rafmacalaba/datause-encoder-data huggingface dataset:
and one-third of the self-employed appear to share characteristics that give them the potential to be owners of somewhat larger firms, while the remaining two-thirds to three-quarters look much more like wage earners. Quasi-experimental evidence from Mexico (Bruhn (2013)) and Brazil (Fajnzylber, Maloney, and Montes-Rojas (2011)) suggest that only the high-potential informal business owners who have characteristics similar to the owners of formal businesses are likely to formalize after the simplification of business entry and tax regime, albeit the effect is small particularly in the case of Mexico. Our finding on the heterogeneous effect on tax registration based on firm revenues thus complements the existing literature on formalization and suggests that rather than wasting resources and efforts to encourage formalization of the entire informal sector, policymakers would be much better off by targeting the high-potential informal firms for formalization from the perspective of increasing the revenue source. Also these high potential firms are the ones that could potentially reap the benefits of formalization most in terms of increased market and credit access; as such a well-targeted effort could be a win-win situation both for the policymakers and for the high-potential informal firms.

# **6.3 Heterogeneous Direct Effects: Intensity of Treatment at the Market Level**

Treating a varying number of firms in the different markets is interesting to analyse in order to determine whether the treatment effects are a function of the intensity of the treatment at the market level. We do so following the usual tabular structure seen before and present these results in Table 4. The results suggest that the number of treated firms in a market does not affect the direct treatment effects as the point estimates are quite similar (and statistically indistinguishable) for the above- versus below-median number of treated firms in a market. As will be shown later, this result is encouraging since we are also able to perform the same test for the spillover or indirect treatment effects ( _ITE_<sup>_′_</sup> _s_ ) which will allow us to assess the saliency and perception of the threat of punishment for control firms when neighboring firms receive a visit from the NBR.

**** TABLE 4 HERE****

14
-23 -

provided for changes in lung function that are likely to occur after exposure to certain forms and levels

# of particulate matter.

Table 2 sunmarizes the morbidity outcomes associated with particulates in this review.

Table 2: Morbidity Effects of 10 pg/r<sup>3</sup> Change in PM1O

|Morbidity|Central|High'|
|---|---|---|
|RHA/100,000|12.0|15.6|
|ERV/100,000|_235.4_|342.5|
|RAD/person|0.575|0-903|
|LRI/child/per asthmatic|0.0169|0.0238|
|Ashma attacksfper asthmatic*|0.326|2-73|
|Respiratory Symptoms/person|1.83|2.74|
|Chronic Bronchitisll00,000|61.2|91.8|

*Applies to the 8.25 percent of the Indonesian population that is assumed asthmatic. #A high (low) estimte is obtained by increasing (decreasing) the coefficient by one estimated standard derivation.

# 2. Sulfur Dioxide

Effects of sulfur dioxide (SO2 on the respiratory system have been observed after either shortterm (less than one hour average) or longer term (24-hour average or longer) exposures. Several recent epidemiologic studies indicate _that_ changes in 24-hour average exposure to S02 my affect lung funion, the incidence of respiratory symptoIs and diseases, and risks of mortality. These studies have been conducted in different geographic locations and climates, and with different populations and covarying pollutants. Although many of these investigations also indicate that particulate matter or ozone was associated with these adverse health outcomes, several studies appear to show an effect of SO2 alone. Furthemnore, in some of the publications reporting an effect of both S02 and parciculates, they are highly correlated, but in others, the correlation of the daily levels is only weak to moderate. Thus, it is possible
in post-compulsory schooling (grades 9 to 11) increases. This finding for post-compulsory schooling is consistent with stronger sheepskin effects for men in urban areas, which results from the substantial difference in the labor-force participation rates of men and women. This finding also implies that lowering the cost of schooling, by providing free and accessible schooling, is not sufficient to eradicate the gender gap in Turkey, despite its substantial positive effect on girls. School availability must be combined with other policies that especially target girls to eradicate the gender gap.

The rest of the paper is organized as follows. Next, we explain the education system as well as the new compulsory schooling policy in Turkey. In section II, we discuss the conceptual framework for the interpretation of our findings. In section III, we present the data and descriptive statistics. In section IV, we discuss the identification method and estimation. This section is followed by a presentation of the results in section V. Section VI concludes.

_I. EDUCATION SYSTEM IN TURKEY AND THE NEW COMPULSORY SCHOOLING POLICY_ Prior to 1997, the education system in Turkey was built on a 5+3+3 system, which meant five years of compulsory primary, three years of noncompulsory lower secondary, and three years of upper secondary schooling. Schools in Turkey are coeducational. In 1997, just before the implementation of the policy, the net enrollment rate in secondary schooling, the first level of non-compulsory schooling, was only 52 percent (World Bank 2013). In addition, the gender gap and urban-rural gap in education were substantial. According to the 1998 wave of the Turkish Demographic and Health Survey, among 11- to 15-year-old boys, while 79.4 percent of urban residents were enrolled in school, this figure was 67.1 percent among rural residents. The urban-rural gap was much wider among girls of the same age: 38.3 percent of rural residents were enrolled in school compared to 64.5 percent of urban residents.

6
17

**Table 4: The Contribution of Income Sources to Total Household Income Inequality in 1981, 1993 and 2004.**

||**Total**<br>**Household**<br>**Income per**<br>**Capita**|**Total Earnings**<br>**from**<br>**Employment***|<br>**Total Income**<br>**from Self-**<br>**Employment****|**Total Employer**<br>**Income*****|**Total Social**<br>**Insurance**<br>**Transfers #**|**All Other**<br>**Incomes ##**|
|---|---|---|---|---|---|---|
||||**1981**||||
|Mean|336.71|196.33|58.04|32.92|32.02|17.41|
|E(2)|1.447|2.097|5.147|31.000|11.502|33.106|
|Correlation with household<br>income (ρf)|1|0.709|0.268|0.472|0.356|0.429|
|Relative mean (χf)<br>ρ<br>_f_|1|0.583|0.172|0.098|0.095|0.052|
|Absolute factor contribution<br>(_S_f)<br>χ<br>_f_|1.447|0.720|0.126|0.309|0.138|0.153|
|**Proportionate factor**<br>**contribution (sf)**|**1**|**0.498**|**0.087**|**0.214**|**0.095**|**0.106**|
|<br>E(2), yf>0|1.447|1.352|1.658|1.193|2.325|4.413|
|Pop share with yf>0|1|0.713|0.382|0.054|0.235|0.146|
||||**1993**||||
|Mean|320.73|166.15|57.80|37.55|45.27|13.95|
|E(2)|2.308|3.115|7.626|51.177|9.386|49.332|
|Correlation with household<br>income (ρf)|1|0.615|0.319|0.584|0.345|0.400|
|<br>Relative mean (χf)<br>ρ<br>_f_|1|0.518|0.180|0.117|0.141|0.044|
|<br>Absolute factor contribution<br>(_S_f)<br>χ<br>_f_|2.308|0.854|0.241|0.743|0.227|0.243|
|**Proportionate factor**<br>**contribution (sf)**|**1**|**0.370**|**0.104**|**0.322**|**0.098**|**0.105**|
|E(2), yf>0|2.308|2.106|2.467|2.510|2.288|7.433|
|Pop share with yf>0|1|0.721|0.365|0.058|0.282|0.159|
||||**2004**||||
|Mean|393.88|196.06|60.76|44.12|76.82|16.11|
|E(2)|1.618|2.101|6.801|43.301|6.925|23.090|
|Correlation with household<br>income (ρf)|1|0.569|0.310|0.598|0.443|0.299|
|<br>Relative mean (χf)<br>ρ<br>_f_|1|0.498|0.154|0.112|0.195|0.041|
|<br>Absolute factor contribution<br>(_S_f)<br>χ<br>_f_|1.618|0.522|0.158|0.561|0.289|0.088|
|**Proportionate factor**<br>**contribution (sf)**|**1**|**0.323**|**0.098**|**0.347**|**0.179**|**0.054**|
|<br>E(2), yf>0|1.618|1.365|1.991|2.115|1.923|6.567|
|<br>Pop share with yf>0|1|0.717|0.341|0.060|0.326|0.300|

* Includes all earnings from both formal (com carteira) and informal (sem carteira) employment.

** Includes all income from own-account (conta-própria) activities.

*** Includes all incomes described as labor remuneration to employers.

# Includes all occupational pensions, retirement incomes and other social security incomes, but NOT social assistance transfers. ## Includes all social assistance transfers, capital incomes, and incomes from rents. Notes: all incomes are in per capita terms, and are measured in September 2004 Reais. <u>Source: Author’s calculations from PNAD 1981, 1993 and 2004.</u>

C. Ferreira (1994).
<!-- Start of picture text --> 45 40<br>4.0<br>35<br>3.0<br>2<br>1.6<br>20 20 20 20 2.0<br>. 00 80.0 00<br>GSE OTP EP OE<br><!-- End of picture text --> <!-- Start of picture text --> 12 1100<br>1.0<br>08<br>0.6 05<br>oo oo ff<br>PP SOESPOSE<br><!-- End of picture text -->
# **I Introduction**

There is substantial variation in intergenerational mobility (IGM) in education–measured as the likelihood of children completing at least primary education when parents did not complete primary school–across provinces and districts of 24 countries in the Latin America and the Caribbean (LAC) region (see Munoz, 2021a).<sup>1</sup> This variation in upward mobility can be the result of sorting of families (i.e., the fact that different families choose to live in different places) or, alternatively, can be the result of the existence of causal effects of regions (i.e., the place where people live affects the level of upward mobility independently of the characteristics of the family).

In this paper, I use 21 census samples that span 11 countries in LAC to investigate how much of the variation in upward mobility can be attributed to the effect of being exposed to certain places during childhood. In particular, I use families that moved across districts/provinces and exploit variation in children’s age at the time of the move to isolate sorting from regional childhood exposure effects. This empirical strategy that relies on observational data was first proposed in Chetty and Hendren (2018a) to study the effect of commuting zones on IGM in income in the US and adapted by Alesina, Hohmann, Michalopoulos, and Papaioannou (2021) to the context of educational IGM in developing countries.

I find evidence of regional childhood exposure effects as well as significant sortingselection. I estimate a convergence rate of 3.5% per year of exposure between the ages 1 to 11, which implies that children who move at age 1 would pick up 10 _×_ 3 _._ 5 = 35% of the observed difference in permanent residents’ outcomes between their origin and destination regions. In addition, I find significant selection effects of approximately 42%, which implies that individuals who move to a region where permanent residents have 1 percentage point higher upward mobility have close to 0.42 higher mobility themselves purely due to selection. Given that exposure effects are identified under the strong assumption that the timing of

> 1See Figure A1 in the Appendix for a reproduction of the map of IGM at the district-level.

2
Table 7: Climate Change Impacts from AOGCM Scenarios to Small Farms.

||Land Value:|Net Revenue:|Land Value:|Land Value:|
|---|---|---|---|---|
||All|All<br>Small Far|Rainfed<br>ms|Irrigated|
|Baseline|1728|449.3|1748|1807|
|2020|||||
|CCC|-272** (-16%)|-81** (-18%)|-239** (-14%)|-235** (-13%)|
|CCSR|-223** (-13%)|-68** (-15%)|-189** (-11%)|-121 (-7%)|
|PCM|13 (+1%)|-30** (-7%)|230** (+13%)|1007** (+56%)|
|2060|||||
|CCC|-570** (-33%)|-160** (-38%)|-529** (-30%)|-519** (-29%)|
|CCSR|-387** (-22%)|-108** (-24%)|-368** (-21%)|-172 (-10%)|
|PCM|-121** (-7%)|-69** (-15%)|98* (+6%)|944** (+52%)|
|2100|||||
|CCC|-1060** (-61%)|-288** (-64%)|-1004** (-57%)|-702 (-39%)|
|CCSR|-617** (-36%)|-170** (-38%)|-590** (-34%)|-438* (-24%)|
|PCM|-228** (-13%)|-91** (-20%)|-5 (0%)|1082** (+60%)|

Impacts to small farms in the sample measured in USD/ha. .

* implies a significance at 95% confidence level and ** at 99% confidence level.

34
educated segment of Mexico’s labor force. Table 5 presents growth rates by schooling levels over the 1996-2015 period.

# **Table 5. Growth Rates of the Labor Force by Schooling Levels, 1996–2015**

||**_A_**<br>|**_nnual average growth_**<br>|**_rate_**<br>|
|---|---|---|---|
||**_WAP_**|**_EAP_**|**_Sample_**|
|**Incomplete Primary**|−1.22|−1.68|−3.39|
|**Complete Primary**|0.85|0.81|−1.12|
|**Incomplete Junior High**|0.44|0.45|−1.99|
|**Complete Junior High**|5.03|5.03|3.45|
|**Incomplete Senior High**|1.19|0.75|0.00|
|**Complete Senior High**|6.18|6.16|5.77|
|**University**|4.67|4.39|4.79|
|**All**|2.20|2.31|2.32|
|**Years of schooling**|—|—|—|

_Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

Note: _WAP_ (Working-age Population) includes all persons 18 years of age or older. _EAP_ (Economically Active Population) is a subset of _WAP_ who participate in the labor market. _Sample_ population consists of private sector employees between 18 and 65 years of age, living in localities of 100,000 inhabitants or more, and working between 30 and 48 hours a week.

Four facts are highlighted from the resulting changes in the schooling composition of the labor force over the same period. First, there is a substantive change in the schooling composition of the WAP: in 1996, 48.4 percent had at most completed primary school, and only 18.9 percent had at least completed high school; by 2015, these shares changed to 29.3 and 33.0 percent, respectively. The average years of schooling are 4.7, 7.8, and 9.8 years respectively for WAP, EAP, and the sample in 1996. By 2015, the years have increased to 9.2, 9.8, and 11.5. (See Figure 1).

16
# **Table** 5: **Restricted SURE Estimates**

||(1)|(2)|
|---|---|---|
|In Yt|<br>0.120|0.207|
||(16.93)|(14.13)|
|rt|0.061|0.101|
||(3.42)|(3.62)|
|flit|-0.003|-0.010|
||(-3.125)|(-8.440)|
|n, t|0.065|0.168|
||(3.498)|(10.696)|
|govst|-0.745|-0.660|
||(-14.425)|(-16.446)|
|NOBS|160|144|
|LR|0.000|0.000|

Notes:

1. t-statistics in parenthesis.

2. LR denotes the marginal significance level of the likelihood ratio test on the equality across countries of the long run coefficients.

47
**Table 10** <u>SYS-GMM parameter estimates</u>

|Variable|Coefficient|Std error|
|---|---|---|
|_lag(privinvt)_|.54575***|.0912674|
|_findev_|122.0128**|71.0817|
|_exchraterisk_|18.28363***|8.137896|
|_indepreg_|364.9427*|257.483|
|Obs.|306||
|Fisher test|F(4, 28) = 23.08***||
|Arellano Bond test - Order 1|-2.45***||
|Arellano Bond test - Order 2|-0.07||
|Hansen-J|chi2(23) = 24.34||
|Dif-Hansen|chi2(12)= 12.44||

# **7. Putting together the effects of the overall economic and financial sector development levels on investment in the power sector**

The results discussed so far indicate that economic growth and financial development are both key determinants of the volume of private investment in developing countries’ energy projects. More specifically, our analysis has shown that higher economic and financial development is associated to higher private investment. We therefore express this latter as a function of both variables as described by equation (1) to check whether these effects still hold when the potential interactions between these variables are taken into account by applying the same methodology as in the previous sections. Tables 11 and 12 present our empirical results.

From Table 11 we see that both economic growth and financial sector development indeed remain positively and significantly related to the level of private investment in energy projects, thereby confirming our previous conclusion that these variables are key determinants of private involvement in infrastructure projects’ financing. When the institutional and risk variables are added to the model, none of them appear to significantly affect private investment. We therefore use the model _growth_ and _findev_ only to investigate the consistency of the importance of the existence of an independent energy sector regulator for private investors.

Does the existence of an autonomous regulator still matter? We investigate this question by adding 18
Jaffee, S. (2003) From Challenge to Opportunity: Transforming Kenya’s Fresh Vegetable Trade in the Context of Emerging Food Safety and Other Standards in Europe. World Bank, Agriculture and Rural Development Discussion Paper No. 2. Washington, D.C.

Lee, Dae-Seob, P. Lynn Kennedy, and Stanley Fletcher (2003) Examining the Latin American Peanut Industry: A Multi-Country Econometric Trade Model. Paper prepared for the International Conference of Agricultural Policy Reform and the WTO. Capri, Italy. June 23-26, 2003.

Mathews, K, J. Bernstein, and J. Buzby (2003) International Trade of Meat/Poultry Products and Food Safety Issues, in Buzby (editor).

OECD (2003) Trade Effects of the SPS Agreement. Joint Working Party on Agriculture and Trade, Paris.

Otsuki, T., J. Wilson, and M. Sewadeh (2001) “Saving Two in a Billion: Quantifying the Trade Effect of European Food Safety Standards on African Exports,” Food Policy, 26, 5 (October): 495-514.

Park, D., H. Njapau, and E. Boutrif (1999) “Minimizing Risks Posed by Mycotoxins Utilizing the HACCP Concept. Third Joint FAO/WHO/NEP International Conference on Mycotoxins, Tunis.

Pauwelyn, J. (1999). The WTO Agreement on Sanitary and Phytosanitary (SPS) Measures as Applied in the First Three SPS Disputes. _Journal of International Economic Law_ , <u>2</u> 94), 641649.

Perry, B.D., Randolph, T.F., Ashley, S., Chimedza, R., Forman, T., Morrison, J., Poulton, C., Sibanda, L., Stevens, C., Tebele, N. and Yngstrom, I. (2003). _The Impact and Poverty Reduction Implications of Foot and Mouth Disease Control in Southern Africa_ . International Livestock Research Institute, Nairobi.

Plantconsult (2003) Eurepgap—Introduction Among Small-Scale Producers of Fresh Fruit and Vegetables in Developing Countries. Report prepared for the Dutch Ministry of Agriculture, Ministry of Foreign Affairs, and Central Bureau for Provision Trade.Emmeloord, the Netherlands.

Reardon, T. and Berdegue, J.A. (2002). The Rapid Rise of Supermarkets in Latin America: Challenges and Opportunities for Development. _Development Policy Review_ , 20 (4), 371-388.

Reddy, D., K. Thirumala-Devi and others (2002) Estimation of Aflatoxin Levels in Selected Foods and Feeds in India, in Food Safety Management in Developing Countries. Proceedings of the International Workshop, CIRAD-FAO, December 11-13, 2003.

41
**Figure 3: Five bank concentration index (2007-2017, equally weighted)**

<!-- Start of picture text --> 92<br>83 85 86<br>75 80 78 77 75 78 73 80<br>70 68 69<br>64<br>60<br>29<br>20072017 20072017 20072017 20072017 20072017 20072017 20072017 20072017 20072017<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Global Financial Development Database (World Bank).

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Third, state ownership of banks is a key feature in the ECA region (Figure 4). State ownership is especially high in Eastern Europe and in Russia. Even in large high-income countries like Germany, the public authorities are still active players in the banking sector. After the GFC, governments stepped in to rescue distressed banks by injecting capital in order to stabilize markets (for instance in Belgium, The Netherlands, Slovenia and the United Kingdom). Government ownership naturally brings up issues of efficiency and political influence (Altunbas et al., 2001; Bonin et al., 2005; Dinç, 2005). Within the context of capital regulations, state ownership affects transparency, risk-taking and market discipline.

**Figure 4: State Ownership (% of Banking Sector Assets, equally weighted)**

<!-- Start of picture text --> 66.0<br>39.7<br>37.5 37.5<br>33.9 32.9<br>30.5<br>14.2 17.9 16.414.3<br>11.2 9.0 9.4<br>5.5 6.6 3.6 3.1<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>2008 2018<br>80<br>60<br>40<br>20<br>Banking system's assets government-controlled (%) 0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank and national sources. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus – data not available for 2008; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Data for ECA sub-regions is as per year end 2018 and is obtained from national sources with the help of colleagues in Finance, Competitiveness and Innovation global practice of the World Bank. Data from the ROW is per year-end 2016.

4
# **Table 13.** **<u>Vulnerability decomposition and simulations</u>**

|||Consumption|||Income||
|---|---|---|---|---|---|---|
|||Without|Without||Without|Without|
||Factual|idiosyncratic|covariate|<sup>Factual</sup>|<br>idiosyncratic|<br>covariate|
||(1)|shocks<br>(2)|shocks<br>(3)|<br>(1)|shocks<br>(2)|shocks<br>(3)|
|Poverty rate*|0.80|0.28|0.70|0.80|0.24|0.63|
|Mean vulnerability|0.63|0.39|0.58|0.67|0.33|0.57|
|Vulnerability rate**|0.98|0.64|0.95|0.96|0.46|0.86|
|Poverty induced vulnerability|0.80|0.28|0.70|0.80|0.24|0.63|
|<br>Risk induced vulnerability|0.18|0.36|0.25|0.16|0.22|0.23|
|_Poverty induced/Risk induced vulnerability_|4.42|0.77|2.75|5.11|1.10|2.70|
|Idiosyncratic vulnerability (unobserved)|0.96|0.60|0.94|0.92|0.41|0.83|
|Covariate vulnerability (unobserved)|0.82|0.30|0.73|0.82|0.27|0.67|
|_Idiosyncratic/Covariate vulnerability (unobserved)_|1.17|2.01|1.28|1.12|1.56|1.22|
|Idiosyncratic vulnerability (observed)|0.91|0.00|0.84|0.89|0.00|0.77|
|<br>Covariate vulnerability (observed)|0.91|0.39|0.00|0.90|0.32|0.00|
|_Idiosyncratic/Covariate vulnerability (unobserved)_|1.00|-|-|1.00|-|-|
|(Extreme) Poverty rate*|0.40|0.06|0.29|0.40|0.06|0.24|
|<br>Mean vulnerability|0.46|0.23|0.41|0.45|0.16|0.34|
|Vulnerability rate**|0.87|0.28|0.73|0.76|0.17|0.54|
|Poverty induced vulnerability|0.40|0.06|0.29|0.40|0.06|0.24|
|<br>Risk induced vulnerability|0.47|0.22|0.44|0.36|0.11|0.30|
|_Poverty induced/Risk induced vulnerability_|0.85|0.29|0.66|1.10|0.60|0.82|
|Idiosyncratic vulnerability (unobserved)|0.80|0.27|0.69|0.66|0.15|0.47|
|Covariate vulnerability (unobserved)|0.44|0.07|0.32|0.45|0.08|0.28|
|_Idiosyncratic/Covariate vulnerability (unobserved)_|1.83|3.79|2.16|1.47|1.93|1.68|
|Idiosyncratic vulnerability (observed)|0.62|0.00|0.50|0.58|0.00|0.40|
|Covariate vulnerability (observed)|0.60|0.11|0.00|0.56|0.10|0.00|
|_Idiosyncratic/Covariate vulnerability (observed)_|1.04|-|-|1.03|-|-|

Source: Own computations using _Haitian Vulnerability and Food Security Survey_ , 2007.

Notes: *The poverty line is chosen so that 80% (resp. 40%) of households have expected mean consumption below it. The poverty rate is the percentage of households whose expected mean consumption is below the poverty line. **The vulnerability threshold is 29%.

36