rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix Table I. 3: Effect of Reforms on Unorganized Sector Log (Employment)**

||(1)<br>(2)|(3)|
|---|---|---|
||De-licensing<br>Trade Liberalization|FDI Liberalization|
||_Other-sector Employment Spi_|_llovers_|
|Horizontal Employment Spillovers from|||
|Organized Sector|0.3499***<br>0.3117***<br><br>|0.2225***<br>|
||(0.0416)<br>(0.0344)|(0.0298)|
|Backward Employment Spillovers from|||
|Organized Sector|1.3286***<br>1.0602***|1.4175***|
||(0.1954)<br>(0.1818)|(0.1720)|
|Forward Employment Spillovers from|||
|Organized Sector|0.1016<br>0.2099|0.1415|
||(0.2715)<br>(0.3030)|(0.2734)|
|Reforms|-0.6272<br>0.0393**|-4.5688|
||(1.2758)<br>(0.0173)|(3.9059)|
|Reform*Horizontal Spillovers from|||
|Organized sector|-0.1909***<br>0.0022***|-0.0460++|
||(0.0315)<br>(0.0003)|(0.0340)|
|Reform*Backward Spillovers from|||
|Organized sector|-0.0786<br>-0.0055***|0.0701|
||(0.1168)<br>(0.0015)|(0.1831)|
|Reform*Forward Spillovers from|||
|Organized sector|-0.3024***<br>0.0028*|0.2460|
||(0.1083)<br>(0.0015)|(0.3617)|
||_Other-sector Productivity Spi_|_llovers_|
|Average TFP of Horizontal-linked|||
|Organized Plants|0.0725<br>0.0922+|0.0861++|
||(0.0601)<br>(0.0604)|(0.0611)|
|Average TFP of Backward-linked|||
|Organized Plants|0.3967***<br>0.4802***|0.6593***|
||(0.1356)<br>(0.1377)|(0.1516)|
|Average TFP of Forward-linked Organized|||
|Plants|0.6814***<br>0.5305***|0.5327***|
||(01553)<br>(01583)|(01539)|
||.<br>.<br>_Own-sector Employment Spi_|.<br>_llovers_|
|Forward Employment Spillovers from|||
|Unorganized Sector|0.2373<br>0.0170|0.2987|
||(0.3046)<br>(0.3834)|(0.3270)|
|Backward Employment Spillovers from|||
|Unorganized Sector|1.0265***<br>0.9559***|0.9489***|
||(0.1637)<br>(0.2110)|(0.1595)|
|Reform*Backward Spillovers from|||
|Unorganized sector|0.1194*<br>0.0012|0.1245|
||(0.0700)<br>(0.0011)|(0.1179)|
|Reform*Forward Spillovers from|||
|<br>Unorganized sector|0.4080***<br>-0.0029**|0.0096|
||(0.0952)<br>(0.0014)|(0.2631)|
|Constant|-31.4778***<br>-25.7196***|-32.3953***|
||(5.3179)<br>(5.6820)|(5.4840)|
|Observations|5888<br>5888|5888|
|R-squared|0.1676<br>0.1645|0.1513|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 56
20 percent higher following EU accession. Column 2 presents the OLS results controlling for FDI and Exports, column 3 preesnts the results clustered at the country level to assuage concerns of serial correlation. 27 Clustering does indeed substantially raise the standard errors indicating that there may be serial correlation among countries; nevertheless the results remain highly significant at the 6 percent level. Finally column 4 presents the results clustered at the country level and additionally controlling for FDI and exports, in this final regression the results are less significant. Nevertheless, they remain significant at the 12 percent level and the robustness of the coefficient to these various specifications is encouraging.

The impact of EU accession on enrollment in tertiary education identified above, lends support to the assumption of the endogeneity of human capital formation and hence the hypothesis that increased migration opportunities encourage greater levels of human capital formation that may, given the right parameters, offset or even overpower the brain drain that results from the relocation of skilled labor. Such a substantial increase in enrollment suggests that even in the presence of large outflows of human capital, there is the potential for a positive net effect.

Further work may wish to redo the above analyses after some time has passed. This would allow a number of refinements. Firstly, it would enable a comparison of the post accession trends of the Czech Republic and Slovakia therefore providing a check on the validity of the difference in difference approach. Secondly, the pass of time would provide more data to analyse the impact of the Eastern expansion of 2004, it would also increase the sample by enabling the inclusion of the most recent expansion of 2007 to include Romania and Bulgaria. Lastly, given data continuing to a later date, it may be interesting to look more closely at the possibility of lagged effects.

> 27Bertrand, Duflo, and Mullainathan (2004) present an analysis of the dangers in ignoring serial correlation in panel data, using Monte Carlo data and randomly generated placebo laws to illustrate frequency of inaccurate conventional standard errors. Using standard differencein-difference standard errors they find a statistically significant effect in 45% of the placebo interventions.

23
- **Bursztyn, Leonardo and Robert Jensen** , “Social image and economic behavior in the field: Identifying, understanding, and shaping social pressure,” _Annual Review of Economics_ , 2017, _9_ , 131–153.

- **Castro, Silvia and Clarissa Mang** , “Breaking the Silence-Group Discussions, Social Pressure, and the Adoption of Health Technologies,” _Social Pressure, and the Adoption of Health Technologies (April 11, 2022)_ , 2022.

- **Chandrasekhar, Arun G. and Randall Lewis** , “Econometrics of sampled networks,” Technical Report 2012.

- **Cheema, Ali, Asim I Khwaja, Farooq Naseer, and Jacob N Shapiro** , “Glass walls: Experimental evidence on access constraints faced by women,” Technical Report, Mimeo, Harvard University 2019.

- **Chiappori, Pierre-Andre and Maurizio Mazzocco** , “Static and Intertemporal Household Decisions,” _Journal of Economic Literature_ , September 2017, _55_ (3), 985–1045.

- **Clark, Shelley** , “Son preference and sex composition of children: Evidence from India,” _Demography_ , 2000, _37_ (1), 95–108.

- **, Caroline W Kabiru, Sonia Laszlo, and Stella Muthuri** , “The impact of childcare on poor urban women’s economic empowerment in Africa,” _Demography_ , 2019, _56_ (4), 1247–1272.

- **Comola, Margherita and Silvia Prina** , “Treatment Effect Accounting for Network Changes,” _The Review of Economics and Statistics_ , July 2021, _103_ (3), 597–604.

- **Davis, Jessica, Joseph Vyankandondera, Stanley Luchters, David Simon, and Wendy Holmes** , “Male involvement in reproductive, maternal and child health: a qualitative study of policymaker and practitioner perspectives in the Pacific,” _Reproductive Health_ , July 2016, _13_ (1), 81.

- **Desai, Sonalde and Reeve Vanneman** , “India Human Development Survey-II (IHDS-II), 2011-12: Version 6,” Technical Report, ICPSR - Interuniversity Consortium for Political and Social Research, New Delhi, India 2015.

- **Diaz-Martin, Lucia, Akshara Gopalan, Eleonara Guarnieri, and Seema Jayachandran** , “Greater than the Sum of the Parts? Evidence on Mechanisms Operating in Women’s Groups,” _Unpublished manuscript_ , 2021.

- **Fafchamps, Marcel and Susan Lund** , “Risk-sharing networks in rural Philippines,” _Journal of development Economics_ , 2003, _71_ (2), 261–287.

- **Feigenberg, Benjamin, Erica Field, and Rohini Pande** , “The economic returns to social interaction: Experimental evidence from microfinance,” _Review of Economic Studies_ , 2013, _80_ (4), 1459–1483.

- **Field, Erica, Seema Jayachandran, Rohini Pande, and Natalia Rigol** , “Friendship at Work: Can Peer Effects Catalyze Female Entrepreneurship?,” _American Economic Journal: Economic Policy_ , May 2016, _8_ (2), 125–153.

- **Gallup** , “Americans Satisfied With Number of Friends, Closeness of Friendships,” March 2004.

- **Godlonton, Susan and Rebecca Thornton** , “Peer effects in learning HIV results,” _Journal of Development Economics_ , January 2012, _97_ (1), 118–129.

- **GoI** , “National Family Health Survey, 2015-16,” Technical Report, Ministry of Health and Family Welfare, Mumbai 2017.

28
- Katz, L. F. and K. M. Murphy (1992). Changes in Relative Wages, 1963-1987: Supply and Demand Factors. The Quarterly Journal of Economics 107(1), 35–78.

- Lakner, C. and B. Milanovic (2016). Global income distribution: from the fall of the berlin wall to the great recession. World Bank Economic Review, vol. 30 (2), pp. 203-232.

- Lewis, A. (1954). “Economic Development with Unlimited Supplies of Labor.” _The Manchester School of Economics and Social_ 28: 139-91.

- López-Calva, Luis Felipe, and Nora Claudia Lustig, eds. Declining inequality in Latin America: A decade of progress? Brookings Institution Press, 2010.

- Maloney, William F.; Molina, Carlos A. (2016). Are automation and trade polarizing developing country labor markets, too? Policy Research Working Paper; no. WPS 7922. Washington, DC, World Bank Group.

- Milanovic, B. (2013). Global income inequality in numbers: in history and now. Global Policy 4 (2), 198-208.

- Orcutt, G.H. (1957). “A New Type of Socio-Economic System.” _Review of Economics and Statistics._ 39(2):116-23.

- Piketty, T. (2014). _Capital in the Twenty-First Century._ Cambridge: The Belknap Press of Harvard University Press.

- Ravallion, M. (2013). "How Long Will It Take to Lift One Billion People Out of Poverty?" World Bank Research Observer, World Bank Group, vol. 28(2), pages 139-158, August

- Stiglitz, J. (2012). _The Price of Inequality –How Today’s Divided Society Endangers Our Future._ New York: W.W. Norton & Company.

- Van der Mensbrugghe, D.,2013. Modeling the Global Economy - Forward Looking Scenarios for Agriculture. In P.B. Dixon & D.W. Jorgenson (Eds.), _Handbook of Computable General Equilibrium Modeling._ North Holland Elsevier B.V., pp. 933-994.

- Van Der Mensbrugghe, D. (2011). Linkage Technical Reference Document, version 7.1. Development Prospects Group, The World Bank, Washington DC.

- World Bank (2015). Global Monitoring Report 2015/16: Development Goals in an Era of Demographic Change. Washington DC: World Bank.

- World Bank (2011). _Global Development Horizons 2011 – Multipolarity: The New Global Economy._ The World Bank, Washington DC.

- World Bank (2007). _Global Economic Prospects 2007 – Managing the Next Wave of Globalization._ The World Bank, Washington DC.

- World Bank (2006). _World Development Report 2006 – Equity and Development._ The World Bank, Washington DC.

- Young, A. Inequality, the urban-rural gap, and migration. The Quarterly Journal of Economics, 128(4):1727{1785, 2013.

33
available with the data.<sup>6</sup> The DHS wealth index is constructed from selected household level variables, including housing characteristics, ownership of land and selected durable goods (Rutstein and Johnson, 2004).

For child undernutrition analysis, we computed anthropometric indicators from age, height/length and weight data following the WHO 2006 growth standards. We calculated height-for-age, height-for-weight, and weight-for-age z-scores and then stunting, wasting and underweight levels for children aged 0 to 59 months. Child mortality rates (NMR, U5MR and IMR) are calculated using the standard DHS methodology, using all data on all children deaths in the 5-year period preceding the survey (Rutstein and Rojas, 2006). We calculated prevalence of modern contraception use by currently married women, ANC4+, and SBA from the women’s (aged 15-49 years) questionnaire.

# _2.3 Data Analysis_

We examined differential progress for groups by wealth ranking, place of residence and education. For wealth groups, we looked at socioeconomic inequalities in health by wealth ranking between the worse off (bottom 40%) and the better off (top 60%) and between the poorest (1<sup>st</sup> quintile) and the richest (5<sup>th</sup> quintile). The spatial dimension we investigated is place of residence: rural vs. urban residents. On education, we considered

> 6 For DHS 2000, we constructed quintiles from the wealth index. The bottom 40% comprises the first and the second quintiles, i.e., the poorest and the next poorest quintiles, and the top 60% represents the remaining three quintiles. The interest on the Bottom 40% of the wealth group is following the World Bank Gorup’s current goals to end extreme poverty and promote shared - prosperity. <u>http://www.worldbank.org/content/dam/Worldbank/document/WB goals2013.pdf</u>

8
tax handle to the size of the tax base for that tax handle. Comparing the implicit tax rates with the legal tax rates provides an indication of the level of tax compliance in the economy (table 6).

# **Table 6 Implicit and legal tax rates**

||Legal tax rate|Implicit<br>rate|tax|Implicit<br>rate<br>as<br>percent of legal rate|
|---|---|---|---|---|
|Personal income tax|16|5.8||36.3|
|Corporate income tax|16|5.4||33.8|
|Social contributions|43|20.8||46.8|
|VAT|18|10.8||60.0|

_Data pertains to 2010 except for VAT which is 2009_ _Source: Eurostat, Romanian National Institute of Statistics, Romanian Ministry of Public Finance, Fiscal Council, and authors’ calculations_ Tax compliance with the personal and corporate income tax is particularly poor; less than 40 percent for both tax handles which probably reflects the large share of informal employment in the economy – estimated at between 20 percent and 25 percent of total employment - as well as tax evasion. Tax compliance has also weakened since the onset of the economic crisis in 2008. The Romania Fiscal Council estimated that the losses of personal income tax arising from tax evasion from undeclared work amounted to 1 percent of GDP in 2009, while losses of social contributions from undeclared work amounted to 3.3 percent of GDP.

Romania replaced a progressive income tax, with rates between 18 and 40 percent, with a flat tax in 2005, set at 16 percent. One of the merits of a flat tax is that it is supposed to encourage compliance and simplify tax administration, but the implicit tax rate on personal income was only fractionally higher in 2010 than it was prior to the introduction of the flat tax (figure 8). For the corporate income tax, the implicit tax rate in 2010 was lower than it was prior to the 23
Table 1. Firms’ Environmental Performance Ratings by the Green Watch Program in Jiangsu Province, China (% Representation in Parentheses)

||Year|Green|Blue|Yellow|Red|Black|Total|
|---|---|---|---|---|---|---|---|
|Pilot program<br>|1999|3|25|40|13|10|91|
|in Zhenjiang<br>City||(3.30)|(27.47)|(43.96)|(14.29)|(10.99)||
||2000|2|58|21|12|2|95|
|||(2.10)|(61.05)|(22.11)|(12.63)|(2.10)||
|Province-wide|2001|77|512|288|141|41|1059|
|program||(7.27)|(48.35)|(27.20)|(13.31)|(3.87)||
||2002|182|1196|655|398|77|2508|
|||(7.26)|(47.69)|(26.12)|(15.87)|(3.07)||
||2003|267|1545|789|367|106|3074|
|||(8.69)|(50.26)|(25.67)|(11.94)|(3.44)||
||2004|329|2659|1467|525|114|5094|
|||(6.46)|(52.20)|(28.80)|(10.31)|(2.24)||
||2005|530|4016|2614|702|143|8005|
|||(6.62)|(50.17)|(32.65)|(8.77)|(1.79)||
||2006|702|5414|3944|1000|155|11215|
|||(6.26)|(48.27)|(35.17)|(8.92)|(1.38)||

Sources: Pilot program in Zhenjiang: Wang et al. (2004). Province-wide program: the Legislative Affairs Office of the China State Council (2007) (available at <u>http://www.chinalaw.gov.cn/article/dfxx/dffzxx/js/200706/20070600021431.shtml; last assessed</u> on May 19, 2009) .
the lowest poverty line for each region. The DHB applies half a minimum salary as the poverty line<sup>11</sup> .

# **3. Evolution of Poverty and Income Inequality**

What happened to poverty in the last decades? This section shows that there has been tremendous diversity in outcomes in the Northeast region as well as in Brazil as a whole, across dimensions such as states, municipalities, households, and individuals. Both income poverty and nonincome poverty are considered in the analysis. Investment in human capital, such as education and health, and in infrastructure services for the poor are posited to reduce poverty. Investment in human capital reduces poverty by raising productivity, employability and wages; investment in infrastructure services for the poor, for example, water, sanitation and transportation reduce poverty by raising the time available for income generating activities and, hence, increase total income, monetary and non-monetary, of the poor. This section is organized in subsections that consider: (1) income and poverty before and after 1990; (2) education and child labor; and (3) other welfare indicators. Next, we provide a brief overview of the recent picture and evolution of poverty before focusing on a more disaggregated poverty profile.

## **3.1. Income, inequality  and poverty in the 1970-91 period**

In this section we apply the DHB data for the period from 1970 to 1991. The data reveal that the average income measured in units of 1991 minimum wages has more than doubled in Brazil and the Northeast region in the 1970 to 1991 period (Table B1). However, in 1991 in the latter region the average income is barely 65 percent of the listed minimum wage (mw) while in the Southeast region and Brazil as a whole it is 177 percent and 131 percent, respectively. These figures illustrate the huge income dispersion between the Northeast and the Southeast regions. Moreover, the average citizen in Rio Grande do Norte has an income of 0.72 mw, which ties Rio Grande do Norte and Sergipe (0.73mw) as the states with the second highest average income in the Northeast after Pernambuco (0.81mw). Maranhão is the poorest state of the Northeast and has an average income of

> 11 The different poverty lines used in sections 3.1 and 3.2 explain the differences between the levels and the similarity of the trends of the poverty indices between this section and the later ones.

8
and time-specific. Substituting implies the following theoretical relationship: _Oit_ =<sup>_<u>A</u>_</sup> Γ<sup>_·R_</sup> _it_<sup>_<u>it</u>_</sup> that can be expressed in logarithmic terms as: Our estimating equation is thus where lower-case notations indicate natural logarithm of corresponding upper-case variables and _εit_ is the disturbance term that we assume to be independently and identically distributed across sites and time. We examined the estimated _εit_ to ensure symmetry and normality. The results of the model validation and estimation are reported in A.4.3. A linear relationship between crude oil extraction and radiant heat would imply _β_ 1 = 1.

Under the assumption that the relationship estimated in equation 2 is valid after the onset of the war, estimates ˆ _oit_ of oil production are obtained according to ˆ where _α_ , _β_<sup>ˆ</sup> 1, and _β_<sup>ˆ</sup> 2 are estimated by ordinary least squares (OLS).<sup>7</sup> A consistent estimate of the level of oil production needs to adjust for the fact that the expectation of the exponential of a random variable is not equivalent to the exponential of its expectation.

# **A.4.1 Consolidating oil production and field characteristics data**

To construct the dataset used to estimate equation 2, we combine data on field-level characteristics (including oil output and GOR) with flare-level radiant heat measures. In this section, we describe the sources and preparation of calibration data.

> 7The statistical software package used to that end is STATA version 14.

38
**Table 5: Proportion of Learning Studies Included in each Review by Region**

||||Number of studie<br>Kremer, Brannen,|s evaluatinglearn<br>Krishnaratne,|inginterven|tions||
|---|---|---|---|---|---|---|---|
||Conn<br>2014|Glewwe et<br>al. 2014|& Glennerster<br>2013|White, &<br>Carpenter 2013|<br>McEwan<br>2014|Murnane &<br>Ganimian 2014|All<br>reviews|
|_East Asia and Pacific_|0%|22%|7%|21%|29%|13%|19%|
|_Europe and Central Asia_<br>_Latin America and the_|0%|3%|0%|2%|0%|2%|2%|
|_Caribbean_|0%|28%|10%|26%|18%|30%|25%|
|_Middle East and North Africa_|0%|0%|0%|0%|0%|4%|2%|
|_South Asia_|4%|19%|33%|16%|21%|22%|16%|
|_Sub-Saharan Africa_|100%|21%|40%|35%|33%|27%|34%|
|_Low and Middle Income_|100%|90%|90%|98%|98%|84%|90%|
|_High Income_|0%|13%|10%|2%|3%|16%|11%|
|Total number of studies|56|67|30|43|66|92|227|

Note: A small number of studies include results from countries from more than one region. We include studies in the count for all regions for which they cover at least one country. As such, the sum of percentages of studies by region within a given review may exceed 100%.

27
consumption occurred in 2008 when agents finally realized that they were “overconsuming”.

Blanchard et al.( _forthcoming_ ) and L’Huiller (2012) find qualitatively the same results as our paper using US data. They both find first a slow learning for consumers about changes in the permanent component of productivity. Second they find an overshooting of their beliefs -on long-run productivity in the first paper, and on permanent productivity in the second- around 2005, followed by a sudden downward adjustment in 2008.

The paper continues as follows. Section II presents the basic version of the BBL model. Section III describes the estimation methodology and presents the recovered shocks and states from the estimation. Section IV concludes.

# **2 The BLL basic model**

## **2.1 The model**

We consider a framework where productivity is driven by two disturbances: a permanent and a transitory shock. A key assumption in this model is that consumers do not observe the two shocks separately, but only the realized level of productivity. The permanent shock introduces uncertainty about the economy’s long-run fundamentals. The presence of the transitory shock implies that consumers cannot back out the permanent shock from productivity observations, thus creating a signal extraction problem. We assume that consumers have access to an additional source of information, as they observe a noisy signal of the permanent component of productivity. This adds a third source of fluctuations, a shock to the error term in the signal, which is called in Lorenzoni (2009) and Blanchard et al. ( _forthcoming_ ) a “noise shock”, and which has the features of aggregate demand shocks, i.e. the shock increases output only temporarily. Consumers solve their signal extraction problem, form expectations about future productivity, and choose spending based on these expectations. Finally, we assume the existence of nominal rigidities in the economy, which implies that spending determines output in the short-run. We explicitly list below the specific assumptions of the model.

Productivity _at_ (in logs) is the sum of two components, the permanent _xt_ , and the transitory component _zt_ : The permanent component follows a unit root process: The transitory component follows a stationary process: The coefficients _ρx_ and _ρz_ are in [0,1[, and _εt_ and _ηt_ are independently and identically distributed (i.i.d.) draws from a normal distribution with zero mean and with variances _σε_<sup>2and</sup><sup>_σ_</sup> _η_<sup>2.</sup> 3