rafmacalaba/datause-encoder-data huggingface dataset:
# _b. Related with inequality_

<!-- Start of picture text --> 100<br>Redistribution Distribution<br>Social justice<br>75<br>Income<br>distribution<br>50<br>25<br>Social inclusion<br>0<br>%<br>1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> Note: This Figure shows the percentage of presidential speeches on each decade that contained at least once each of the keywords and were calculated pooling the presidential speeches of 10 Latin American countries. See Appendix A for more details about the data.

If only the way poverty and inequality are discussed has changed, then, as the forms of expression shift, one would expect to observe a decline in the prevalence of previously used synonyms that coincides roughly with the “political enlightenment.” This, however, is not what we observe. For the topic of poverty, synonyms and related terms, such as hunger and indigence do not decline in prevalence during the enlightenment decades. In fact, indigence becomes a more frequent term, referenced in 2% of speeches from the 1960s and 18% of the speeches from the 2010s. Hunger evolves rather erratically, but there is no obvious decline in usage. Moreover, the fact that all the terms are infrequent (mentioned in less than 30% of speeches) further supports the fact that the increase in references to poverty we observe starting in the 1960s constitutes a political enlightenment.

The evolution of terms related to inequality is somewhat more heterogeneous, but it does not indicate that the enlightenment simply reflected a change in the words used to discuss the subject. This is particularly true considering the frequency of many of these terms _increased_ during the enlightenment period. For example, despite a marked decline in the usage of “social justice,” “redistribution,” and “distribution” beginning in the 1980s, these terms also increased in frequency during the two preceding decades (1960-1980), the period during which the early stage of the political enlightenment took place. In sum, the evolution of terms related to poverty and inequality does not support the view that the enlightenment reflected a change in the language used to discuss poverty and inequality, instead of an increasing awareness about these issues.

13
<!-- Start of picture text --> 3.4 Domestic expenditure<br>_ uec oct ~.<br>a 3 ¢<br>oo “7<br>ao>BH5529 ae < ’<br>o iS} t “<br>ba 3 Y<br>528 a<br>seo ¢<br>oc o<br>a B 2.7 ¢ 4<br>3D 67<br>ecce LZ A<br>© S26 °<br>Ee0T ge<br>7 - — baseline<br>———~ halved migration cost<br>2.5<br>25 30 35 40 45 50<br>age<br><!-- End of picture text --> <!-- Start of picture text --> 0.35 Welfare ><br>;<br>03 — halved migrationigrati cost ;J welfare gain. of<br>] , 0.056 std. dev.<br>|<br>0.25 1t)|<br>q<br>> 02 p<br>@ /<br>5 y<br>70.15 J<br>y<br>0.1 y<br>/<br>‘<br>0.05<br>0 :<br>-6 -4 -2 0 2 4<br>welfare (standardized to baseline)<br><!-- End of picture text -->
6

# C. The chartist model (moving average model)

The previous approach may be subject to criticism on the grounds that, since there are several break-points in the sample that are known to the researcher ex-post, it is diffic.ult from an operational viLewpoin **t** o know at every point in time if there has been a change in the mean or variance models. To avoid this problem, a simple approach coimcmnly used by financial markets practitioners is to compare the variable (IMV) with its mo),irng average. Based on Granger causality tests, the 6-month moving average was selected. A signal is flashed when the IMV exceeds the 6-month moving average.

# D. The ARIMA residuals model

Finally, one could hypothesize that a crisis is more likely to happen when the set of leading indicators are behaving "strangely". The "normal" or regular behavio: is described by an ARIMA model for the IMV, so the residuals summarize the deviations from the "normal" behavior. In theory, the residuals must be a white noise process and have mean zero. But some residuals will randomly be positive. We're interested in mrore permanent positive deviations, so we constructed a moving average of the residuals and a signal was generated when this statistic exceeds zero. Conceptually, this filter is similar to applying any detrending methodology, except that it considers a richer set of information besides the trend in the variable's history.

# IV. **Evaluation Criteria and Results**

With the four models described above (deviations from trend, simple, chartist and ARIMA residuals) different signals were generated. With their empirical clistributions, as well as those of the crises, we evaluated the models using a 24-month window prior to each crisis<sup>13</sup> . Contemporaneous signals were not counted because they are not ':ad signals and they are not leading variables either. Similarly if a crisis occurs withi l4 months of another one, they are counted as one episode.

# A. Evaluation criteria

Our evaluation of each model is based on four 4 statistics; the sizes of Type I and Typt II errors, the noise to signal ratio, and the probability that a crisis occurs given that a sigiial was produced. A short description of each one and details on the computation follow: We borrow a very useful table from Kaminsky-Lizondo and Reinhart (KLR) (1997) to visualize the different criteria:

**13** 24 months is the most common size for the window. The results don't change dramatically if an 18month window is used, but a noticeable improvement is achieved with the wider one.
difference exists in the probability of ever attending school. In some counties, these differences are exacerbated as children progress through the school system. In particular, in Indonesia, Jamaica, and Romania, where the gap at the start of schooling is on the order of 30 to 45 percentage points, the shortfall in grade completion increases to about 60 or 70 percentage points by grade 8.

**Figure 2. Grade “survival” profiles for ages 10 to 17: Kaplan-Meier estimates of the probability of** **<u>completing a grade.</u>**

<!-- Start of picture text --> Benin 2000 Cambodia 1999 Cambodia 2000<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Indonesia 2003 Jamaica 1998 Mongolia 2000<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br>Mozambique 1996 Romania 1995<br>100 100<br>80 80<br>60 60<br>40 40 Without disability<br>With disability<br>20 20<br>0 0<br>1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9<br><!-- End of picture text --> _Note_ : Estimates account for right hand censoring. Jamaica 2000 and Myanmar are not reported because there are too few observations to estimate survival profiles.

12
# **5.2 Indicators and proxies**

This section describes the empirical proxies we employ to estimate equations (22) and (23). It also documents the sources of data.

## **5.2.1 The technology selection variable**

As mentioned, our model suggests that countries face discretely different technological choice sets. Therefore, to construct variable _Rt_<sup>_c_werelyonthe</sup> theory, according to which the key determinants of the technological group to which a country belongs are IPRs of the destination country, _φ_<sup>_d_</sup> , relative _w_<sup>_d_</sup> factor prices of the destination country, _wfz_<sup>_d_, relative technology expenditures,</sup> _e_<sup>_d_</sup> _<u>jz</u>_<sup>factorendowmentsoftechnologytradingpartners,</sup><sup>_Vo_,anddistanceto</sup> _e_<sup>_d_</sup> _jf_<sup>,</sup> technology suppliers, _d_<sup>_o,d_</sup> _o∈D_<sup>.</sup> Our baseline model for the selection equation considers variables strictly related to technology adoption such as IPRs of the destination country, relative factor endowments (capital/labor), which we use to proxy _ee_<sup>_d_</sup> _jf_<sup>_d_</sup> _<u>jz</u>_<sup>,and</sup> the same variables for technology trading partners. The latter variables are weighted by the inverse of the distance between trading countries. To test the robustness of our specification, we add factor price ratio, namely the ratio of the manufacturing wages over bank lending interest rates, and national TFP levels to control for Hicks-neutral technological differences. Our proxy for _Rt_<sup>_c_</sup> is the first component in the principal component analysis of the variables employed to construct variable _Rt_<sup>_c_.</sup>

## **5.2.2 Data**

## **Factor endowments**

Data on capital stocks come from Serven and Calder ́ _o_ n[33], who extend the series provided by the Penn World Tables. The labor force is from the International Labor Organization (ILO), and it refers to economically active population defined as the 25-64 age group. To calculate endowments of highand low-skilled labor, we use data on educational attainment from Barro and Lee[2]. Skilled workers are defined as the population economically active with at least one year of secondary school. The rest are considered unskilled 17
investors who stand ready to lend to households and firms in our model economy at rate _R_ . However, lending to the OECS countries is risky, so international lenders demand a risk-premium when making loans to these countries. Thus, we model _R_ following Neumeyer and Perri (2005) as consisting of two components – a rate charged for risky assets in the international markets (independent of developing country status), _R*_ , and a country spread over the risky assets rate paid by borrowers in an OECS country, _D_ . Thus, the interest rate faced by the OECS country is �� � ��∗��. Because there is only one international asset in the economy, the interest rate on that asset faces by all agents is the same and equal to ��.

We will calibrate _R*_ as a U.S. rate for risky assets and model it as a stochastic exogenous process, independent of the fundamentals of the OECS economy: where �� is the autocorrelation coefficient, and ��� are i.i.d. mean-zero normal innovations to �<sup>�</sup> �∗. In the annual data for the U.S. we find that �� �0.4305 and the standard deviation of �<sup>�</sup> �∗ is equal to 1.30%. We calibrate the parameters of equation (9) to match these moments.

In modeling the shocks to risk-premium _D_ we consider two possibilities. In the first case, we assume that only exogenous factors (contagion, foreign shocks, etc.) affect the country riskpremium. In this case, the process for _Dt_ is given by a simple AR(1) process with persistence coefficient of ��: where �� is the autocorrelation coefficient and ��� is i.i.d. normal innovations.

In the second approach, we assume that domestic fundamentals determine country riskpremium. We model this in a simple way where country risk is induced by domestic default probability which in turn is a function of domestic productivity shocks. In particular, we use where �� is a constant reflecting the extent to which risk-premium responds to expected productivity shocks; ��� is i.i.d. normally distributed innovation. In this case, the process for �� becomes This approach is consistent with the models of default in Eaton and Gersovitz (1981), Arellano (2008) and others. It also corresponds closely to the modeling strategy in Neumeyer and Perri (2005).

Of course, factors other than productivity may influence the country risk-premium in the OECS countries. For instance, risk-premium may also be positively related to changes in government expenditures, where greater spending (or deficits) are accompanied by higher risk14
# **References**

- Abadie, Alberto, Susan Athey, Guido W Imbens, and Jeffrey Wooldridge (2017), “When should you adjust standard errors for clustering?” Technical report, National Bureau of Economic Research.

- Alm ̊as, Ingvild, Maximilian Auffhammer, Tessa Bold, Ian Bolliger, Aluma Dembo, Solomon M Hsiang, Shuhei Kitamura, Edward Miguel, and Robert Pickmans (2019), “Destructive behavior, judgment, and economic decision-making under thermal stress.” Working Paper 25785, National Bureau of Economic Research, URL `http://www.nber.org/papers/w25785` .

- Anderson, Craig A (1989), “Temperature and aggression: Ubiquitous effects of heat on occurrence of human violence.” _Psychological Bulletin_ , 106, 74.

- Anderson, Craig A (2001), “Heat and violence.” _Current Directions in Psychological Science_ , 10, 33–38.

- Anderson, Craig A, Kathryn B Anderson, Nancy Dorr, Kristina M DeNeve, and Mindy Flanagan (2000), “Temperature and aggression.” In _Advances in Experimental Social Psychology_ , volume 32, 63–133, Elsevier.

- Baron, Robert A and Paul A Bell (1976), “Aggression and heat: The influence of ambient temperature, negative affect, and a cooling drink on physical aggression.” _Journal of personality and social psychology_ , 33, 245.

- Barreca, Alan, Karen Clay, Olivier Deschˆenes, Michael Greenstone, and Joseph S Shapiro (2015), “Convergence in adaptation to climate change: Evidence from high temperatures and mortality, 1900-2004.” _American Economic Review_ , 105, 247–51.

- Baylis, Patrick (2020), “Temperature and temperament: Evidence from twitter.” _Journal of Public Economics_ , 184, 104161.

- Becker, Gary S (1968), “Crime and punishment: An economic approach.” In _The economic dimensions of crime_ , 13–68, Springer.

- Behrer, A Patrick and Valentin Bolotnyy (forthcoming), “Heat and law enforcement.” Policy Research Working Paper Series XXXX, World Bank, Washington, DC.

- Blakeslee, David, Ritam Chaurey, Ram Fishman, Deepak Malghan, and Samreen Malik (2018), “In the heat of the moment: economic and non-economic drivers of the weather-crime relationship.” _Working Paper_ .

- Boyanowsky, Ehor (1999), “Violence and aggression in the heat of passion and in cold blood: The ecs-tc syndrome.” _international Journal of Law and psychiatry_ , 22, 257–271.

- Braga, Alf ́esio Lu ́ıs Ferreira, Antonella Zanobetti, and Joel Schwartz (2001), “The time course of weather-related deaths.” _Epidemiology_ , 12, 662–667.

- Braga, Anthony A, David M Hureau, and Andrew V Papachristos (2011), “The relevance of micro places to citywide robbery trends: A longitudinal analysis of robbery incidents at street corners and block faces in boston.” _Journal of Research in Crime and Delinquency_ , 48, 7–32.

- Bruederle, Anna, J ̈org Peters, and Gareth Roberts (2017), “Weather and crime in south africa.”

37
||PP<br>Title|R Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|---|
|WPS84|Black Markets for Foreign Exchange,<br>Real Exchange Rates and Inflation:<br>Overnight vs. Gradual Reform in<br>Sub-Saharan<br>Africa|Brian Pinto|September 1988|<br>S. Fallon<br>61680|
|WPS85|Wage Responsiveness<br>and Labor Market<br>Disequilibrium|Ramon E. Lopez<br>Luis A. Riveros|September 1988|<br>L. Riveros<br>61762|
|WPS86|External Balance, Fiscal Policy and<br>Growth In Turkey|Ritu Anand<br>Ajay Chhibber<br>Sweder van Wijnbergen|September 1988|<br>A. Chhlbber<br>60102|
|WPS87|Vocational<br>and Technical Education<br>in Peru|Peter R. Moock<br>Rosemary T. Bellew|||
|WPS88|Costs, Payments, and Incentives<br>in<br>Family Planning Programs|John A. Ross<br>Stephen L. Isaacs|September 1988|<br>S. Alnsworth<br>31091|
|WPS89|Export Quota Allocations,<br>Export<br>Earnings and Market Diversifications|<br>Taeho Bark<br>Jaime de Melo|September 1988|<br>C. Cabana<br>61539|
|WPS90|A Framework for Analysis of Mineral<br>Tax Policy in Sub-Saharan<br>Africa|Robert F. Conrad<br>Zmarak M. Shalizi|September 1988|<br>A. Bhalla<br>60359|
|WPS91|<br>Israel's<br>Stabilization<br>Program|Nissan Liviatan|September 1988|<br>N. Liviatan<br>61763|
|WPS92|A l4odel<br>of Cocoa Replanting and New<br>PlanTing in Bahia, Brazil: 1966-1985|Pravin K. Trivedi|September 1988|<br>D. Gustafson<br>33714|
|WPS93|The Effects of Education,<br>Health and<br>Social Security on Fertility in<br>Developing<br>Countries|Susan H. Cochrane|September 1988|<br>S. Ainsworth<br>31091|
|WPS94|The World Bank's Population Lending<br>and Sector Review|George B. Simmons<br>Rushikesh Maru|September 1988|<br>S. Ainsworth<br>31091|
**Figure 4** Impulse response functions for different ordering of fiscal decisions

<!-- Start of picture text --> a) Government expenditure b) Tax revenue<br>1 0.2<br>0.8 0<br>0.6 -0.2<br>0.4 -0.4<br>0.2 -0.6<br>0 -0.8<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Years Years<br>Baseline Alternative ordering Baseline Alternative ordering<br><!-- End of picture text --> The robustness checks hence confirm the main findings presented above. Different estimations of the cyclicality result in somewhat higher pro-cyclicality of both revenues and spending, but the latter always remains more pro-cyclical. And the identification of fiscal shocks seems very much appropriate, given that a whole battery of sensitivity checks results in fundamentally the same conclusions.

# **8. Discussion**

Procyclical public spending and a positive expenditure multiplier imply that fiscal policy in South Asia amplifies boom-and-bust cycles. When growth accelerates, both tax revenue and government spending increase, but spending increases stronger than revenue. The impact of larger tax revenue on subsequent economic activity is not significant but that of larger public spending is, which further accelerates economic growth. And the reverse is true in economic downturns, with the deceleration of economic activity being amplified by contractionary fiscal policy. The correlations between growth and fiscal variables presented above suggest that the amplification of boom-and-bust cycles may be especially severe in Pakistan and Bangladesh, and sizeable in Bhutan. In addition, in some countries in South Asia, tax revenue and economic growth are not significantly correlated, meaning that governments miss out on much needed revenue and that taxes are not acting as automatic stabilizers. In these countries, broadening the tax base and ensuring tax payments by the sectors contributing to growth is crucial. And in Bhutan and Nepal, the inflation tax even decreases in economically good times.

The arguments in favor of a countercyclical fiscal policy can be challenged. In traditional Keynesian models, such fiscal policy is justified by an objective function that penalizes deviations of output from trend. However, some distortions and constraints, like political distortions (Tornell and Lane 1999; Talvi and Végh 2000) or borrowing constraints (Aizeman, Gavin, and Hausmann 1996; Gavin and Perotti 1997), can rationalize a pro-cyclical fiscal policy. Clearly, the spending needs of South Asian countries are immense and postponing spending in times of high growth to preserve fiscal space for times of low growth comes with costs. Developing this argument in a macroeconomic model would allow to assess the welfare implication of South Asia’s procyclical fiscal policy.

16
# **Table 5: Political questions and enumerator characteristics**

||DP<br>(1)|FDC<br>(2)|NRM<br>(3)|UPC<br>(4)|DP<br>(5)|FDC<br>(6)|NRM<br>(7)|UPC<br>(8)|
|---|---|---|---|---|---|---|---|---|
|Enumerator gender (male)|-0.563***|-0.403**|-0.107***|-0.513***|-0.353**|-0.302**|-0.051|-0.390***|
||(0.150)|(0.158)|(0.034)|(0.116)|(0.157)|(0.144)|(0.030)|(0.131)|
|Enumerator age|-0.021|-0.005|0.002|-0.034**|-0.004|0.005|0.006|-0.026|
||(0.021)|(0.020)|(0.004)|(0.016)|(0.024)|(0.019)|(0.004)|(0.016)|
|Enumerator education|0.113|0.136|-0.075**|0.066|0.162|0.076|-0.000|0.076|
||(0.110)|(0.121)|(0.035)|(0.111)|(0.113)|(0.107)|(0.028)|(0.124)|
|Enumerator from urban locality|0.455**|0.568***|0.001|0.429***|0.546***|0.721***|-0.057|0.515***|
||(0.184)|(0.203)|(0.039)|(0.149)|(0.138)|(0.122)|(0.038)|(0.107)|
|Work experience as enumerator|||||0.006***|0.006***|0.000|0.004***|
||||||(0.002)|(0.002)|(0.000)|(0.001)|
|Want people to know how good can be at<br>work|||||-0.069|0.124|-0.084**|-0.015|
||||||(0.127)|(0.116)|(0.032)|(0.103)|
|Strongly motivated by the wage can earn|||||0.004|-0.087|0.071***|-0.053|
||||||(0.098)|(0.096)|(0.017)|(0.085)|
|Enjoy handling new problems|||||0.122|-0.033|0.008|-0.017|
||||||(0.143)|(0.140)|(0.033)|(0.131)|
|Enjoy trying to solve complex problems|||||0.129|0.119*|0.065***|0.157**|
||||||(0.082)|(0.065)|(0.023)|(0.070)|
|Curiosity is a driving force for her actions|||||0.034|0.106|0.001|0.084|
||||||(0.081)|(0.077)|(0.011)|(0.076)|
|Observations|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|
|R-squared|0.485|0.458|0.233|0.478|0.496|0.473|0.238|0.487|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Work experience as enumerator_ is the number of months previously worked as an enumerator. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master’s program. _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ are categorical variables taking value from 1 to 4 (decreasing). Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

30
**Table 6: Comparison of Hospital vs. Contem orary Industr Corporate) Boards**

|Large (14-50)|Small (7-10)|Srnall (7)|Small (5-9)|
|---|---|---|---|
|Broadly<br>representative|Expertise focused|Expertise focused,<br>but inadequately|Politically representative.<br>Possibly inadequately expertise<br>focused.|
|Long terms of<br>office|Short term of<br>office|Long term of office|Short, but renewable with no<br>limits|
|Many<br>committees|Few comiittees|Few commnittees|No committees provided for by<br>design|
|Monthly<br>meetings|Quarterly<br>meetings|Weekly and/or<br>bimonthly<br>meetings|Weekly meetings and/or<br>bimonthly meetings|
|2-3 hour<br>meetings|6-8 hour meetings|2-3 hour meetings|2-3 hour meetings|
|Cumbersome<br>decision making|Rapid decision<br>making|Rapid decision<br>making|Cumbersome decision making|
|Consensual<br>orientation|Pragmatic<br>orientation|Consensual<br>orientation|Unclear orientation, elements<br>of conflict to date due to<br>differential political leverage<br>and lack of definition of<br>prerogatives|
|Stewardship<br>orientation|Growth/Risk<br>orientation|Mostly stewardship<br>orientation<br>Growth/risk<br>orientation at the<br>beginning|Unclear orientation to date. No<br>fiduciary or legal responsibility<br>to mitigate risk-taking|
|Process<br>orientation|Results<br>orientation|Results orientation|Unclear pattem to date. Legal<br>structure is very process-<br>oriented in key areas|
|Members seldom<br>paid|Most members<br>paid|Members not paid|Members paid|

Adapted from Steven Shortell, "New Directions in Hospital Governance," _Hospital Governance 34:1 Spring 1989._ Table 7 reveals good news and bad news about the Implementation Decrees under Law #544 in Lebanon. The good news is that boards are small in size, their meetings are relatively frequent, and their members are remunerated (although the real incentive may in fact be of a more important, non-pecuniary currency). The bad news about hospital boards in Lebanon is that the law does not guarantee that they be expertise-focussed at a time when expertise has become the single most important asset a board member can bring to a hospital (see Appendix A, Eid 1999b). In addition, the system places no limits on board term renewal and risks creating boards that are either politically representative or politicized, but not necessarily expertise focussed. The system also transfers very little financial risk to the hospital manager and board, and results in cumbersome decision-making because of excessive _ex ante_ controls (Eid 1998; Mubarak 1999). Furthermore, the system does not yet define the distribution of decision rights between the board and the director, a problem that has lead to costly 35