rafmacalaba/datause-encoder-data huggingface dataset:
expand on the findings from more geographically limited studies. We accomplish this expansion by using three large-scale surveys: the Integrated Values Survey (IVS), the Life in Transition Survey (LiTS), and the Russia Longitudinal Monitoring Survey (RLMS). Indeed, our analysis may be the first in-depth study of the relation between age and well-being using a large-scale _panel_ data set from a non-Western country (i.e. the Russian Federation). Second, by adding indicators for birth decades into our regressions, we are able to disentangle some of the age-specific correlation with subjective well-being from the variation attributable to cohort effects. Our results generally support the presence of a U-shaped relation for most of the lifecycle, even when the panel data accounts reasonably well for the cohort effects. We also, however, find evidence of another turning point later in life (during the 60s or 70s), after which well-being in ECA begins to decline again, giving rise to an S-shape across the entire lifespan.

The remainder of the paper proceeds as follows: Section 2 describes our three data sets and our different methodological approaches to determining the shape of subjective well-being over the lifespan in the ECA region. Section 3 reports our empirical results, together with the conclusions drawn about the (non-)universality of the U-shape. Section 4 rounds out the paper by summarizing the contributions our methodological approach makes to the research stream.

# **2. Data and methods**

## _2.1 Data_

The empirical results of this study are based on three different data sets: the Integrated Values Survey (IVS), the Life in Transition Survey (LiTS), and the Russia Longitudinal Monitoring Survey (RLMS). The IVS data set merges the four-wave European Values Study (EVS, 1981– 2009) with the five-wave World Values Survey (WVS, 1981–2008). The EVS, begun in 1981 and repeated every 9 years, is a large-scale, cross-national, longitudinal survey that provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe. Nationally representative of the population 18 years and older, in its first wave, it sampled 1,000 individuals per country, and then for its fourth wave, administered in 2008, expanded to a multistage random sampling of 1,500 interviews per country. This last wave covers 25 of the 30 ECA countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kosovo Republic, Latvia, Lithuania, FYR Macedonia, Moldova, Montenegro, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Turkey and Ukraine.

The WVS, administered in six waves from 1981 to 2014, is a global research project that investigates the changes in individual’s beliefs and values and how these changes influence social and political development throughout the world. It samples a minimum of 1,000 interviewees aged 18–85 in 23 ECA countries: Albania, Armenia, Azerbaijan, Belarus, Bosnia 4
# **9 Annexes**

## **9.1 Survey Data - Construction of Individual Selection Variables**

_Public Sector Motivation (PSM)_ – In order to measure bureaucrats’ and politicians’ motivation to serve in the public sector, we use Perry’s Public Sector Motivation Index (Perry, 1996). Annex I presents the version used in the survey in Uganda: it is composed of 36 questions measuring motivation in six different dimensions – Attraction to Policy Making, Commitment to Public Interest, Social Justice, Civic Duty, Compassion and Self-Sacrifice. As discussed by Perry (1996), each dimension captures distinct motivations to serve identified previously in the public administration literature. Interviewees are asked how much they agree with each of the questions on a scale of 1 (Strongly Disagree) to 5 (Strongly Agree). We follow the established methodology in the personnel economics literature, as in Dal Bo, Finan and Rossi (2013) and Callen et al. (2016): we first recode all questions so that higher values are associated with normatively positive answers (i.e. higher agreement with “I think there are many public causes which are worth defending” but disagreement with “I hardly think of the welfare of people I do not know personally”), and then create an index for each dimension by taking simple averages of its answers. Finally, we create an aggregate PSM Index for each individual by taking an average of the z-scores of each of the six dimensions.

_Behavioral and Personality Traits_ – The module used to assess behavioral and personality traits is the same as the one used in the World Bank STEP (Skills Towards Employability and Productivity) surveys . Annex II presents the 25 questions used to measure eight distinct dimensions of personality. Taken together, the five first dimensions – Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability – are usually referred to as the Big Five Inventory (BFI), which have been shown to approximate the main dimensions used to describe personalities (John and Srivastava, 1999). Whereas the BFI concerns very broad personality traits, the module also collects data on three other more specific dimensions of behavior – Grit, Decision-Making and Hostility Bias. Interviewees 64
stronger upward mobility both in the medium term and the long term.<sup>20</sup> These results provide further supportive evidence that our estimation results are robust to attrition.

# _Other Robustness Checks_

We examine a battery of other robustness checks and offer a brief summary of the main findings here. First, we investigate whether estimation results change if we analyze the household panel data instead of the individual panel data. We use two different definitions of a household panel: one whereby any household member remains in the panel data over time, and another whereby half or more household members remain the same over time.<sup>21</sup> Using both definitions offers qualitatively similar results (Appendix 1, Table 1.5). Second, we examine whether adjusting for household equivalence scale may affect our estimates. We employ two different scale adjustment methods, one by the OECD (2009) and another with a different scale parameter (i.e., using an economy-of-size parameter of 0.8).<sup>22</sup> Estimation results are also similar, especially for mobility in the long term (Appendix 1, Table 1.6). Third, following Gorodnichenko _et al._ (2010), who showed that accounting for differences in the cost of living between regions could reduce consumption inequality, we also make a similar adjustment. Estimation results suggest somewhat higher upward mobility in the medium term but are generally similar (Appendix 1, Table 1.7). Fourth, instead of dividing the income distributions into three terciles, we use an alternative

> 20 In fact, even if we only rely on the adjustments offered by Wooldridge’s method, the finding that there was more upward conditional mobility than downward mobility in both medium-term periods is not very different from our finding that this finding holds for the long term and mostly for the short term.

> 21 More precisely speaking, we analyze the panel of household heads, where we define heads according to the RLMS survey manual’s recommendations: (1) the oldest working-aged male in the household, (2) if no working-aged males, then the oldest working-age female, (3) if no working-age females, then the youngest retirement-age male, (4) if no retirement-age males, then the youngest retirement-age female, (5) if no retirement-age females, then the oldest child. 22 There is no established equivalence scale for Russia. Different equivalence scales are often applied in studies of poverty using the RLMS-HSE data (e.g., Lokshin _et al._ , 2000; Denisova, 2012).

27
**Table II. Summary Statistics**

|**Variables**|**Mean**|**Std. Dev.**|
|---|---|---|
|U.S. Short-term Government Bond|1.8|2.9|
|U.S. Long-term Government Bond|2.6|3.1|
|U.S. Stock|7.1|29.8|
|U.S. ROE|1.3|3.9|
|Japanese Short-term Government Bond|0.4|4.0|
|Japanese Long-term Government Bond|2.1|4.3|
|Japanese Stock|7.6|34.1|
|Japanese ROE|0.8|4.5|
|U.S. CPI-Inflation|4.3|3.6|
|Japanese CPI-Inflation|3.9|5.6|
|Real Exchange Rate (Change)|-2.0|24.5|

This table shows the summary statistics of quarterly real returns from 1960 to 2005. The assets considered in the analysis are: short-term government bonds, long-term government bonds, stocks, and ROE for both the U.S. and Japan. It also shows the sample summary statistics of the CPI-inflation rates and the real exchange rate. Real returns are reported in local currency. Data is in annual percentage units.

Source: _Global Financial Database._ 47
# **<mark>Table 4 - Stunting (Prob (HAZ<-2SD)) Equations</mark>**

|**Eq.#**|**Independent Variable Specification**|∂**=0.5**|∂**=1**|∂**=2**|∂**=3**|
|---|---|---|---|---|---|
|||**Mod**<br>|**el 5.1**<br>|||
|1|q=1|0.208***|0.103***|0.0511***|0.0401***|
|||(0.0613)|(0.0304)|(0.0150)|(0.0116)|
|2|q=2|0.0307***|0.0152***|0.00750***|0.00583***|
|||(0.00907)|(0.00449)|(0.00221)|(0.00170)|
|3|q=3,Cat 2(1993-1996)|0.230***|0.114***|0.0565***|0.0444***|
|||(0.0651)|(0.0323)|(0.0160)|(0.0124)|
||q=3,Cat 1(1996-2001)|0.205|0.102|0.0510|0.0412|
|||(0.165)|(0.0823)|(0.0408)|(0.0329)|
|4|q=4|0.0579|0.0283|0.0136|0.00962|
|||(0.0691)|(0.0343)|(0.0170)|(0.0133)|
|5|q=5|0.134***|0.0664***|0.0325***|0.0246**|
||_N_|_3202_|_3202_|_3202_|_3202_|
|||(0.0496)|(0.0246)|(0.0122)|(0.00966)|
|||**Mod**<br>|**el 5.2**<br>|||
|6|d<br>�<sup>, in utero</sup>|0.373*|0.186*|0.0923*|0.0737*|
|||(0.211)|(0.105)|(0.0520)|(0.0414)|
||d<br>�<sup>, 0-2years old</sup>|0.240***|0.119***|0.0591***|0.0460**|
|||(0.0908)|(0.0452)|(0.0224)|(0.0179)|
||d<br>�<sup>, 3-5years old</sup>|0.169**|0.0838**|0.0415**|0.0332**|
|||(0.0738)|(0.0365)|(0.0179)|(0.0135)|
|**Source:** 201|_N_<br>5/2016 Tanzania DHS. Standard errors in parentheses, Stand|_3103_<br>ard Errors clustered at the clu|_3103_<br>ster level. In addition to clust|_3103_<br>er fixed effects, the regression|_3103_<br>controls for the gender of the|

**<mark>Source:</mark>** <mark>2015/2016 Tanzania DHS. Standard errors in parentheses, Standard Errors clustered at the cluster level. In addition to cluster fixed effects, the regression controls for the gender of the head, the birth order and gender of the child but also mother and child year of birth fixed effects; * p < 0.10, ** p < 0.05, *** p < 0.01.</mark>

31
|**Table 8. Export Intensities by Federal O**|**krug**<br>_Sectoral Export_|_s as a Percentag_|_e of Production_|||||
|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|Railway transportation|24.5|14.6|18.3|21.8|17.1|30.8|25.0|
|Pipelines transportation|21.5|1.2|37.3|31.7|89.8|9.4|15.1|
|Maritime transportation|81.8|63.0|76.1|79.9|91.1|73.9|82.1|
|Air transportation|48.3|45.9|45.9|48.3|43.1|44.9|48.8|
|Other transportation|11.7|7.6|9.2|13.1|9.2|12.7|10.0|
|Telecommunications|6.9|6.8|6.7|6.9|6.6|6.7|6.9|
|Science & science servicing|2.7|2.6|2.6|2.7|2.6|2.6|2.7|
|Financial services|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|Ferrous metallurgy|39.0|40.3|43.3|31.1|37.6|36.2|34.6|
|Non-ferrous metallurgy|53.7|56.5|57.6|57.8|52.9|46.0|34.6|
|Chemical & oil-chemical industry|41.1|25.3|26.8|34.8|50.8|46.9|20.4|
|Mechanical engineering & metal-working|29.1|27.4|29.7|15.6|24.4|47.2|24.1|
|Timber & woodworking & pulp & paper industry|31.9|5.9|12.2|16.0|13.6|48.8|71.7|
|Construction materials industry|4.4|3.0|3.0|3.9|6.2|6.4|2.8|
|Food industry|6.4|6.6|11.7|3.9|3.8|4.4|14.8|
|Other industries|14.3|14.3|14.3|14.3|14.3|14.3|14.3|
|Public services, culture and arts|0.4|0.4|0.4|0.4|0.4|0.4|0.4|
|Agriculture & forestry|1.0|1.0|1.0|1.0|1.0|1.0|1.0|
|Coalmining|23.0|23.0|23.0|23.0|23.0|23.0|23.0|
|Housing and communal services|0.7|0.7|0.7|0.7|0.7|0.7|0.7|
|Construction|2.7|2.7|2.7|2.7|2.7|2.7|2.7|
|Electric industry|1.7|1.7|1.7|1.7|1.7|1.7|1.7|
|Gas|26.8||26.8|26.8|26.8|26.8|26.8|
|Crude oil extraction|55.9||55.9|55.9|55.9|55.9|55.9|
|Oil refining and processing|32.1|32.1|32.1|32.1|32.1|32.1|32.1|
|Other goods-producing sectors|11.5|11.5|11.5|11.5|11.5|11.5|11.5|
|Post|9.3|9.3|9.3|9.3|9.3|9.3|9.3|
|Wholesale and retail trade|21.6|9.0|16.8|21.3|58.7|25.1|22.5|
|Textiles and apparel|11.4|11.4|11.4|11.4|11.4|11.4|11.4|

42
that the composition of the surge matters to predict sudden stops: debt-led surges are more likely to precede capital inflow reversals.

# **3. Defining Surges in Capital Flows**

This section describes the methodology used to identify episodes of surges in gross capital inflows using quarterly data information for 71 countries (23 industrial economies and 48 developing countries) from 1975 to 2010. We then describe the sources of data used to obtain our data on capital flows. Finally, we count the number of surge episodes using alternative criteria for these massive inflows and two different measurements: the first measure uses the overall amount of gross inflows whereas the second one considers only the private component of gross inflows. The identification criteria follows in this paper is: (a) CDMN criterion adapted from Cowan, De Gregorio, Micco and Neilson (2008), and (b) FW criterion as implemented in Forbes and Warnock (2012a, b).

# **3.1 Methodology**

We first describe the procedure to identify episodes of surges in gross inflows. Our first criterion to identify surge episodes in gross inflows mainly follows the methodology implemented by Forbes and Warnock (2012a, b) —which we denote as the FW criterion. According to this criterion, we first compute the cumulative sum of gross inflows over the last four quarters (i.e. cumulative annual inflows in period t). We denote Ct, as the sum of quarterly gross capital inflows (GIF) over the last four quarters. In addition, we define ∆Ct as the year-over-year changes in Ct. We can express these variables as follows: where: Next, we calculate the rolling mean and standard deviation of ∆Ct for each country. Surges in gross inflows are defined as periods when ∆Ct increases more than one standard of deviations above its rolling mean. Consequently, the end of surge periods indicates that ∆Ct drops below one standard of deviation above its rolling mean. Therefore, surges in gross capital inflows are captured by a dummy variable that takes the value of one whenever the conditions stated above hold, and zero otherwise.

We apply similar criteria as stated above to gross inflows as a ratio to GDP. This is in line with our second identification criterion —which follows the strategy outlined in Cowan, De Gregorio, Micco and Neilson (2008). We use gross overall inflows and we divide them by GDP. The identification of surges in gross capital inflows normalized by GDP is denoted as the CDMN10
11

# IV. **Antidumping Practice in Latin America**

Antidumping activity has increased significantly in the recent decade across the world and also in Latin America GATT records show that during 1985-92 there were 1,148 antidumping cases, an average of over 150 cases per year, while there were no more than 12 case per year from 1947-68. Between 1985 and 1992, the United States brought some 300 cases, Australia 282 cases, the European Union 242, Canada 129 cases, Mexico 84, and Brazil 13. The concern is that these measures have become protectionist devices. The increase is associated with the liberalization of the trade regimes. Moreover, with the appreciations of exchange rates particularly with countries that adopted nominal anchor strategies, antidumping was an appealing weapon to reduce external competition in "sensitive" sectors and to help with the increased current account deficits that resulted from external shocks.<sup>6</sup> The incidence of antidumping actions are shown in Table 2. From 1988 through 1994, Argentina led the way in Latin America during that period with 135 petitions alleging dumping or subsidizations of imports<sup>7</sup> . Of these, 50 petitions resulted in the opening of an investigations and of those 19 resulted in the application of temporary or permanent remedial measures. Antidumping activity in Brazil is a relatively new phenomena. The early 1990s witnessed a dramatic increase in the number of antidumping cases brought before the Brazilian authorities. Not surprisingly this coincide with the implementation of trade liberalization policies. Seven cases were initiated in Brazil in 1991, another seven in 1992 and 18 in 1993, with favorable determinations of 1, 5 and 5 for the respective years. The most significant increases in antidumping occurred during 1994 and 1996. Argentina led the Latin American countries in the use of antidumping. Mexico and Brazil came second and third respectively. In the consideration of all those cases the respective competition policies agencies had no role, advisory or otherwise. They were total strangers, when indeed they should have been siblings of the antidumping agencies if not soul mates. The increase in antidumping in 1996 to some extent is associated with the postponment of actions to fully implement Uruguay Round

- 6 Recently, Latin American countries had by and large not taken recourse to the balance of payments provision for limiting imports under Article XXVIII (b) of GATT.

7These cases are not reported on Table 2 since we do not have the breakdown by year.
|**_Table D2b. Heterogeneity by Women's Beliefs: Ma_**|**_rried Child De_**<br>(1)|**_pendent Sa_**<br>(2)|**_mple and rest_**<br>(3)|**_ricted to tho_**<br>(4)|**_se countries f_**<br>(5)|**_or which we_**<br>(6)|**_information_**<br>(7)|**_on WVS_**<br>(8)|
|---|---|---|---|---|---|---|---|---|
|||||T|ime Spent on(|Hoursper day)|...||
||Business cl|osed (1=Yes;|||Care and|Domestic|Care and|Domestic|
||<br>0=|<br>No)|Paid|Work|Respon<br>(Self/M|sibilities<br>arried)|Respon<br>(Spouse|sibilities<br>/Married)|
||_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19=1|0.692|-0.306|2.098|-14.549**|17.871***|0.605|7.361|2.663|
||[0.57]|[0.19]|[8.48]|[6.24]|[6.40]|[2.78]|[5.98]|[5.39]|
|Agree Men and women should have equal opportunities|||||||||
|<br>(Women)|0.252|0.142|10.419***|3.017|8.317**|10.109***|7.495***|-1.543|
||[0.18]|[0.10]|[3.53]|[2.62]|[4.07]|[1.69]|[2.25]|[2.16]|
|School closure due to COVID-19=1 # Agree Men and|||||||||
|<br>women should have equal opportunities (Women)|-0.724|0.331|-2.079|15.534**|-18.409***|-0.855|-7.852|-2.542|
||[0.61]|[0.21]|[9.00]|[6.67]|[6.78]|[3.02]|[6.34]|[5.76]|
|Constant|<br>-0.120|<br>0.079|<br>3.145|<br>7.648**|<br>-4.467|<br>-4.672**|<br>1.512|<br>6.823**|
||[0.26]|[0.14]|[4.28]|[3.26]|[4.14]|[1.91]|[2.58]|[2.73]|
|Observations|5652|13245|3311|7869|5652|13245|4295|10196|
|Adjusted R-squared|0.11|0.09|0.11|0.13|0.13|0.04|0.06|0.06|
|<br>Mean|0.16|0.16|6.80|7.62|6.50|4.05|3.70|6.51|
|Notes:* significant at 10% level, ** significant at 5% level, *** significant at 1%<br>[1] School Closure due to COVID-19 is a dummy variable where 1 = schools c<br>_men and women should have equal opportunities_measured prior to the CO<br>towards gender equality.<br>[2] OLS pooled regressions include Region of the world and Time Fixed Ef<br>recreation/workplaces); GDP Per Capita; and Demographic Controls (Married<br>[3] Sample is restricted to business owners who are married or living with a<br>versus MALE (Columns (2), (4), (6), and (8)).|level.<br>losed in country at<br>VID-19 pandemic. W<br>fects and full set o<br>and Types of Depe<br>partner with any ch|time of survey wa<br>omen's Commun<br>f controls includi<br>ndents). Academi<br>ild dependents ag|ve. Women's Indiv<br>ity Norm is the co<br>ng Firm Controls<br>c Break control is<br>ed 18 years or yo|idual Belief is the<br>untry-level aggre<br>(Sector + Size, Ag<br>included in the re<br>unger in the hous|proportion of wom<br>gate of the perceiv<br>e and Role in Bus<br>gressions but not s<br>ehold and is split b|en in the country<br>ed social norm as<br>iness); Mobility C<br>hown in the table.<br>y gender with FEM|who agreed with t<br>reported by wom<br>ontrols (Moveme<br> <br>ALE (Columns (1|he statement that<br>en in the sample<br>nt around retail &<br>), (3), (5), and (7))|

61
would be minimized. This is reflected in the fact that some 75 percent of families were grouped in the second class (just above the indigent; see Malanima 2006, Table 3, p. 9).<sup>29</sup> Malanima, however, revises these original data, uses information about salaries and other sources of income, and constructs a new distribution (which we use here) composed of nine groups, each consisting of 10 percent of the population, and the top decile divided into three groups (see Malanima 2006: Appendix). We thus obtain an income distribution composed of twelve groups ranked by their estimated per capita income.

# **Population and area** . Malanima (2006: p.3).

**Urbanization rate** . Malanima (2006: Table 7, p. 15)

**Mean income in $PPP** . Obtained as the ratio between the mean income of the Kingdom of Naples as calculated from Malanima data (65.8 ducats per capita per annum) and the subsistence minimum (31 ducats per capita for a five-member family in rural areas, and up to 50 ducats per capita in urban areas). Taking relative shares of urban and rural populations (85 and 15 percent), we estimate the subsistence minimum at 35 ducats per person annually. Mean income is thus 1.9 times the subsistence. Taking $PPP 400 for the subsistence, results in mean income of  $PPP 752. This can be contrasted with Maddison’s (2004) estimate of Italy’s 1820 GDI per capita of  $PPP 1117. Since Kingdom of Naples was poorer than most of Italy (north of Naples), the difference seems plausible.

# REFERENCES

Malanima, Paolo (2006), “Pre-modern equality: income distribution in the Kingdom of Naples (1811),” paper presented at XIV International Congress of Economic History, August 2006, Helsinki. Available at <u>http://www.helsinki.fi/iehc2006/papers3/Malanima.pdf.</u>

> 29 It is notable however that the quota for the indigent which was 16.6 percent was not fulfilled: in total, only 14.4 percent of families were placed in this group and thus tax-exempt.

59