rafmacalaba/datause-encoder-data huggingface dataset:
Bank credit to the private sector has been identified empirically as an important factor in growth by various studies.<sup>35</sup> The possibility that government domestic debt might crowd-out of bank credit to the private sector would thus raise a concern regarding the impact of the increased government domestic debt on economic growth.

The growth of government domestic debt is larger than the growth of bank credit to the private sector in many countries over the period studied (Table 4). Moreover, there seems to be a negative relationship between the growth of government debt and the growth of private sector credit.<sup>36</sup> Of course, the observed relationship reflects changes in many factors over the period studied, new financial developments, and statistical issues.<sup>37</sup>

> 35 For example, see King and Levine, 1993; Levine and Zervos, 1998; and Levine, Loyaza, and Beck, 2000. 36 A simple regression for the period 1994-2004 yields the following result: Change in Bank Credit  =  7.83  -0.55 Change in Government Domestic Debt ’94-‘04 R<sup>2</sup> adj.<sup>= .21</sup> to the Private Sector (S. Error of Coeff.=.24, _t_ Statistic=-2.29, _P_ -value _=_ 0.039) Note the regression excludes countries that do not show private sector and public enterprise credits separately in the IFS—the Eastern European countries, China, India, Morocco, and Tunisia in the sample. 37 The growth of government domestic debt partly reflects the resolution of financial crises that began after 1994, as discussed above, and the slow measured growth of private credit may also reflect the crises. There are also statistical issues. In the resolution of the crisis, injections of government domestic debt into the weak banks were matched by a withdrawal of weak loans from the banks. These credits may still exist outside the banks, in debt resolution agencies, or may have been bought-up, including by the original borrowers. Hence, although bank credit may have gone down, it may be in other hands. There is also the issue of liquidity credits granted by the central bank during the crisis that are not counted in bank credit to the private sector.

19
**Table 2. Social Spending in Brazil by program or transfer, 2019**

||**Brazilian**<br>**Reais**<br>**(in millions)**|**Share of**<br>**expenditures**<br>**(%)**|**GDP**<br>**(%)**|**Included**<br>**in the**<br>**analysis?**|**Source**|
|---|---|---|---|---|---|
|**Total Expenditure**|3,450,294|100.0|49.3||a|
|**Social Spending**|1,718,071|49.8|24.5|||
|Social Protection|1,389,407|40.3|19.8|||
|**Direct cash transfers**|552,400|16.0|7.9|||
|**Conditional/Unconditional Cash Transfers**|56,761|1.6|0.8|||
|_Bolsa Família_|31,159|0.9|0.4|Yes|d|
|_Salario Família_|3,502|0.1|0.1|Yes|e|
|_Abono Salarial_|17,603|0.5|0.3|Yes|f|
|_Other social transfers_|4,505|0.1|0.1|Yes||
|**Noncontributory Pensions**|137,210|4.0|2.0|Yes||
|_Ruralpension_|81,882|2.4|1.2|Yes|g|
|_BPC_|55,328|1.6|0.8|Yes|f|
|**Near Cash Transfers***|n.a.|||No||
|**Other**|164,457|4.8|2.3|No|b|
|**Social insurance**|837,007|24.3|12.0|||
|**Old-Age Pensions**|572,739|16.6|8.2|Yes|a|
|**Unemployment**|37,521|1.1|0.5|Yes|a|
|**Other**|226,748|6.6|3.2|No|a|
|Education|166,204|4.8|2.4|||
|**Pre-school**|51,448|1.5|0.7|Yes|b|
|**Primary**||||||
|**Secondary**|20,167|0.6|0.3|Yes|b|
|**Post-secondary non-tertiary**|n.a.|||No||
|**Tertiary**|80,049|2.3|1.1|Yes|b|
|**Education n.c.p. **|14,542|0.4|0.2|No||
|Health|153,325|4.4|2.2|Yes|b,h|
|Housing& Urban|9,135|0.3|0.1|No|b|
|**Subsidies(to corporations)****|18,309|0.5|0.3|No|c|
|**Economic affairs**|99,329|2.9|1.4|No|b|
|**Defense, public order and safety spending**|233,294|6.8|3.3|No|b|
|**Other**|1,381,290|40.0|19.7|No|b|

Sources: a. Brazil (2022b). b. Brazil (2020). c. IMF (2023). d. Open Data Portal Brazil Government. e. World Bank (2018). f. Fiscal transparency Portal Brazil Government. g. ECLAC (2023). h. Brazil (2022a). Notes: “n.a.” stands for not applicable. BPC stands for Benefício de Prestação Continuada.* Includes Food, School, Uniforms, etc. ** While Higgins and Pereira (2014) include indirect subsidies to electricity consumption in the analysis, we find that the subsidies directly targeting families (e.g., social tariff, universalization, rural) amounted to no more than 0.1 percent of the GDP in 2019 with a limited number of beneficiaries and effects over the welfare results.

# _Other in-kind income_

The estimation of households’ market income incorporates the value of goods produced for own consumption and the imputed value of owner-occupied dwellings and other income sources. None of these measures are readily available in the survey data. We now describe our approach to incorporate them in the analysis.

Value of own consumption was estimated in the following way. First, self-consumption was estimated from the POF survey using the ratio of households’ reported value (of withdrawals from business or own production) to disposable income. Next, the average of these ratios by state and percentile was estimated and then imputed to households in PNAD-C according to their state of residence and position in the income distribution. Each household’s monetary value of own consumption was obtained by multiplying the imputed ratio by disposable income.

18
Table B6: Full variance decomposition for UG

|||Absolute<br>|contributi<br>|ons(in s.d.<br>|units)<br>|||R<br>|elative sha<br>|res(in %)<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|
|σ��<br>�|0.17|0.18|0.18|0.13|0.20|0.19|2.94|3.12|3.13|1.75|4.05|3.59|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.06)|(0.06)|(0.06)|(0.04)|(0.07)|(0.06)|
|σ�<br>�|0.53|0.39|0.35|0.56|0.31|0.38|28.03|15.57|12.22|31.03|9.35|14.72|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.25)|(0.19)|(0.17)|(0.27)|(0.15)|(0.18)|
|σ�<sup>�</sup>|0.31|0.40|0.55|0.14|0.53|0.44|9.49|15.77|30.39|2.03|28.11|19.44|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.25)|(0.32)|(0.45)|(0.12)|(0.43)|(0.36)|
|2Σ��|0.10|0.25|-0.04|0.13|0.18|0.22|0.96|6.38|-0.13|1.73|3.15|4.78|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.06)|(0.16)|(0.02)|(0.08)|(0.11)|(0.14)|
|2Σ���|0.14|0.11|0.09|0.16|0.09|0.11|1.89|1.31|0.86|2.47|0.74|1.31|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.05)|(0.04)|(0.04)|(0.06)|(0.03)|(0.04)|
|2Σ���|-0.11|-0.10|-0.06|0.04|-0.08|0.18|-1.25|-0.92|-0.37|0.19|-0.59|3.34|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.05)|(0.05)|(0.03)|(0.02)|(0.04)|(0.09)|
|σ�<sup>�</sup>|0.76|0.77|0.73|0.78|0.74|0.73|57.94|58.77|53.90|60.79|55.20|52.82|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.25)|(0.25)|(0.24)|(0.26)|(0.25)|(0.24)|
|σ�<br>�|1.00|1.00|1.00|1.00|1.00|1.00|100.00|100.00|100.00|100.00|100.00|100.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|
|ρ��|0.03|0.20|-0.00|0.11|0.10|0.14|.|.|.|.|.|.|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|

Note: following equations (3b) and (3c), the table sets out the absolute and relative variance shares attributable to each component of the test score; absolute shares are in standard deviation units; different models/estimators are indicated in the columns – (a) to (c) refer to results from a partitioned iterative algorithm for different choices of initialization scalar _π_ , (d) is the household upper bound, (e) is the school upper bound, and (f) is a mixed linear model; _ρhs_ is the estimated correlation coefficient between household and school effects; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003).

Source: own calculations.

51
# **Table C.35: Role model effects: Main results using husbands’ data**

**Panel A: Effects on income and decision-making power**

||(1)<br>Husband’s personal<br>income(Taka)|(2)<br>Household<br>income<br>(Taka)|(3)<br>Wife’s Decision-<br>making (Index)|
|---|---|---|---|
|exposed|_−_203_._58|_−_567_._41|0_._02|
||(332_._82)|(761_._76)|(0_._11)|
|N|449|449|449|
|Non-exposed mean|10304_._84|21235_._56|0_._10|
|Non-exposed SD|5327_._06|7414_._98|1_._17|

**Panel B: Effects on assignable goods expenditures and remittances**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes as %|in household i|ncome)||
|exposed|0_._41|0_._16|_−_0_._69|_−_0_._32|_−_0_._93|
||(0_._89)|(0_._39)|(0_._51)|(0_._47)|(1_._25)|
|N|449|144|449|146|449|
|Non-exposed mean|6_._55|1_._82|5_._48|2_._30|9_._04|
|Non-exposed SD|6_._51|2_._22|5_._32|2_._52|12_._80|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Sharpened q-values in squared brackets. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory, enumerator and month dummies. The estimation in Panel A, Column 2 excludes the household income variable from the set of possible controls. The sample in Panel B, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

97
International Energy Agency (IEA) (2020). CO2 Emissions from Fossil Fuel Combustion. IEA, Paris.

- Jorgenson, Dale W., Richard Goettle, Mun S. Ho and Peter J. Wilcoxen (2015). Carbon Taxes and Fiscal Reform in the United States. National Tax Journal, Vol. 68 (1), pp. 121-37.

Lay, J., R. Thiele and M. Wiebelt (2006). Resource Booms, Inequality, and Poverty: The Case of Gas in Bolivia. Kiel Working Paper No. 1287. The Kiel Institute for the World Economy, Kiel, Germany.

Maloney, W. (2004). “Informality Revisited”, _World Development_ 32(7), pp. 1159-1178. McKibbin, Warwick J., Adele C. Morris, Peter J. Wilcoxen and Cai, Yiyong (2015). Carbon Taxes and U.S. Fiscal Reform. National Tax Journal, Vol. 68 (1), pp. 139-55.

Morales, Rolando, Danilo Agramont, Erick Gomez, Monica Cueto, Estefany Parisaca Quispe, Franz Gomez-Soto, Jazmin Illanes-Yujra, and Ximena Soruco (2017). Trade bust, labor and wage policy in Bolivia: A CGE approach. Working Paper #16, Partnership for Economic Policy.

Peña, Ximena (2013). The formal and informal sectors in Colombia: country case study on labour market segmentation. Employment working paper; No.146. International Labor Office, Geneva.

Rada, Codrina (2009). Formal and Informal Sectors in China and India: An Accounting-Based Approach. Working Paper, Department of Economics, University of Utah, United States. Robilliard, Anne-Sophie, François Bourguignon and Sherman Robinson (2011). Examining the Social Impact of the Indonesian Financial Crisis Using a Macro-Micro Model.

Stifel, David C. and Erik Thorbecke (2003). A dual-dual CGE model of an archetype African economy: trade reform, migration and poverty. _Journal of Policy Modeling_ , Volume 25, Issue 3, Pages 207-235.

Telaye, Andualem; Benitez, Pablo; Tamru, Seneshaw; Medhin, Haileselassie; Toman, Michael (2019). Exploring Carbon Pricing in Developing Countries : A Macroeconomic Analysis in Ethiopia. Policy Research Working Paper; No. 8860. World Bank, Washington, DC.

Timilsina, Govinda R (2018a). Where Is the Carbon Tax after Thirty Years of Research? Policy Research Working Paper, WPS 8493. World Bank, Washington, DC.

36
_APPENDIX_ **_1._** _ENROLLMENTS AND EXPENDiTURES ON EDUCATION_

**Table 1. Enrollments by type of school, 1994**

|**Education level**|**Public**|**Private**|**Others**|**Total**|
|---|---|---|---|---|
|Primary|13,593,797|895,913|40,689|14,530,399|
|Lower Secondary|4,661,522|388,806|12,004|5,062,332|
|Upper Secondary|2,386,758|778,587|49,385|3,214,730|
|Tertiary|1,461,189|530,754|1,503|1,993,446|

Source: ENIGH 94

**Table 2. Enrollments by type of school, 1996**

||**Public**|**Private**|**Others**|**Total**|
|---|---|---|---|---|
|Primary|13,802,395|768,748|1,746|14,572,889|
|Lower Secondary|4,972,116|326,229|4,153|5,302,498|
|Upper Secondary|2,767,993|875,129|15,782|3,658,904|
|Tertiary|1,459,820|580,962|7,680|2,048,462|

Source: ENIGH 96

**Table 3. Federal Expenditure (Thousands of 1996 constant pesos)**

||**1994**|**1996**|
|---|---|---|
|Primary|32,351,871|33,328,323|
|Lower Secondary|15,508,552|13,394,898|
|Upper Secondary|11,916,903|10,884,850|
|Tertiary|23,688,868|21,651,986|

Source: DGPPyP, SEP 12
using DHS surveys from developing countries. World Bank (2007) finds that low selfesteem, spirituality, school connectedness, abuse in the home, abuse in the community, connectedness to institutions, poverty and gender are the factors most associated with risky behaviors and negative outcomes in Brazil.<sup>8</sup> Youth in the English-speaking Caribbean who have lower risky behaviors<sup>9</sup> are also those who are more connected to parent/family, attending religious services, feel little rage, have not been abused or witnessed parental violence, do not have family members who have attempted suicide, have mentally healthy parents, and have households free of illegal substances (World Bank 2003). A series of papers by Brook, et. al. (2001, 2002a, 2002b) find similar correlations between household factors, poverty, and community factors for explaining substance use and violence among Colombian youth, while Hutz and Silva (2003) find that young Brazilian men who have been incarcerated for violence are disproportionately the sons of poor, uneducated, and violent fathers.

The ecological risk framework can be stated more formally. A person _i_ has a set of behaviors,<sup>_B_</sup> _i_<sup>, that are determined by a vector of risk factors,</sup><sup>_r_</sup> _i_<sup>, and protective factors,</sup><sup>_p_</sup> _i_<sup>,</sup> determined at the individual (<sup>_I_</sup> _i_<sup>), micro (</sup><sup>_c_</sup> _i_<sup>), and macro (</sup> _M i_ ) levels.

If an element in any of the vectors<sup>_I_</sup> _i_<sup>,</sup><sup>_c_</sup> _i_<sup>, or</sup> _M i_ leads to a positive behavior in<sup>_B_</sup> _i_<sup>, it will</sup> take a positive value in _p_ ( _I i_ , _ci_ , _M i_ ) and a 0 in _r_ ( _I i_ , _ci_ , _M i_ ) . Likewise, an element that leads to risky behavior in<sup>_B_</sup> _i_<sup>will take a 0 value in</sup> _p_ ( _I i_ , _ci_ , _M i_ ) and a positive value in _r_ ( _I i_ , _ci_ , _M i_ ) . A weighted average of the risk (<sup>_r_</sup> _i_<sup>) and protective factors (</sup><sup>_p_</sup> _i_<sup>) specific to each</sup> person will predict the behavior elements in the vector<sup>_B_</sup> _i_<sup>. Behaviors include elements such</sup> as unprotected sex, school truancy, or substance use.

The outcomes of these behaviors are a function of the behaviors, the individual, micro, and macro environments, and luck ( δ ). The outcome, _Oi_ , is given by

> 8 Behaviors and outcomes studied include: grade repetition, early labor force entry, early sexual initiation, risk taking sexual practices, alcohol use tobacco use, illegal drug use, violence and suicide attempt.

> 9 Behaviors and outcomes studied include: perception of general health, ever had sexual intercourse, ever attempted suicide, violent behavior, problems due to alcohol and drugs.

5
# **_Table of Figures_**

|_Figure 1: Growth Diagnostics _____________________________________________ 6_|
|---|
|<br>_Figure 2: Economic Growth in Thailand, East Asia and the World, 1960 to 2003 _____ 9_|
|_Figure 3: GDP Expenditure Components, 1980 to 2003 ________________________ 11_|
|_Figure 4: Expenditure Composition of GDP by Quarter, Q1 2004 to Q2 2005 ______ 12_|
|_Figure 5: Thailand Trade by Country, 1980 to 2003 ___________________________ 14_|
|_Figure 6: GDP Production Components, 1951 to 2004_________________________ 16_|
|_Figure 7: Manufacturing Industries ________________________________________ 18_|
|_Figure 8: Growth Accounting and Labor Productivity _________________________ 20_|
|_Figure 9: Capital Flows, Monetary Aggregates and Exchange Rates______________ 23_|
|_Figure 10: Current Account, Reserves, and Credit ____________________________ 25_|
|_Figure 11: Investment Climate ____________________________________________ 29_|
|_Figure 12: Infrastructure Indicators _______________________________________ 32_|
|_Figure 13: Investment Indicators __________________________________________ 34_|
|_Figure 14: Mega-Infrastructure Projects and Public and Private Infrastructure Spending,_<br>_2003_________________________________________________________________ 37_|
|_Figure 15: Thailand’s Region_____________________________________________ 39_|
|_Figure 16: Regional Composition of Gross Domestic Product, 1970 to 2004________ 41_|
|_Figure 17: Urbanization Indicators ________________________________________ 43_|
|_Figure 18: Primacy Indices ______________________________________________ 45_|
|_Figure 19: Spatial Distribution of Manufacturing _____________________________ 47_|
|_Figure 20: Employment Composition_______________________________________ 49_|
|_Figure 21: Education Indicators __________________________________________ 52_|
|_Figure 22: The 1999 Trends in International Mathematics and Science Study and_|
|_National Income Levels__________________________________________________ 54_|
|_Figure 23: Labor Market Indicators________________________________________ 56_|
|_Figure 24: Returns to Education __________________________________________ 58_|
|_Figure 25: Relative Wages, Labor Demand and Labor Supply ___________________ 60_|
|_Figure 26: Annual Real Growth in Wages of Monthly Wage Earners, 1991 to 2004 __ 61_|

# **Acknowledgements**

I am grateful to Indermit S. Gill, Kazi Mahbub-Al Matin and Roberto N. Zagha for suggesting this work and providing advice throughout. Comments on an early draft from Barry Eichengreen, Ricardo Hausmann, Dani Rodrik and Andrés Velasco helped greatly to improve the paper. Norbert Schady and Carolina Sanchez-Paramo were very generous in providing me with Stata codes to calculate relative labor demand and supply. Numkrit Jeraputtiruk patiently explained to me how to analyze the Thailand Labor Force data.

4
- 16 -

# **_5.2. Econometric Analysis of MWRs_**

Most empirical studies examine the differences of MWRs across different classes of annuities without testing whether these differences are significant. The large dataset of individual annuities in Chile enables a more formal examination of the main determinants of MWRs, and the testing of whether the relationships identified above are significant. For this purpose we specify the MWR as a function of individual annuitant characteristics, as in equation (3): (3) MWRi,t = f (genderi,t, agei,t , premiumi,t , guaranteei,t , defermenti,t) Where MWRi is the money’s worth ratio of the annuity bought by individual i at time t, regressed against the gender and age of the individual annuitant, the size of the annuity premium expressed in logs, and the guaranteed and deferment periods. Since the bulk of the market is constituted by joint annuities, the equation was estimated using this type of annuity as the base variable and dummies included for single male and single female annuities. Likewise, 1999 was considered as the base year and dummies were included for 2002, 2003, 2004, and 2005. Table 6 shows the results obtained through least squares with robust standard errors. This specification was selected after conducting a number of specification tests, including the White test for heteroskedasticity of the residuals.

**Table 6: Main Determinants of MWRs**

**Dependent Variable: 100*MWR;  Least Squares with Robust Standard Errors Pooled Data for 1999, 2002, 2003, 2004, and 2005; Observations: 6526**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|C|62.39024|0.722912|86.30404|0.0000|
|AGE|0.410145|0.008974|45.70317|0.0000|
|LOG(PREMIUM)|1.618070|0.073313|22.07059|0.0000|
|GUARANTEE|-0.134448|0.008383|-16.03824|0.0000|
|DEFERMENT|0.016582|0.007399|2.241063|0.0251|
|Male|1.345882|0.206458|6.518928|0.0000|
|Female|4.023704|0.089566|44.92436|0.0000|
|2002|10.66352|0.149209|71.46677|0.0000|
|2003|5.699579|0.152080|37.47739|0.0000|
|2004|8.253581|0.150549|54.82318|0.0000|
|2005|6.507061|0.156551|41.56508|0.0000|
|R-squared|0.639507|Mean dep|endent var|104.9609|
|Adjusted R-squared|0.638954|S.D. depe|ndent var|5.600486|
|S.E. of regression|3.365172|Akaike inf|o criterion|5.266519|
|Sum squared resid|73778.36|Schwarz|criterion|5.277954|
|Log likelihood|-17173.65|F-sta|tistic|1155.747|
|Durbin-Watson stat|1.754037|Prob(F-s|tatistic)|0.000000|

Source: Authors’ estimations on SVS data.
8 (2008) included a secret ballot in their survey of rural households in West Bengal to examine the correlates of voting for the incumbent Left Front political parties. They find that an individual household’s vote for the incumbent party is significantly correlated only with private and recurring benefits received, in the form of personal help in times of need or benefits from welfare schemes, and not with the availability of local infrastructure. This suggests that the vote-returns from providing infrastructure broadly as a public good can be less than the vote-returns from providing privately targeted recurring benefits. We return to discuss this issue after presenting the results on how budget composition across states varies with voter turnout in state assembly elections.

If the share of infrastructure spending is negatively correlated with voter turnout, falling when turnout increases, this would suggest that infrastructure provision may not be as effective as other types of spending at winning votes. If the share of spending on social programs that provide recurrent and private benefits like welfare transfers and salaries is positively correlated with turnout, increasing when turnout increases, these types of government programs may be more effective at winning votes.

# **Variation of budget composition across states over time**

Political agents with decision-making power over budget composition, however, might not be motivated to satisfy voter demands and win re-election alone, but also to use the powers available to them to extract private benefits or rents for themselves. Persson and Tabellini (2000) provide a review of models of rent-seeking, some of which show that when voters are too demanding politicians might make their policy choices solely on the basis of what allows them to extract more private benefits for themselves. The cross-country analysis of Keefer and Knack