance|0.526|0.642|-0.116***<br>(0.014)|-0.097***<br>(0.013)|-0.065***<br>(0.006)|
|Number of disabilities|0.243|0.197|0.046***<br>(0.005)|0.082***<br>(0.005)|0.057***<br>(0.003)|
|Teen pregnancy [14-19]|0.128|0.111|0.016**<br>(0.007)|0.021***<br>(0.007)|0.008*<br>(0.005)|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : SE clustered at department level in parenthesis. Column 4 includes gender and age fixed effects and column 5 also includes department and urban fixed effects. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

are greater than the differences between Afro-Argentines and the rest of the population on average. While Afro-Argentines obtain, on average, statistically significant worse outcomes with respect to home ownership, being legally married and number of disabilities, they obtain statistically significant better outcomes regarding years of education, use of computer and being economically active.

Another benchmark for comparison is the differences between Natives and non-Natives in the US, see Tables A2 and A3 in the Appendix. These tables are constructed using the five-year sample (2010-2014) of the American Community Survey (see Ruggles et al., 2021). We chose outcomes that are somewhat comparable with the outcomes in Argentina, including home ownership, years of education, being economically active, unemployment, precarious housing, health insurance, number of disabilities and teen motherhood. Natives face a disadvantage in both countries for all outcomes except health insurance. For health insurance, Natives have a small advantage in the US thanks to the Indian Health Services, while Natives experience a disadvantage in Argentina. For the other seven outcomes in which Natives face a disadvantage in both countries, the disadvantage is larger in Argentina than in the US for three outcomes, and smaller for the other 8
**Business Regulations and Growth** Raian Divanbeigi and Rita Ramalho<sup>1</sup>

# JEL classification: **K20, O43, L51**

Keywords: competition, entrepreneurship, firm creation, growth, law, private sector development, regulation.

> 1 World Bank Group, Global Indicators Group of Development Economics Vice-Presidency. The authors may be contacted at rdivanbeigi@worldbank.org and rramalho@ifc.org . We would like to thank – without implication – Carolin Geginat, Adrian Gonzalez, Aart Kraay, Frederic Meunier, Douglas Randall and Hulya Ulku for helpful discussions and comments.
ENIGH parameters generate smaller program effects compared to the results in Table 1 because all the parameters are a little smaller in size than ENCEL-generated parameter estimates. These results confirm the findings of Leite (2007) for Brazil, which show that estimations based on a more heterogeneous sample such as the one surveyed in the national representative PNAD leads to smaller parameter estimates. By estimating the same set of parameters for a subsample of homogenous households, Leite (2007) found a much higher impact on the enrollment rate and reduction of child work.<sup>30</sup>

**Table 3: Estimating Key Parameters from BFL model** Using Baseline 1997 and Enigh 1996 Surveys

|_Boys and Girls_|_Baseline - ENCEL_|_Enigh_|
|---|---|---|
||_8-17y.o._<br>_12-17y.o._|_12-17y.o._|
|α0|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.018<sup>******</sup>|
||(0.008)<br>(0.008)|(0.009)|
|α1|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.017<sup>******</sup>|
||(0.009)<br>(0.008)|(0.009)|
|α2|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.018<sup>******</sup>|
||(0.009)<br>(0.008)|(0.009)|
|M|53%<sup>******</sup><br>54%<sup>******</sup>|61%<sup>******</sup>|
||(2.02%)<br>(3.09%)|(5.01%)|
|D|65%<sup>******</sup><br>76%<sup>******</sup>|76%<sup>******</sup>|
||(14.8%)<br>(12.2%)|(9.9%)|

_Source_ : Baseline Survey 1997 and ENIGH 1996 _Notes_ :<sup>**</sup> Significant at 5% level;<sup>*</sup> Significant at 10% level. Standard Deviations (in parentheses) computed by bootstrapping.

The BFL model also makes the estimation of program effects on poverty and inequality possible, which is useful to examine the importance of _PROGRESA_ as an anti-poverty program in the short 30 Leite (2007) estimates the set of parameters of interest using a sub-sample of children around the means test threshold. This is done by defining two homogenous groups – potential beneficiaries who are below the means test threshold (treatment) and non-beneficiaries who are above the threshold (control) – who have made their choices subject to similar income constraints. He finds that 65 percent of the movement of children out of the labor market that were already in school can be explained as a response to the Bolsa Escola. These two groups around the means test threshold, according to the Regression Discontinuity Design, are more likely to have similar observable and unobservable characteristics which would make it a good approximation of a randomized experiment.

24
- McMillan, M, D Rodrik, and I Verduzco. 2014. “Globalization, Structural Change and Productivity Growth, with an Update on Africa.” _World Development_ 63: 11–32.

- Nagler, Paula, and Wim Naudé. 2014. “Non-Farm Enterprises in Rural Africa: New Empirical Evidence.” _World Bank Policy Research Working Paper_ No. 7066.

- Pardey, Philip G. 2014. “African Agricultural R&D and Productivity Growth in a Global Setting.” In _Frontiers in Food Policy: Perspectives on Sub-Saharan Africa_ , edited by Walter P Falcon and Rosamond Naylor. Stanford Center on Food Security and the Environment.

- Pingali, Prabhu L, Yves Bigot, and Hans P Binswanger. 1987. _Agricultural Mechanization and the Evolution of Farming Systems in Sub-Saharan Africa._ Johns Hopkins University Press.

- Rodrik, Dani. 2014. “An African Growth Miracle.” 2014. Richard H. Sabot Lecture, Center for Global Development. Washington, D.C.

Ruthenberg, Hans. 1971. _Farming Systems in the Tropics._ Oxford: Clarendon Press.

- Timmer, C Peter. 1988. “The Agricultural Transformation.” In _Handbook of Development Economics, Vol ume1_ , edited by H B Chenery and T N Srinivasan, I:275–331. Amsterdam: Elsevier.

- Vollrath, Dietrich. 2014. “The Efficiency of Human Capital Allocations in Developing Countries.” _Journal of Development Economics_ 108 (0) (May): 106–118.

World Bank Group. 2014. “World Development Indicators.”

- http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=wo rld-development-indicators.

- Young, Alwyn. 2012. “The African Growth Miracle.” _National Bureau of Economic Research Working Paper Series_ No. 18490.

20
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_-5-_

# **II. Initial Conditions**

This section analyzes the initial macro conditions under which the liberalization of the financial sector and the opening of the capital account took place, to assess whether the overall macro-economy was benign. It also analyzes the weaknesses in the institutional and incentive framework of financial institutions and corporates at the onset of the capital inflow period. In particular, it will explore:

- existence of imbalances at the macro level;

- structure, conditions, and incentive framework of financial institutions; and

- corporate governance, monitoring and performance in the real sector.

## **1. The Macro Environment**

Following trends evident since 1975, the early 1980s were characterized by large macro imbalances fueled by rapid domestic credit expansion and loose fiscal policy. Domestic demand pressures and an inflexible exchange rate policy led to an appreciation of the real effective exchange rate, a faltering export performance and a large current account deficit over 7 percent of GDP in the late 1970s and early 1980s. In addition., the Thai economy was negatively affected by several external shocks in the late 1970s and early 1980s. These included the second oil shock in 1979, and a decline in Thai export commodity prices that, combined, resulted in a large deterioration in the TOT equivalent to 8 percent of GDP (Kochhar and others, 1996).

**Table 1. Macro Adjustment during the 1980s**

|||**1980**|**1981**|**1982**|**1983**|**1984**|**1985**|**1986**|**1987**|**1988**|
|---|---|---|---|---|---|---|---|---|---|---|
|**GDP**|**(Teal %**change)|5.2%|5.9%|5.4%|5.6%|5.8%|4.6%|5.5%|9.5%|13.3%|
|Exports (GNFS)|**(%**change in USD)|26.6%|7.2%|0.4%|-4.7%|14.1%|-2.2%|22.0%|32.1%|39.3%|
|Investments|(% of GDP)|29.1%|29.7%|26.5%|30.0%|29.5%|28.2%|25.9%|27.9%K|<br>32.6%|
|National Savings|**(%**ofGDP)|22.1%|21.S%|23.3%|22.1%|24.0%|23.9%|25.9%|26.7%,|29.6%|
|**Current Account**|**(%**of GDP)|-6.4%|-7.4%|-2.7%|-7.2%|-5.0%|-4.0%|0.6%|-0.7%1,|<br>-2.7%|
|**Fiscal Balance**|(% of GDP - cy basis)|-4.7%|-4.2%|5.9%|-3.9%|-3.9%|-5.1%|-3.8%|**-1.5%,**|1.3%|
|**M2**|**(%**change)|22.4%|-4.2%|24.1%|23.3%|20.2%|10.3%|13.2%|20.40/,|18.2%|
|Domestic Credit|**(%**change)|18.1%|-4.2%|21.5%|26.3%|17.8%|8.4%|6.0%|17.8%|_15.6%_|
|REER|(1980=100)|100.0|102.8|105.9|108.7|107.3|95.3|85.0|79.9|77.4|
|Inflation|**(%**change in CPI)|19.7%|12.7%|5.3%|3.7%|0.9%|2.4%|1.8%|2.5%i|3.8%|

Source: World Bank Data Base

## **a. The Stabilization Program of 1984-1987**

In response, Thailand implemented a macro stabilization program during the period 1984-87. The program combined a large devaluation of the nominal exchange rate in late 1984 with tighter financial policies. Its main features were as follows:
inputs employed by the firm conditional on industry and the overall amount of material inputs consumed. As noted in the introduction, theory would suggest that productivity can be enhanced through lower diminishing returns through better suited inputs. By contrast, we do not model directly how much of each input is used or their relative concentrations (e.g., a HerfindahlHirschman Index) as we do not know the optimal mix of inputs for each industry. That is, it may be quite beneficial to an industry to have a small amount of a specific input, but it would never be the case that they would want equal proportions of that input to the others utilized.

# **3. Patterns of Input Usage in the Indian Manufacturing Sector**

This section provides a broad descriptive foundation for input usage in Indian manufacturing. Panel A of Table 1a starts by documenting the overall level of input usage by sector of the economy. The first column provides descriptive statistics for the organized sector, while the second through fourth columns consider the unorganized sector. These descriptive statistics are unweighted representations of the raw survey data.

The mean level of input usage for plants in the organized sector is 2.60 inputs, with a standard deviation of 1.79. This level is higher than in the unorganized sector, where the average is 2.35 inputs, and this difference is statistically significant at a 10% level. In both sectors, the median value is 2 inputs. Within the unorganized sector, female-owned businesses have an even lower input usage at 1.91 inputs, compared to the 2.44 average for male-owned businesses. This partially reflects the smaller and often household-based nature of these businesses.

Panels B and C repeat Panel A and separate establishments into those located in urban versus rural areas.<sup>5</sup> There is a clear pattern of higher input usage in urban areas compared to rural areas for all types of establishments. For the organized sector, plants in urban areas utilize 2.78 inputs compared to 2.32 inputs in rural areas. The raw gap is even larger in the unorganized 5 Whether or not a plant is located in an urban area is collected directly from the survey and is not part of the sample stratification. The definition of an urban setting for our surveys is (a) All statutory places with a municipality, corporation, cantonment board or notified town area committee, etc., or (b) A place satisfying the following three criteria simultaneously: i) a minimum population of 5,000; ii) at least 75% of male working population engaged in non-agricultural pursuits; and iii) a density of population of at least 400 per sq. km. (1,000 per sq. mile). The 2000 surveys use classifications based upon the 1991 Census for these demarcations.

7
environment matter, but there is less agreement as to whether absolute differences or differences vis-à-vis competitors are important. Galindo, Micco, and Serra (2003) argue that absolute legal, regulatory and institutional differences across countries can increase entry costs and reduce the participation of banks in foreign countries. Using bilateral foreign banking data for 176 countries, the authors find results that support their hypotheses. In other words, they find that foreign bank penetration is higher between countries that have similar legal origins, banking regulations, and institutional set ups. On the other hand, Claessens and Van Horen (2006) argue that what is important in driving foreign bank participation is not the difference between the institutional environment in the host and source country, but rather the difference taking into account the institutional quality of the competitors from other source countries. Using data for most banks in all developing countries, the authors find that absolute levels of institutional differences between the host and source countries do not matter once they control for the relative advantage the source country has compared to its competitors from other countries operating in the same host.

To summarize, there is substantial evidence that factors such as market opportunities, regulatory barriers in the host countries along with economic, cultural and institutional similarities between home and host economies influence the decision of banks to operate overseas. Contrary to the wealth of evidence on these factors, the role that crises can play in bringing about foreign bank participation has only been discussed anecdotally. For example, in discussing the rise in foreign bank participation in Argentina, Brazil and Mexico, Peek and Rosengren (2000) mention the Tequila Crisis in Mexico and Argentina and the 1999 Brazilian crises as catalytic events. Moreno and Villar (2006) also point to the experience of Mexico in stating that foreign bank entry might be stimulated by the need for countries to recapitalize their banking systems in the aftermath of crises. Similarly, Tschoegl (2003) discusses the importance of crises in bringing about the subsequent entry of foreign banks in emerging markets focusing on 12 countries. Yet, solid empirical evidence on the role of crises is lacking. Section 4 will attempt to fill the gap in the literature by exploring the statistical association between crises and foreign bank participation. We turn next to the implications of foreign bank participation for the degree of competition, efficiency, stability and access to credit in the host countries.

9
- Djankov, Simeon, Rafael La Porta, Florencio López-de-Silanes, and Andrei Shleifer. 2002. “The Regulation of Entry.” _Quarterly Journal of Economics_ 117 (1): 1–37.

- Dollar, David, Mary Hallward-Driemeier, and Taye Mengistae. 2005. “Investment Climate and Firm Performance in Developing Countries.” _Economic Development and Cultural Change_ 54 (1): 1–31.

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- ———. 1987b. “Tests of Alternative Theories of Firm Growth.” _Journal of Political Economy_ 95: 657– 74.

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- Gelb, Alan, Vijaya Ramachandran, Manju Kedia Shah, and Ginger Turner. 2007. “What Matters to African Firms? The Relevance of Perceptions Data.” Policy Research Working Paper 4446, World Bank, Washington, DC.

- Hall, Bronwyn H. 1987. “The Relationship between Firm Size and Firm Growth in the U.S. Manufacturing Sector.” _Journal of Industrial Economics_ 35: 583–606.

- Hausmann, Ricardo, Dani Rodrik, and Andrés Velasco. 2005. “Growth Diagnostics.” John F. Kennedy School of Government, Harvard University, Cambridge, MA.

- IDB (Inter-American Development Bank). 2007. _Living with Debt: How to Limit the Risks of Sovereign Finance._ Economic and Social Progress Report. Washington, DC: IDB.

Jovanovic, Boyan. 1982. “Selection and Evolution of Industry.” _Econometrica_ 50: 649–70.

- Klapper, Leora, Luc Laeven, and Raghuram Rajan. 2004. “Business Environment and Firm Entry: Evidence from International Data.” NBER Working Paper 10380, National Bureau of Economic Research, Cambridge, MA.

19
# **<mark>Table 2. First-stage probit regression of child care attendance on the instrumental variable (marginal effects)</mark>**

|Explanatory variables|Depende<br>|nt variable is child care a<br>|ttendance<br>|
|---|---|---|---|
||Pooled sample|Children age 1-3|Children age 3-5|
|Instrument (child born in|0.092***|0.080***|0.097***|
|December)|(0.017)|(0.018)|(0.024)|
|Age|0.046***|0.033**|0.048***|
||(0.013)|(0.014)|(0.017)|
|Age squared|-0.639***|-0.548**|-0.697***|
||(0.189)|(0.213)|(0.249)|
|Ethnic minority|0.021|-0.029|0.049|
||(0.022)|(0.021)|(0.032)|
|Number of years of schooling|0.016***|0.012***|0.022***|
||(0.002)|(0.002)|(0.003)|
|Dummy year 2010|_Reference_|||
|Dummy year 2012|0.025|-0.033|0.013|
||(0.021)|(0.021)|(0.032)|
|Dummy year 2014|0.039*|0.015|0.089***|
||(0.022)|(0.024)|(0.033)|
|Dummy year 2016|0.078***|0.025|0.088***|
||(0.023)|(0.024)|(0.032)|
|Observations|3,863|1,718|2,145|
|Pseudo R2|0.029|0.072|0.038|

This table reports the marginal effects from the logit regression of child care attendance on the instrumental variable and control variables of women. The observations in these regressions are women of children age 1-6. Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1. Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

34
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